Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
type: channel
description: Journal CERITA (Creative Education of Research in Information Technology and Artificial Informatics) merupakan jurnal ilmiah nasional peer-reviewed yang diterbitkan oleh Universitas Raharja sebagai media publikasi dan diseminasi hasil penelitian ilmiah serta pengembangan teknologi di bidang Teknologi Informasi, Sistem Informasi, Informatika, Rekayasa Perangkat Lunak, dan Artificial Intelligence. Jurnal ini memprioritaskan naskah yang menyajikan kontribusi teknis dan/atau empiris yang terukur melalui pengembangan, implementasi, pengujian, evaluasi, optimasi, maupun penerapan metode komputasi dan teknologi informasi. Fokus publikasi mencakup kecerdasan buatan, machine learning, data science, computer vision, sistem pendukung keputusan, transformasi digital, pengembangan aplikasi web/mobile, IoT, keamanan informasi, serta evaluasi infrastruktur dan analisis performa perangkat lunak. Journal CERITA tidak memprioritaskan naskah yang kontribusi utamanya berada di luar disiplin ilmu komputasi, seperti ilmu komunikasi, manajemen, pemasaran, ekonomi, ilmu sosial, atau pendidikan, yang tidak membawa kontribusi substantif bagi perkembangan teknologi informasi. Journal CERITA bertujuan untuk menjadi wadah akademik bagi dosen, peneliti, praktisi, mahasiswa, dan profesional dalam menyebarluaskan hasil penelitian dan inovasi yang berkontribusi terhadap pengembangan ilmu pengetahuan dan teknologi informasi. Jurnal ini juga berperan dalam mendukung pertukaran pengetahuan ilmiah serta mendorong kolaborasi penelitian di tingkat nasional maupun internasional. Journal CERITA diterbitkan secara berkala dua kali dalam setahun, yaitu pada bulan Februari dan Agustus, dengan seluruh proses pengelolaan naskah dilakukan melalui sistem Open Journal Systems (OJS). Setiap artikel yang diterbitkan telah melalui proses Double-Blind Peer Review untuk menjamin kualitas, objektivitas, dan integritas publikasi ilmiah. Sejak Volume 4 Nomor 2 Tahun 2019, Journal CERITA diterbitkan dalam format elektronik…
publisher: UNIVERSITAS RAHARJA
language: en
publication name: Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
issn: 2461-1417
items: Perbandingan Algoritma Naïve Bayes dan KNN pada Analisis Sentimen Kepuasan Pelanggan E-Commerce
items: Pengukuran Kepuasan Pengguna Mobile JKN pada Play Store Menggunakan Naïve Bayes Dan SVM
items: Fine-tuning Transformer Phi-3 Mini dengan QLoRA untuk Article Generator Sumber Daya Terbatas
items: Perbandingan MobileNet V2 EfficientNet Lite dan DenseNet169 dalam Pengenalan Jamur Berbasis Android
items: Pengembangan Aplikasi Untuk Monitoring Laporan Bulanan Dan Pembayaran PPATQ RF Pati
items: Sistem Peringatan Dini Banjir Berbasis Urgensi Wilayah Menggunakan Metode Fuzzy Berbasis Internet of Things Di Kota Palopo
items: Prototipe Game Edukasi Interaktif untuk Meningkatkan Minat Belajar dan Keterampilan Motorik Anak Berkebutuhan Khusus
items: Peningkatan Efisiensi Transaksi dan Pelaporan Penjualan Melalui Rancang Bangun Sistem Informasi Kasir Berbasis Website
items: Evaluasi Implementasi Sistem Manajemen Informasi Kepegawaian Berbasis PIECES dalam Mendukung Good Governance di Provinsi Banten
items: Optimasi Algoritma Random Forest dengan PSO untuk Peningkatan Akurasi Klasifikasi Kampanye Digital
items: Prediksi Intensitas Curah Hujan di Kota Surabaya Menggunakan Principal Component Analysis PCA Dan Long ShortTerm Memory LSTM
items: Identifikasi Kerusakan Buah Kakao Akibat Serangan Hama Menggunakan Algoritma Yolov9
items: Penerapan Metode RAD dalam Rancang Website Pemasaran Rumah Pada PT Bumi Lingga Pertiwi
items: Implementasi Teknologi Laravel dalam Sistem Pemesanan Catering Inovasi Digital pada AlFaruq Catering Kudus
items: Sistem Pemantauan Kedisiplinan Santri Berbasis Citra Raspberry Pi Dan Internet Of Things
items: Peningkatan Akurasi Pada Slowfast Network Menggunakan MultiHead Self Attention Layer
items: Implementasi Vision Transformer untuk Klasifikasi Penyakit Pneumonia melalui Citra Chest XRay
items: Analisis Sentimen Komentar Instagram Terkait Persepsi Hidup Sehat Menggunakan Algoritma BERTLSTM
items: MONITORING PEMETAAN KUALITAS UDARA MENGGUNAKAN METODE FUZZY BERBASIS INTERNET OF THINGS
items: Sistem Pendukung Keputusan Mata Pelajaran Pilihan Siswa Menggunakan Metode Simple Additive Weighting dan Weighted Product
items: RANCANG BANGUN SISTEM INFORMASI PENJUALAN TEMBAKAU BERBASIS WEBSITE PADA METRIC TOBACO
items: Perancangan Sistem Prediksi Keuangan Menggunakan Moving Average Dan Prototype Method
items: Perancangan Website ECommerce Berbasis MobileFirstDesign untuk Optimalisasi Efisiensi Layanan di Apotek Mumtaz Husada
items: Analisis Performa Choke Dalam Prediksi Well Testing Menggunakan Algoritma LongShort Term Memory
items: Analisis Ulasan Ecommerce Menggunakan Fine Grained Sentiment Analysist dan Convolutional Neural Network
items: Pengembangan Model Aplikasi Ujian Online Siswa Berbasis Website Menggunakan Metode PIECES
items: Analisis Performansi Forecasting Harga Saham Amazon Dengan ARIMA ANN dan LSTM
items: Optimalisasi Sistem Deteksi Parkir Menggunakan YOLOv7 dan CLAHE
items: Penerapan Metode Waterfall Dalam Rancang Website Penerimaan Karyawan Pada PT XYZ
items: Sistem Pendukung Keputusan untuk Seleksi Karyawan dengan Metode AHP dan Profile Matching

Perbandingan Algoritma Naïve Bayes dan KNN pada Analisis Sentimen Kepuasan Pelanggan E-Commerce
type: item
description: The rapid growth of e-commerce in Indonesia has led to a significant increase in customer reviews on digital platforms. Customer reviews serve as an important source of information for companies to understand customer satisfaction regarding products and services. However, the large volume of review data and the characteristics of unstructured text make manual evaluation inefficient and prone to subjectivity. Therefore, a machine learning-based approach is required to automatically classify sentiments. This study aims to compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in sentiment analysis of customer satisfaction in e-commerce at PT XYZ. The dataset consisted of 3,500 customer reviews, divided into 80% training data (2,800 data) and 20% testing data (700 data). The research stages included text preprocessing consisting of case folding, cleaning, tokenization, stopword removal, and stemming, followed by feature weighting using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process was performed using Naïve Bayes and KNN algorithms with a parameter of K = 5. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results indicate that the KNN algorithm achieved the best performance compared to Naïve Bayes, with an accuracy of 92.57%, precision of 91.56%, recall of 92.73%, and F1-score of 92.14%, while Naïve Bayes achieved an accuracy of 86.85%, precision of 86.30%, recall of 88.50%, and F1-score of 87.30%. Therefore, the KNN algorithm is considered more effective in classifying customer satisfaction sentiment on PT XYZ e-commerce review data. The findings of this study are expected to assist companies in understanding customer perceptions more efficiently and support service quality improvement through data-driven decision-making
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: K-Nearest Neighbor (KNN)
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Customer Satisfaction
  topic: keywords
creator: Wahyu Hidayat
creator: Fahri Shaftian
creator: Emelia Lopez
creator: Ajay Supriyadi
creator: Riyan Hermawan
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 162
ending page: 177
doi: 10.33050/cerita.v12i2.4434

Pengukuran Kepuasan Pengguna Mobile JKN pada Play Store Menggunakan Naïve Bayes Dan SVM
type: item
description: Mobile JKN is a digital service developed by BPJS Kesehatan to facilitate public access to various healthcare administrative services online. As the number of users continues to increase, user reviews on Google Play Store have become an important source of information for evaluating service quality and user satisfaction. This study aims to analyze user sentiment toward Mobile JKN and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms for sentiment classification. The dataset was collected through a web scraping process from Google Play Store, resulting in 7,000 reviews. After data cleaning, 6,995 reviews were retained for analysis. The preprocessing stage included cleaning, case folding, tokenization, stopword removal, and stemming. Sentiment classification was then conducted into three categories: positive, neutral, and negative, using Naïve Bayes and SVM algorithms. The results show that the sentiment distribution consists of 60.76% positive, 17.08% neutral, and 22.16% negative reviews. SVM achieved the best performance with an accuracy of 92.10%, precision of 91.20%, recall of 90.50%, and F1-score of 90.80%, while Naïve Bayes achieved an accuracy of 89.50%, precision of 88.70%, recall of 87.90%, and F1-score of 88.30%. The findings indicate that users appreciate the convenience and accessibility of the services provided by Mobile JKN, although complaints related to login issues, OTP verification, and system stability remain prevalent. This study demonstrates that SVM outperforms Naïve Bayes in sentiment classification of Mobile JKN reviews and can provide valuable insights for evaluating and improving BPJS Kesehatan’s digital services.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Mobile JKN
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Text Mining
  topic: keywords
subject: Sentiment Classification
  topic: keywords
creator: Meri Mayang Sari
creator: Eduard Hotman Purba
creator: Dedeh Supriyanti
creator: Rivqi Firdaus
creator: Erna Astriyani
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 248
ending page: 261
doi: 10.33050/cerita.v12i2.4405

Fine-tuning Transformer Phi-3 Mini dengan QLoRA untuk Article Generator Sumber Daya Terbatas
type: item
description: The rapid development of Transformer‑based Large Language Models (LLMs) has led to significant progress in text generation tasks, including news article generation. However, the deployment of LLMs typically requires substantial computational resources, limiting their applicability in low‑resource environments. This study investigates the use of a lightweight Transformer model, Phi‑3 Mini, adapted using Quantized Low‑Rank Adaptation (QLoRA) for news article generation under constrained computational settings. The research adopts an experimental research methodology by fine‑tuning the model on an open, structured news dataset and evaluating the generated outputs. All experiments were conducted using the Google colab free tier with an NVIDIA T4 GPU, without relying on paid infrastructure. The experimental results show that the fine‑tuned model is capable of generating new news articles based on given titles; however, the quality of the generated text remains limited in terms of coherence, contextual relevance, and informational completeness. These findings indicate that while the combination of Small Language Models and QLoRA enables feasible LLM experimentation in low‑resource environments, further optimization of data, training configurations, and fine‑tuning strategies is required to improve text generation quality.
subject: Articles
  topic: section
subject: Transformer Phi‑3 Mini
  topic: keywords
subject: QLoRA fine-tuning
  topic: keywords
subject: news article generator
  topic: keywords
subject: small language models
  topic: keywords
subject: low‑resource computing
  topic: keywords
creator: Dafa Al Farezi
creator: Abdul Hamid Arribathi
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 207
ending page: 219
doi: 10.33050/cerita.v12i2.4281

Perbandingan MobileNet V2 EfficientNet Lite dan DenseNet169 dalam Pengenalan Jamur Berbasis Android
type: item
description: Kasus keracunan jamur sering terjadi akibat salah identifikasi antara jamur beracun dan jamur yang aman untuk dikonsumsi. Teknologi pengenalan citra berbasis Convolutional Neural Network atau CNN merupakan salah satu solusi yang dapat digunakan untuk mengatasi permasalahan ini. Namun, penelitian sebelumnya umumnya masih mengandalkan perangkat dengan spesifikasi tinggi atau memerlukan koneksi internet dalam implementasinya, sementara implementasi praktis pada perangkat seluler atau ponsel belum banyak dibahas. Penelitian ini bertujuan untuk mengembangkan sebuah aplikasi Android praktis yang mampu mengidentifikasi jamur beracun, serta membandingkan performa tiga arsitektur CNN ringan, yaitu MobileNet V2, DenseNet-169, dan EfficientNet. Hasil penelitian menunjukkan bahwa ketiga model memiliki akurasi tinggi dalam mengidentifikasi jamur, tetapi menunjukkan perbedaan signifikan dalam efisiensi penggunaan sumber daya perangkat serta ketahanan dalam berbagai kondisi pengambilan gambar. Penelitian ini diharapkan menjadi solusi praktis untuk pengenalan jamur bagi masyarakat sekaligus menjadi acuan bagi pengembangan aplikasi serupa di masa depan
subject: Articles
  topic: section
subject: Deteksi Jamur Beracun, Pengenalan Citra, Jaringan Syaraf Tiruan, Aplikasi Android, Convolutional Neural Network
  topic: keywords
creator: Mahardika Virgo Wuryantoro
creator: Achmad Junaidi
creator: Hendra Maulana
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 312
ending page: 327
doi: 10.33050/cerita.v12i2.3913

Pengembangan Aplikasi Untuk Monitoring Laporan Bulanan Dan Pembayaran PPATQ RF Pati
type: item
description: The Tahfidzul Qur'an Raudlatul Falah Children's Islamic Boarding School, located in Bermi, Gembong, Pati, was founded on June 1 2009 by the Raudlatul Falah Foundation. This Islamic boarding school aims to produce a generation of young people who memorize the Koran with education based on religion and morals. However, the increasing number of students and the increasing complexity of data management give rise to various administrative challenges. To overcome this, this research developed a web-based application to make it easier to manage monthly reports related to finances, Tahfidz Program and student payments. Development uses Agile methods, which allows for flexibility and iterative adjustments based on user needs. The stages include planning, gradual development, evaluation and continuous improvement by involving the Islamic boarding school as stakeholders. This system is integrated with a database to ensure accurate and real-time data management. The implementation results show that this application can make it easier to record and monitor student data efficiently, increase management transparency, and reduce human error. By implementing this application, Islamic boarding school administrative processes become more structured and support the achievement of educational goals in Islamic boarding schools
subject: Articles
  topic: section
subject: Finance, Tahfidz Program, Agile Method
  topic: keywords
creator: Mukhamad Ulul Albab
creator: Tri Listyorini
creator: Endang Supriyati
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 22
ending page: 30
doi: 10.33050/cerita.V12i1.3626

Sistem Peringatan Dini Banjir Berbasis Urgensi Wilayah Menggunakan Metode Fuzzy Berbasis Internet of Things Di Kota Palopo
type: item
description: Flood is a natural disaster where there is an excessive volume of water that submerges land. Floods sometimes come without the public knowing, so an early warning system is needed for floods based on the Internet of Things (IoT) with spatial method integration to map the level of regional urgency in Palopo City. This system utilizes an ultrasonic HC-SR04 sensor and rainfall connected to an ESP32 microcontroller to collect environmental data in real-time. The data obtained is analyzed using a fuzzy algorithm to determine the flood status in three categories: Safe, Alert, and Danger. This information is then displayed on WebGIS and conveyed to the public via Telegram bot notifications. The research methodology used is Research and Development (R&D), which involves the stages of problem identification, literature study, system design, testing, and implementation. System testing is carried out through simulations to evaluate hardware and software performance. The results of the study show that the system successfully provides early warning information with high accuracy, an average response time of 3-5 seconds for Telegram notifications, and real-time visualization on WebGIS. In addition, this system is able to map flood-affected areas accurately based on sensor data.
subject: Articles
  topic: section
subject: Flood, IoT, HC-SR04, WebGIS, Telegram, Fuzzy Algorithm
  topic: keywords
creator: Miswar Rasyid
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 117
ending page: 122
doi: 10.33050/cerita.v12i1.3602

Prototipe Game Edukasi Interaktif untuk Meningkatkan Minat Belajar dan Keterampilan Motorik Anak Berkebutuhan Khusus
type: item
description: Children with special needs (CSN) require extra attention regarding motor skills to enhance their independence and quality of life. This research developed an interactive educational game prototype aimed at increasing learning interest and motor skills in CSN, with a case study conducted at SLB BC Cahaya Pertiwi. The method employed is the Multimedia Development Life Cycle (MDLC), a framework ensuring structured multimedia application development. The game utilizes a drag and drop mechanism involving animal images, specifically designed to improve fine motor skills. Test results involving ten CSN actors (with intellectual disabilities/Tunagrahita) demonstrate the game's effectiveness. There was an average increase in motor skill scores of 33.23% and an increase in remaining time of 18.52% after seven days of use. This game successfully serves as an optimal, engaging, and adaptive innovative learning method, supporting the motor development and learning interest of CSN.
subject: Articles
  topic: section
subject: Children with Special Needs
  topic: keywords
subject: Motor
  topic: keywords
subject: Games
  topic: keywords
subject: Multimedia Development Life Cycle (MDLC)
  topic: keywords
subject: drag and drop
  topic: keywords
creator: Muhammad Nur
creator: Rudi Budi Agung
creator: Rafly Pandu Ariansyah
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 71
ending page: 79
doi: 10.33050/cerita.V12i1.4269

Peningkatan Efisiensi Transaksi dan Pelaporan Penjualan Melalui Rancang Bangun Sistem Informasi Kasir Berbasis Website
type: item
description: In many small and medium enterprises (SMEs), sales transaction and reporting processes are still conducted manually, which often leads to recording errors, delays in report generation, and difficulties in monitoring inventory in real time. These issues are also experienced by Toko Kue XYZ, where manual recording is still applied in daily operations. This study aims to design and develop a web-based cashier information system to improve transaction efficiency and sales reporting. The system was developed using the Agile/Scrum methodology, which allows iterative development and adaptability to user requirements. Data were collected through observation, interviews, and documentation studies. The developed system includes features for product management, sales transactions, inventory control, and automatic real-time sales reporting. System testing was conducted using Black Box Testing and User Acceptance Testing (UAT). The results show that all system functions operate properly and meet operational requirements. The implementation of the web-based cashier information system is proven to accelerate transaction processing, improve the accuracy of sales records, and simplify sales reporting. Therefore, the proposed system serves as an effective digital solution to enhance operational efficiency in SMEs.
subject: Articles
  topic: section
subject: Good Governance
  topic: keywords
subject: Personnel Information System
  topic: keywords
subject: Information Technology
  topic: keywords
subject: PIECES
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Dedi Martono
creator: Lahuddin Lahuddin
creator: Dedeh Supriyanti
creator: Ajay Supriadi
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 50
ending page: 61
doi: 10.33050/cerita.v12i1.4270

Evaluasi Implementasi Sistem Manajemen Informasi Kepegawaian Berbasis PIECES dalam Mendukung Good Governance di Provinsi Banten
type: item
description: The implementation of information systems in personnel management is an important strategy in supporting the realization of Good Governance principles in the government environment. The Banten Provincial Government has implemented the Personnel Management Information System (SIMPEG) as an effort to improve the effectiveness, transparency, and accountability of civil servant data management. However, the implementation of the information system needs to be evaluated to ensure that the implemented system functions optimally and makes a real contribution to improving organizational performance. This study aims to analyze the implementation and evaluate the performance of SIMPEG in supporting Good Governance in the Banten Provincial Government. The research method uses a descriptive approach with the PIECES evaluation framework that covers the aspects of Performance, Information, Economy, Control, Efficiency, and Service. Data were collected through documentation studies, observations of personnel business processes, and interviews with system users. The results of the study indicate that SIMPEG is able to accelerate the personnel administration process, improve the quality and integration of information, reduce administrative costs, and strengthen control mechanisms and data security. In addition, this system contributes to improving work efficiency and the quality of personnel services. This research provides a scientific contribution in the form of a comprehensive evaluation of SIMPEG implementation using the PIECES framework at the provincial government level, which shows the strategic role of personnel information systems in strengthening the principles of information technology-based good governance.
subject: Articles
  topic: section
subject: Good Governance
  topic: keywords
subject: Personnel Information System
  topic: keywords
subject: Information Technology
  topic: keywords
subject: PIECES
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Fajar Nur Iman
creator: Meri Mayang Sari
creator: Nur Azizah
creator: Mohammad Rakhmansyah
creator: Fahri Shaftian
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 1
ending page: 11
doi: 10.33050/cerita.V12i1.4223

Optimasi Algoritma Random Forest dengan PSO untuk Peningkatan Akurasi Klasifikasi Kampanye Digital
type: item
description: Digital marketing campaigns are becoming increasingly complex with large and diverse data volumes. This research proposes an innovative method for classifying digital marketing campaign success by optimizing the Random Forest model with Particle Swarm Optimization (PSO). The purpose of this study to improve prediction accuracy and reduce overfitting in campaign data analysis. Using a digital marketing dataset consisting of 8000 data points and 20 variables, the research compares the performance of standard Random Forest with PSO-optimized Random Forest. Results show that the PSO + Random Forest approach consistently provides performance improvement, with the highest accuracy reaching 90.19%, precision of 89.36%, and F1 Score of 88% in the 60%:40% data split. This method effectively reduces prediction errors and produces a more stable classification model. The research contributes to the development of digital marketing data analysis methods and demonstrates the potential of machine learning algorithm optimization for more accurate data-driven decision-making.
subject: Articles
  topic: section
subject: Digital Marketing
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Classification
  topic: keywords
subject: Hyperparameter Optimization
  topic: keywords
creator: Mochamad Fachri Ardiansyah
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 291
ending page: 299
doi: 10.33050/cerita.v12i2.3810

Prediksi Intensitas Curah Hujan di Kota Surabaya Menggunakan Principal Component Analysis PCA Dan Long ShortTerm Memory LSTM
type: item
description: Surabaya frequently experiences high rainfall, which has the potential to cause flooding due to its suboptimal drainage system. Therefore, accurate rainfall prediction is crucial for disaster mitigation and urban planning. This study aims to measure the extent to which Principal Component Analysis (PCA) influences the accuracy improvement of the Long Short-Term Memory (LSTM) model in predicting rainfall intensity in Surabaya. PCA is used to extract features from meteorological variables such as air pressure, temperature, humidity, sunlight duration, and wind direction to reduce data dimensionality and enhance model efficiency. This study evaluates three scenarios with different numbers of PCA variables: 5, 6, and 7 variables. The results indicate that the scenario with 6 PCA variables achieves the best performance, with an MAE of 0.012, RMSE of 0.033, dan MSE of 0.0010. This scenario demonstrates an optimal balance between the number of variables and model accuracy. The findings confirm that dimensionality reduction using PCA can improve the efficiency and accuracy of LSTM-based rainfall prediction models; however, the number of selected variables must be optimal to avoid significant information loss. The developed model is expected to serve as a reference and guideline for improving rainfall prediction accuracy.
subject: Articles
  topic: section
subject: Rainfall
  topic: keywords
subject: Prediction
  topic: keywords
subject: Principal Component Analysis (PCA)
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Time Series
  topic: keywords
creator: Cintya Juanita Elizabeth Kolimon
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 62
ending page: 70
doi: 10.33050/cerita.V12i1.3809

Identifikasi Kerusakan Buah Kakao Akibat Serangan Hama Menggunakan Algoritma Yolov9
type: item
description: Pertanian di Indonesia memiliki peran penting dalam perekonomian nasional, dengan kakao sebagai salah satu komoditas utama. Indonesia merupakan produsen kakao terbesar ketiga di dunia. Namun, produktivitas kakao sering terganggu oleh serangan hama. Penelitian ini bertujuan untuk mengidentifikasi kerusakan buah kakao akibat serangan hama menggunakan algoritma YOLOv9 berbasis pengolahan citra. Fokus penelitian ini adalah hama penggerek buah kakao (Conopomorpha cramerella) dan hama penghisap buah kakao (Helopeltis spp.), yang dipilih karena dampaknya yang signifikan terhadap penurunan kualitas dan kuantitas hasil panen. Algoritma YOLOv9 dipilih karena keunggulannya dalam mendeteksi objek dengan akurasi tinggi dan kecepatan pemrosesan. Hasil penelitian menunjukkan bahwa model YOLOv9 mampu mengidentifikasi kerusakan dengan akurasi yang tinggi, mencapai mAP sebesar 99.5%. Dengan hasil ini, model yang dikembangkan dapat menjadi alat yang efektif untuk mendukung petani dalam memantau dan mengelola serangan hama secara lebih efisien. Penggunaan YOLOv9 dalam identifikasi kerusakan buah kakao diharapkan dapat memberikan solusi yang lebih efektif dalam mengurangi risiko penurunan hasil panen akibat serangan hama. Selain itu, teknologi ini membuka peluang untuk diintegrasikan dalam aplikasi mobile smart farming, sistem sortir buah kakao, dan sistem monitoring otomatis guna meningkatkan hasil panen kakao di Indonesia. Kata kunci: Kakao, Hama, Citra, YoloV9
subject: Articles
  topic: section
subject: Kakao, Hama, Citra, YoloV9
  topic: keywords
creator: Andi Syam Aswandi
creator: Ingrid Nurtanio
creator: Abdul Jalil
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-02-08
publication date: 2025-02-08
starting page: 40
ending page: 48
doi: 10.33050/cerita.v11i1.3483

Penerapan Metode RAD dalam Rancang Website Pemasaran Rumah Pada PT Bumi Lingga Pertiwi
type: item
description: Industri properti terus berkembang seiring meningkatnya kebutuhan akan hunian yang layak. PT Bumi Lingga Pertiwi (BLP Property) berupaya meningkatkan kepercayaan konsumen melalui pengembangan situs web pemasaran yang dilengkapi Sistem Pendukung Keputusan (SPK) berbasis metode AHP-SMARTER. Penelitian ini menggunakan metode Rapid Application Development (RAD) untuk mempercepat proses pengembangan situs web dengan melibatkan pengguna secara aktif. Framework Laravel, React, dan MySQL digunakan untuk membangun sistem yang efisien dan terstruktur. Hasil implementasi menunjukkan bahwa situs web ini dapat menyajikan informasi properti secara interaktif serta memberikan rekomendasi hunian yang sesuai dengan preferensi pengguna. Pengujian menggunakan blackbox mendapatkan hasil yang sesuai dan berjalan sesuai dengan fungsionalitasnya. Dengan adanya situs web ini, BLP Property diharapkan dapat memperkuat citra positif perusahaan, meningkatkan pengalaman pengguna, dan mendukung pemasaran properti secara efektif.
subject: Articles
  topic: section
creator: Masfi Ulil Affandi
creator: Yisti Vita Via
creator: Retno Mumpuni
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 282
ending page: 292
doi: 10.33050/cerita.v11i2.3735

Implementasi Teknologi Laravel dalam Sistem Pemesanan Catering Inovasi Digital pada AlFaruq Catering Kudus
type: item
description: Al-Faruq Catering, like many other catering businesses, faces challenges in managing orders and catering management which are done manually. Inefficient processes often lead to problems such as miscommunication of orders, delays, and suboptimal resource utilization. To overcome these problems, this study aims to implement Laravel technology in the development of a website-based catering ordering system for Al-Faruq Catering Kudus. This system is designed to improve efficiency, accessibility, and operational management through online ordering features, menu management, and digital payment integration. The software development method used is the Waterfall method, which ensures that each stage of development is carried out in a structured and systematic manner, from needs analysis to maintenance. The results of the study show that the use of Laravel as a development framework provides high flexibility, security, and scalability. The implementation of this system has succeeded in significantly improving customer experience and business efficiency. This study contributes to supporting digital transformation in the MSME sector, especially in the catering service sector. By using a Laravel-based approach, Al-Faruq Catering Kudus can meet customer needs more effectively.
subject: Articles
  topic: section
creator: Nabila Shania Putri
creator: Tri Listyorini
creator: Endang Supriyati
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 272
ending page: 281
doi: 10.33050/cerita.v11i2.3775

Sistem Pemantauan Kedisiplinan Santri Berbasis Citra Raspberry Pi Dan Internet Of Things
type: item
description: The pesantren, as a traditional Islamic educational institution, faces significant challenges in maintaining discipline due to the large number of students and the extensive area. To address these challenges, an innovative discipline monitoring system using Internet of Things (IoT) technology, Raspberry Pi, and image recognition has been developed. This system employs a webcam connected to the Raspberry Pi to capture and analyze students' faces, activating a buzzer in response to detected disciplinary patterns. The system was installed in strategic locations, with real-time audio responses provided by the buzzer and data processed and recorded via a web-based platform. Analysis of the system's performance reveals that violations most frequently occur in the afternoon, accounting for 45.7%, followed by daytime violations at 30.4%. The system demonstrates high accuracy, efficiency, and reliability in detecting and managing disciplinary issues. These findings, illustrated in the accompanying charts and pie diagram, underscore the system’s operational efficiency, high detection accuracy, and effective data management capabilities, significantly enhancing discipline management and the overall quality of education in the pesantren.
subject: Articles
  topic: section
subject: Pesantren, Sistem Pemantauan Disiplin, IOT, Raspberry Pi, Pelanggaran
  topic: keywords
creator: Khaerul Qadri
creator: Mashur Razak
creator: Abdul Jalil
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 264
ending page: 271
doi: 10.33050/cerita.v11i2.3500

Peningkatan Akurasi Pada Slowfast Network Menggunakan MultiHead Self Attention Layer
type: item
description: Keselamatan berkendara merupakan aspek krusial dalam sistem transportasi modern, di mana perilaku pengemudi seperti kantuk dan kehilangan fokus menjadi faktor utama penyebab kecelakaan. Penelitian ini mengusulkan sistem deteksi perilaku pengemudi berbasis video dengan menggunakan arsitektur SlowFast Network yang dikombinasikan dengan mekanisme Self-Attention. SlowFast Network memungkinkan pemrosesan dua jalur informasi temporal secara paralel, yaitu gerakan cepat dan lambat, untuk menangkap pola ekspresi dan dinamika visual pengemudi. Mekanisme Self-Attention ditambahkan untuk memperkuat kemampuan model dalam mengenali fitur penting secara kontekstual. Dataset yang digunakan adalah SUST Driver Drowsiness Dataset, yang telah melalui proses segmentasi dan normalisasi. Model dilatih menggunakan pembagian data pelatihan, validasi, dan pengujian, serta dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa penambahan Self-Attention meningkatkan performa model secara signifikan, dengan akurasi mencapai 96% pada data uji seimbang. Visualisasi attention map dan filter Conv3D mendukung interpretasi bahwa model mampu menangkap pola perilaku tanpa memerlukan deteksi eksplisit bagian wajah. Sistem ini menunjukkan potensi untuk diterapkan dalam sistem peringatan dini berbasis video guna meningkatkan keselamatan berkendara secara real-time.
subject: Articles
  topic: section
subject: Deteksi Kantuk Pengemudi
  topic: keywords
subject: Jaringan SlowFast
  topic: keywords
subject: Self-Attention
  topic: keywords
subject: Computer Vision
  topic: keywords
creator: Muhammad Reza
creator: Arief Setyanto
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 255
ending page: 263
doi: 10.33050/cerita.v11i2.3870

Implementasi Vision Transformer untuk Klasifikasi Penyakit Pneumonia melalui Citra Chest XRay
type: item
description: Pneumonia is a type of respiratory infection in the respiratory tract that is often caused by viruses or bacteria. Poor air quality in Jakarta's urban areas increases people's risk of developing pneumonia, acute respiratory infections (ARI), and asthma. 2019 showed that more than 740,180 children under the age of 5 died from pneumonia cases, about 14% of all early childhood deaths. In overcoming pneumonia, medical researchers have conducted many studies related to the problem of early diagnosis of pneumonia. One of the techniques to detect pneumonia is through chest x-rays that have been developed for classification. Vision Transformer (ViT) is one of the Deep Learning architectures developed specifically for image processing. The purpose of this study is to implement the classification task of pneumonia with ViT which is expected to help detect pneumonia early so that it can be treated faster and better. The results of the study show that the ViT model has good performance after applying several variations of augmentation, and is stable both in training and testing. at a small Learning Rate of 0.00001, it produces 80% accuracy for the case of pneumonia disease classification through Chest X-Ray Images.
subject: Articles
  topic: section
creator: Raihan Thobie Nabil Maulana
creator: Yisti Vita Via
creator: Eka Prakarsa Mandyartha
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 249
ending page: 254
doi: 10.33050/cerita.v11i2.3599

Analisis Sentimen Komentar Instagram Terkait Persepsi Hidup Sehat Menggunakan Algoritma BERTLSTM
type: item
description: The perception of a healthy lifestyle is crucial for physical and mental well-being, especially with the rising cases of non-communicable diseases in Indonesia. Instagram is an effective platform for education, enabling sentiment analysis of the perception of a healthy lifestyle using comment data from posts on the @ayosehat.kemkes account. This involves data collection and preprocessing, labeling, and splitting the data into training, validation, and testing sets. By combining the BERT-LSTM algorithm to classify sentiment into positive, negative, and neutral, the testing results showed that the best model achieved an accuracy of 89.20%, with a precision of 89.49%, recall of 89.20%, and an F1-score of 88.74%, with an 80:10:10 dataset split ratio.
subject: Articles
  topic: section
creator: Audiva Tartila Daning Putri
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 241
ending page: 248
doi: 10.33050/cerita.v11i2.3526

MONITORING PEMETAAN KUALITAS UDARA MENGGUNAKAN METODE FUZZY BERBASIS INTERNET OF THINGS
type: item
description: Peningkatan jumlah kendaraan bermotor di Kota Baubau, Provinsi Sulawesi Tenggara, menjadi salah satu penyebab utama memburuknya polusi udara. Fokus utama dari penelitian ini adalah pada pengukuran emisi gas buang kendaraan dan parameter kualitas udara seperti suhu dan polusi di lokasi lampu lalu lintas. Penelitian ini bertujuan untuk mengembangkan sistem pemantauan kualitas udara berbasis IoT menggunakan sensor MQ-135 dan DHT22 di Kota Baubau. Dengan sistem ini, data real-time tentang kualitas udara dapat dikumpulkan, dianalisis, dan diakses secara luas. Dengan menggunakan sensor MQ-135 dan DHT22, sistem IoT dapat memberikan data yang akurat dan kontinu tentang berbagai parameter kualitas udara. Sensor MQ-135 memiliki kemampuan mendeteksi berbagai gas berbahaya seperti amonia, nitrogen oksida, benzena, asap, dan karbon dioksida,sementara sensor DHT22 dapat mengukur suhu sekitar. Kombinasi kedua sensor ini memungkinkan pemantauan yang lebih holistik dan mendalam terhadap kondisi kualitas udara di sekitar lampu lalu Hasil analisis menunjukkan bahwa kualitas udara pada pagi hari tergolong baik, pada siang hari tergolong sedang dan pada malam hari tergolong baik. Sistem ini berhasil mengumpulkan dan mengolah data real-time dari sensor MQ-135 dan DHT22, Algoritma fuzzy logic mampu menentukan tingkat polusi udara dengan akurat dan IoT menyebarluaskan Informasi kualitas udara sehingga sistem ini efektif dalam mendeteksi dan menganalisis kualitas udara di perkotaan juga Sistem ini membantu mengurangi dampak kesehatan akibat polusi udara dan mendukung kebijakan pengendalian polusi.
subject: Articles
  topic: section
subject: Udara
  topic: keywords
subject: MQ135
  topic: keywords
subject: DHT22
  topic: keywords
subject: IoT
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
creator: Alman Arapa
creator: Wardi Wardi
creator: Abdul Jalil
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 233
ending page: 240
doi: 10.33050/cerita.v11i2.3539

Sistem Pendukung Keputusan Mata Pelajaran Pilihan Siswa Menggunakan Metode Simple Additive Weighting dan Weighted Product
type: item
description: Sistem pendukung keputusan memiliki peran krusial dalam memfasilitasi pengambilan keputusan yang tepat, terutama dalam bidang pendidikan. Di SMA Negeri 2 Bojonegoro, penerapan Kurikulum Merdeka Belajar memberikan siswa kebebasan untuk memilih mata pelajaran yang sesuai dengan minat dan kemampuan mereka. Namun, siswa masih sering mengalami kesulitan dalam menentukan jurusan yang tepat, yang bisa berdampak buruk pada karier mereka di masa depan. Untuk mengatasi masalah ini, dikembangkanlah sebuah sistem pendukung keputusan yang memanfaatkan metode Simple Additive Weighting dan Weighted Product. Penelitian menunjukkan bahwa penerapan metode Simple Additive Weighting dan Weighted Product dalam sistem pendukung keputusan sangat efektif, dengan skor pengujian system usability scale masing-masing sebesar 84,75 dan 86. Skor ini masuk dalam kategori excellent dengan grade scale B, menandakan bahwa sistem ini layak digunakan. Sistem pendukung keputusan tidak hanya membantu siswa dalam memilih mata pelajaran yang sesuai, tetapi juga meningkatkan efisiensi guru dalam memberikan bimbingan yang lebih tepat dan terstruktur. Sistem pendukung keputusan yang dikembangkan di SMA Negeri 2 Bojonegoro dapat memudahkan penyajian informasi nilai rapor siswa saat kelas 10, memberikan rekomendasi jurusan kuliah, serta menentukan mata pelajaran pilihan yang sesuai dengan kemampuan dan minat siswa. Dengan demikian, sistem ini memiliki potensi untuk meningkatkan kualitas keputusan siswa serta prestasi akademik mereka.
subject: Articles
  topic: section
subject: Sistem pendukung keputusan, simple additive weighting, weighted product, pemilihan mata pelajaran.
  topic: keywords
creator: Rischa Nuril Fadila
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 223
ending page: 232
doi: 10.33050/cerita.v11i2.3507

RANCANG BANGUN SISTEM INFORMASI PENJUALAN TEMBAKAU BERBASIS WEBSITE PADA METRIC TOBACO
type: item
description: Penelitian ini bertujuan untuk merancang serta mengembangkan sebuah sistem informasi berbasis web yang mendukung proses penjualan tembakau di Metric Tobacco Pekanbaru.. Sistem ini dirancang untuk mempermudah proses penjualan, pengelolaan data stok barang, serta pembuatan laporan secara efektif dan efisienPenelitian ini menerapkan metode waterfall, yang terdiri dari beberapa tahap, yaitu analisis kebutuhan, perancangan, implementasi, pengujian, serta pemeliharaan. Dalam perancangan sistem, digunakan alat bantu seperti Data Flow Diagram (DFD) untuk menggambarkan aliran data dan Entity Relationship Diagram (ERD) untuk merancang basis data. Penelitian ini menghasilkan sebuah sistem informasi berbasis web yang dirancang untuk mengelola transaksi penjualan, mengatur stok barang, serta menyusun laporan secara sistematis. Sistem ini dikembangkan menggunakan bahasa pemrograman PHP, HTML, CSS, dan JavaScript.
subject: Articles
  topic: section
creator: Nadila Rizki Firdaus Rizki Firdaus
creator: Tutik Khotimah
creator: Ahmad Jazuli
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 214
ending page: 222
doi: 10.33050/cerita.v11i2.3739

Perancangan Sistem Prediksi Keuangan Menggunakan Moving Average Dan Prototype Method
type: item
description: The significant development of the construction sector in Indonesia requires sophisticated and accurate financial management systems. This research aims to design a web-based financial prediction system for PT. Las Duco Jaya using Moving Average algorithm and Prototype method. The research background is the urgent need for digital transformation in financial management, particularly in construction companies still using conventional recording methods. The research methodology employs a quantitative approach with stages of literature study, field observation, and web-based prototype system development using PHP Laravel and MySQL. The Simple Moving Average (SMA) algorithm is implemented to perform quarterly financial predictions by calculating historical averages. Research results demonstrate the system's ability to generate financial predictions with an accuracy rate of 85-92%, featuring multi-level user authentication, predictive analytics modules, and comprehensive reporting mechanisms. Blackbox testing validated the entire system's functionality, proving its reliability and effectiveness in supporting digital financial management. The research concludes that this financial prediction system is more than just a recording tool; it is a strategic partner empowering organizations to understand and control financial dynamics with high precision. The study suggests continuous development, improvement of prediction algorithms, and integration of cutting-edge technologies to optimize system performance in the future
subject: Articles
  topic: section
creator: terrano windrianto darmawan
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 203
ending page: 213
doi: 10.33050/cerita.v11i2.3876

Perancangan Website ECommerce Berbasis MobileFirstDesign untuk Optimalisasi Efisiensi Layanan di Apotek Mumtaz Husada
type: item
description: The digitization of the healthcare sector has driven the development of e-commerce to enhance pharmacy service efficiency. Mumtaz Husada Pharmacy faces the issue of long queues during peak hours, which reduces customer satisfaction. This study aims to design a Mobile-First e-commerce website using the Waterfall method to optimize service efficiency at the pharmacy. The Mobile-First design ensures an optimal user experience on mobile devices, while the Waterfall method facilitates structured system development through stages of requirement analysis, design, implementation, testing, and maintenance. The results show that the e-commerce platform effectively reduces customer waiting times through features like online prescription ordering and scheduled pickups. The system also includes product management, transactions, and sales reporting to enhance operational efficiency. Laravel 11 and TailwindCSS support the development of a responsive interface and robust backend logic. The results is that a Mobile-First e-commerce website is an effective solution for minimizing queues, improving efficiency, and increasing customer satisfaction at Mumtaz Husada Pharmacy.
subject: Articles
  topic: section
subject: E-Commerce, Mobile-First Design, Pharmacy, Waterfall, Website
  topic: keywords
creator: Novia Marsheila Louisyana
creator: Tri Listyorini
creator: Endang Supriyati
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 192
ending page: 202
doi: 10.33050/cerita.v11i2.3590

Analisis Performa Choke Dalam Prediksi Well Testing Menggunakan Algoritma LongShort Term Memory
type: item
description: Well testing adalah proses yang penting dalam industri perminyakan hal ini karena well testing berfungsi untuk memprediksi produksi minyak dan gas, yang memungkinkan perusahaan membuat keputusan strategis mengenai optimasi produksi dan alokasi sumber daya. Metode konvensional untuk well testing membutuhkan waktu dan biaya yang besar. LSTM, jenis Recurrent Neural Network (RNN) dengan penambahan memory cell agar dapat menyimpan informasi jangka panjang, diharapkan dapat mengatasi kelemahan ini. penelitian ini menggunakan data time series, yang digunakan untuk mempelajari pola produksi. Hasil penelitian menunjukkan bahwa model LSTM dengan skenario terbaik pada choke 38 didapatkan pada pada pembagian data 90%, hidden size 150 dan 250, dengan nilai MSE 0.001 dan nilai RMSE 0.044, untuk choke 40 didapatkan pada pembagian data 90%, hidden size 150, dengan nilai MSE 0.006 dan nilai RMSE 0.078, dan untuk choke 42 didapatkan pada pembagian data 90%, hidden size 150, dengan nilai MSE 0.007 dan nilai RMSE 0.086, hasil penelitian juga menunjukan bahwa hasil produksi menggunakan choke 42 menunjukan performa terbaik dibandingkan dengan kedua choke lainnya. Penelitian ini menunjukkan bahwa LSTM adalah metode yang efektif untuk prediksi well testing dan dapat menjadi alternatif bagi perusahaan minyak dalam mengoptimalkan produksi dan pengambilan keputusan dalam melakukan produksi sumur.
subject: Articles
  topic: section
creator: Rendi Cahya Saputra
creator: Fetty Tri Anggraeny
creator: Agung Mustika Rizki
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 184
ending page: 191
doi: 10.33050/cerita.v11i2.3575

Analisis Ulasan Ecommerce Menggunakan Fine Grained Sentiment Analysist dan Convolutional Neural Network
type: item
description: Beragam ulasan dan komentar dari konsumen Aplikasi e-Commerce seringkali ditinggalkan pada kolom komentar merupakan pengalaman mereka saat mengadakan transaksi jual beli pada platform e-commerce dari yang sangat positif hingga sangat negatif dapat memberikan informasi berharga tentang kepuasan atau ketidakpuasan pelanggan. Ulasan seringkali di tulis dalam bahasa alami yang tidak terstruktur sehingga sulit dianalisis secara manual karena dalam skala besar. Penelitian ini dilakukan untuk menganalisis ulasan pada aplikasi e-commerce platform Bukalapak dan Tokopedia menggunakan metode Fine Grained Sentiment Analysis dan Convolutional Neural Network dengan 1000 dataset yang di scrawling dari google play store menggunakan google colab sebagai toolsnya. Penelitian ini bertujuan untuk memberikan informasi bagi perusahaan dari analisis sentimen yang diperoleh sehingga dapat merespons dengan cepat terhadap umpan balik pelanggan, dan kemudian bisa meningkatkan kualitas layanan, dan mengoptimalkan pengalaman belanja secara online. Penelitian ini menggunakan 5 kelas sentimen yaitu : sangat positif, positif, sangat negatif, negatif dan netral. Dari hasil eksperimen yang telah dilakukan hasil akurasi yang diperoleh dari aplikasi e commerce Tokopedia dengan epoch 10, 20, 40, 60, 80, 100 adalah 62.50 %, 59.26 %, 57.58 %, 48.39%, 51.85%, 65.62%, pada aplikasi Bukalapak adalah 62.50 %, 55.17 %, 62.86 %, 50.00%, 75.00%, 51.72%.
subject: Articles
  topic: section
subject: Ulasan
  topic: keywords
subject: Analisis sentimen
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: FGSA
  topic: keywords
subject: CNN
  topic: keywords
creator: Rusni Harun
creator: Mashur Razak
creator: Abdul Jalil
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 174
ending page: 183
doi: 10.33050/cerita.v11i2.3529

Pengembangan Model Aplikasi Ujian Online Siswa Berbasis Website Menggunakan Metode PIECES
type: item
description: The rapid advancement of information technology compels educational institutions to adopt digital-based systems to enhance the efficiency and effectiveness of learning and assessment processes. This study aims to design and develop a web-based online examination system at SMK Al Hikma. The system is expected to provide a solution to the limitations of conventional paper-based exams, which are time-consuming, prone to human error, and inefficient in terms of scoring. The system development method used is prototyping, with requirements analysis based on the PIECES framework (Performance, Information, Economic, Control, Efficiency, and Service). The system is built using PHP programming language and MySQL as the database. Testing is conducted using the black-box method. The results of the study indicate that the developed system improves time efficiency, reduces operational costs, and enhances the accuracy of exam results.
subject: Articles
  topic: section
creator: Meri Mayang Sari
creator: Dedi Iskandar
creator: Dedeh Supriyanti
creator: Nice Cleveronica Elizabeth Panjaitan
creator: Febby Cahya Firdaus
rights: Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 164
ending page: 173
doi: 10.33050/cerita.v11i2.4032

Analisis Performansi Forecasting Harga Saham Amazon Dengan ARIMA ANN dan LSTM
type: item
description: Stocks are financial instruments traded for profit but come with high risks. Many investors struggle to understand price movements and predict market trends. Various methods, such as ARIMA, ANN, and LSTM, have been developed to forecast stock prices using historical data. This study compares these methods in predicting Amazon's stock prices. The results show that LSTM outperforms the others with a learning rate of 0.001, achieving a MAPE of 1.34% and an RMSE of 3.0501. These findings confirm that LSTM provides higher accuracy for stock market analysis.
subject: Articles
  topic: section
subject: Saham
  topic: keywords
subject: ARIMA
  topic: keywords
subject: ANN
  topic: keywords
subject: LSTM
  topic: keywords
subject: prediksi
  topic: keywords
creator: Chandra Wibawa Syahputra
creator: Basuki Rahmat
creator: M. Muharrom Al Haromainy
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2025-08-08
publication date: 2025-08-08
starting page: 293
ending page: 301
doi: 10.33050/6sy0cm92

Optimalisasi Sistem Deteksi Parkir Menggunakan YOLOv7 dan CLAHE
type: item
description: The significant increase in the number of four-wheeled vehicles in Indonesia, particularly in urban areas, presents challenges in the efficient management of parking spaces. One potential solution is the implementation of an automated parking detection system based on digital image processing and artificial intelligence. This study aims to develop a prototype system for detecting vacant parking spots using the YOLOv7 algorithm, optimized with the Contrast Limited Adaptive Histogram Equalization (CLAHE) method. The image dataset was captured from a single-level parking prototype under various lighting conditions. CLAHE preprocessing was applied to enhance the visibility of essential image features, particularly in low-light scenarios. The training results showed that the use of CLAHE improved detection accuracy compared to YOLOv7 without optimization. Evaluation metrics included precision, recall, F1-score, and mean Average Precision (mAP). The developed system demonstrates significant potential in enhancing the efficiency of parking space usage, reducing search time, and lowering vehicle emissions. This research contributes to the advancement of efficient and environmentally friendly smart parking technologies
subject: Articles
  topic: section
subject: YOLOv7, CLAHE, deteksi parkir, pengolahan citra, deep learning
  topic: keywords
creator: Mochammad Fahmi Yasir
creator: Achmad Junaidi
creator: Muhammad Muharrom Al Haromainy
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 220
ending page: 231
doi: 10.33050/cerita.v12i2.3920

Penerapan Metode Waterfall Dalam Rancang Website Penerimaan Karyawan Pada PT XYZ
type: item
description: The mining industry in the current era is growing with the number of enthusiasts applying for a job at the company. PT XYZ seeks to improve an efficient employee recruitment system by building a recruitment website that uses a decision support system based on the Preference Selection Index method. This research adopts the Waterfall method as a system development approach to build a structured, efficient, and optimal website. In its implementation, the backend development is done by utilising the Laravel framework, while the user interface (frontend) is developed using ReactJS. As for database management, this research uses MariaDB as its database management system. The results of the website implementation show that this web can perform the recruitment system well, displayed based on the accuracy results. Evaluation of the value to measure accuracy using the Confusion Matrix method produced with accuracy reaching 90%. With this website, PT XYZ is expected to increase the number of competent applicants.
subject: Articles
  topic: section
subject: Waterfall
  topic: keywords
subject: PSI
  topic: keywords
subject: Website
  topic: keywords
subject: Decision Support System
  topic: keywords
subject: Employee Recruitment
  topic: keywords
creator: Harith Hakim
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
date: 2026-02-08
publication date: 2026-02-08
starting page: 31
ending page: 42
doi: 10.33050/cerita.V12i1.3812

Sistem Pendukung Keputusan untuk Seleksi Karyawan dengan Metode AHP dan Profile Matching
type: item
description: The manual employee selection process at PT. Mutiara Putri Gemilang still faces several challenges, including subjectivity in assessment, inefficient data management, and unstructured documentation of selection results. This study aims to develop a Decision Support System (DSS) for employee selection by integrating the Analytical Hierarchy Process (AHP) and Profile Matching (PM) methods. The AHP method is used to determine the priority weights of five selection criteria, namely Education, Skills, Work Experience, Interview, and Supporting Certifications, while Profile Matching is used to measure the degree of conformity between candidates’ actual profiles and the predefined ideal profile through GAP, Core Factor, Secondary Factor, and final score calculations. The AHP calculation results produced criterion weights of 0.4694, 0.2740, 0.1459, 0.0754, and 0.0352, respectively, with a Consistency Ratio (CR) of 0.0841. This value satisfies the consistency threshold of CR ≤ 0.10, indicating that the pairwise comparison matrix is consistent. The Profile Matching results generated a candidate ranking based on the degree of conformity with the ideal profile. For the Sales position, the highest-ranked candidate obtained a score of 100.00, followed by the other candidates according to their respective final scores. The developed web-based system can assist HR personnel and management in conducting employee selection in a more structured, transparent, and efficient manner while providing documented ranking results to support decision-making.
subject: Articles
  topic: section
subject: Analytical Hierarchy Process
  topic: keywords
subject: Profile Matching
  topic: keywords
subject: Decision Support System
  topic: keywords
subject: Employee Selection
  topic: keywords
subject: Candidate Ranking
  topic: keywords
creator: Nur Racana Kuslaila
creator: Rizka Hadiwiyanti
creator: Reisa Permatasari
rights: Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics
date: 2026-08-08
publication date: 2026-08-08
starting page: 328
ending page: 339
doi: 10.33050/cerita.v12i2.3915