Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics adalah jurnal ilmiah nasional yang diterbitkan oleh Universitas Raharja Tangerang guna mempublikasikan ringkasan hasil penelitian civitas akademika pada bidang informatika dan komputer. Journal Cerita terbit setiap 6 bulan periode Februari dan Agustus. Penerbitan jurnal ini dimaksudkan sebagai media dokumentasi dan informasi ilmiah yang sekiranya dapat membantu para dosen, staff dan mahasiswa dalam menginformasikan/mempublikasikan hasil penelitian, opini, tulisan dan kajian ilmiah lainnya kepada berbagai komunitas ilmiah. Journal Cerita: Creative Education of Research in Information Technology and Artificial informatics diterbitkan dalam bentuk media online ISSN : 2655-2574, mulai edisi Vol. 4, No. 2, April 2019. This work is licensed under a Lisensi Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Indexed By : Journal CERITA sudah terakreditasi sinta 4
Pengembangan Aplikasi Untuk Monitoring Laporan Bulanan Dan Pembayaran PPATQ RF Pati
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
Copyright (c) 2026 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
Sistem Peringatan Dini Banjir Berbasis Urgensi Wilayah Menggunakan Metode Fuzzy Berbasis Internet of Things Di Kota Palopo
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.
Flood, IoT, HC-SR04, WebGIS, Telegram, Fuzzy Algorithm
Prototipe Game Edukasi Interaktif untuk Meningkatkan Minat Belajar dan Keterampilan Motorik Anak Berkebutuhan Khusus
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.
Multimedia Development Life Cycle (MDLC)
Peningkatan Efisiensi Transaksi dan Pelaporan Penjualan Melalui Rancang Bangun Sistem Informasi Kasir Berbasis Website
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.
Evaluasi Implementasi Sistem Manajemen Informasi Kepegawaian Berbasis PIECES dalam Mendukung Good Governance di Provinsi Banten
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.
Prediksi Intensitas Curah Hujan di Kota Surabaya Menggunakan Principal Component Analysis PCA Dan Long ShortTerm Memory LSTM
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.
Identifikasi Kerusakan Buah Kakao Akibat Serangan Hama Menggunakan Algoritma Yolov9
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
Copyright (c) 2025 Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
Penerapan Metode RAD dalam Rancang Website Pemasaran Rumah Pada PT Bumi Lingga Pertiwi
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.
Implementasi Teknologi Laravel dalam Sistem Pemesanan Catering: Inovasi Digital pada AlFaruq Catering Kudus
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.
Sistem Pemantauan Kedisiplinan Santri Berbasis Citra Raspberry Pi Dan Internet Of Things
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.
Pesantren, Sistem Pemantauan Disiplin, IOT, Raspberry Pi, Pelanggaran
Peningkatan Akurasi Pada Slowfast Network Menggunakan MultiHead Self Attention Layer
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.
Implementasi Vision Transformer untuk Klasifikasi Penyakit Pneumonia melalui Citra Chest XRay
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.
Analisis Sentimen Komentar Instagram Terkait Persepsi Hidup Sehat Menggunakan Algoritma BERTLSTM
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.
MONITORING PEMETAAN KUALITAS UDARA MENGGUNAKAN METODE FUZZY BERBASIS INTERNET OF THINGS
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.
Sistem Pendukung Keputusan Mata Pelajaran Pilihan Siswa Menggunakan Metode Simple Additive Weighting dan Weighted Product
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.
Sistem pendukung keputusan, simple additive weighting, weighted product, pemilihan mata pelajaran.
RANCANG BANGUN SISTEM INFORMASI PENJUALAN TEMBAKAU BERBASIS WEBSITE PADA METRIC TOBACO
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.
Perancangan Sistem Prediksi Keuangan Menggunakan Moving Average Dan Prototype Method
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
Perancangan Website ECommerce Berbasis MobileFirstDesign untuk Optimalisasi Efisiensi Layanan di Apotek Mumtaz Husada
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.
E-Commerce, Mobile-First Design, Pharmacy, Waterfall, Website
Analisis Performa Choke Dalam Prediksi Well Testing Menggunakan Algoritma LongShort Term Memory
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.
Analisis Ulasan Ecommerce Menggunakan Fine Grained Sentiment Analysist dan Convolutional Neural Network
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%.
Pengembangan Model Aplikasi Ujian Online Siswa Berbasis Website Menggunakan Metode PIECES
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.
Analisis Performansi Forecasting Harga Saham Amazon Dengan ARIMA ANN dan LSTM
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.
Penerapan Metode Waterfall Dalam Rancang Website Penerimaan Karyawan Pada PT XYZ
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.
Penerapan Swin Transformer Pada Klasifikasi Bentuk Wajah Untuk Rekomendasi Bentuk Kacamata
Facial shape serves as an identity marker that reflects the unique physical characteristics of each individual. In general, facial shapes are categorized into five main types: square, round, oval, heart, and oblong. Information regarding facial shapes can be utilized in various aspects of personalization, including the selection of suitable eyeglass models. As the fashion industry evolves, eyeglasses are no longer solely functional as vision aids but also serve as aesthetic elements that enhance one’s appearance. Therefore, selecting eyeglass models based on facial shape is a crucial factor in improving an individual's visual appeal. In online shopping, customers often face difficulties in determining the most suitable eyeglass model. Thus, an AI-based automated system is required to provide optimal recommendations. This study implements the Swin Transformer model to classify facial shapes as a basis for eyeglass model recommendations. Swin Transformer is a Deep Learning architecture that employs a hierarchical patch-based image partitioning approach and processes images through a series of transformer blocks in a progressive manner. The results of this study demonstrate that the model performs well in classification tasks. With a learning rate of 0.0001, a batch size of 32, and 64 epochs, the model achieved a training accuracy of 99.15%, a validation accuracy of 98.47%, and a test accuracy of 97.63%. The small accuracy difference (0.68%) indicates that the model does not exhibit significant overfitting. Furthermore, the consistently low loss values across all data subsets indicate the model’s stability. These findings confirm that the developed system is capable of accurately classifying facial shapes and providing appropriate eyeglass recommendations.
Deteksi Seatbelt Pada Pengendara Mobil Melalui CCTV Menggunakan Metode YOLOv7
The use of seatbelts is crucial for vehicle safety, yet many drivers still neglect it, increasing the risk of injuries and fatalities. This research develops a real-time detection system for seatbelt usage using CCTV footage and the YOLOv7 (You Only Look Once version 7) algorithm. The method involves capturing traffic video, processing it into image frames, and detecting seatbelt usage with high accuracy. Data collection was conducted in several strategic locations in Surabaya, followed by preprocessing and annotation for object detection. The best model was achieved with an 80:20 data split, resulting in a mean Average Precision (mAP) of 0.964, Precision of 0.949, and Recall of 0.949, demonstrating high accuracy and reliability. This system is expected to support traffic law enforcement and enhance safety monitoring through real-time detection
Seatbelt Detection, YOLOv7, Computer Vision, Real-Time Detection, Traffic Safety
Sistem Monitoring Pemakaian Air Berbasis IoT Menggunakan Fuzzy Type2
Uncontrolled water consumption can lead to wastage of resources and contribute to the future clean water crisis. To overcome these problems, this research aims to design and implement an Internet of Things (IoT)-based water usage monitoring system equipped with Type-2 fuzzy logic to deal with data uncertainty from flow sensors. The system uses ESP32 microcontroller as the main processing unit, YF-B2 flow sensor to measure the water rate, solenoid valve as automatic flow regulator, I2C LCD to display the information locally, and Blynk platform as the user interface to monitor the data in real-time through mobile devices. Type-2 fuzzy logic is used to classify the level of water usage (low, medium, high) based on the volume and duration of use, and provide an automatic response in the form of a warning or stoppage of water flow. Test results show that the system is able to monitor water usage accurately and responsively to actual conditions. Thus, this system can contribute to water saving efforts, increase public awareness, and support the conservation of water resources in a sustainable manner.
Internet of Things, Type-2 Fuzzy Logic, ESP32, Water Usage, Blynk
Perancangan Sistem Aplikasi Point Of Sales Berbasis Website Pada Toko Bintang Kudus
Various MSME sectors in Indonesia, including small and medium enterprises, have felt a significant impact from the rapid development of information technology in the digital era. Toko Bintang has been operating for more than five years. This shop is located in Temulus Village, RT.01 Rw.05, Mejobo District, Kudus, Central Java. This shop functions as a minimarket that sells various daily necessities and has a wide market because products such as basic necessities and household necessities are always needed by the community. Even though it has been developing for quite a long time, Toko Bintang still relies on manual methods for recording transactions, stock management and financial reports. Using books for recording has proven to be less efficient, prone to errors and data loss. In designing this application, the Waterfall methodology was used to ensure that each development stage was carried out systematically and structured. The point of sales (POS) application will be developed using PHP with the Laravel framework, Bootstrap, MySQL, Tailwind CSS, and Vite. The main aim of creating this application is to increase operational efficiency, automate recording and reporting, reduce the risk of data loss or damage which often occurs with manual methods in stores, and make it easier to manage payments via Midtrans for convenient transactions.
Point of Sales, Laravel, Minimarket, Midtrans
Pendekatan Design Thinking dan Google Lighthouse untuk Peningkatan Usability UI/UX Website Wisma Lansia Aisyiyah Kudus
This study aims to implement and test a website-based data management system for the Aisyiyah Kudus Elderly Home which is intended for administrators and families of the elderly with a Design Thinking approach. In the digital era, an efficient information system is very important to improve the quality of service at the Aisyiyah Kudus elderly home, especially in website development. The research method includes data collection using the System Usability Scalesuvey to evaluate user satisfaction. The result indicate that developed website achieved an average score of 72.75, categorizing it as “Good” in term of usability. Responsiveness testing using Google Lighthouse obtained an average performance of around 82 or showed good performance with smooth access. This findings emphasize the importance of a user-friendly interface design and the alignment of features with the needs of caregivers and residents of the elderly home. This research provides significant contributions to the development of information systems in social service, especially in managing data for elderly, and offers recommendations for further relevant and sustainable development in the future.
Usability, Design Thinking, elderly, Google Lighthouse
Perbandingan Adam dan SGD Menggunakan Faster RCNN MobileNet pada Deteksi Bahasa Isyarat
This research aims to evaluate the performance of two different optimizations, namely Adam's optimization and SGD. The faster RCNN backbone used is the MobileNet v3 backbone, and non-maximum suppression (NMS) is applied to strengthen the results of American Sign Language hand gesture detection. Through analysis, it was found that Adam's optimization obtained mAP accuracy of 88.97% on test data, mAP of 86.05% on validation data, and average f1-score of 88.54% on test data. SGD optimization showed mAP accuracy of 88.35% on test data, mAP of 87.23% on validation data, and an average f1-score of 84.56% on test data. Thus, although both optimizations show similar performance, Adam's optimization has a slightly superior performance in terms of mAP and f1-score on the test data. This research implies that using Backbone MobileNet v3 and Adam's optimization can be a practical approach to support American Sign Language hand gesture detection.
Object Detection, Faster RCNN, MobileNet, Sign Language
Implementasi Metode Hibrida CNNELM Dalam Deteksi Citra Deepfake
The existence of Artificial Intelligence (AI) today has played a significant role in human life. In addition to bringing positive impacts, AI also has negative effects that can be detrimental to humans, one of which is Deepfake. Deepfake is the use of deep learning to forge someone's face in an image or video. This research introduces a hybrid method combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to detect deepfake images. The goal of this research is to create image detection to verify the authenticity of an image in order to avoid deepfake. With the advantage of feature extraction from the CNN model and the efficient computational speed of the ELM model, the CNN-ELM hybrid method can accurately and efficiently train and test data. This research uses various scenarios to find the best parameter configuration. The results of this hybrid method achieved an average accuracy of 85.77% using 600 hidden neurons, RMSprop optimization, and ReLu activation function. This research also developed a simple GUI to allow free input of photos to verify their authenticity. This research can be one approach to detecting deepfake images.