CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) merupakan salah satu wadah publikasi jurnal yang diterbitkan oleh Universitas Raharja, melalui Raharja Enrichment Centre selaku LPPM-Mutu untuk multidisiplin ilmu Meliputi bidang pendidikan, politik, manajemen, sosial, budaya, kesehatan, hukum, ekonomi, IT (Teknik Informatika), Teknik Sipil, Teknik Elektro, Teknik Mesin dan bidang ilmu lainnya. Frekuensi terbitan CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) setiap 6 bulan atau 2 kali dalam setahun pada periode Februari dan Agustus. dengan Nomor ISSN 2356-5209 (media cetak) dan e-ISSN 2655-3058 (media online) mulai edisi Vol. 5, No. 1, Februari 2019. >> cices@raharja.info | https://raharja.ac.id/ This work is licensed under a Lisensi Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Indexed By
Perbandingan Kinerja Algoritma Apriori dan Eclat Equivalence Class Transformation Dalam Menerapkan Rekomendasi Barang Diskon pada Data Transaksi
This research aims to compare the performance of the Apriori and ECLAT algorithms in applying discounted goods recommendations on sales transaction data. The research method used involves six main stages, namely collecting transaction data from CV SOSO Group Jombang, preprocessing data to clean and prepare data, implementing the Apriori and ECLAT algorithms, forming association rules, evaluating algorithm performance, and visualizing analysis results. The transaction dataset used comes from sales during October and is processed using the Market Basket Analysis approach. Apriori and ECLAT algorithms are tested to find customer purchase patterns by considering the support, confidence, and lift factors in the association rules. Performance evaluation is done by measuring the number of intemsets generated, computation time, and the quality of the rules obtained. The results showed significant differences between the two algorithms. Apriori is easier to implement and provides rules that are more easily understood by decision makers, while ECLAT is superior in time efficiency and memory usage, especially on small to medium-sized datasets. After a comparative analysis, it was found that the ECLAT algorithm is faster in finding transaction patterns with a shorter execution time than Apriori. However, Apriori algorithm is more effective in displaying association rules that are clearer and easier to interpret. With these results, this research recommends the use of algorithms based on specific needs, namely Apriori for more interpretative analysis and ECLAT for analysis with larger and more complex datasets.
Copyright (c) 2026 CICES (Cyberpreneurship Innovative and Creative Exact and Social Science)
Analisa Yuridis Dalam Konteks Pembaharuan Hukum Pidana di Indonesia
The purpose of this paper is to analyze criminal law reform in Indonesia, which is crucial to ensure justice and security in society. Criminal law itself is a branch of law that regulates criminal acts and the sanctions imposed on perpetrators. The methodology used in this paper is a juridical-normative approach to legal research that focuses on applicable legal norms or regulations based on statutes. The results of this analysis indicate the need for efforts to improve the quality of criminal law enforcement in Indonesia, such as improving the quality of investigations and trials, and strengthening the independence of law enforcement agencies. Overall, criminal law reform is crucial to ensure justice and security in society. However, it is important to remember that criminal law reform is insufficient if it is not accompanied by sound and effective law enforcement.
Performa Model Faster R-Cnn Untuk Klasifikasi Penyakit Daun Jagung
Image classification is one of the main challenges in the field of digital image processing and computer vision. This study aims to develop an image classification system using the Faster R-CNN object detection method. The Faster R-CNN model is utilized to detect objects in images while also classifying the extracted features from those images. The dataset used in this study was sourced from Kaggle and data taken directly by researcher. The data underwent several preprocessing stages, including data transformation, splitting into training / validation / testing sets, and model performance evaluation based on metrics such as accuracy, precision, recall, anda F1-Score. The experimental results demonstrate that the Faster R-CNN method can achieve high classification performance, with accuracy reaching up to 98,98% at a data ratio of 60:20:20. In addition, the model also shows performance stability at other data ratios. This study confirms that deep learning methods can deliver excellent results in image classification tasks. This research is expected to be a reference for the development of other image classification systems, especially those that require accurate object detection and classification.
Aplikasi Mobile Penjualan Makanan Sisa Dengan Geolocation Dan Metode Haversine
Food waste is a critical global issue with significant economic and environmental implications. In 2021, approximately 40% of the total global food production, equivalent to 2.5 billion tons, was wasted. This figure marks a notable increase of 1.2 billion tons from the estimated food waste in 2011. Globally, Indonesia ranks second in terms of the highest levels of food waste, with 39.4% of the total waste generated in the country being food waste. This statistic makes food waste the predominant type of waste in Indonesia. This research aims to design and develop a mobile geolocation based application for the sale of surplus food, utilizing the haversine method to facilitate this process. The application is designed to allow users to access the locations of surplus food sellers within a maximum radius of 25 kilometers from their position. The results indicate that the developed application successfully displays the distance between consumers and surplus food sellers. Black box testing successfully ensures that all application functionality runs well. The level of user acceptance of the application is at the acceptable level, and the application grade is B.
Copyright (c) 2025 CICES (Cyberpreneurship Innovative and Creative Exact and Social Science)
Implementasi Aplikasi CRM Berbasis Mobile dengan Waterfall Development untuk Efisiensi Kerja Karyawan di PT Kobe Boga Utama Kudus
Penelitian ini bertujuan untuk mengembangkan aplikasi Customer Relationship Management (CRM) berbasis Android dengan menggunakan model pengembangan Waterfall untuk meningkatkan efisiensi kerja karyawan di PT Kobe Boga Utama, Kudus. Aplikasi ini dirancang dengan memanfaatkan Flutter sebagai framework pengembangan dan bahasa pemrograman Dart, yang mendukung fitur-fitur unggulan seperti notifikasi real-time, manajemen tugas, proyek, jadwal, serta komunikasi berbasis chat. Penelitian menggunakan metode kualitatif dengan pengumpulan data melalui wawancara, observasi, dan studi literatur. Hasil implementasi menunjukkan bahwa aplikasi ini mampu mengatasi kendala operasional seperti koordinasi yang kurang efektif dan pengelolaan tugas manual. Pengujian menggunakan metode Black Box Testing memastikan seluruh fungsi aplikasi berjalan sesuai spesifikasi, seperti pengelolaan tugas, integrasi kalender, dan notifikasi real-time. Hasil penelitian ini menunjukkan bahwa aplikasi CRM berbasis Android tidak hanya meningkatkan efisiensi kerja karyawan tetapi juga memberikan kontribusi positif terhadap pertumbuhan bisnis secara berkelanjutan. Kata Kunci— Customer Relationship Management, Flutter, Efisiensi Kerja, Waterfall Development, Aplikasi Mobile
Sistem Informasi Kpi Untuk Analisis Kinerja Karyawan Menggunakan Naive Bayes Di Pt Jembatan Data Pangrango
Penelitian ini mengembangkan sistem analisis kinerja karyawan berbasis web untuk menganalisis dan memprediksi kinerja karyawan menggunakan metode Recurrent Neural Network (RNN) di PT Jembatan Data Pangrango, sebuah perusahaan penyedia layanan internet di Sukabumi. Sistem analisis kinerja karyawan manual yang sebelumnya digunakan menyebabkan inefisiensi dalam memantau dan mengevaluasi kinerja. Dengan pendekatan Rapid Application Development (RAD), sistem ini dibangun untuk mencatat absensi, mendeteksi anomali, dan menganalisis pola kinerja karyawan berdasarkan data historis. Sistem ini juga mampu memprediksi performa karyawan secara individual dan memberikan rekomendasi pelatihan yang sesuai untuk peningkatan kinerja. Hasil pengujian menunjukkan bahwa sistem dapat mendeteksi pola ketidakhadiran, keterlambatan, dan lembur, sehingga memudahkan perusahaan dalam melakukan evaluasi kinerja dan perencanaan pengembangan karyawan secara lebih efektif.
Optimasi Model Prediksi Kelulusan Mahasiswa Berbasis Principal Component Analysis dan Modified KNearest Neighbor
Angka drop-out mahasiswa di perguruan tinggi masih menjadi permasalahan serius karena berdampak pada pemborosan sumber daya dan perkembangan institusi. Identifikasi dini terhadap mahasiswa berisiko drop-out sangat penting, namun metode manual dan algoritma K-Nearest Neighbor (KNN) konvensional masih memiliki keterbatasan, seperti sensitivitas terhadap outlier dan data berdimensi tinggi. Penelitian ini mengusulkan integrasi Principal Component Analysis (PCA) dan Modified K-Nearest Neighbor (MKNN) untuk meningkatkan akurasi klasifikasi kelulusan mahasiswa. PCA digunakan untuk mereduksi 14 variabel menjadi 2 variabel utama, sedangkan MKNN memodifikasi KNN dengan teknik weight voting berbasis jarak serta validasi data latih guna mengurangi perngaruh outlier. Model diujikan dengan skema pembagian data 60:40 (latih:uji) dan parameter optimal k=9. Hasil penelitian menunjukkan bahwa kombinasi PCA dan MKNN mampu mencapai akurasi 99,31%, meningkat 0,93% dibanding KNN standar, serta menghasilkan presisi, recall, dan F1-Score sebesar 99,3%. Temuan ini menegaskan bahwa integrasi reduksi dimensi dan weight voting efektif dalam meningkatkan kinerja klasifikasi, sehingga model ini berpotensi menjadi alat prediksi drop-out yang andal di lingkungan pendidikan tinggi.
Analisis User Experience Pada Website Pendaftaran Santri Pondok Pesantren Salafiyyah AlHidayah Kudus Menggunakan Metode Heuristic Evaluation
Penelitian ini menganalisis User Experience pada website pendaftaran santri Pondok Pesantren Salafiyyah Al-Hidayah Kudus menggunakan metode Heuristic Evaluation. Evaluasi ini penting dilakukan untuk memastikan website yang dikembangkan dapat memenuhi kebutuhan pengguna secara optimal dalam proses pendaftaran santri baru yang sebelumnya dilakukan secara manual. Penelitian menggunakan pendekatan kuantitatif dengan menyebarkan kuesioner kepada 20 responden yang pernah menggunakan website tersebut. Kuesioner dirancang berdasarkan 10 prinsip Heuristic Evaluation yang dikembangkan oleh Jakob Nielsen. Hasil analisis menunjukkan bahwa seluruh aspek yang dievaluasi berada pada tingkat Severity Rating 1 (Cosmetic Problem), yang mengindikasikan adanya masalah-masalah kecil yang tidak terlalu mengganggu kenyamanan pengguna. Aspek Aesthetic and Minimalist Design (1.275) dan Consistency and Standards (1.225) memerlukan perhatian lebih dalam perbaikan, sementara aspek Visibility of System Status (0.95) menunjukkan performa terbaik. Hasil evaluasi ini dapat menjadi dasar untuk pengembangan dan perbaikan website guna meningkatkan kualitas pengalaman pengguna dalam proses pendaftaran santri.
Sistem Informasi Distribusi LPG 3 Kg Pada PT Explorindo Solusi Gasindo Kudus Berbasis Website
The 3 kg LPG distribution system plays an important role in ensuring the fuel supply is well-targeted. 3 kg Liquefied Petroleum Gas (LPG) is an important subsidized fuel for households and small businesses in Indonesia. PT Explorindo Solusi Gasindo Kudus faces obstacles in the distribution and management of new base registration manually, such as long lines, data errors, and payment complexity. This research develops a web-based information system to improve administrative efficiency, simplify registration, and increase service transparency. This research aims to develop a web-based LPG 3 KG Distribution Information System for PT Explorindo Solusi Gasindo Kudus. This system is designed to facilitate distribution data management, including base registration, cross-check for base acceptance, and real-time base data reporting. This system is expected to support modern and effective management of LPG distribution. The implementation results show an increase in operational efficiency, data transparency, and ease of access for related parties, supporting the optimization of LPG distribution in the Kudus area.
Pengembangan Sistem Monitoring dan Manajemen ATK Berbasis Website dengan Fitur RealTime Reporting di Pengadilan Negeri Kudus
Pengelolaan alat tulis kantor (ATK) yang optimal menjadi elemen penting dalam mendukung kelancaran administrasi di instansi pemerintahan, termasuk di Pengadilan Negeri Kudus. Namun, masih terdapat tantangan dalam melakukan pemantauan terhadap ketersediaan stok, permintaan, serta distribusi ATK secara real-time. Penelitian ini bertujuan untuk merancang dan membangun sistem berbasis web yang dapat memantau serta mengelola ATK guna meningkatkan efisiensi pengelolaannya. Metode penelitian melibatkan analisis kebutuhan, desain sistem menggunakan framework Laravel, serta implementasi dan pengujian fitur utama, seperti manajemen stok, pemrosesan permintaan, dan pelaporan otomatis. Hasil penelitian menunjukkan bahwa sistem ini memudahkan bendahara dalam memantau ketersediaan ATK sekaligus mengelola permintaan dari pengguna. Selain itu, sistem menyediakan laporan yang dapat diakses secara real-time untuk mendukung proses pengambilan keputusan yang lebih baik. Dengan demikian, sistem ini diharapkan mampu meningkatkan efisiensi operasional serta transparansi dalam pengelolaan ATK.
Komunikasi Interpersonal Coach dengan Atlet Pebasket Klub CLS Surabaya
Perkembangan kemampuan serta keterampilan atlet memerlukan komunikasi serta hubungan antara coach dengan atlet harus berjalan dengan baik. Hubungan yang dibangun tersebut tentu saja memerlukan adanya proses komunikasi. Komunikasi interpersonal merupakan komunikasi yang tepat untuk digunakan dalam hubungan tersebut. Komunikasi interpersonal merupakan proses komunikasi yang terjadi secara langsung. Dengan adanya permasalahan tersebut peneliti tertarik untuk membahas mengenai bagaimana proses komunikasi interpersonal antara coach dan atlet di klub CLS Surabaya. Komunikasi interpersonal antara coach dengan altet pebasket klub CLS Surabaya dilihat melalui cir-ciri serta proses komunikasi interpersonal yang terjalin. Peneliti menggunakan pendekatan kuasi-kualitatif dengan memerlukan teori yang mendasarinya, dan dari pengumplan data yang didapatkan melalui wawancara dan dokumnetasi. Hasil penelitian ini didapatkan bahwa komunikasi interpersonal yang terjalin antara coach dan atlet di CLS Surabaya menunjukan beberapa ciri komunikasi interpersonal, yaitu selektif, sistematis, unik, processual, transaksional, individual, pengetahuan personal, serta menciptakan makna. Serta proses atau tahapan dalam komunikasi interpersonal yang dilalui oleh coach dengan atlet. Namun, ditemukan pula dalam beberapa kasus terjadi kemerosotan komunikasi yang dapat diperbaiki melalui pendekatan komunikasi yang lebih efektif. ​ Kata Kunci—Atlet, Coach, Komunikasi Interpersonal
Analisis Perbandingan Test Case Prioritization Menggunakan Algoritma Greedy dan Algoritma Hill Climbing
Software Testing merupakan salah satu langkah penting dalam memastikan suatu software tetap unggul serta dapat berjalan dan digunakan oleh masyarakat luas, software testing dapat dilakukan menggunakan Regression Testing untuk memastikan software tidak mengalami kerusakan atau terdapat bug baru setelah perubahan terjadi. Regression Testing digunakan untuk menghemat waktu dan biaya pengujian dengan menerapkan Test Case Prioritization yang berfungsi agar software tester dapat memilih test case yang paling penting untuk dilakukan pengujian berdasarkan penggunaan algoritma Greedy dan algoritma Hill Climbing yang digabungkan dengan parameter lain seperti Severity Rating, dan matriks Average Percentage Fault Detection (APFD). Fokus penelitian ini adalah mencari urutan prioritas test case berdasarkan hasil pengujian Regression Testing pada website demoblaze.com berdasarkan perbandingan algoritma Greedy dan algoritma Hill Climbing yang digabungkan dengan parameter Severity Rating berdasarkan nilai APFD tertinggi. Hasil dari penelitian menunjukkan penggunaan algoritma Hill Climbing lebih efektif dalam menentukan prioritas test case dengan memperoleh nilai APFD sebesar 0.91, sedangkan algoritma Greedy mendapatkan nilai APFD sebesar 0.9.
ELearning Responsif Berbasis Laravel untuk Mendukung Pembelajaran Daring di SMP Negeri 1 Kudus
The advancement of information technology has driven educational institutions to adapt, particularly in implementing responsive and integrated online learning. This study aims to design and develop a Laravel-based e-learning system at SMP Negeri 1 Kudus to support digital teaching and learning processes. The system was designed using the Waterfall software development method, which includes requirement analysis, system design, implementation, testing, and maintenance. This system provides key features such as user management, learning material management, assignments, exams, and grade reports, accessible via both desktop and mobile devices. During development, web technologies such as HTML, CSS, JavaScript, and the MySQL database were utilized. Testing results showed that the application operated according to specifications and positively impacted the effectiveness of online learning, particularly in enhancing interactions between teachers and students. Additionally, the system is responsive and supports cross-device accessibility, facilitating students and teachers in various situations. This research is expected to offer an innovative solution to overcome the limitations of conventional learning, support digital transformation in education, and provide a more interactive and adaptive learning experience in line with technological advancements.
Pengukuran Hasil Belajar Mata Kuliah Agama Islam Berbasis Multiple Intelligence di Universitas Raharja Tangerang
Islamic Religious Education held at Higher Education in Indonesia generally has the same problem, namely the lack of methodology in learning so that it is less interesting. For this reason, innovation is needed in Islamic Religious Education. One solution is to use multiple intelligences-based learning. The formulation of the problem in this study is: 1. Is there a direct or indirect influence between kinesthetic intelligence, visual-spatial intelligence and language intelligence mediated by mathematical intelligence on the results of learning Islamic Religious Education at Raharja University in Tangerang? 2. Is there a direct or indirect influence between interpersonal intelligence, intrapersonal intelligence and existential intelligence mediated by naturalist intelligence on the results of learning Islamic Religious Education at Raharja University in Tangerang? This study is a quantitative study using the Smart-PLS Version 4.0 application for data analysis. The results of the study are: 1. Based on the results of the analysis, the R Square value of 0.651 was obtained for language intelligence, indicating that 65.1% of the variation in this variable can be explained by kinesthetic intelligence and visual-spatial intelligence in the model, with 34.9% influenced by external factors. This can be explained that the results of learning Islamic religion at Raharja University are influenced by language intelligence supported by kinesthetic intelligence and visual-spatial intelligence which are significant even though there are external factors on learning outcomes.2. Likewise, the R Square value of 0.646 for mathematical intelligence indicates that 64.6% of the variation in this variable can be explained by kinesthetic intelligence and visual-spatial intelligence in the model, with 35.5% influenced by external factors. From these results, it can also be said that the results of studying Islamic religion at Raharja University students have a significant influence on mathematical intelligence mediated by kinesthetic intelligence and visual-spatial intelligence. It can be said that there is a positive correlation between the three intelligences. 3. Based on the results of the analysis, the R Square value of 0.806 for existential intelligence indicates that 80.6% of the variation in this variable can be explained by intrapersonal intelligence and interpersonal intelligence in the model, with 19.4% influenced by external factors. Likewise, the R Square value of 0.697 for naturalist intelligence indicates that 69.7% of the variation in this variable can be explained by intrapersonal intelligence and interpersonal intelligence in the model, with 31.3% influenced by external factors.
Penerapan Website EGovernment Dalam Pendataan Infrastruktur Dan Fasilitas Publik
The development of science and technology in the management system of social and public facilities will assist in improving the performance of the sub-district government. Currently, the Pasar Kemis District office does not yet have a web-based infrastructure and facility management system to overcome the problem of collecting data on social and public facilities that still use a semi-computer system, namely recording infrastructure and facility data using paper, then recapitulation is carried out to make reports on social facilities. And general. This system still has shortcomings such as difficulty in finding data, allowing errors in data collection, data piling up and requiring a long time in the reporting process. To overcome the problems faced, the authors propose a web-based social and public facility management system so that data collection on facilities becomes easier, shortens time, is organized in data storage. The author uses the method of Observation, Interview, and Literature Study in collecting data. All data are summarized and then clearly described in the Unified Modeling Language (UML). Making a prototype system using Adobe XD application and in analyzing the advantages and disadvantages of the system, the researcher uses the SWOT method. This system is made using the PHP programming language with the Codeigniter framework. The system was tested using the Black Box Testing method. The computerized system makes it easier to manage infrastructure and facilities data so as to assist in the data collection process, data retrieval and reporting of social and public facilities.
Copyright (c) 2024 CICES (Cyberpreneurship Innovative and Creative Exact and Social Science)
Implementasi ANOVA Pada Algoritma KNN++ Untuk Klasifikasi Serangan DDoS UDP Flood
Cybersecurity has become a critical issue in the digital era, particularly with the significant rise of Distributed Denial of Service (DDoS) UDP Flood attacks, which increased by 59.77% in 2023. This study implements the K-Nearest Neighbors++ (KNN++) algorithm, enhanced with feature selection using Analysis of Variance (ANOVA) and data balancing through the Synthetic Minority Oversampling Technique (SMOTE), to detect UDP Flood DDoS attacks based on network traffic anomalies. The CICDDoS2019 dataset is processed through comprehensive preprocessing stages, including the removal of 20 irrelevant features, Min-Max normalization, and class distribution balancing. Experimental results show the best performance at K=3, achieving an accuracy of 99.994%, precision of 99.998%, recall of 99.996%, and an F1-score of 99.997%. The application of SMOTE successfully increased the minority class recall by 2.51% compared to the model without SMOTE, with a computation time of 3,846 seconds, which is considered efficient for large-scale datasets. These findings demonstrate the effectiveness of the combined KNN++, ANOVA, and SMOTE approach as a comprehensive solution for anomaly-based intrusion detection systems
Implementasi Portal Akademik Berbasis Website Untuk Digitalisasi Administrasi Sekolah di SMP 2 Jekulo Kudus Menggunakan Metode Waterfall
The implementation of an Academic Portal based on a website for digitalizing school administration at SMP 2 Jekulo Kudus aims to enhance the efficiency of administrative tasks through a modern digital system. The traditional paper-based approach often led to delays and errors in managing student data, grades, attendance, and other administrative aspects. This research adopts the Waterfall method, a sequential development approach, to build a user-friendly academic portal tailored to the needs of SMP 2 Jekulo. The study outlines the system requirements, design, implementation, and testing phases. The development process includes features such as student registration, grade input, attendance monitoring, and reporting. The results of this implementation are expected to streamline administrative work, reduce errors, and provide real-time access to academic information. The academic portal offers benefits to both administrative staff and students, providing a more organized and efficient system. The study concludes that digitalizing administrative tasks is essential for improving the overall management of educational institutions.
Sistem Presensi Karyawan SISKA Menggunakan RFID dan NodeMCU Pada CV Mulia Abadi
This research aims to develop an Internet of Things-based employee attendance system using RFID and NodeMCU technology in CV Mulia Abadi Jepara. The system is designed to replace the presence of conventional or manual methods. In this study, the attendance reader machine was designed using a NodeMCU ESP8266 microcontroller and an MFRC-522 RFID module for RFID card reading. This system is also equipped with a PHP and MySQL-based website to monitor and recapitulate attendance data automatically. The waterfall method is used in the development of this system which consists of five stages, namely requirement analysis, system design, implementation, testing, and maintenance. System testing is carried out with two types of tests, namely blackbox testing and User Acceptance Testing (UAT). The test results show that the RFID presence system using a 13.56MHz RFID card can function well with an average card reading distance of 4 cm and a user satisfaction rate of 89%. With this implementation, the employee attendance system can increase efficiency in managing attendance recapitulation data that is practical and accurate for the company.
Implementasi Metode Waterfall dalam Rancang Bangun Website ELearning untuk Pembelajaran di SMP 2 Jekulo Kudus
Digital development in education is driven by advances in information technology, including at SMP 2 Jekulo Kudus. The purpose of this research is to create and build an e-learning website that uses the Waterfall method effectively to support online learning. Requirement analysis is the first step in the development process, which determines functional requirements such as user management, learning material management, online exam system, discussion forum, and data security. In the design stage, the system is designed to be scalable, accessible, and easy to use. Implementation is done using contemporary web technology and thoroughly tested to ensure that all features run according to specifications. The results show that the e-learning platform can help teachers and students learn better by providing an interactive platform. Systems such as exams and online discussion forums increase student participation in learning, while structured material management makes it easier for teachers to deliver material. The website also provides solutions to conventional learning constraints, supports flexibility of access, and strengthens digital transformation efforts in the school environment.
Komparasi Kernel SVM Pada Klasifikasi Citra Tumor Otak Menggunakan Ekstraksi Fitur GLCM
The human brain contains more than 100 billion cells that function to transmit information. Brain tumors occur due to the abnormal growth of cells within or around the brain. Early detection is crucial to prevent tumors from progressing to more severe stages. Manual processes such as biopsies are time-consuming and prone to errors. Therefore, computer technology, such as machine learning, offers an effective solution to support brain tumor diagnosis. This study applies brain tumor image classification using the GLCM feature extraction method and the SVM algorithm as a classifier with four different kernels: linear, polynomial, RBF, and sigmoid with default parameter. The dataset used consists of 2,800 images divided into two classes: tumor and no-tumor. Testing results indicate that the linear kernel achieved an accuracy of 97%, while the polynomial kernel also reached 97%. The RBF kernel achieved 75% accuracy, while the sigmoid kernel produced the lowest accuracy at 48%. These results demonstrate that the linear and polynomial kernels are better at capturing patterns compared to other kernels for brain tumor image classification. With a high confusion matrix score, the linear kernel is recommended for similar image classification tasks.
Pengembangan Sistem Informasi Pengelolaan Distribusi Bantuan Bencana Berbasis Web Pada Koramil 09 Kaliwungu Kudus Untuk Peningkatan Efektivitas Respon Bencana
Disaster is an event that causes damage and suffering, whether caused by natural or human factors. In disaster management, Koramil 09/Kaliwungu has a very important role, especially in distributing aid to affected communities. However, Koramil 09/Kaliwungu faces several problems in managing aid distribution, such as the use of Microsoft Excel to record aid recipient data that is inefficient and prone to errors. This results in data that is not well integrated, leading to the risk of providing aid that is not on target or multiple recipients. In addition, the problem of inefficient logistics and distribution coordination, as well as the lack of transparency in monitoring aid, worsens the situation. The lack of a support system also has an impact on the quality of service and slow response time in distributing aid. As a solution, the author proposes the development of a web-based aid distribution management information system, which can integrate data accurately, improve logistics efficiency, and provide transparency and accountability in aid distribution. This system is expected to facilitate distribution management, monitor aid in real-time, and support personnel in making faster and more targeted decisions
Predictive Maintenance dengan Klasifikasi OnevsRest dan Random Search
This study develops a predictive model for machine maintenance using machine learning, focusing on optimizing the Random Forest algorithm with a One-vs-Rest (OvR) approach. The research is motivated by the industrial need for accurate predictive maintenance systems to anticipate machine failures early, addressing the limitations of currently dominant reactive and preventive maintenance approaches. The main challenge lies in significant class imbalance, where the "No Failure" class dominates 80% of the dataset while other failure classes comprise only 2-5%. The research methodology includes data preprocessing, exploratory data analysis, hyperparameter optimization using Random Search, and model evaluation with various metrics. Results show the model achieves 99% accuracy and a macro ROC-AUC of 0.9408, but fails to predict minority classes like Tool Wear Failure (TWF) and Random Failures (RNF) with 0.00 precision and recall. These findings confirm the negative impact of class imbalance on model performance. The study makes important contributions by identifying Random Forest's limitations for imbalanced data while recommending specialized handling techniques for future research. The results provide a foundation for developing more robust predictive maintenance systems in industrial applications.
Implementasi Metode KNN Untuk Klasifikasi Penyakit Daun Teh Berdasarkan Fitur Tekstur GLCM
This study evaluates the tea leaf disease classification method using Gray Level Co-occurrence Matrix (GLCM) texture feature extraction and the K-Nearest Neighbor (K-NN) algorithm. The dataset used consists of 1,800 tea leaf images divided into six categories, namely Red Leaf Spot, Algal Leaf Spot, Anthracnose, Bird Eye Spot, Brown Blight, and Healthy. Each image undergoes a preprocessing process in the form of conversion to grayscale, resizing, and normalization. The extracted GLCM global texture features include contrast, correlation, energy, and homogeneity. The classification process uses the K-NN algorithm with variations in K values ​​from K = 1 to k = 10, and is carried out on three training and test data division scenarios, namely 90:10, 80:20, 70:30. The evaluation was carried out using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the model with a combination of GLCM and K-NN produces the highest accuracy of 95.83% at a value of K = 1 with a data division scenario of 80:20. These findings indicate that this approach is effective in classifying tea leaf diseases.
Optimalisasi Penggunaan Pemanfaatan Pemindahtanganan Pemusnahan dan Penghapusan Barang Milik Daerah
The purpose of this research is to minimize potential losses and ensure that the rental process of assets owned by the South Tangerang City Government is carried out efficiently and in accordance with applicable regulations by identifying potential risks that may arise in the process of renting local assets, such as the risk of damage to goods, legal non-compliance, or financial risk. The methodology used in the study of risk profiles and risk management patterns in projects utilizes the COSO method in this case, activities such as Focus Group Discussions (FGD) and field surveys are conducted for verification Stakeholders involved in the FGD activities include Government Agencies, Businesses, Academics, and Practitioners. The risk of renting local government property has important implications for the local government Some of the implications that may arise, as well as recommendations for managing these risks, include risk implications and risk management recommendations Taking the risk of renting local government property seriously and implementing these recommendations can help the local government manage risks better and maintain stability and reputation in rental operations.
Klasifikasi Nilai Ujian Menggunakan Algoritma C50 di SMA Hangtuah 2 Sidoarjo
Hangtuah 2 Sidoarjo High School is one of the educational institutions that implements intensive data management practices in all its operational activities. These activities start from processing student personal data, teacher lesson schedules, teaching management, attendance management, library management, financial management as well as evaluating and reporting student grades. In the evaluation process, teachers at Hangtuah 2 Sidoarjo High School often make mistakes in filling in the value data because there is too much data, so that the management of student grades becomes slow and calculation errors occur, resulting in ineffective classification of student grades. For this reason, this research aims to design an academic management system and test score classification using the C5.0 algorithm. With this system, it will be easier for administrators, teachers, librarians and finance departments to store student data and see their grades directly through the system. The results of the average percentage of all the questions that have been distributed have concluded that the UAT test results from the academic management system have very good criteria. The system that has been built can help teachers at Hangtuah 2 Sidoarjo High School to classify exam results based on the parameters of tuition arrears, number of books borrowed. , absenteeism, average UTS and UAS scores, accuracy results obtained from data from 37 students, test data, namely 70%..
Pengaruh Manajemen Piutang Terhadap Profitabilitas Studi Industri Di Tangerang
Working capital in management is very important for the survival of an organization. It is also important for the growth of an organization. Management of receivables is an important component of working capital management carried out in a company. The current study empirically tests the effect of the efficiency of receivables management as measured by the debtor turnover ratio in the commercial industry in Indonesia on the profitability of the company. Profitability is measured using the returned capital. The study was conducted in the period 2019 to 2021. The findings show a significant positive relationship between the debtor turnover ratio and the company's profitability. This indicates that receivables management must be the main focus in increasing the profitability of industrial companies.
Pemanfaatan Model ResNet50 dan SVM untuk Klasifikasi Penyakit Daun Tebu
Indonesia is an agrarian country with an economy that heavily relies on the agricultural sector, including the sugarcane plantation sub-sector for sugar production. Although domestic sugar production continues to increase, the demand for sugar consumption also grows, leading to dependency on imports and fluctuating sugar prices in the domestic market. Therefore, efforts to maintain and enhance the productivity of sugarcane crops are crucial. One of the main challenges in sugarcane cultivation is the attack of pests and diseases such as yellow disease, redrot, mosaic, and rust, which often affect sugarcane plants and reduce their productivity. These diseases must be detected promptly as they significantly impact the quality and quantity of the sugarcane to be harvested. However, manual identification processes are prone to human error and are inefficient for large-scale plantations. To address this, machine learning technology using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) was employed. This approach uses CNN for feature extraction and SVM for classification. Through a series of experiments, the study shows that the CNN and SVM models can achieve high accuracy of 90.32% with a computational time of 181.53 seconds.
Sistem Rekomendasi Makanan Sesuai Budget Bagi Wisatawan Menggunakan Algoritma Genetika Berbasis Website
The development of a web-based food recommendation system using Laravel aims to help tourists in Surabaya find places to eat that suit their preferences, overcoming the constraints of limited information and the many choices that are often confusing. This system utilizes a genetic algorithm, which was chosen for its ability to optimize recommendations based on various criteria such as budget and location. Restaurant data in Genteng and Bubutan Districts is collected and analyzed for use in the recommendation process. Functional testing shows that this system functions well, with a genetic algorithm that is able to produce optimal recommendations based on adequate fitness values ​​and fast execution times. To improve this system, it is recommended to improve the user interface design, optimize algorithm parameters, and perform additional testing to ensure system performance and security.
Recognition of Handwritten Hangeul Characters Using Convolutional Neural Network
The rapid increase in Indonesian tourists visiting South Korea has highlighted a growing interest in Korean culture, largely fueled by the Korean Wave. However, the inability to read the Korean alphabet (Hangeul) leaves many tourists vulnerable to scams. This paper proposes a novel solution to address this issue by developing a system for recognizing handwritten Hangeul characters, aimed at assisting Indonesian tourists in navigating South Korea safely. The research introduces a hybrid algorithm that integrates Vision Transformers (ViTs) with Convolutional Neural Network (CNN), aiming to overcome the limitations of CNN in capturing global features. The dataset utilized comprises 2,400 images of handwritten Hangeul characters, categorized into consonants and vowels. The study involved pre-processing, training, validation, and testing with three data split ratios (60:20:20, 70:15:15, 80:10:10) and two learning rates (0.001 and 0.0001) over 10 epochs. This hybrid model approach is designed to enhance recognition accuracy and improve the system's adaptability to diverse inputs.
Implementasi Logika Fuzzy Untuk Pemeriksaan Gizi Berdasarkan IMT Pada Aplikasi Fitpriority
Public awareness of the importance of leading a healthy lifestyle and exercising has significantly increased in recent years. However, various issues such as a lack of understanding of proper exercise techniques and nutrition that do not align with fitness goals remain prevalent. To address these challenges, this study designs a web-based application called Fitpriority to connect users with professional trainers. One of the main features of this application is a nutritional status check based on Body Mass Index (BMI). This algorithm was chosen for its ability to tolerate small changes in nutritional values, unlike the rigid traditional logic methods. The study employs the Mamdani method to determine nutritional status through the processes of fuzzification, implication function application, rule aggregation, and defuzzification using the centroid method. Additionally, the System Usability Scale (SUS) method is used to assess the usability and user experience of the developed application. The results of this study are expected to provide an effective solution for individuals aiming to achieve their fitness goals more efficiently and comfortably, and to serve as a valuable tool for a wide range of users in maintaining optimal physical health and fitness.