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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris TEKMAPRO Journal of Industrial Engineering and Management Prosiding SNATIF Jurnal Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Mantik Penusa SISFO Jurnal Riset Informatika Ekonomi dan Bisnis Aisyah Journal of Informatics and Electrical Engineering bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal Dialektika Informatika (Detika) Journal La Multiapp Bulletin of Computer Science Research Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Bina Informatika dan Komputer (BINER)
Ahmad Abdul Chamid
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muria Kudus
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Design of an Exam Cheating Detection System Application Based on Machine Learning with the Computer Vision Method Hendrawan, Andra Putra; Wijayanti, Esti; Chamid, Ahmad Abdul
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2704

Abstract

Exam cheating is a persistent problem facing educational institutions worldwide. This cheating not only harms honest students but also undermines the integrity of the education system. In today's digital age, various forms of cheating are increasingly difficult to detect using manual proctoring methods. For example, test takers can use hidden technological devices or engage in non-verbal communication that is difficult for human proctors to detect. This suggests that traditional proctoring is less effective in addressing increasingly sophisticated cheating. Abstract Exam cheating is a serious problem that can compromise the integrity of the education system. Manual proctoring is often ineffective in identifying suspicious behavior that occurs during exams. This study aims to design and develop a machine learning-based exam cheating detection system with computer vision methods. This system uses facial recognition technology, motion tracking, and object detection to identify suspicious activities such as the use of prohibited devices or unusual movements automatically and in real-time. The method used involves a Convolutional Neural Network (CNN) algorithm for participant face verification, pose estimation for motion analysis, and You Only Look Once (YOLO) for object detection. The results of this system development show that the system can improve efficiency and accuracy in detecting cheating behavior, as well as reduce reliance on manual proctoring.
COMPARATIVE MACHINE LEARNING ALGORITHMS FOR YOUTUBE SENTIMENT ANALYSIS ON DPR DEMONSTRATION 2025 USING LEXICON Samsudin, Syafri; Abdul Chamid, Ahmad; Jazuli, Ahmad
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.470

Abstract

The high volume of public comments on YouTube regarding the DPR Demonstrasion August 2025, which reached 43,910 raw data, presents a significant challenge in conducting efficient sentiment analysis. Time and cost limitations in manual labeling for large-scale datasets are a major obstacle in the development of predictive models. This study aims to address this problem by proposing a hybrid approach that integrates Lexicon-Based auto-labeling with a comparative evaluation of five Machine Learning algorithms. The research methodology included a text preprocessing stage that generated 40,097 unique comments, feature extraction using TF-IDF, and data sharing with an 80:20 ratio. The performance of the Support Vector Machine algorithm was comprehensively compared to Random Forest, Decision Tree, K-Nearest Neighbors, and Naive Bayes. The results of the experiment showed that the SVM model recorded the most superior performance with an accuracy of 96.5% and a weighted F1-Score of 0.966. This score significantly outperformed other benchmarking algorithms, where Random Forest came in second place with 89.2% accuracy, followed by Decision Tree at 85.6%, KNN at 84.6%, and Naive Bayes at the lowest with 84.0%. These findings validate that the integration of Lexicon-Based labeling with SVM classification is a highly accurate, robust, and efficient solution for handling sentiment analysis on large-scale social media data in Indonesia.
Sistem Informasi Pembayaran Berbasis Website Untuk Efisiensi dan Transparansi Administrasi Kos Puri Delima Dersalam Nurainin, Enno Siti; Meimaharani, Rizkysari; Chamid, Ahmad Abdul
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12367

Abstract

Penelitian ini dilakukan untuk mengatasi permasalahan administrasi pembayaran kos di Kos Puri Delima Dersalam yang sebelumnya masih dilakukan secara manual melalui buku catatan dan konfirmasi pesan WhatsApp, sehingga sering menimbulkan kesalahan pencatatan, keterlambatan rekap data, serta kurangnya transparansi informasi bagi penghuni. Untuk menyelesaikan permasalahan tersebut, dikembangkan sistem informasi pembayaran kos berbasis website yang mampu mengelola data penghuni, kamar, tagihan, dan pembayaran secara terpusat. Penelitian ini menggunakan metode Waterfall, meliputi analisis kebutuhan, perancangan dengan Flowchart, Data Flow Diagram (DFD), dan Entity Relationship Diagram (ERD), implementasi menggunakan PHP dan MySQL, serta pengujian sistem melalui metode Black Box. Hasil implementasi menunjukkan bahwa fitur-fitur utama seperti pembuatan tagihan otomatis, unggah bukti pembayaran, verifikasi pembayaran oleh admin, dan penyajian laporan pembayaran dapat berfungsi dengan baik sesuai kebutuhan pengguna. Pengujian Black Box juga menunjukkan bahwa seluruh proses berjalan sesuai spesifikasi. Sistem ini mampu meningkatkan efisiensi, akurasi, dan transparansi dalam proses pembayaran kos, sehingga dapat menjadi solusi digital yang layak untuk mendukung pengelolaan administrasi kos secara lebih modern dan terstruktur.
Sistem Informasi Booking Layanan Pemasangan Lampu Variasi Berbasis Website pada Bengkel DVM Motor Daelami, Fahri Muhammad; Mei Maharani, Rizky Sari; Chamid, Ahmad Abdul
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12368

Abstract

Penelitian ini bertujuan untuk merancang dan membangun sistem informasi booking berbasis website guna mendukung layanan pemasangan lampu variasi pada Bengkel Variasi DVM Motor. Permasalahan utama yang dihadapi bengkel adalah proses pemesanan layanan yang masih dilakukan secara manual melalui telepon atau WhatsApp, sehingga sering menimbulkan kesalahan pencatatan, antrean panjang, dan overbooking. Untuk mengatasi permasalahan tersebut, dikembangkan sistem berbasis web dengan fitur pembatasan slot pelayanan harian, manajemen data pelanggan, serta konfirmasi otomatis oleh admin. Metode pengembangan yang digunakan adalah model Waterfall yang terdiri dari analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Hasil implementasi menunjukkan bahwa sistem dapat memfasilitasi pelanggan dalam melakukan pemesanan dengan mudah, memberikan transparansi jadwal, serta membantu admin mengelola layanan secara efisien. Pengujian black-box membuktikan seluruh fungsi utama berjalan sesuai kebutuhan, sementara hasil uji usability menunjukkan bahwa sistem mudah digunakan dan dapat meningkatkan efisiensi operasional bengkel.
Co-Authors Aditya Akbar Riadi Ahdi Riyono Ahmad Hariyadi, Ahmad Ahmad Jazuli Ahmad Jazuli Akbar, Fadhil Akh Sokhibi Alif Catur Murti Alif Catur Murti, Alif Catur Aprilianto, Mohammad Andrean Jaya Ariyanto, Iqbal Haqiqi Bayu Surarso Daelami, Fahri Muhammad Dimas Satrio Oktavianto Endang Supriyati Endang Supriyati Esti Wijayanti Esti Wijayanti Esti Wijayanti, Esti Evita Noor Sofiana Dewi Hakim, Alvin Rainaldy Hendra Dwi Kurniawan, Hendra Dwi Hendrawan, Andra Putra Ida Nur Aeni Ida Nur Aeni, Ida Nur Ivan Bagus Prasetiyo Juwanda, Farikhin Mahendra, Bagus Rosa Maula Yudhananta, Naufal Maulana, Sahidin Achmad Noor Mohammad Kanzunnudin, Mohammad Muhammad Imam Ghozali Muhammad Rieza Maulana Muhammad Rizki Naila Rizki Salisa Noor Azizah Nurainin, Enno Siti Prayogo, Sandi Ratih Nindyasari Riasti, Savira Rijal, Syaifur Rina Fiati Rizky Sari Mei Maharani, Rizky Sari Rizkysari Meimaharani Safitri, Sabrina Alya Samsudin, Syafri Sentanu, Arga Seva Amartya Shafian Syah, Aliffaza Sholihul Ibad Sokhibi, Akh Sugiharto, Wibowo Harry Teguh, Aris Wicakosno Umam, Arfiyan Khusnul Wibowo, Yuniar Satrio Wicaksono, Abraham Wahyu