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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL PENDIDIKAN TAMBUSAI Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Review Pendidikan dan Pengajaran (JRPP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Komtekinfo SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sovia, Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Journal : Bulletin of Computer Science Research

 1 
Analisis Data Mining dengan Metode K-Means Clustering Dalam Pengelompokan Penggunaan Alat Kontrasepsi Rahmad, Rahmad; Defit, Sarjon; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.750

Abstract

Family Planning (KB) is a strategic government effort to suppress population growth and improve the quality of life. The availability of various types of contraceptives can delay unwanted pregnancies, including in women facing increased pregnancy risks. Based on this, this study aims to cluster contraceptive use. The K-Means Clustering method is an unsupervised learning algorithm used to group data into several clusters based on similar characteristics. This algorithm works by minimizing the distance between the data and the cluster center (centroid). The advantages of K-Means are its simplicity and speed in processing large data. This research variable uses data from the 2024 Family Data Collection of the BKKBN Representative Office of West Sumatra Province in West Pasaman Regency. Based on the application of the K-Means Clustering method to the contraceptive use data, the grouping is obtained into three clusters: low use of MKJP contraceptives, moderate use of MKJP contraceptives, and high use of MKJP contraceptives. This study contributes in the form of a data mining-based analysis model that is able to group contraceptive use patterns in a more structured and objective manner. By applying the K-Means Clustering method, this study produces information that can be used to identify the characteristics of each user group, so that relevant agencies can design more targeted contraceptive counseling and distribution strategies.
Deteksi Pelanggaran Tata Tertib Siswa Sistem Cerdas Menggunakan Face Recognition dengan Metode Convolutional Neural Network Syafril, Syafril; Yuhandri, Yuhandri; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.753

Abstract

Student disciplinary violations are a social problem increasingly common in schools and can negatively impact students' academic and moral development. This phenomenon requires an effective identification system so that prevention and mitigation efforts can be carried out quickly and accurately. This research aims to develop a student face detection system based on Digital Image Processing (DIP) technology that functions to identify and classify adolescent disciplinary violations. The designed system utilizes a camera as an image acquisition device, then processes it to detect the presence of student faces in real-time. The face detection process is carried out using the Haar Cascade Viola-Jones method, which is known to be able to recognize faces with high speed and accuracy. Once a face is detected, the system continues the analysis process using the Convolutional Neural Network (CNN) method to classify facial expressions and behavioral patterns that could potentially indicate violations. The integration between Haar Cascade and CNN allows the system to work efficiently in identifying signs of negative behavior based on visual data. System testing shows satisfactory results, with a high level of facial detection accuracy and fairly reliable behavior classification capabilities. This technology has the potential to be used as a monitoring tool in the school environment, allowing teachers and school management to quickly identify students who need special attention. With the implementation of this system, it is hoped that schools will be able to provide timely guidance, prevent the escalation of deviant behavior, and create a more conducive learning environment. The use of digital image processing-based technology for detecting and classifying student behavior is a relevant innovation in the modern education era, while also supporting efforts to prevent juvenile disciplinary violations through a systematic and measurable approach.
Prediksi Jumlah Kebutuhan Biji Kopi Berdasarkan Pola Konsumsi Konsumen dengan Algoritma Apriori Sutri, Ridwan; Hendrik, Billy; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.757

Abstract

Coffee bean prediction is needed for optimal inventory management to maintain efficiency. This data grouping is taken from customer shopping consumption patterns. Based on the research aims to predict the amount of coffee bean needs based on consumer consumption patterns by applying the Apriori algorithm. Utilization of processed transaction data can provide what steps should be taken in the future. Based on this, this study aims to predict the amount of coffee bean needs based on consumer consumption patterns with the Apriori algorithm. The Apriori algorithm forms association rules based on a combination of data indicators used. These data indicators are sourced from Freehand Coffee. Based on the use of the Apriori algorithm in predicting coffee bean needs based on consumer consumption patterns, the results showed that the Apriori algorithm is able to provide product recommendations in the form of associative or consumer transaction patterns by collecting transaction data and then experimenting with existing data indicators. The contribution of this research can help Freehand Coffee to estimate coffee bean needs and optimize stock management, this research also helps in selecting drinks based on consumer consumption.
Analisis Kepuasan Masyarakat Terhadap Proses Pengurusan Sertipikat Analog Ke Elektronik Menggunakan Metode Naïve Bayes Al-Arrafi, Muhammad Ikhsan; Sovia, Rini; Ramadhanu, Agung
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.758

Abstract

The certificate media conversion program from analog to electronic implemented by the Ministry of ATR/BPN in Sejati Village requires evaluation to ensure its effectiveness. The main problem faced is the limited use of quantitative, data-driven analysis in identifying the factors that influence public satisfaction. This study aims to analyze the level of public satisfaction using the Naïve Bayes method to classify and predict the influence of related variables. Data were obtained from 250 respondents through questionnaires based on digital public service indicators, covering demographic variables, perceived benefits, obstacles, support, service speed, and procedural simplicity. The results show that the level of public satisfaction is in the high category, with procedural simplicity and service speed proven to be the most significant variables influencing satisfaction prediction. The Naïve Bayes model achieved an accuracy of 94%, demonstrating its effectiveness in predicting satisfaction levels. These findings serve as a basis for improving policies and strategies to enhance the quality of digital public services, particularly in the implementation of electronic certificate media conversion in the future.
Co-Authors Adinda Syalsabila Aditra Agus Salim, David Al-arrafi, Muhammad Ikhsan Awal, Hasri Borianto, B Chairunnissa Deliva Akbar, Syifa Devia Kartika Dila, Rahmah Effendy, Geraldo Revanska Elmi Rahmawati, Elmi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Gema, Rima Liana Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Harnaranda, Jefri Hartika Zain, Ruri Hartika Hendri Irawan Hendrik, Billy Huda, Ramzil Islam, Md Ataul Khomsi, Ahmad Lubis, Fitri Amelia Sari maha rani Nabila, Tuti Nurdiansyah, Ali Nursyahrina Permana, Randi Permana, Randy Putra, Kharisma Utama Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rais, Edo Rinaldi Ramadhanu, Agung Renaldi, Encik Yoega Ricki Ardiansyah, Ricki Ridwan Sutri Rinaldi Chan, Fajri Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Sandawa, Hoka Muhgrah Saputra, Randy Sarjon Defit Selfi Melisa Siregar, Diffri Sumijan Sumijan Sumijan Sutri, Ridwan Syafril Syafril Syaiffullah, Afif Tika Christy Wahyudi, Anggy Wedo, Mhd Wirdawati, Wira Yanti, Rahma Yasmin, Nabilla Yenila, Firna Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri, Y