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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JMM (Jurnal Masyarakat Mandiri) Sebatik JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH EXPLORE Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Teknologi Informasi dan Pendidikan Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence EXPLORE Jurnal Komtekinfo Journal of Computers and Digital Business SmartComp JOURNAL OF COMMUNITY SERVICE AND APPLICATION SCIENCE (JCSAS) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pustaka Robot Sister
Sovia, Rini
Universitas Putra Indonesia YPTK Padang, Padang
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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; Sarjon Defit; Rini Sovia
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; Rini Sovia
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 Ridwan Sutri; Billy Hendrik; Rini Sovia
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 Muhammad Ikhsan Al-Arrafi; Rini Sovia; Agung Ramadhanu
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.
Analisis Pengelompokan Jenis Anomali Aktivitas Pengguna Pada Log Sistem Informasi Klinik Menggunakan Lof Dan K-Means Puja M Alca; Sumijan Sumijan; Rini Sovia
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.972

Abstract

Digital transformation in the healthcare sector has driven the adoption of clinic information systems for computerized management of patient medical records. Sensitive data security is threatened by user behavior deviations, requiring immediate detection mechanisms. This study aims to identify anomalous activity patterns and indicators from user log records, including unusual database operation frequencies, abnormal access times, and suspicious data manipulation patterns.The Local Outlier Factor algorithm functions to systematically calculate the local density score of each data point relative to its nearest neighbors. This method detects user activities that deviate significantly from normal patterns in daily clinic operational systems. The K-Means Clustering algorithm groups detected anomalous data into clusters based on similarity of user activity feature characteristics. The clustering facilitates administrator categorization of occurring anomaly types along with threat severity levels to the system.Research data were obtained from user activity log records of the clinic information system at Klinik Utama RIDDA Payakumbuh, which underwent preprocessing stages including data cleaning, feature transformation, value normalization, and handling of missing values.Test results demonstrate that the combination of LOF and K-Means achieved accuracy of 89.5%, precision of 87.3%, and recall of 85.7% on the test dataset. These validation metrics prove that the method effectively addresses user behavior deviation detection in the clinic environment. The test results affirm that the hybrid approach can identify suspicious activities with minimal error rates, ensuring reliability. The research contribution provides practical impact for clinic information system administrators in supervising patient data security through integrated early warning mechanisms.
Integrasi Principal Component Analysis dan Logistic Regression untuk Analisis Sentimen Kepuasan Pelanggan Berdasarkan Ulasan Online Tsalsabila Jilhan Haura; Rini Sovia; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1029

Abstract

Customer reviews on digital platforms are an important source of information for evaluating service quality and customer satisfaction levels. However, the unstructured nature of review data and its high feature dimensionality pose challenges in the sentiment analysis process. This study aims to develop a customer sentiment analysis model by integrating Principal Component Analysis (PCA) and Logistic Regression. The data used are 679 Indonesian-language reviews obtained through web scraping techniques from Google Reviews at ten d'Besto EBM branches in Padang City. The research stages include text preprocessing, TF-IDF weighting, dimensionality reduction using PCA, and sentiment classification using Logistic Regression. The results show that PCA is able to reduce data complexity by producing two principal components that explain 85.7% of the total data variance. The Logistic Regression model built on the features resulting from PCA reduction achieved an accuracy of 82%, demonstrating the model's ability to effectively classify positive and negative sentiments. In addition to improving computational efficiency, the use of PCA also helps reduce feature redundancy in high-dimensional text data. The contribution of this research is to produce a simpler and more efficient sentiment analysis approach to process customer reviews and provide data-based information that can be used to support service quality evaluation and decision-making in the culinary industry.
Co-Authors Abuzar Gafari Adiddo Restiady Adinda Syalsabila Aditra Agung Ramadhanu Agus Salim, David Ahsan Firdaus Ali Nurdiansyah Amin Amirul Mukminin, Andi Anam, M Khairul Anggy Wahyudi Aulia Fitrul Hadi Aulia Fitrul Hadi Ayu Mahessya, Raja Billy Hendrik Borianto, B Chairunnissa Deliva Akbar, Syifa Dede Pratama Deny Suyandi Deval Gusrion Devi Maryuni Devia Kartika Dila, Rahmah Dwi Andhara Valkyrie Dwiki Aulia Fakhri Edo Rinaldi Rais Effendy, Geraldo Revanska Eka Praja Wiyata Mandala Elmi Rahmawati Elmi Rahmawati, Elmi Encik Yoega Renaldi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Firdaus daus Firna Yenila Gema, Rima Liana Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Hadrian Erlanda Hanippa Prima Putra Harnaranda, Jefri Hartika Zain, Ruri Hartika Hasri Awal Hendri Irawan Hendri Irawan Heriyanto Hoka Muhgrah Sandawa Huda, Ramzil Ika Melinia Sapitri Fitriyanti Ilsa Hidayat Irzal Arif Wisky Islam, Md Ataul Jimmy Febio Julsapargi Nursam Kharisma Utama Putra Khomsi, Ahmad Lidya Adriani Darma Lony Armawati Tambunan Lubis, Fitri Amelia Sari Lusinia, Shary Armonitha Maha Rani maha rani Mardhiah, Sitty Mhd Wedo Muhammad Aidil Rahman Muhammad Ikhsan Al-Arrafi Muhammad Reza Putra Muhammad, Abulwafa Mutiana Pratiwi Nabilla Yasmin Niken Rindiana Nugraha, Fajri Nurdiansyah, Ali Nursam, Julsapargi Nursyahrina Oriza Rama Saputra Permana, Randi Permana, Randy Prihandoko Puja M Alca Putra, Kharisma Utama Putri Melati Putri Melati Rahma Yanti Rahmad Rahmad Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rahmi, Nadya Alinda Ramadani, Sela Randa Mahardika Randy Permana Randy Permana Retno Devita Revi Gusriva ricki ardiansyah Ricki Ardiansyah Ricki Ardiansyah Ricki Ardiansyah, Ricki Ridwan Sutri Ridwan Sutri Rinaldi Chan, Fajri Rindhani Aditia, Mellya Riska Amelia Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Ruri Hartika Zain Ruri Hartika Zain S, Sumijan Sandawa, Hoka Muhgrah Saputra, Charisman Fajri Saputra, Oriza Rama Saputra, Randy Sarjon Defit Sarjon Defit Selfi Melisa Selvia, Dina Shally Amna Silky Safira Siregar, Diffri Sulastri Sulastri Sumijan Sumijan Syafri Arlis Syafril Syafril Syafril Syafril Syafril Syafril Syaiffullah, Afif Tika Christy Tsalsabila Jilhan Haura Tsalsabila Jilhan Haura Tuti Nabila Wahyudi, Anggy Widya Nursanty Wifra Safitri Wirdawati, Wira Yanti, Rahma Yanto, Musli Yanto, Musli Yasmin, Nabilla Yesi Betriana Roza Yuhandri Yuhandri, Yuhandri Zainal A. Haris