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All Journal Ekasakti Jurnal Penelitian dan Pengabdian (EJPP) Jurnal Sains Informatika Terapan (JSIT) TOFEDU: The Future of Education Journal
Radiyan Rahim
Universitas Nahdlatul Ulama Sumatera Barat
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Other

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Sosialisasi Pencegahan Kekerasan terhadap Anak dan Perempuan dalam Perspektif Hukum Perlindungan Anak dan Perempuan di Nagari Kasang, Kecamatan Batang Anai, Kabupaten Padang Pariaman Rangga Prayitno; Radiyan Rahim; Rica Azwar; April zahmi; Ade Triawan; Gustri Efendi
Ekasakti Jurnal Penelitian dan Pengabdian Vol. 5 No. 2 (2025): Ekasakti Jurnal Penelitian dan Pegabdian
Publisher : LPPM Universitas Ekasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/ejpp.v5i2.1313

Abstract

Violence against children and women is a serious issue that threatens welfare, security, and human rights. In Padang Pariaman Regency, particularly in Nagari Kasang, cases still occur that reflect the community's low awareness of legal protection mechanisms. This outreach activity aims to increase public understanding of legal protection for children and women in accordance with Law No. 23 of 2004 on the Elimination of Domestic Violence and Law No. 35 of 2014 on Child Protection. The implementation methods included interactive legal lectures, group discussions, educational video screenings, and case reporting simulations to the authorities. Evaluation results from pre-tests and post-tests indicated a significant improvement in participants’ legal knowledge, from an average of 58% to 87%. The community also demonstrated increased awareness in reporting violence cases to the relevant authorities. Supporting factors included the commitment of local government officials and participants’ enthusiasm, while challenges faced were patriarchal culture and limited complaint facilities. This activity proves that community-based participatory outreach is effective in promoting violence prevention and strengthening legal protection for victims.
Sistem Pakar Diagnosa Kerusakan Mobil Avanza Dengan Metode Certainty Factor Menggunakan Bahasa Pemrograman PhP Dan Database MySQL Edo Cahyadi; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.792

Abstract

The development of automotive technology has led to increased vehicle system complexity, creating the need for intelligent solutions to assist in fault diagnosis. One of the common problems faced by Avanza car users is the difficulty in detecting engine damage at an early stage. Therefore, this study aims to develop an expert system capable of diagnosing Avanza car malfunctions using the Certainty Factor method in a web-based environment. The system was developed using PHP as the programming language and MySQL as the database to manage symptoms, malfunction data, and certainty values for each diagnosis. The Certainty Factor method was implemented to calculate the level of confidence in potential malfunctions based on user-inputted symptoms. The results indicate that the method provides accurate diagnostic outcomes by presenting certainty values for each possible malfunction. This expert system helps both technicians and users obtain preliminary information about the vehicle’s condition before performing direct inspections at the workshop. Therefore, the developed expert system improves efficiency, accuracy, and service quality in diagnosing Avanza car malfunctions.
The Implementation of the K-Means Clustering Algorithm Based on the Severity Level of Diabetes in Patients Using a Website Platform Syaputri Maharani; Yumai Wendra; Melladia Melladia; Radiyan Rahim
The Future of Education Journal Vol 4 No 7 (2025): Continued
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i7.933

Abstract

Diabetes mellitus is a chronic non-communicable disease with a steadily increasing prevalence worldwide, posing a significant public health challenge due to its potential for severe complications if not managed properly. In many healthcare facilities, including RSUD Pariaman, there is still no structured system to classify patients according to the severity of their condition, which hampers timely intervention and optimal resource allocation. This study aims to develop and implement a web-based system for clustering the severity levels of type 2 diabetes mellitus using the K-Means Clustering algorithm as a decision support tool for medical staff. A quantitative system development research design was applied, utilizing secondary medical records from January 2023 to December 2024, with five clinical variables: Hemoglobin A1c (HbA1c), Fasting Blood Glucose (GDP), Systolic Blood Pressure (TDS), Diastolic Blood Pressure (TDD), and Body Mass Index (BMI). The system was built using the CodeIgniter PHP framework, MySQL database, and Bootstrap-based interface, following the Knowledge Discovery in Database (KDD) process for data preprocessing. K-Means clustering was configured into three categories (mild, moderate, and severe). Validation using RapidMiner confirmed that the clustering results from the web-based system were consistent with the benchmark model, ensuring the correctness of the algorithm’s implementation. The developed system enables real-time data processing, displays results in both tabular and graphical forms, and provides an intuitive interface for medical personnel, thus supporting clinical decision-making and improving healthcare service quality.
Pemanfaatan Machine Learning Dalam Analisis Data Untuk Mendukung Pengambilan Keputusan Cerdas Berbasis Data Modern Dinul Akhiyar; Hari Marfalino; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1194

Abstract

Perkembangan teknologi digital telah menghasilkan volume data yang sangat besar dan kompleks sehingga membutuhkan metode analisis yang lebih efektif dibandingkan pendekatan konvensional. Machine Learning merupakan salah satu cabang kecerdasan buatan yang mampu mengolah data dalam jumlah besar untuk menghasilkan informasi yang mendukung pengambilan keputusan secara cerdas. Penelitian ini bertujuan menganalisis pemanfaatan Machine Learning dalam proses analisis data modern serta kontribusinya terhadap peningkatan kualitas pengambilan keputusan. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif melalui pengumpulan berbagai sumber ilmiah terkait algoritma Machine Learning, analisis data, dan sistem pendukung keputusan. Hasil penelitian menunjukkan bahwa algoritma Machine Learning seperti Decision Tree, Random Forest, dan Neural Network mampu meningkatkan akurasi prediksi, mengidentifikasi pola tersembunyi, serta memberikan rekomendasi berbasis data secara otomatis. Implementasi teknologi ini telah diterapkan pada berbagai sektor seperti bisnis, kesehatan, pendidikan, dan keuangan. Dengan demikian, Machine Learning menjadi teknologi strategis yang mampu mendukung organisasi dalam menghasilkan keputusan yang lebih cepat, akurat, dan efektif di era data modern.
Pemanfaatan Machine Learning Dalam Analisis Data Untuk Mendukung Pengambilan Keputusan Cerdas Berbasis Data Modern Dinul Akhiyar; Hari Marfalino; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1194

Abstract

Perkembangan teknologi digital telah menghasilkan volume data yang sangat besar dan kompleks sehingga membutuhkan metode analisis yang lebih efektif dibandingkan pendekatan konvensional. Machine Learning merupakan salah satu cabang kecerdasan buatan yang mampu mengolah data dalam jumlah besar untuk menghasilkan informasi yang mendukung pengambilan keputusan secara cerdas. Penelitian ini bertujuan menganalisis pemanfaatan Machine Learning dalam proses analisis data modern serta kontribusinya terhadap peningkatan kualitas pengambilan keputusan. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif melalui pengumpulan berbagai sumber ilmiah terkait algoritma Machine Learning, analisis data, dan sistem pendukung keputusan. Hasil penelitian menunjukkan bahwa algoritma Machine Learning seperti Decision Tree, Random Forest, dan Neural Network mampu meningkatkan akurasi prediksi, mengidentifikasi pola tersembunyi, serta memberikan rekomendasi berbasis data secara otomatis. Implementasi teknologi ini telah diterapkan pada berbagai sektor seperti bisnis, kesehatan, pendidikan, dan keuangan. Dengan demikian, Machine Learning menjadi teknologi strategis yang mampu mendukung organisasi dalam menghasilkan keputusan yang lebih cepat, akurat, dan efektif di era data modern.
Co-Authors April Zahmi Azwar, Rica Dinul Akhiyar Edo Cahyadi Gustri Efendi Marfalino, Hari Melladia, Melladia Prayitno, Rangga Syaputri Maharani Triawan , Ade Yumai Wendra