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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Journal of Information System, Applied, Management, Accounting and Research INTI Nusa Mandiri SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi Jurnal Pengabdian Masyarakat Ekonomi, Manajemen dan Akuntansi (JPMEMA)
Dinata, Riadi Marta
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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SISTEM DETEKSI GEMPA BERBASIS IOT DENGAN VISUALISASI REAL-TIME DAN NOTIFIKASI CERDAS Dinata, Riadi Marta; Ariman, Ariman; Yamin, Muhammad Ikrar
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6394

Abstract

Indonesia, as a region with high seismic activity, requires a fast, accurate, and reliable disaster mitigation system. However, most existing earthquake detection systems still focus primarily on data collection without automatic notifications, which delays response times in emergency situations. This study develops an Internet of Things (IoT)-based early earthquake detection system that integrates a gyroscope sensor, the ThingSpeak cloud platform, and an Android application to provide real-time information to users. The system detects orientation changes along the X, Y, and Z axes, calculates vibration magnitude through a calibrated algorithm, and sends automatic notifications via WhatsApp to mitigation officers. Testing was conducted through simulations using Wokwi to validate the algorithm and physical implementation in real-world conditions, demonstrating that the system achieves high accuracy in detecting seismic activity, with an average accelerometer magnitude of 3.35 and a gyroscope magnitude of 4.19. Data visualization on ThingSpeak, along with graphical displays in the Android application, enables intuitive and real-time earthquake monitoring. The integration of smart notifications via WhatsApp ensures a fast response from mitigation officers, making it an effective and applicable solution for earthquake risk mitigation.
The Penguatan Literasi Digital melalui Pengabdian Masyarakat untuk Peningkatan Kapasitas Guru dan Operator Sekolah di Desa Kademangan Dinata, Riadi Marta; Marhaeni, Marhaeni; Mustika, Lely
Jurnal Pengabdian Masyarakat Ekonomi, Manajemen dan Akuntansi (JPMEMA) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Ekonomi, Manajemen dan Akuntansi (JPMEMA - Juni 20
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jpmema.v4i1.296

Abstract

Kegiatan pengabdian masyarakat di Desa Kademangan ini bertujuan untuk meningkatkan literasi digital para guru dan operator sekolah di wilayah rural melalui pelatihan berbasis simulasi dan praktik langsung. Hasil evaluasi menunjukkan adanya peningkatan pemahaman yang signifikan terhadap manfaat teknologi dalam pembelajaran dan administrasi sekolah, yang dibuktikan dengan kenaikan skor rata-rata sebesar 8,70 poin dari tahap pra-pelatihan (PRA) ke pasca-pelatihan (PASCA). Analisis statistik menggunakan Wilcoxon Signed-Rank Test mengonfirmasi bahwa perubahan ini signifikan pada tingkat 0.05. Dampak praktis dari program ini terlihat jelas dari peningkatan keterampilan serta kesadaran peserta akan pentingnya integrasi teknologi dalam pendidikan. Oleh karena itu, program ini sangat direkomendasikan untuk direplikasi di wilayah lain dengan dukungan infrastruktur yang memadai serta kolaborasi antara institusi pendidikan, pemerintah, dan masyarakat. 
Analisis Keamanan Situs Web Rumah Sakit Menggunakan Metode Penetration Testing OWASP dinata, riadi marta; Alzril, Muhammad; Yamin, Muhammad Ikrar; Effendi, Harlan; Febriansyah, Muhammad
SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI Vol 35 No 2 (2025): Jurnal Penelitian dan Pengkajian Sains dan Teknologi
Publisher : Institut Sains dan Teknologi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37277/stch.v35i2.2383

Abstract

Di era digital yang semakin terkoneksi, keberadaan situs web sebagai wajah utama sebuah institusi menjadi sangat krusial, terutama dalam sektor pelayanan kesehatan. Penelitian ini mengkaji keamanan situs web rumah sakit rsjsh.co.id dengan pendekatan penetration testing yang merujuk pada panduan OWASP Testing Guide v4. Fokus utama penelitian adalah mengidentifikasi dan memverifikasi kerentanan yang berpotensi dimanfaatkan oleh pihak tidak bertanggung jawab. Metode yang digunakan mencakup pengumpulan informasi (footprinting), pemindaian (scanning), eksploitasi kerentanan, serta evaluasi risiko menggunakan CVSS v3.1 dan OWASP Risk Rating. Hasil analisis menunjukkan adanya 11 kerentanan, di antaranya tiga berkategori sedang: tidak adanya kebijakan Content Security Policy (CSP), anti-clickjacking header, serta keberadaan mixed content. Eksploitasi dilakukan menggunakan berbagai alat seperti Owasp Zap, Burp Suite, dan seography.io. Meskipun beberapa kerentanan tidak dapat dieksploitasi akibat sistem pertahanan yang memadai, temuan valid mengindikasikan perlunya implementasi kebijakan keamanan tambahan, seperti CSP dan konfigurasi HSTS. Penelitian ini merekomendasikan perbaikan proaktif sebagai langkah penting dalam menjaga kerahasiaan data pasien dan meningkatkan kepercayaan publik terhadap sistem digital rumah sakit Kata kunci: penetration testing, keamanan web, rumah sakit, OWASP, Content Security Policy
PENILAIAN KUANTITATIF KEDISIPLINAN ASISTEN LABORATORIUM FSTT ISTN MENGGUNAKAN SISTEM ABSENSI BERBASIS GEOFENCING DAN GPS Purnomo, Niko; Dinata, Riadi Marta; Marhaeni, Marhaeni; Atmadja, Kurniawan
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 1 (2026): JISAMAR (February 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i1.2320

Abstract

Ensuring accurate and analyzable attendance data is essential for managing human resources and discipline in university laboratories. This study analyzes the attendance patterns of 13 laboratory assistants at the Computer Laboratory of FSTT ISTN using a descriptive statistical approach applied to records produced by a GPS-based attendance system with geofencing. The proposed framework, termed the LABKOM Geolocation Attendance Framework (KAGELAB), collects 4,300 attendance records over 485 days and processes them through measures of central tendency and dispersion, tardiness frequency distributions, Pearson correlations among tardiness, early arrival, and overtime, the Jarque–Bera normality test, and a confidence interval for mean tardiness. The results indicate that most attendances are on time, while a smaller subset of high tardiness events generates a right-skewed distribution and a considerable amount of extra time contributed through early arrivals and overtime. Correlations among attendance variables are weak, yet daily, hourly, assistant-level, and monthly analyses reveal consistent patterns of discipline variations. These findings demonstrate that combining a GPS–geofencing attendance system with descriptive statistical analysis provides a robust basis for monitoring discipline and designing data-driven attendance policies in educational laboratory settings.
Personalized Behavioral Analytics for GPS-Validated Attendance Systems Using K-Means Clustering and Individual-Baseline Anomaly Detection Abidin, Ashari; Dinata, Riadi Marta; Satrio, Bambang; Petrus, Risma; Lamsir, Seno
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.38881

Abstract

This study develops and evaluates a GPS-based attendance analytics framework integrating three complementary analytical layers for higher education environments. The proposed system combines spatial validation using Haversine-based geofencing, behavioral segmentation through K-Means clustering with multi-metric validation, and personalized anomaly detection employing individual-baseline Z-Score computation. Empirical evaluation utilized 4,300 attendance records from 13 lecturers at FSTT ISTN Jakarta over a 16-month period. K-Means clustering with K=3 achieved a Silhouette Score of 0.634 and a Davies-Bouldin Index of 0.621, identifying three behavioral segments: High Performers (30.8%), Moderate (38.5%), and Improvement Needed (30.8%). The personalized Z-Score method detected 19.9% more anomalies compared to population-based thresholds and reduced detection inequity across lecturer groups. Practically, the framework transforms passive attendance logging into a decision-support tool that enables differentiated monitoring, early behavioral change detection, and fairer evaluation policies. However, the study is limited by a relatively small sample size (13 lecturers) within a single institutional context, which may affect model generalizability. Broader validation across larger and multi-institutional datasets is recommended for future work.
Co-Authors Abidin, Ashari Alzril, Muhammad ariman, ariman Atmadja, Kurniawan effendi, Harlan Febriansyah, Muhammad Lamsir, Seno Marhaeni, Marhaeni Mustika, Lely Petrus, Risma Purnomo, Niko Yamin, Muhammad Ikrar