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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Muhammad Zakariyah
Universitas Teknologi Yogyakarta, Daerah Istimewa Yogyakarta
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Sistem Deteksi dan Monitoring Terpusat Penyakit Daun Padi Berbasis Mobile Menggunakan MobileNetV2 Yogi Pranoto; Muhammad Zakariyah
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11256

Abstract

Rice leaf diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut are a major threat to agricultural productivity in Indonesia because manual identification by farmers remains subjective, slow, and error prone. This study aims to develop a rice leaf disease detection system that integrates a Flutter based mobile application with a web based monitoring dashboard, so that diagnosis can be performed independently by farmers while being monitored centrally by system administrators. The method applied is a Convolutional Neural Network with a transfer learning approach on the MobileNetV2 architecture, trained using an expanded dataset of 300 rice leaf images obtained from primary field observation data in Klaten and secondary data from the Kaggle platform, then converted to TensorFlow Lite format to enable on-device inference on mobile devices. The system backend was built using Flask and a MySQL database to manage user data and detection history. Experiments on four hyperparameter scenarios showed that a configuration of 20 epochs, batch size 32, and learning rate 0.001 produced the best performance with a test accuracy of 95.24 percent and a loss of 0.1506 without significant indication of overfitting. The final model size of 9.71 MB with an average inference time of under 2 seconds proves that this system is feasible to be applied as an accurate, lightweight, and responsive rice disease diagnosis tool in the field.
Klasterisasi Pola Seismik: Perbandingan K-Means dan ST-DBSCAN pada Data Gempa Spasio-Temporal Muhammad Putra Pratama Budiman; Muhammad Zakariyah
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11257

Abstract

Earthquakes are natural disasters with significant socio-economic impacts; thus, understanding their distribution patterns and characteristics is essential for disaster mitigation. Indonesia is situated along the Pacific Ring of Fire with intense seismic activity, and the volume of earthquake data recorded by BMKG is too vast for manual analysis. This study compares K-Means and ST-DBSCAN using 93,815 spatio-temporal data points from a historical catalog (2008–2023) and data from the final 30 days of 2026. The number of K-Means clusters was determined using the elbow method supported by the Silhouette Score and Davies-Bouldin Index (DBI); both algorithms were evaluated using the Silhouette Score, DBI, and Adjusted Rand Index (ARI) across six tectonic zones. K-Means (k = 4) outperformed ST-DBSCAN across all three metrics (Silhouette Score: 0.1563, DBI: 2.7180, ARI: 0.4137), whereas ST-DBSCAN produced 20 clusters with a single cluster containing 70.9% of the data. Three K-Means clusters corresponded to the Sulawesi–Maluku, Sunda Arc, and Sumatra regions, while one represented a temporal artifact from the time_days feature. An ablation test excluding time_days (k = 3) improved the ARI to 0.5102. Results are presented via the Flask-based NusaSeis web dashboard with interactive maps and automated interpretations by Gemini 2.5 Flash; all 23 black box scenarios were validated. NusaSeis serves as an open seismic exploration platform enabling academics and disaster management agencies to interactively access national seismicity summaries without programming expertise, though it is not yet suitable for official tectonic zoning or hazard assessment.
Rancang Bangun Purwarupa Sistem Pewarisan Aset Digital Berbasis Dead Man's Switch Dengan Enkripsi AES-256 llham Aditya Pratama; Sutarman Sutarman; Muhammad Zakariyah
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11258

Abstract

Digital assets may become inaccessible to heirs when their owners die or experience emergencies. This study develops a web- and mobile-based digital inheritance prototype integrating a Dead Man’s Switch (DMS), server-side AES-256 encryption, and folder-based access control. Development followed the Waterfall model and Unified Modeling Language (UML), using Laravel for the RESTful API backend, React Native for the interface, MySQL as the database, and a cron job to check inactivity every minute. Users perform periodic check-ins; once the time limit expires, the account status changes to released and access is processed according to heirs’ permissions. Email notifications contain a download link valid for three days and a One-Time Password (OTP) for heir authentication. Black-box testing of eight functional scenarios showed that all scenarios operated as expected. Across 25 repetitions of the access-release scenario, emails arrived 4–12 seconds after the time limit was reached. Testing was limited to functionality and notification timing; load testing, penetration testing, and user evaluation were not conducted. The prototype integrates activity monitoring, data protection, and controlled cross-platform access, but does not replace a legally valid will. Further security validation and user evaluation are needed before the system is used to manage high-value digital assets.
Co-Authors llham Aditya Pratama Muhammad Putra Pratama Budiman Sutarman Sutarman Yogi Pranoto