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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA sudo Jurnal Teknik Informatika
Mutaqin Akbar
Universitas Mercu Buana Yogyakarta, Yogyakarta
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Peningkatan Efisiensi Pembaruan Aplikasi Android Tap On Bus Menggunakan Sistem OTA Update Berbasis REST API Rohmat Julianto; Mutaqin Akbar
TIN: Terapan Informatika Nusantara Vol 6 No 10 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i10.9406

Abstract

Managing application updates for more than 400 Android validator devices of the Tap On Bus system, deployed across various public transportation routes in Indonesia, presents significant challenges in efficiency and scalability. The existing manual update process, relying on physical storage media (flash drives) and direct technician intervention at each device location, has proven inefficient, poses risks to data integrity, and causes version inconsistencies across devices. Using commercial Mobile Device Management (MDM) platforms as an alternative also introduces vendor dependency and high recurring license costs. This research aims to develop and implement an independent (in-house) Over-The-Air (OTA) Update system based on REST API to automate the centralized and remote distribution and installation of Android application updates. The system was developed using the Research and Development (R&D) method with the Waterfall model, implemented using Go (Golang) for the REST API backend with a MySQL database running on the XAMPP web server, Kotlin with the PackageInstaller API for the Android client on Telpo T10 devices, and the Vuetify framework for the monitoring dashboard. Functional testing using the Black Box Testing method across 19 test scenarios covering device registration, startup-triggered update checking, version validation, APK package download, download-failure and installation-failure handling with recovery, and automatic installation yielded a 100% success rate. Comparative analysis demonstrated that the OTA system successfully reduced the average update time per device from 16 minutes (manual) to approximately 1.2 minutes (automatic), equivalent to a 92.5% efficiency improvement, while eliminating technician travel costs. The system also demonstrated the ability to maintain transaction data integrity during updates and centrally log all update activities in the database, confirming its readiness for deployment in large-scale public transportation environments.
Segmentasi Citra Wayang Kulit Pandawa Berkompleksitas Visual Tinggi Menggunakan Model U-Net Berbasis Convolutional Neural Network Krisna Refiansyah; Mutaqin Akbar
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10013

Abstract

Shadow puppetry (wayang kulit) is one of Indonesia's cultural heritages with significant historical and artistic value. The complexity of digital image backgrounds in wayang kulit poses a major challenge in automatic segmentation, particularly due to lighting variations, intricate carving (tatahan) details, and the limitations of conventional methods in handling high visual variability. This study aims to implement a U-Net architecture based on Convolutional Neural Network (CNN) for segmenting images of Pandawa shadow puppet characters encompassing five main characters: Puntadewa, Janaka, Werkudara, Nakula, and Sadewa. The dataset consists of 1,500 independently collected shadow puppet images with ground truth masks divided into 1,093 training, 157 validation, and 250 test data. The U-Net model was trained using the Adam optimizer with an initial learning rate of 1×10⁻⁴, combined Binary Cross-Entropy and Dice Loss function, and 128×128 pixel input size. Early stopping and automatic learning rate adjustment via ReduceLROnPlateau were applied to optimize training and prevent overfitting throughout the learning process. The model achieved Accuracy 95.8%, AUC 98.6%, Dice Coefficient 91.9%, IoU 86.9%, Precision 91.5%, and Recall 95.0% on 250 test data. Previous studies on wayang kulit have been limited to image classification, while U-Net applications have been predominantly found in medical and satellite domains, making this study a novel contribution that addresses an existing research gap and supports the digitalization of Indonesian cultural heritage. The contribution of this study is to provide the first deep learning-based image segmentation model specifically designed to automatically separate Pandawa wayang kulit silhouettes from their backgrounds, demonstrating the effectiveness of U-Net architecture on cultural heritage objects with high visual complexity, and establishing a segmentation performance baseline for the Indonesian visual cultural heritage domain that can serve as a reference for future wayang kulit digitalization system development.
Sistem Pendukung Keputusan Penentuan Lokasi Early Warning System Tanah Longsor Menggunakan TOPSIS Aang Ma'ruf Perdana; Mutaqin Akbar
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10605

Abstract

As one of the areas in the Special Region of Yogyakarta with a relatively high level of landslide vulnerability, Gunungkidul Regency requires targeted mitigation measures, including implementing an Early Warning System (EWS) to detect potential disasters early. The obstacle faced by the Gunungkidul Regency Regional Disaster Management Agency (BPBD) is the absence of a system that can support the process of determining priority locations for EWS installation objectively by considering various existing risk parameters. This research aims to develop a Decision Support System (DSS) by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to produce priority recommendations for landslide EWS installation locations. The study utilized historical landslide occurrence data from BPBD Gunungkidul Regency covering the period of 2022–2025 and the Gunungkidul Regency Disaster Risk Assessment Document 2026–2030, with 116 villages (kalurahan) as the decision alternatives. The evaluation was conducted using four criteria: hazard, vulnerability, capacity, and landslide occurrence frequency. The results indicate that Sawahan Village achieved the highest preference value of 1.0000, followed by Mertelu Village with 0.9336 and Ngalang Village with 0.7481, making them the highest-priority locations for landslide EWS installation. The developed system successfully automates the TOPSIS calculation process, provides ranking results in a fast and transparent manner, and fulfills all functional requirements based on Black Box Testing. The proposed system is expected to support BPBD Gunungkidul Regency in determining priority locations for landslide Early Warning System (EWS) installation through a more measurable, data-driven, and objective decision-making process.
Analisis Sentimen Program Makan Bergizi Gratis Menggunakan Claude Sonnet 4.5 dengan pendekatan Zero-Shot Classification Yassir Ahmad Nugroho; Mutaqin Akbar
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9308

Abstract

The Free Nutritious Meal Program (Program Makan Bergizi Gratis, MBG) is a national policy aimed at improving public nutritional quality, particularly among school-aged children. Despite its strategic objectives, the program’s implementation has generated diverse public responses, widely expressed through social media platforms. This study aims to analyze public sentiment toward the Free Nutritious Meal Program using the Claude Sonnet 4.5 model with a zero-shot classification approach. The study was conducted online using YouTube user comments on videos discussing the MBG program as the data source. Data were collected through the YouTube Data API between January 5 and January 20, 2026, yielding a total of 5,036 comments. After preprocessing, 4,737 clean comments were retained for analysis. Sentiment classification was performed without model retraining by leveraging the contextual understanding capabilities of Claude Sonnet 4.5. Model performance was evaluated using a Confusion Matrix by comparing automatic classification results with manual labels on 20% of the data as an evaluation sample. The results indicate that relevance classification achieved an accuracy of 97.89%, while sentiment classification reached an accuracy of 94.60%. Sentiment distribution was dominated by negative sentiment at 57.8%, followed by neutral sentiment at 21.5% and positive sentiment at 20.7%. This study contributes by proposing a Large Language Model–based framework for public policy sentiment analysis using Claude Sonnet 4.5 with a zero-shot classification approach, enabling accurate analysis of Indonesian-language public opinion without reliance on labeled training data.
Pengembangan Media Pembelajaran Interaktif Sistem Pernafasan Hewan Berbasis 3D Augmented Reality Fahrul Advis Kafilahudin; Mutaqin Akbar
sudo Jurnal Teknik Informatika Vol. 3 No. 1 (2024): Edisi Maret
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v3i1.469

Abstract

Augmented Reality (AR) merupakan teknologi yang menggabungkan dimensi dunia nyata dengan dunia maya secara real-time, menawarkan pengalaman interaktif yang mendalam. Penelitian ini berangkat dari masalah menurunnya minat belajar siswa yang disebabkan oleh visualisasi materi pembelajaran sistem pernapasan hewan yang kurang menarik. Tujuan utama penelitian ini adalah untuk mempermudah visualisasi materi tersebut sehingga siswa dapat belajar dengan lebih interaktif dan menyenangkan. Metode penelitian yang digunakan adalah Software Development Life Cycle (SDLC), yang meliputi tahapan perancangan sistem, pembuatan database Vuforia, pengembangan model 3D sistem pernapasan hewan, hingga pembuatan aplikasi media pembelajaran menggunakan Unity. Hasil dari penelitian ini adalah sebuah aplikasi Android berbasis AR yang mampu menampilkan model 3D sistem pernapasan hewan melalui penanda kertas, yang kemudian ditampilkan pada layar perangkat Android lengkap dengan penjelasan mengenai organ-organ pernapasan. Dengan adanya aplikasi ini, diharapkan minat dan pemahaman siswa terhadap materi pelajaran dapat meningkat secara signifikan, menciptakan lingkungan belajar yang lebih dinamis dan mendukung pembelajaran aktif serta menyenangkan.
Klasifikasi Penyakit Tuberculosis Berdasarkan Citra Rontgen Thorax Menggunakan Multi-Scale Convolutional Neural Network Bondan Surya Nusantara; Mutaqin Akbar
sudo Jurnal Teknik Informatika Vol. 3 No. 1 (2024): Edisi Maret
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v3i1.483

Abstract

Tuberculosis (TB) merupakan penyakit menular yang disebabkan oleh bakteri mycobacterium tuberculosis, patogen yang paling sukses menginfeksi paru-paru. Teknik diagnosis penyakit TB yang paling sering digunakan adalah melalui pemeriksaan rontgen thorax (paru-paru). Hasil rontgen dapat dilihat secara citra/visual oleh dokter untuk melihat apakah terdapat ciri dan pola adanya penyakit TB pada pasien. Pada penelitian ini befokus pada Artificial Intelligence (AI) untuk membantu dokter dan memberikan alternatif solusi yang efisien dalam mendiagnosa pasien, apakah pasien tersebut menderita TB atau tidak dengan lebih cepat. Penelitian ini dirancang menggunakan Multi-Scale Convolutional Neural Network (CNN) untuk mengklasifikasi penyakit tuberculosis berdasarkan citra rontgen thorax. Data yang digunakan berupa citra X-ray thorax yang digunakan sebagai input untuk proses image processing. Dataset yang dikumpulkan berjumlah 1400 data yang terdiri dari 2 kelas yaitu paru-paru normal dan paru-paru penderita tuberculosis. Model CNN terdiri dari 3 lapisan konvolusi yang berukuran 3x3, 3 lapisan penggabungan (Maxpool) berukuran 2x2 dan 1 lapisan fully connected yang menggunakan aktivasi softmax. Filter yang digunakan pada setiap lapisan konvolusi adalah 128. Penelitian ini menggunakan algoritma Adam Optimizer. Dataset dibagi menjadi 1120 data pada skenario 1 dan 978 data pada skenario 2 untuk training atau latih dan 280 data pada skenario 1 dan 422 data pada skenario 2 untuk testing atau uji. Dalam proses pelatihan, digunakan nilai epoch 20 diperoleh nilai akurasi sebesar 100% pada semua skenario. Pada tahap pengujian dihasilkan nilai akurasi sebesar 99,29% pada skenario 1 dan 97,67% pada skenario 2.
Co-Authors Aang Ma'ruf Perdana Bondan Surya Nusantara Fahrul Advis Kafilahudin Krisna Refiansyah Rohmat Julianto Yassir Ahmad Nugroho