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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Pendidikan Informatika dan Sains Jurnal CoreIT JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Infomedia DoubleClick : Journal of Computer and Information Technology JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi CSRID (Computer Science Research and Its Development Journal) JURNAL SURYA ENERGY Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) INTI Nusa Mandiri Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JURMATIS : Jurnal Manajemen Teknologi dan Teknik Industri Jurnal Generic Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Digital Transformation Technology (Digitech) MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Riau Jurnal Teknik Informatika Scientific Journal of Informatics IPSSJ
Dedy Hermanto
Universitas Multi Data Palembang
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Physics Public Health Social Sciences Other

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Perancangan UI/UX dan Evaluasi Usability Sistem Cerdas Prediksi Titik Api Sumatera Selatan Hotspot Monitor Muhammad Rizky Pribadi; Dedy Hermanto; Hafiz Irsyad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i2.17243

Abstract

South Sumatra faces recurring forest and land fires, yet hotspot information remains difficult for lay users to interpret. This study designs and evaluates the user interface (UI/UX) of Sumsel Hotspot Monitor, accommodating a representation of AI-based wildfire hotspot prediction via the Design Thinking method (Empathize, Define, Ideate, Prototype, Test). Interviews with residents, disaster officers, and meteorological operators revealed a need for plain language and color-coded indicators, translated into a map prototype displaying illustrative output from LightGBM (spread probability) and ConvLSTM (movement direction) models, adopted as design references; their training and quantitative validation against real historical data are planned for future research. Usability testing (SUS) with 24 respondents yielded an average score of 78.54 (Grade B+, "Good"), indicating the prototype is acceptable for use. This research bridges AI-based hotspot prediction with user-centered UI/UX design, offering practical recommendations for an accessible mitigation application; empirical validation of the AI component remains necessary before full adoption by disaster agencies.
Pengembangan Model Matematika Penyebaran Api Berbasis Vektor dan Filter Titik Panas Industri untuk Sistem Peringatan Dini Karhutla Muhammad Rizky Pribadi; Dedy Hermanto; Hafiz Irsyad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.17244

Abstract

Forest and land fires (karhutla) in the tropical peatland ecosystem of South Sumatra pose recurring ecological threats and transboundary haze disasters during every dry season. Existing early warning systems generally rely on satellite hotspot detections without accounting for the direction and rate of fire spread, and remain vulnerable to false alarms caused by persistent industrial heat sources such as refineries, palm oil mill flare stacks, and power plants. This study develops a deterministic, vector-based mathematical model to predict the direction, rate, and hazard-zone geometry of fire spread in near real-time, complemented by a spatial-temporal filtering algorithm that eliminates industrial heat sources. The model derives a propagation bearing from wind direction, a base rate of spread from four environmental factors, and constructs three risk zones as cone-shaped polygons in geospatial coordinates. The model was implemented in the Sumsel Hotspot Monitor system, processing VIIRS and MODIS data from NASA FIRMS. Evaluation using Intersection over Union (IoU) and Dice Similarity Coefficient against real satellite ground truth shows that model performance degrades as the prediction time horizon increases. These results confirm that the model can run at low computational cost and is suitable as an early prediction baseline, although its accuracy still requires further parameter calibration before full adoption by the regional disaster management agency.
KLASIFIKASI JENIS IKAN AIR TAWAR MENGGUNAKAN ALGORITMA CNN DAN ARSITEKTUR ALEXNET Ahmad Rizky; Dedy Hermanto
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.8025

Abstract

Freshwater fish are an important commodity in the fishing industry that requires an accurate classification system. This study aims to develop a freshwater fish classification system using the Convolutional Neural Network (CNN) algorithm with AlexNet architecture, as well as applying data augmentation techniques to improve model accuracy. The dataset used consists of 488 images of five types of freshwater fish, namely catfish, baung fish, tapah fish, juaro fish, and patin fish, which were then augmented into 68,400 images. The model was trained using the Adam optimizer, with a batch size of 16, a learning rate of 1e-5, and 200 epochs. The results of the experiment show that the model achieved a training accuracy of 71.09%, a validation accuracy of 85.00%, and a testing accuracy of 80.29%. Precision reached 0.8310, Recall 0.7909, and F1-score 0.7912, indicating the model's excellent performance in classifying freshwater fish species. This research is expected to support the development of an automatic classification system for the freshwater fisheries industry
Analysis of Optimal Epoch Selection for YOLO26 Model in Detecting Graves and Free Slots Using UAV Photogrammetry Hafiz Irsyad; Muhammad Rizky Pribadi; Dedy Hermanto; Dina Lestari Putri
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.11573

Abstract

Purpose – This study investigates the optimal number of training epochs for the YOLO26 model in detecting graves and free burial slots from UAV photogrammetry imagery, with particular attention to model convergence, generalization, and detection performance. Design/methods/approach – A publicly available cemetery dataset containing two object classes, namely graves and free burial slots, was preprocessed using auto-orientation, 2×2 tiling, resizing to 640 × 640 pixels, grayscale conversion, and data augmentation. The YOLO26 model was trained using transfer learning under six epoch configurations: 50, 100, 150, 200, 250, and 300 epochs. Performance was evaluated using precision, recall, F1-score, mAP@50, mAP@50–95, confusion matrices, and training and validation loss curves. Findings – Model performance improved substantially as training progressed and began to stabilize after approximately 200 epochs. The highest observed performance occurred at epoch 289, with a precision of 98.78%, recall of 98.26%, F1-score of 99%, mAP@50 of 99.40%, and mAP@50–95 of 90.94%. Although the 300-epoch configuration produced similarly strong results, the additional gains were marginal, indicating diminishing returns after convergence. Research implications/limitations – The findings provide practical guidance for selecting an appropriate training duration in UAV-based cemetery mapping and small-object detection. However, the study relies on a relatively small, single-source dataset, which may limit generalizability across different cemetery layouts, environmental conditions, and UAV imaging configurations. Originality/value – This study provides a domain-specific multi-epoch benchmark for YOLO26 and demonstrates the importance of metric- and convergence-based checkpoint selection rather than relying solely on the maximum predefined number of epochs.
Co-Authors Abdul Rahman Adikara Alif Nurrahman Ahmad Rizky Ahmad, Prima Anam, M. Syaiful Andreas Saputra, Andreas Daniel Daniel Deka Putra Pamungkas Deris Stiawan Dermawan, Sepri Desy Iba Ricoida Dina Lestari Putri Eka Puji Widiyanto Eldo Suryadi Emi Yuliati Evan Susanto Feby Ardianto Hafiz Irsyad Iis Iis Pradesan Inayatulah Inayatulah Indri Ramayanti Ivan Ricardo Jiwanata Jonathan, Jasen Joseph Alberto Jovian Aristo Kgs. M. Ammar Yazid Kornelia* Kornelia* Kynta, Diva Putri Laksono, Ivan Luthfi Latius Hermawan Lucretia, Jolyn Meilinda, Meilinda Muhammad Fahmi Muhammad Mauladi Muhammad Rizky Pribadi Mulyati Mulyati Negara, Edi Surya Nova Ariansyah Pibriana, Desi Qois Al Qorni Richardo Anggara Rizqie, Muhammad Qurhanul Rusbandi rusbandi rusbandi, rusbandi Saputra, Darwin Sepri Dermawan Shinta Puspasari Suluh Arif Wibowo Syakurah, Rizma Adlia Theng, Arifin Therino Elevan, Rivaldo Thingkilia Finnatia Husin Tri Hartati Utami Valen Julyo Armando Davincylin Valentino, Calvin Bertnas Vannes Wijaya Wijaya, Frisky Yuniarti, Erliza