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Hafiz Irsyad
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Universitas Multi Data Palembang, Kampus Rajawali. Jl. Rajawali no 14 Palembang
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INDONESIA
Algoritme Jurnal Mahasiswa Teknik Informatika
Published by Universitas Multi Data Palembang
ISSN : -     EISSN : 27758796     DOI : https://doi.org/10.35957/algoritme.v2i2
Core Subject : Science,
Computer Science & IT
Jurnal Algoritme menjadi sarana publikasi artikel hasil temuan Penelitian orisinal atau artikel analisis. Bahasa yang digunakan jurnal adalah bahasa Inggris atau bahasa Indonesia. Ruang lingkup tulisan harus relevan dengan disiplin ilmu seperti: - Machine Learning - Computer Vision, - Artificial Inteledence, - Internet Of Things, - Natural Language Processing, - Image Processing, - Cyber Security, - Data Mining, - Game Development, - Digital Forensic, - Pattern Recognization, - Virtual & AUmented Reality,. - Cloud Computing, - Game Development, - Mobile Application, dan - Topik kajian lainnya yang relevan dengan ilmu teknik informatika.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 122 Documents
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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.

Page 13 of 13 | Total Record : 122

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