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All Journal IJISTECH (International Journal Of Information System & Technology) Brahmana : Jurnal Penerapan Kecerdasan Buatan IJISTECH
Edra, Anggun Purnama
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Evaluating Urban Green Space Dynamics in Makassar City Through NDVI-Based Analysis of Sentinel-2 Imagery Edra, Anggun Purnama; Azzahra, Cantika; Nurhayati, Shifa
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.392

Abstract

Urban expansion in rapidly growing cities like Makassar has considerable implications for vegetation loss and ecosystem degradation. This study investigates vegetation cover changes in Makassar City by analyzing the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery for the years 2020 and 2024. A threshold of NDVI > 0.5 was applied to identify areas classified as dense vegetation. Image pre-processing, NDVI computation, and binary classification were performed to quantify and map vegetation extent. The analysis reveals a noticeable reduction in densely vegetated areas. This study analyzes the changes in vegetation health in Makassar City using the Normalized Difference Vegetation Index (NDVI) for the years 2020 and 2024. The NDVI threshold of >0.5 was used to identify areas of healthy vegetation. The spatial analysis and classification maps reveal a significant decline in vegetated areas, with a decrease from 4,792.93 hectares in 2020 to 1,157.23 hectares in 2024. This trend highlights a substantial reduction in healthy vegetation cover, potentially caused by urban development, land-use changes, and environmental pressures. The findings underscore the need for sustainable land management and green infrastructure policies to mitigate the adverse effects of vegetation loss and promote ecological balance in urban areas.
Evaluating Urban Green Space Dynamics in Makassar City Through NDVI-Based Analysis of Sentinel-2 Imagery Edra, Anggun Purnama; Azzahra, Cantika; Nurhayati, Shifa
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.392

Abstract

Urban expansion in rapidly growing cities like Makassar has considerable implications for vegetation loss and ecosystem degradation. This study investigates vegetation cover changes in Makassar City by analyzing the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery for the years 2020 and 2024. A threshold of NDVI > 0.5 was applied to identify areas classified as dense vegetation. Image pre-processing, NDVI computation, and binary classification were performed to quantify and map vegetation extent. The analysis reveals a noticeable reduction in densely vegetated areas. This study analyzes the changes in vegetation health in Makassar City using the Normalized Difference Vegetation Index (NDVI) for the years 2020 and 2024. The NDVI threshold of >0.5 was used to identify areas of healthy vegetation. The spatial analysis and classification maps reveal a significant decline in vegetated areas, with a decrease from 4,792.93 hectares in 2020 to 1,157.23 hectares in 2024. This trend highlights a substantial reduction in healthy vegetation cover, potentially caused by urban development, land-use changes, and environmental pressures. The findings underscore the need for sustainable land management and green infrastructure policies to mitigate the adverse effects of vegetation loss and promote ecological balance in urban areas.
Performa Model YOLOv8 untuk Deteksi Kondisi Mengantuk pada pengendara mobil Armin, Edmund Ucok; Edra, Anggun Purnama; Alifin, Fakhri Ikhwanul; Sadidan, Ikhwanussafa; Sary, Indri Purwita; Latifa, Ulinnuha
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.279

Abstract

Driving while drowsy is identified as a significant risk factor in traffic accidents, yet awareness of this risk is often lower compared to other hazards. Phenomena such as microsleep have been shown to increase the risk of inattention and accidents on the road. This study proposes a novel approach utilizing Deep Learning, specifically YOLOv8, to detect and address the risk of driver drowsiness. To train the model, the researchers employed a secondary dataset consisting of 3708 images, partitioned into 80% for model training and 20% for validation. Multiple models were compared during the training process, and the results indicated that the YOLOv8 model outperformed previous models, achieving a recall value of 0.95261, precision of 0.94655, F1-SCORE of 0.9496, and mAP of 0.98055. This research contributes to the development of more effective drowsiness detection systems using Deep Learning approaches, with promising evaluation results.
Co-Authors Armin, Edmund Ucok Azzahra, Cantika Fakhri Ikhwanul Alifin Ikhwanussafa Sadidan, Ikhwanussafa Latifa, Ulinnuha Nurhayati, Shifa Sary, Indri Purwita