Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Kelautan Tropis Journal of Applied Data Sciences
Sugiana, I Putu
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Identifying Key Factors Causing Flooding Using Machine Learning Gama, Adie Wahyudi Oktavia; Dennatan, Monalisa; Dharmayasa, I Gusti Ngurah Putu; Maw, Me Me; Sugiana, I Putu; Suryanti, Irma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.463

Abstract

The impact of flooding extends beyond physical and infrastructural damage, affecting social, economic, and environmental dimensions. This study aims to identify the key factors influencing flooding by developing a decision tree model. The research method applies the C4.5 algorithm to build a decision tree model using flood factors such as rainfall, soil type, elevation, land use, and distance from rivers. The model is then applied to 57 past flood data events to determine key contributors to flooding in Denpasar City, Bali, Indonesia. The analysis showed that land elevation is the most influential factor, with areas below 28 meters above sea level having a 71% likelihood of being flood vulnerability. Additionally, the model reveals unknown patterns contributing to flood vulnerability among the factors considered. These insights give a deeper understanding of how these factors combine to affect flood vulnerability. The model's effectiveness was evaluated using a confusion matrix, resulting in an accuracy rate of 90%, a precision rate of 100%, a sensitivity rate of 90%, a specificity rate of 100%, and a F1 Score rate of 94%, demonstrating its strong predictive power in identifying areas at risk of flood vulnerability. Although this study is limited by the availability of data, the focus on Denpasar City, and the potential omission of other relevant attributes, it advances flood risk assessment by applying machine learning to provide practical insights that could enhance flood management strategies, with potential applications to other urban areas facing similar risks.
Validasi Mangrove Health Index Berbasis Sentinel-2 di Bali dan Jawa Timur, Indonesia As-syakur, Abd. Rahman; Novanda, I Gede Agus; Sugiana, I Putu; Dewi, I Gusti Ayu Istri Pradnyandari; Andiani, Anak Agung Eka; Aryunisha, Putu Echa Priyaning; Riskianisya, Adhisti; Wijana, I Made Sara
Jurnal Kelautan Tropis Vol 29, No 1 (2026): JURNAL KELAUTAN TROPIS
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jkt.v29i1.30565

Abstract

Hutan mangrove memegang peranan yang penting dalam perlindungan pesisir dan penyerapan karbon, namun ancaman degradasinya terus meningkat. Teknologi penginderaan jauh menawarkan solusi pemantauan efisien melalui Indeks Kesehatan Mangrove atau Mangrove Health Index (MHI) berbasis citra Sentinel-2 (MHI-S2). Penelitian ini mengevaluasi akurasi model empiris MHI-S2 dibandingkan dengan pengukuran MHI lapangan (MHI-L), pada ekosistem mangrove di Bali dan Jawa Timur. Data dikumpulkan dari 194 plot pada 6 lokasi berbeda di Bali dan Jawa Timur dengan pengamatan yang mencakup parameter tutupan tajuk, diameter batang, dan kerapatan pancang/sapling, yang kemudian disandingkan dengan MHI-S2 pada periode waktu berdekatan. Evaluasi kinerja menggunakan koefisien korelasi Pearson (r dan R²), metrik galat/error (RMSE, MAE, bias), koefisien korelasi konkordansi Lin (Lin’s CCC), serta analisis Bland–Altman. Hasil menunjukkan hubungan moderat antara MHI-S2 dan MHI-L (r = 0,71; R² = 0,50). Meski demikian, ditemukan bias positif yang cukup tinggi yaitu sebesar 20,6 yang mengindikasikan kecenderungan overestimasi oleh model MHI-S2. Rendahnya nilai Lin’s CCC (0,36) dan lebarnya rentang batas kesepakatan 95% hasil analisis Bland–Altman (–17 hingga +59) menegaskan akurasi yang terbatas pada skala plot. Temuan ini mengimplikasikan bahwa model MHI-S2 yang tersedia saat ini belum sepenuhnya andal untuk estimasi nilai absolut MHI-L. Oleh karena itu, diperlukan pengembangan model empiris lanjutan serta validasi lapangan yang lebih intensif untuk meningkatkan presisi pemantauan berbasis satelit, demi mendukung keberhasilan manajemen konservasi mangrove.
Co-Authors Abd. Rahman As-syakur Adie Wahyudi Oktavia Gama Andiani, Anak Agung Eka Aryunisha, Putu Echa Priyaning Dennatan, Monalisa Dewi, I Gusti Ayu Istri Pradnyandari I Gusti Ngurah Putu Dharmayasa I Made Sara Wijana Irma Suryanti Maw, Me Me Novanda, I Gede Agus Riskianisya, Adhisti