Article Per Year (5 Year)
p-Index From 2021 - 2026
3.215
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MIND (Multimedia Artificial Intelligent Networking Database) Journal Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL Brahmana : Jurnal Penerapan Kecerdasan Buatan Bulletin of Information Technology (BIT) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Pedagogy and Education Science TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi BEduManageRs Journal : Borneo Educational Management and Research Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Muhammad Ibnu Saad Saad
STMIK Widya Cipta Dharma
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Nonlinear Modeling of Agricultural and Environmental SDG Indicators in ASEAN Using Extreme Learning Machine Algorithms Ita Arfyanti; Muhammad Ibnu Sa'ad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7041

Abstract

This study presents a data-driven approach for modeling Sustainable Development Goal (SDG) indicators in ASEAN countries using the Extreme Learning Machine (ELM) algorithm. Focusing on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land), we utilized FAOSTAT datasets from 2020 to 2024 to forecast key indicators such as undernourishment, water use efficiency, and forest area. ELM, known for its rapid learning speed and capability to model nonlinear relationships, outperformed baseline models Linear Regression and Support Vector Machine (SVM) in terms of R² score, RMSE, and MAE. Specifically, ELM achieved R² values exceeding 0.93, with up to 54% RMSE reduction compared to linear models. The model successfully captured national development trends, including deforestation in Indonesia and Cambodia, water stability in Brunei, and varied progress in sustainable agriculture across the region. This study underscores the effectiveness of the Extreme Learning Machine (ELM) in forecasting Sustainable Development Goal (SDG) indicators and provides actionable insights to support evidence based policy planning, particularly in resource-constrained settings. The findings demonstrate that ELM’s combination of interpretability, computational efficiency, and scalability positions it as a highly valuable tool for real-time monitoring of sustainable development across Southeast Asia.
Co-Authors Ahmad Abul Khair Ahmad Fitriadi Andi Aslindah Anggra Prima Azahari Daud Hasiholan, Jundro Dovist Calvino Eka Selvi Handayani Ekawati, Hanifah Gamar Al Haddar Hasbi Sjamsir Heny Pratiwi Heny Pratiwi Intan Nur Safikah Irianto Ita Arfyanti Junirianto, Eko Muhammad Ibnu Sa’ad NUR FITRIANI Nur Yanti Nur, Nurul Hikmah Nurul Hikmah Otovianus Oscar Pitrasacha Adytia Pratiwi, Heny Salmon Sarifmata Purnomo Siti Halimah Syamsuddin Mallala Wahyuni - Wahyuni Widyatmike Gede Mulawarman Zakaria, Muhammad Alamsyah