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All Journal Jurnal Manajemen Update CommIT (Communication & Information Technology) Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Consumer Science Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab IJIS - Indonesian Journal On Information System Jurnal Ipteks Terapan ComTech: Computer, Mathematics and Engineering Applications Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Tekno Kompak Jurnal Teknologi Informasi dan Pendidikan JUTIS : Jurnal Teknik Informatika Journal of Information Systems and Informatics Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat TEPIAN International Journal Of Science, Technology & Management (IJSTM) Dharma Sevanam : Jurnal Pengabdian Masyarakat Jurnal Komunikasi, Sains dan Teknologi (JKST) jabdimasunipem International Transactions on Artificial Intelligence (ITALIC) Journal of Applied Research In Computer Science and Information Systems Proletarian : Community Service Development Journal
Erick Fernando B311087192
Bina Nusantara University
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Journal : JOIV : International Journal on Informatics Visualization

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Comparison of Classification Algorithms in Bamboo Distribution Mapping for Identification of Industrial Supporting Raw Materials Veritawati, Ionia; Maspiyanti, Febri; Mastra, Riadika; Fernando, Erick; Murtako, Amir
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3072

Abstract

This study aims to address the challenges in the widespread supply of bamboo raw materials and the lack of coordination between bamboo-producing regions, as well as to conduct a comprehensive inventory and mapping of bamboo resources. In addition, this study also explores the factors that influence the distribution and growth characteristics of bamboo, such as soil type, altitude, and rainfall. The main problems faced in the bamboo industry are the uneven distribution of raw materials and the lack of coordination between regions, which hinder the development of a strong and sustainable bamboo industry value chain. The lack of in-depth information on the ecological factors that influence bamboo growth also exacerbates this situation. The method used in this study involves mapping bamboo potential through aerial photography data collection, which is then analyzed using machine learning technology. The three algorithms used in the classification process are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. The study was conducted in an area rich in bamboo vegetation, especially Bojongmangu District in Bekasi, West Java, Indonesia. From the analysis results, the SVM algorithm showed the best performance with a classification accuracy ranging from 80% to 90%. These results indicate that this method is very effective in mapping bamboo vegetation areas with high precision. This study also identified other variables, such as soil type and altitude, that play a role in bamboo distribution. With this more holistic approach, the study is expected to provide deeper insights into bamboo ecology and improve sustainable bamboo resource management.
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