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Journal : JOIV : International Journal on Informatics Visualization

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.
Co-Authors ., Adiyanto ., winanti Agistiawati, Eva Ali Sadikin Andwiyan, Denny Angel, Mary Anugrah, Sandy Asri Mulyani Basuki, Sucipto Beny Beny Cahyadi, Larissa Belva Chidir, Gusli Clarissa, Eleane Cyrilla Condrobimo, A. Raharto Dede Kurniadi Denny Prabowo, Yulius Derist Touriano Dina Fitria Murad Eldora, Karin Eugenia, Janice Florence Fauziyah, Asyifa Fayzhall, Miyv Faza, Ahmad Fernando, Andhika Fianty, Melissa Indah Firman Anindra Fitriawati, Nora Gatc, Jullend Goestjahjanti, Francisca Sestri Haloho, Heppy New Year Hartati, Ria Hendri Hendri Hetty Rohayani Himmy'azz, Istajib Kulla Kulla Himmy’azz, Istajib Kulla Hulu, Paulinus Hussein Hamid, Muhammad Hutagalung, Dhaniel Ikhsan, Ridho Bramulya Ionia Veritawati Irza, Dhany Kharisma JAINURI, JAINURI Johan, Monika Evelin Juliater Simamarta Karnawi Kamar, Karnawi Kumoro, Dwi Ferdi Cahya Kurniabudi, Kurniabudi Kusumawardani, Fadilla Liga, Wendy Madani, Muchlishina Maspiyanti, Febri Mastra, Riadika Meyliana Meyliana Mulyono, Herry Murtako, Amir Nuraini Sari, Nuraini Nurasiah ., Nurasiah Nurasiah Nurasiah Oktarina, Thina Parlindungan, Davis Roganda Pattymahu, Gracia Christabel Henrietta Po Abas Sunarya Prabowo, Dimas Aditya Rachman, Andi Ridho Riyanto Riyanto Rizfie, Muhammad Dala Setiawan, Johan Siagian, Pandapotan Siagian, Pandapotan Silitonga, Nelson Silvian, Vergio Siti Maesaroh Sri Lestari Sudiyono, Rachma Nadhila Sukriyah, Sukriyah Supiana, Nana Surjandy, Surjandy Suroso Suroso Suseno, Bayu Susetyono, Eko Suwita, Jaka Syabanera, Nasya Dochka Tiara, Beby Tjahjana, David Touriono, Derist Winanti WINANTI, WINANTI Wisnu Murti, Nugroho Wiyono, Nuri Yudhatama, Dhimas Yulia, Yayah Yulius Denny Prabowo, Yulius Denny Yunitasari Yunitasari, Yunitasari Yusuf Yusuf