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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Mantik Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal ComunitA Servizio
Rudy Chandra
Institut Teknologi Del
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Social Sciences

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Journal : Jurnal Mantik

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Wood Classification For Efficiency in Preventing Illegal Logging Using K-Nearest Neighbor Rudy Chandra
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Wood is a commodity that is usually used for various purposes. Growing demands for timber have led some humans to commit fraudulent and unlawful moves. One of them is through illegal logging of wood species that are protected by the state. The Ministry of Environment and Forestry apart from forest rangers has several problems in classifying wood species. Then technology is used to overcome these problems through the use of machine learning. One suitable algorithm for classifying is K-NN. There are five types of information wood used, specifically marine resin, teak, kruing, meranti, and ironwood. The total wood photos are 1,300 with a complete dataset of 250 images for each type of wood and 10 images for classification testing. The trial was carried out by finding the most optimal K, ie K = 3, after that it was recorded with a confusion matrix. The results obtained are 76% accuracy, 78.8% recall, 76% precision, and 77.37% F1-Score. The higher the value of K, the greater the number of classifications and the lower the accuracy. The higher the value of K, the more types of classifications you want to test, and the less accurate the percentages in the classification process.
Co-Authors Ahmadi Irmansyah Lubis Anastasia Marsada Uli Simamora Asido Saragih Christian Benedict Lumbantoruan Dame Sisri Haryati Katarina Rumapea Dewi Handayani Emy Sonia Sinambela Erna Budhiarti Nababan Ester Saulina Hutabarat Evan Richardo Sianipar Frengki Simatupang Fritz Marpaung Goklas Henry Agus Panjaitan Henry Agus Panjaitan , Goklas Henry Agus Panjaitan, Goklas Herbeth Augustinus Napitupulu Italiano Wowiling, Gerry Joshua Pratama Silitonga Juan Carlos Munthe Lawy Xenna Lumban Gaol, Tiurma Matthew Alfredo Mula Timbul Sigiro, Marojahan Nico Felix Sipahutar Panca Rahmat Siagian, Iqbal Pangaribuan, Maria Partogi Pardede, Immanuel Pasaribu, Monalisa Perdanasari, Lukie Poibe Leny Naomi Risky Saputra Siahaan Roberd Saragih Samuel Sibuea Sarbaini Sarbaini Sawaluddin Nasution SIAGIAN, WESLY MAILANDER Siahaan, Samuel Jefri Saputra Sihombing, Tahan HJ Simamora, Akdes Simon Simbolon, Amanda Artha Regina Sinaga, Ester Krismayani Siregar, Horas Marolop Amsal Suandika Napitupulu Tegar Arifin Prasetyo Tessalonika Siahaan Tiurma Lumban Gaol Trito Exaudi Manik Wesly Mailander Siagian