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All Journal Bulletin of Electrical Engineering and Informatics JURNAL MUQODDIMAH : Jurnal Ilmu Sosial, Politik dan Hummaniora Anterior Jurnal Martabe : Jurnal Pengabdian Kepada Masyarakat Etika Demokrasi Gorontalo Fisheries Journal Restorica: Jurnal Ilmiah Ilmu Administrasi Negara dan Ilmu Komunikasi Jurnal Review Pendidikan dan Pengajaran (JRPP) JURNAL LENTERA BISNIS Jurnal Ekonomi Manajemen Sistem Informasi Journal of Social Politics and Governance (JSPG) Pencerah Publik Journal of Governance and Local Politics (JGLP) Journal of Social Political Sciences (JSPS) LEGAL BRIEF Journal of Government and Politics (JGOP) Jurnal Pengabdian kepada Masyarakat Jurnal Jeumpa International Journal of Business and Information Technology Edu Sociata : Jurnal Pendidikan Sosiologi Jurnal Dehasen Untuk Negeri Journal of Mathematics Instruction, Social Research and Opinion
Saipul, Saipul
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Journal : Bulletin of Electrical Engineering and Informatics

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A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
Co-Authors Amriadi, Amriadi Anindita Septiarini, Anindita Annisa Anggraini, Annisa Aquarini, Aquarini Arlina Arlina, Arlina Bambang Irawan Buana , Ierene Patra Cathas Teguh Prakoso Cianstury, Irvan Cornelia, Geby Daryono Daryono, Daryono Didit Suprihanto, Didit Dipa, Pradeka Reza Djoko Wijono Ekariana S Pandia Elisabeth, Christine Riani Ernita, Lina Ery Burhandenny, Aji Evi Septiana, Dyna Firdiansyah, Firdiansyah Fitri, Rahmani Hamzani Hamzani Hapidah, Hapidah Harianti Harianti Hastina, Hastina Herlina, Yuni Hidayanti, Rakhmah Idris, Adam Indah Tri Handayani Izza Mahdiana Apriliani Jumiati Siska, Jumiati Junaidi Junaidi Junaidi, H. Kartika Sari, Enda Kristina Kristina Kus, Indarto M. Fatchurahman Marupi, Zamir Maulana Maulana, Maulana Mesterjon, Mesterjon Momoh, Zekeri Muhammad Aqil Muhammad Arsyad Muhammad Ilhamuddin Dahri Muliyani Muliyani Nadriati, Sri Ngabito, Meriyanti Ningsih, Asniati Nonon Saribanon Noorrachmi, Noorrachmi Nur Faliza Nur Wahyunianti Dahri Nurhaerani Tenriwaru Nurhasanah Nurhasanah Nurul Auliyah, Nurul Oktobrian, Ivan Priyodigdo, Achmad Arief Puspitasari, Novianti Raden Biroum Bernardianto Rahim, Muhammad Nur Ramadhona, Yudha Ramadlan, Ahmad Ramdani, M. Yuda Rohelly, Rohelly Rudy Haryanto Rudy Suryadi, Rudy Rusdiansyah Rusdiansyah Sarifudin Sarifudin Sri Ekanti Sabardini Subagyo, Banu Sukirdi, Sukirdi Sundari, Sri Surya , Iman Tejawati, Andi Tiono, Muhamad Ilham Umi Rahmawati Walenta, Abdi Sakti Widyastuti Widyastuti Wulan Oktavia, Ellisa Yulya Muharmi Zico Patera, Rega