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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SISFOTENIKA JURNAL INTEGRASI JRST (Jurnal Riset Sains dan Teknologi) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknoinfo ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL Jurnal Informatika dan Rekayasa Elektronik Sains, Aplikasi, Komputasi dan Teknologi Informasi TEPIAN Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Inovasi Teknologi Masyarakat
Tejawati, Andi
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Betel leaf classification using color-texture features and machine learning approach Novianti Puspitasari; Anindita Septiarini; Ummul Hairah; Andi Tejawati; Heni Sulastri
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5101

Abstract

The existence of machine learning has been exploited to solve difficulties in various fields, including the classification of leaf species in agriculture. Betel leaf is one of the plants that provide health advantages. The objective of using a machine learning approach is to classify the betel leaf species. This study involved several processes: image acquisition, region of interest (ROI) detection, pre-processing, feature extraction, and classification. The feature extraction used the combination features of color and texture. Furthermore, the classification applied four classifiers, including artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM). The evaluation in this study implemented cross-validation with a K-fold value of 5. The method performance produced the highest accuracy value of 100% using the color and texture features with the SVM classifier.
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 Achmad, Rayhan Zidane Ade Chrisvitandy Ahmad Wahbi Fadillah Alameka, Faza Anam, M Khairul Andi Azza Az-Zahra Andi Muhammad Redha Putra Hanafiah Anindita Septiarini, Anindita Anjas, Andi Anton Prafanto Arba, Muhammad Hendra Arief Hidayat Bambang Cahyono Budiman, Edy Budiman, Edy Damayanti, Elok Didit Suprihanto, Didit Eddy Kurniawan Pradana Eka Priyatna, Surya Ery Burhandenny, Aji Fadli Suandi Fahrul Yamani Fairil Anwar Fajar Fatimah Faza Alameka Fernando Elda Pati Ferry Miechel Lubis Firdaus, Muhammad Bambang Friendy Prakoso Hairah, Ummul Hairah, Ummul Hamdani Hamdani Hanif Aulia Hasman, Firnawan Azhari Heni Sulastri indrajit, Indrajit Irfan Putra Pratama Irsyad, Akhmad Joan Angelina Widians, Joan Angelina Kamila, Vina Zahratun Lathifah Lathifah Lathifah Lathifah Lubis, Ferry Miechel M Syauqi Hafizh Masa, Amin Padmo Azam Masna Wati Medi Taruk Muhammad Bambang Firdaus Muhammad Budi Saputra Muhammad Nopri Fauzi Muhammad Nur Ihwan Nariza Wanti Wulan Sari Novianti Puspitasari Pakpahan, Herman Santoso Pasorong, Hillary Bella Pohny Pohny Puspita Octafiani Puspitasari, Novianti Ramadhan, Khefyn Rantetana, Stevie Falentino Renol Sulle Richard Giovanni Ardie Wong Riyayatsyah, Riyayatsyah Rizqi Saputra Rondongalo Rismawati Rosmasari Rosmasari, Rosmasari Saipul, Saipul Setyadi, Hario Jati Sofiansyah Fadli Sukma Dewi Hardi Yanti Syahbana, Syarif Nur Taruk, Medi Wahyudianto, Mochamad Rizky Wahyudin Wahyudin Waksito, Alan Zulfikar Wardhana, Reza Wati, Masna Wenty Dwi Yuniarti, Wenty Dwi Widians, Joan Angelina Zainal Arifin Zainal Arifin