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All Journal EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN SMATIKA InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informasi dan Komputer Jurnal Manajemen Informatika dan Sistem Informasi JURSIMA (Jurnal Sistem Informasi dan Manajemen) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Experimental Student Experiences JURSIMA Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN IPSSJ
Odi Nurdiawan
Unknown Affiliation
Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Optimization of Classification of Tea Leaf Disease Images Using LBP–HOG and MobileNetV2 Ezar Qotrunnada; Odi Nurdiawan; Arif Rinaldi Dikananda; Aris Pratama Putra; Bani Nurhakim
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1861

Abstract

This study was motivated by the need for an accurate and efficient system for detecting tea leaf diseases, given that the current method Manual identification has limitations in terms of consistency, speed, and It also depends on expert labor. To address these challenges, the study It developed a classification model for detecting diseases in tea leaves using a combination of features Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) integrated with the MobileNetV2 architecture. The research method includes the following stages: importing the dataset, data partitioning, exploratory data analysis (EDA), preprocessing, features, and training four model scenarios: baseline MobileNetV2, LBP-based model, HOG-based model, and hybrid LBP–HOG model. Evaluation is done with the metrics of accuracy, precision, recall, and F1-score. The results show that the baseline model achieved 91.67% accuracy, the LBP model achieved 60.67%, the HOG model achieved 68.67% accuracy, and the hybrid model achieved 66.67% accuracy. These findings indicate that MobileNetV2 is still the most optimal model, but the integration of texture features and gradients provides a deeper understanding of the characteristics of disease patterns. This study emphasizes the importance of exploring classic features to enriching visual representation in lightweight CNN models, as well as providing a contribution to the development of plant disease diagnosis systems that are efficient.
Peningkatan Pemahaman Akuntansi Dengan Menggunakan Software Zahir Fidya Arie Pratama; Odi Nurdiawan
Edunomic : Jurnal Ilmiah Pendidikan Ekonomi Fakultas Keguruan dan Ilmu Pendidikan Vol 7 No 2 (2019): EDISI SEPTEMBER
Publisher : Prodi Pendidikan Ekonomi-UGJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33603/ejpe.v7i2.2551

Abstract

STMIK IKMI have a student academic test when the students enter to STMIK IKMI, the result of the test is low. Low in this case will explains by categories the students get D 25% C 37% B 0% and all the students can not get A. The research uses kuasi eksperiments method with time series design that collaborate classroom action research. The result of research shows about pre test 1 until 4, the score of pre test 1 about 54,129, the score of pre test 2 about 55,548, the score of pre test 3 about 56,032, the score of pre test 4 about 56,097. Based on Kruskall Walls Test shows Asymp Sig score about 0,986 it means there is no significance differences between student perception for the first time and students understanding the materials. In second part of research the students learn accounting that use zahir accounting software for 6 meetings. In third part of research the students has a post test for 4 meetings and the results are the score of post test 1 62,6456, the results are the score of post test 2 70,065, the results are the score of post test 3 80,032, the results are the score of post test 4 86,742. The research analyze statistic test focus on pre test and post test by Kruskall Wall Test that shows Asymp Sig score about 0,000 it means there is a differences between the result of pre test and post test. This reality shows accounting learning by Zahir software gives the positive effect for improving (upgrading) student understanding about accounting.
Co-Authors Ade Irma Purnamasari Ade Rizki Rinaldi agus bahtiar Ahmad Bahrum Maula Rahman Ananda Rafly Aria Pratama Arif Nur Hakim Arif Rinaldi Dikananda Aris Pratama Putra Bani Nurhakim Budi Saputra Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dicky Miftakhul Rizki Dwi Teguh Afandi Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Ezar Qotrunnada Fadhil M Basysyar Fadhil M.Basysyar Fathurrohman, Fathurrohman Fidya Arie Pratama Gifthera Dwilestari Iman Nurrohmat Irfan Ali Irvan Himawan Jayawarsa, A.A. Ketut Liyanda Pangestu Maulana Manshur Melawati Melawati Muhamad Sulaeman Mulyawan Mulyawan nur syarief abdullah Rini Astuti Ruli Herdiana Saeful Anwar Shofian Yunus Siti Aisyah Syafi'i Syafi'i Syarif Hidayat, Deden Teguh Abdi Mangun Tio Prasetiya Tio Prasetya