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All Journal International Journal of Electrical and Computer Engineering International Journal of Applied Power Engineering (IJAPE) Jurnal Sosial Ekonomi Pertanian Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Telematika Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi Kinerja: Jurnal Ekonomi dan Manajemen JKTP: Jurnal Kajian Teknologi Pendidikan Jurnal SOLMA JURNAL SINEKTIK LIABILITIES (JURNAL PENDIDIKAN AKUNTANSI) Jurnal Sisfokom (Sistem Informasi dan Komputer) community: Pengawas Dinamika Sosial JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Infortech Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Science and Engineering (JCSE) Jurnal Graha Pengabdian Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Conten : Computer and Network Technology Journal of Electrical Vocational Teacher Education Jurnal Neuroanestesi Indonesia PELS (Procedia of Engineering and Life Science) Jurnal Media dan Teknologi Pendidikan Jurnal Ilmu Ekonomi Mulawarman (JIEM) JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Info Abdi Cendekia Finger : Jurnal Ilmiah Teknologi Pendidikan SATIN - Sains dan Teknologi Informasi Jurnal Komputer Antartika Bulletin of Informatics and Data Science Informatics and Software Engineering (ISE) INOVASI: Jurnal Ekonomi, Keuangan, dan Manajemen Vocational: Journal of Educational Technology Journal Of Biology Education Research (JBER) Jurnal Informatika Jurnal Komputasi Jurnal Elektronika dan Telekomunikasi
Agus Junaidi
Universitas Bina Sarana Informatika
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Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram Sarifah Agustiani; Yoseph Tajul Arifin; Agus Junaidi; Siti Khotimatul Wildah; Ali Mustopa
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2961

Abstract

Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method.
Pemodelan dan Analisis Koordinasi Proteksi Overcurrent Relay pada Sistem Distribusi Tenaga Listrik Menggunakan ETAP Ritonga, Syah Fikri; Saragi, Dian Putra; Junaidi, Agus
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.37833

Abstract

Penyaluran energi listrik pada jaringan distribusi memerlukan sistem proteksi yang handal agar kontinuitas pelayanan tetap terjaga serta peralatan terlindungi dari kerusakan akibat gangguan. Salah satu perangkat yang sering digunakan adalah overcurrent relay (OCR), yaitu relai yang bekerja saat terjadi arus lebih. Gangguan seperti hubung singkat dapat menimbulkan lonjakan arus yang tinggi sehingga berisiko merusak komponen sistem jika tidak segera ditangani. Oleh karena itu, penentuan setting OCR yang tepat sangat penting untuk memastikan relai dapat bekerja secara selektif dan cepat. Penelitian ini dilakukan dengan memodelkan sistem distribusi menggunakan ETAP, dilanjutkan analisis arus gangguan dan penentuan parameter relai seperti arus pickup dan waktu operasi. Hasil simulasi menunjukkan bahwa pengaturan yang tepat mampu menghasilkan koordinasi proteksi yang baik dan efektif dalam mengisolasi gangguan.
Deep Neural Network Classifier for Analysis of the Debrecen Diabetic Retinopathy Dataset Cucu Ika Agustyaningrum; Haryani Haryani; Agus Junaidi; Iwan Fadilah
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.640

Abstract

Diabetic retinopathy (DR) is a serious complication that can occur in individuals who have diabetes. This disease affects the blood vessels in the retina, a part of the eye that is important for vision. Early detection of DR is key to preventing further complications and saving the patient’s vision. The goal of Diabetic Retinopathy Debrecen Data Set Analysis is to get the best, most accurate results for medical professionals to receive appropriate Diabetic Retinopathy Debrecen prediction results through the stages of data collection, evaluation, and classification.   Data is collected from existing secondary sources, then assessed using a deep neural network algorithm with various variations. The classification algorithm in this research uses the Python programming language to measure accuracy, F1-Score, precision, recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 79.94%, the F1 score reaches 79.16%, the precision is 79.58%, the recall is 79.60%, and the AUC is 79.56%. Thus, based on this research, the deep neural network data mining technique with variations of the four hidden layer encoder-decoder, sigmoid activation function, Adam optimizer, learning rate 0.001, and dropout 0.2 is proven to be effective. When compared with other variations   such as decoder-encoder, 3-8 hidden layers, learning rate 0.1 and 0.01, the average difference in values between this variation and the others is 0.07% accuracy, 2.03% F1 score, 0.25% precision, 0.80% recall, and 0.90% AUC. Therefore, the deep neural network algorithm with the variation used shows significant dominance compared to other variations.Diabetic retinopathy (DR) is a serious complication that can occur in individuals who have diabetes. This disease affects the blood vessels in the retina, a part of the eye that is important for vision. Early detection of DR is key to preventing further complications and saving the patient’s vision. The goal of Diabetic Retinopathy Debrecen Data Set Analysis is to get the best, most accurate results for medical professionals to receive appropriate Diabetic Retinopathy Debrecen prediction results through the stages of data collection, evaluation, and classification.   Data is collected from existing secondary sources, then assessed using a deep neural network algorithm with various variations. The classification algorithm in this research uses the Python programming language to measure accuracy, F1-Score, precision, recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 79.94%, the F1 score reaches 79.16%, the precision is 79.58%, the recall is 79.60%, and the AUC is 79.56%. Thus, based on this research, the deep neural network data mining technique with variations of the four hidden layer encoder-decoder, sigmoid activation function, Adam optimizer, learning rate 0.001, and dropout 0.2 is proven to be effective. When compared with other variations   such as decoder-encoder, 3-8 hidden layers, learning rate 0.1 and 0.01, the average difference in values between this variation and the others is 0.07% accuracy, 2.03% F1 score, 0.25% precision, 0.80% recall, and 0.90% AUC. Therefore, the deep neural network algorithm with the variation used shows significant dominance compared to other variations.
Co-Authors AA Sudharmawan, AA Abdul Muin Sibuea Afandi, Marwan Agus Junaidi agusniati, Agusniati Agustiani, Sarifah Ahmad Yani ahmad yani Ahmad Yani Aji Miftahus Salim Ali Mustopa, Ali Alif Rahman AMRI, MOHAMAD SYAIFUL Andi Saryoko Angga Eko Pratama Anggita, Nur Anisa Anis Suryatri AS, Usman Asyiri, Syekh Mohammad Auliabahri, Ananda Putri Ayu Wahyuni Azis, Mochammad Abdul Baharuddin Baharuddin Bakti Dwi Waluyo Butar Butar, Abdul Hakim Candra Sumirat Catra Indra Cahyadi Cucu Ika Agustyaningrum Delani, Desta Denny Haryanto Sinaga Derlina . Devi Angelina Simaremare Dewi Kartika Dewi Kartika Diah Puspitasari Dikki Miswanda DINA AMPERA Dio Caisar Darma Dodi Suryanto, Eka Donna Setiawati Efendi Napitupulu Emiliana, Meutia Raissa Fachry Abda El Rahman Fadilah, Iwan Faez Syahroni Fahmi, Jaman Fany Visella Fawzim, Ahmad febryan_wiraputra febryan Firdaus Idam Fitrayuda Rivaldy Fitriadi Fitriadi Frisma Handayanna Frisma Handayanna Gustin Setyaningsih Halawa, Ratakan Berkat Halimatun, Futria Hardiyan Hardiyan Harun Sitompul Haryani Haryani Haryani Henry Januar Saputra, Henry Januar Hutabarat, Hot Marindo Ikha Listyarini Indra Cahyadi, Catra Indra Permana Putra intan Iwan Fadilah Jananto Watori Jesica Yolanda Br. Sibarani K, Abd Hamid K, Abdul Hamid Kamil, Anton Abdul Basah Khairahmi, Khairahmi Khairul Khairul, Khairul Khairunnisa Zakaria Lesmana, Dicky Lubis, Ali Hamzah Mansur Mariati Mariati Maruloh Maulana, Syukran Meutia Raissa Emiliana Mochammad Abdul Azis Muhammad Amin Muhammad Junaidi Munthe, Erayana Mustaqim, Bima Mustopa, Ali Ningrum, Eri Widya Nur Hafid, Ardika Nur ‘Azah Opetu, Demitila Okola Pangaribuan, Wanapri Panjaitan, Albert Popon Handayani Pratama, Adryansyah Anugrah Pribadi, Denny Priyagus, Priyagus Putri, Rizky Rachma R Mursid RACHMAT HIDAYAT Rachmat Hidayat Rahmaniar Rahmaniar, Rahmaniar Ramadan, Angga Riski Ramadhan, Khoiru Iqbal Ratna Tanjung Riska Aryanti Ritonga, Syah Fikri Rizki Wahyudi Rizky Rachma Putri Rudianto Rudianto Ryan Juska Pratama S.M Santi Winarsih Saddam Saddam, Saddam Sahat Siagian Samsidar Tanjung Samsiyatun Samsiyatun Samudi Sandra Jamu Kuryanti Saprijal, Saprijal Saputra, Agus Saputra, Muhammad Fadhlan Saragi, Dian Putra Sari, Debby Kaumala Setyaningsih, Indah Sinaga, Enny Keristiana Sinaga, Irma Aprilda Siti Khoiriyah Siti Khotimatul Wildah Siti Marlina Siti Nur Khasanah Sobari, Irwan Agus Sopiyan Dalis Sri Adelila Sari Sriadhi Sriadhi Sriadhi, Sriadhi St Wulan Aprianti Sulistiyah Suwarman, S Suwarno Suwarno SYAHPUTRA, MUHAMMAD RIZKI Syekh Mohammad Asyiri Tambunan, Arsita Devi Tarigan, Adi Sastra P Tatang Bisri Teguh Febri Sudarma, Teguh Febri Usman AS Wahyudin Wahyudin Wahyudin Wahyudin Yahaya, Wan Ahmad Jaafar Wan Yosefa Hutajulu, Olnes Yoseph Tajul Arifin Yunita yunita yunita Yusra Jamali Zakaria, Khairunnisa Zubir, Moondra