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All Journal International Journal of Electrical and Computer Engineering 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) 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) 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 Journal of Artificial Intelligence and Digital Business 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) Smatika Jurnal : STIKI Informatika Jurnal Jurnal Informatika Jurnal Komputasi Jurnal Elektronika dan Telekomunikasi
Agus Junaidi
Universitas Bina Sarana Informatika
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Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Sarifah Agustiani; Haryani Haryani; Agus Junaidi; Rizky Rachma Putri; Meutia Raissa Emiliana
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Waste management is an important challenge in protecting the environment and public health. Improperly managed waste can cause pollution and hinder the recycling process. This study aims to classify waste based on images by optimizing three deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to determine the model with the best performance. The dataset comes from the Kaggle platform, consisting of 4,650 images in six categories: battery, glass, metal, organic, paper, and plastic. The research stages include preprocessing, data augmentation, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance with 93% accuracy, followed by ResNet50 with 91%, while MobileNetV2 ranged from 70–73% depending on the optimizer. Variations in optimizers were found to affect model performance, while data augmentation improved generalization capabilities, especially in classes with limited samples. This research confirms the potential of deep learning methods in supporting automatic waste classification systems and provides a basis for the development of technology-based waste management systems in the future.
Pemanfaatan Pucuk Tebu Dan Kotoran Ternak Menjadi Kompos Organik Kaya Nutrisi Zubir, Moondra; Junaidi, Agus; Miswanda, Dikki; Fahmi, Jaman; Lubis, Ali Hamzah; Pratama, Adryansyah Anugrah; Anggita, Nur Anisa
Jurnal SOLMA Vol. 15 No. 1 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i1.22304

Abstract

Background: Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pengetahuan, keterampilan, dan kemandirian masyarakat Desa Banyumas dalam memanfaatkan limbah pucuk tebu dan kotoran ternak menjadi kompos organik melalui penerapan teknologi tepat guna. Metode: Program dilaksanakan melalui pendidikan masyarakat, pelatihan, difusi teknologi, dan pendampingan, dengan introduksi mesin pencacah pucuk tebu serta praktik pembuatan kompos berbasis bahan lokal. Hasil: Hasil kegiatan menunjukkan adanya peningkatan pemahaman masyarakat mengenai pengelolaan limbah pertanian, meningkatnya efisiensi proses pencacahan bahan baku, dan terselenggaranya praktik pengomposan yang lebih terstruktur. Program ini berkontribusi positif terhadap pengurangan limbah pertanian dan membuka peluang pemanfaatan kompos organik bagi kebutuhan pertanian masyarakat. Ke depan, evaluasi mutu kompos secara laboratorium dan pendampingan pemasaran masih diperlukan agar program benar-benar berkelanjutan
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.
Comparative Analysis of Transfer Learning-Based Deep Learning Models for Jatropha Leaf Disease Classification Sarifah Agustiani; Sulistiyah; Agus Junaidi; Cucu Ika Agustyaningrum; Yoseph Tajul Arifin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2325

Abstract

Plant disease identification is essential for enhancing agricultural productivity and promoting sustainable crop management practices. Jatropha curcas has considerable potential as a biofuel-producing plant; however, its growth and productivity can be significantly affected by various leaf diseases. Conventional disease diagnosis often requires substantial time and relies heavily on expert knowledge, creating a need for automated solutions based on deep learning techniques. Although deep learning has been widely applied in plant disease recognition, comparative studies focusing on transfer learning models for Jatropha leaf disease classification remain limited, particularly for datasets characterized by distinctive visual features and relatively small sample sizes. This research conducts a comparative assessment of several deep learning architectures to determine the most effective model for classifying Jatropha leaf diseases. The evaluated architectures include MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and VGG16. All models utilized ImageNet pre-trained weights and were adapted through fine-tuning of the final classification layers to accommodate a dataset containing healthy and diseased Jatropha leaf images. Experimental findings reveal that ResNet50 achieved the highest classification accuracy of 93.81%, followed by VGG16 at 93.58% and EfficientNetB0 at 90.49%. In comparison, DenseNet121 and MobileNetV2 attained accuracies of 85.40% and 74.56%, respectively. Model effectiveness was assessed using accuracy, training duration, confusion matrix analysis, and ROC curve evaluation to examine classification capability across categories. The results demonstrate that ResNet50 offers the most balanced combination of predictive accuracy and performance stability. Overall, the study confirms that transfer learning-based deep learning models are highly effective for Jatropha leaf disease classification, with ResNet50 emerging as the most suitable architecture among those investigated. These findings may serve as a valuable reference for the development of reliable and efficient plant disease detection systems in agricultural environments.
Sistem Informasi Pengelolaan Stok Barang Pada Pabrik Gula Merah UD. Barokah Ardika Nur Hafid; Agus Junaidi
Jurnal Komputer Antartika Vol. 3 No. 4 (2025): Desember
Publisher : Antartika Media Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70052/jka.v3i4.1176

Abstract

Gula merah merupakan salah satu bahan pangan pokok yang banyak digunakan dalam kehidupan sehari-hari masyarakat Indonesia. UD. Barokah sebagai produsen gula merah di wilayah JABODETABEK masih menggunakan metode konvensional dalam pengelolaan stok, yaitu pencatatan manual melalui buku. Cara tersebut menimbulkan risiko kehilangan dan kerusakan data serta menyulitkan penyajian informasi secara cepat dan akurat. Permasalahan ini mendorong perlunya sistem informasi yang mampu mengelola data produksi, stok, dan distribusi barang secara terstruktur dan terkomputerisasi. Penelitian ini bertujuan untuk mengembangkan sistem informasi pengelolaan stok berbasis web guna membantu UD. Barokah dalam menyediakan informasi produksi, stok, dan pengeluaran barang secara efektif dan efisien. Metode penelitian meliputi observasi, wawancara, dan studi pustaka untuk memperoleh data kebutuhan sistem. Model pengembangan perangkat lunak yang digunakan adalah Waterfall, sedangkan implementasi dilakukan dengan framework Laravel, bahasa pemrograman PHP, dan basis data MySQL. Hasil penelitian menunjukkan bahwa sistem informasi yang dibangun dapat berjalan dengan baik dan mendukung pencatatan produksi, stok, serta distribusi secara lebih efisien. Pengujian menggunakan metode Black Box membuktikan bahwa seluruh fungsi sistem bekerja sesuai dengan kebutuhan. Dengan demikian, sistem ini dapat meningkatkan efektivitas dan akurasi pengelolaan stok pada UD. Barokah.   Palm sugar is one of the staple food ingredients widely used in the daily life of Indonesian society. UD. Barokah, as a palm sugar producer in the JABODETABEK area, still applies a conventional method in stock management, namely manual recording using books. This method poses risks of data loss and damage, as well as difficulties in presenting information quickly and accurately. These problems indicate the need for an information system that can manage production, stock, and distribution data in a structured and computerized manner. This study aims to develop a web-based stock management information system to support UD. Barokah in providing accurate, effective, and efficient information related to production, stock, and distribution activities. The research method includes observation, interviews, and literature study to gather system requirements. The software development model applied is the Waterfall model, while implementation is carried out using the Laravel framework, PHP programming language, and MySQL database. The results show that the developed system works properly and supports recording of production, stock, and distribution activities more efficiently. Testing using the Black Box method proves that all system functions run as expected. Thus, this system improves the effectiveness and accuracy of stock management at UD. Barokah.
Information System for Submitting Payment for Operational Costs of PT Selamat Makmur in Tangerang St Wulan Aprianti; Agus Junaidi
Informatics and Software Engineering Vol. 3 No. 1 (2025): June 2025
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v3i1.416

Abstract

Challenges arise from the manual submission of operational cost payments using Microsoft Excel. This manual process is susceptible to issues in payment submissions, complicates the tracking of approved payments, and lacks automated payment reminders. The author utilizes two approaches: the Data Collection method and the Software Development method. The Data Collection method entails conducting observations, interviews, and literature reviews. In regards to software development, the waterfall model is highlighted for its straightforward, sequential implementation. By adopting this system, PT. Selamat Makmur aims to streamline the submission and payment of operational costs, reduce errors, and improve transparency and accuracy throughout the company's operational cost management. This system is also vital for ensuring the efficient functioning of the cosmetic production process and supporting the company's growth in a competitive market.
Analisis Sentimen Wattpad di Play Store Menggunakan Naïve Bayes Berbasis TF-IDF dan SMOTE Farah Diba Azkia; Agus Junaidi; Arif Ismail Husin
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12268

Abstract

Kajian ini menguji performa klasifikasi Multinomial Naive Bayes yang diintegrasikan dengan skema pembobotan TF-IDF untuk memilah ulasan aplikasi Wattpad ke dalam kategori Positif, Negatif, dan Netral, sembari menakar kontribusi Synthetic Minority Oversampling Technique (SMOTE) dalam memitigasi ketimpangan jumlah data. Sebanyak 2.829 ulasan valid hasil web scraping melalui library google-play-srcaper diproses melalui tahapan krusial, meliputi pembersihan teks, case folding, konversi kata tidak baku, tokenisasi, eliminasi 773 stopword, hingga stemming dengan PySastrawi. Transformasi data menjadi vektor TF-IDF dengan batasan max_features=5.000 serta ngram_range=(1,2) menghasilkan matriks berdimensi 2.829 × 4.499. Melalui skema pembagian stratified split 80:20, teknik SMOTE diaplikasikan pada sektor data latih untuk mendongkrak jumlah sampel dari 2.263 menjadi 4.056 agar komposisi antar kelas lebih proporsional. Hasil observasi memperlihatkan pergeseran metrik performa yang cukup signifikan pasca-intervensi SMOTE. Sebelum dilakukan penyeimbangan data, model memang mencatat akurasi 68,37% dengan presisi 62,83% dan F1-score 60,24%, namun sistem menunjukkan kelemahan fatal yakni kegagalan total dalam mengidentifikasi kelas Netral. Setelah SMOTE diterapkan, kendati terjadi penurunan akurasi ke angka 58,83%, model justru menunjukkan ketangguhan lebih baik melalui peningkatan presisi menjadi 65,25% dan F1-score ke level 61,22%, serta lonjakan recall pada kelas Netral dari titik nol menjadi 0,43. Secara keseluruhan, pemetaan sentimen didominasi oleh opini Negatif sebesar 59,51%, disusul opini Positif 26,01%, dan Netral 14,48%, di mana mayoritas keluhan pengguna berhulu pada persoalan teknis aplikasi seperti lonjakan iklan, hambatan login, serta rendahnya stabilitas sistem.
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 Ardika Nur Hafid Arif Ismail Husin AS, Usman Asyiri, Syekh Mohammad Auliabahri, Ananda Putri Ayu Wahyuni Azis, Mochammad Abdul Baharuddin Baharuddin Bakti Dwi Waluyo Candra Sumirat Cucu Ika Agustyaningrum Delani, Desta Denny Haryanto Sinaga 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 Fahmi, Jam’an Fany Visella Farah Diba Azkia 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 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 Meutia Raissa Emiliana Mochammad Abdul Azis Muhammad Amin Mustaqim, Bima Mustopa, Ali Ningrum, Eri Widya Nur ‘Azah Opetu, Demitila Okola Pangaribuan, Wanapri 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 Sahat Siagian Samsidar Tanjung Samsiyatun Samsiyatun Samudi Sandra Jamu Kuryanti Saputra, Agus Saputra, Muhammad Fadhlan Saragi, Dian Putra Sari, Debby Kaumala Setyaningsih, Indah Sinaga, Enny Keristiana 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 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 Zakaria, Khairunnisa Zubir, Moondra