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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Ilmiah KOMPUTASI Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Informasi MURA Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Jurnal Teknologi dan Manajemen Industri Terapan JEECS (Journal of Electrical Engineering and Computer Sciences) MDP Student Conference Jurnal Teknologi Informasi Mura Jurnal Pengabdian Masyarakat Nian Tana
Nur Rachmat
Universitas Multi Data Palembang
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Perbandingan Kinerja Arsitektur MobileneTV2 dan MobileneTV3 Dalam Klasifikasi Penyakit Retina pada Citra Optical Coherence Tomography (OCT) Menggunakan Optimizer AdamW dan SGD Ricko Andreas Kartono; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.915

Abstract

Retinal diseases are serious visual disorders that can lead to decreased visual function and even blindness. The diagnosis of retinal diseases is generally still performed manually by medical professionals through the examination of Optical Coherence Tomography (OCT) images, a process that requires considerable time, high precision, and is prone to diagnostic errors. Previous studies have mostly employed larger and more complex CNN architectures, with optimization limited to a few commonly used optimizers. This study aims to develop an automatic retinal disease classification model using Convolutional Neural Network (CNN) methods by leveraging the lightweight and efficient MobileNetV2 and MobileNetV3 architectures, enabling faster applications that can be deployed on resource-constrained devices. The architectures evaluated include MobileNetV2, MobileNetV3-Large, and MobileNetV3-Small, along with a comparison of two optimizers, namely AdamW and Stochastic Gradient Descent (SGD). The dataset used consists of 4,000 OCT images divided into four classes: Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen. The training process was conducted using a transfer learning approach, and model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the combination of the MobileNetV2 architecture with a batch size of 16 and either the AdamW or SGD optimizer achieved the best performance, reaching an accuracy of 85.75%, which is the highest among all tested configurations. These findings highlight the strong potential of lightweight architectures to be developed into fast, accurate, and field-deployable retinal disease diagnostic applications on mobile devices using deep learning.
Pelatihan Manajemen Website Rumah Sakit Khusus Gigi dan Mulut Provinsi Sumatera Selatan Nur Rachmat; Johannes Petrus
JPMNT JURNAL PENGABDIAN MASYARAKAT NIAN TANA Vol. 4 No. 1 (2026): Januari: Jurnal Pengabdian Masyarakat Nian Tana
Publisher : Fakultas Ekonomi & Bisnis, Universitas Nusa Nipa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59603/jpmnt.v4i1.1187

Abstract

The Special Dental and Oral Hospital of South Sumatra Province has an official WordPress-based website (https://rsgigimulut.co.id/) that functions as a medium for information dissemination, communication, and public services to the community. This website is expected to provide accurate, up-to-date, and easily accessible information for users. However, in practice, the website management staff still face challenges and limitations in independently managing and updating the content, resulting in the suboptimal utilization of the website’s potential. This community service activity aims to provide website management training for the staff by utilizing the WordPress Content Management Sistem (CMS) so that they are able to manage the website effectively and sustainably. The training was conducted on August 11, 2025, in the third-floor meeting room of the Special Dental and Oral Hospital of South Sumatra Province, involving two website management staff members. The implementation methods included lectures, demonstrations, and hands-on practice covering the introduction to the WordPress interface, page layout customization, the addition of new posts and pages, menu creation and management, as well as tips on content management and website maintenance. The results of the training indicate an improvement in participants’ understanding and skills in independently managing the website, which has a positive impact on enhancing the quality of public information presentation and strengthening the hospital’s digital services.
Klasifikasi Penyakit Daun Mangga Menggunakan YOLOv11 Berbasis Deep Learning dan Computer Vision Wijaya, Andrian; Rachmat, Nur
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9168

Abstract

Indonesia’s mango agriculture sector continues to face significant challenges due to leaf diseases that reduce crop productivity. Conventional disease identification methods remain inefficient because they rely on subjective visual observation. This study aims to develop a mango leaf disease classification model using the YOLOv11 deep learning algorithm. YOLOv11 is chosen for its capability in real-time object classification with an optimal balance between accuracy and processing speed. The research will utilize the Mango Leaf Disease dataset from Kaggle, consisting of eight classes (seven disease types and one healthy class). The planned methodology includes preprocessing, image augmentation, data splitting using K-Fold Cross Validation, and hyperparameter tuning on optimizer, learning rate, epoch, and batch size. Model performance will be evaluated using the Confusion Matrix. This research is expected to produce an accurate and efficient classification model that enables objective and rapid early detection of mango leaf diseases. The research utilizes a dataset from Kaggle consisting of 4,000 images across eight classes—comprising seven disease types and one healthy leaf class. The methodology involves preprocessing (resizing to 640x640 pixels and normalization), image augmentation, and data splitting using 10-Fold Cross Validation. Performance was optimized through hyperparameter tuning of the Adam optimizer, a learning rate of 0.001, a batch size of 16, and various epoch settings. The experimental results demonstrate that the YOLOv11s model achieves exceptional and stable performance. Evaluation using a Confusion Matrix shows that the model reached a 100% accuracy, precision, recall, and F1-score on the dataset used in this study. The model recorded an average training loss of 0.0979 and a validation loss of 0.0027. These findings confirm that YOLOv11s is not only highly accurate but also computationally efficient, making it a viable candidate for real-time detection systems on mobile or edge computing devices to support early disease detection in mango orchards. As the main contribution, this study provides a comprehensive evaluation of YOLOv11s for mango leaf disease classification using a 10-Fold Cross Validation scheme, stability analysis based on validation loss, and an assessment of its potential for real-time deployment on mobile and edge computing devices.
Implementasi CBAM pada Arsitektur ResNet50 dalam Klasifikasi Penyakit Daun Tanaman Kentang Yanto, Vicky; Rachmat, Nur
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3411

Abstract

Potato cultivation is inevitably susceptible to various challenges, particularly leaf diseases. Failure to address these issues effectively can lead to a significant decline in both crop yield and harvest quality. This study aims to implement the Convolution Block Attention Module (CBAM) within the ResNet50 architecture for the classification of potato leaf diseases. The dataset utilized in this research comprises 2,152 images categorized into three classes: 152 healthy leaves, 1,000 early blight leaves, and 1,000 late blight leaves. The data was partitioned into training, validation, and testing sets with a ratio of 80:10:10, respectively. Image augmentation techniques were employed to address the class imbalance by increasing the number of healthy leaf images and enhancing dataset variability. Experimental results demonstrate that the ResNet50+CBAM model achieved the highest accuracy of 92% in both Scenario 1 (Adam optimizer, batch size 16) and Scenario 3 (Adam optimizer, batch size 32). Conversely, Scenario 4 (SGD optimizer, batch size 32) yielded the lowest accuracy at 77%.Keyword: CBAM; Classification; CNN; Potato; ResNet50 AbstrakDalam membudidayakan suatu tanaman kentang pastinya tidak terlepas dari permasalahan yang terjadi dalam tanaman kentang salah satunya yaitu pernyakit pada daun kentang, bila tidak diperhatikan dengan baik maka dapat terjadinya penurunan produksi dan penurunan kualitas pada hasil panen. Peneltian ini bertujuan untuk mengimplementasikan Convolution Block Attention Module (CBAM) pada arsitektur ResNet50 dalam klasifikasi penyakit daun tanaman kentang. Dataset yang digunakan dalam penelitian ini berjumlah 2152 gambar yang terdiri dari 3 kategori yaitu 152 daun sehat, 1000 daun early blight dan 1000 daun late blight yang akan dibagi menjadi 80% data latih, 10% data validasi dan 10% data uji. Penelitian ini menggunakan teknik augmentasi gambar yang bertujuan untuk menambah jumlah gambar daun sehat dan meningkatkan variasi data. Hasil pengujian menunjukkan ResNet50+CBAM pada skenario 1 (optimasi Adam dan batch size 16) dan skenario 3 (optimasi Adam dan batch size 32) menghasilkan akurasi yang sama yaitu 92% dan skenario 4 (optimasi SGD dan batch size 32) menghasilkan akurasi terendah yaitu 77%. 
Co-Authors Adhytio Mahendra Andrian Wijaya Antonius Wahyu Sudrajat Ardi, Ardi al Ghifari Bobby Jaya Saputra Calvin Oliver Saputra Caroline, Fionna Davin Hansel Winata Devella, Siska Dicky Ryanto Fernandes Elizabeth Fiona Hartono Farisi, Ahmad Hakiki, Muhammad Anugrah Indrawan Indrawan Johannes Petrus M Ezar Al Rivan Maximilliano, Wesley Michael Lorens Molavi Arman Monica Rizki Ayustin Muhammad Naufal Anugrah Ricko Andreas Kartono Riski Ariza, Agung Rizi, Muhammad Alfa Ubaidillah Ubaidillah Vannes Wijaya Vincent Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes