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All Journal Techno.Com: Jurnal Teknologi Informasi 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
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Penerapan SMOTE dan Regresi Logistik Pada Website Skrining Awal Kesehatan Mental Mahasiswa Wijaya, Vannes; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9046

Abstract

Mental health is a very important aspect in realizing overall health. Students are an age group that experiences a transition from adolescence to adulthood, students tend to experience stress, especially those originating from the academic process. In this study, a website-based questionnaire system was developed to predict mental health profiles consisting of optimal mental health profiles (+-), maximum mental health profiles (++), minimal mental health profiles (--), and minimal mental health profiles (-+). The questionnaire questions and grouping of mental health profiles used the SKM-12 mental health measurement tool. The dataset used was obtained from 78 students at Multi Data University Palembang. The method used in this research is Logistic Regression using the data imbalance method, namely SMOTE with parameter solver newton-cg with data division, 70% training data and 30% test data. The results obtained in this study using confusion matrix model evaluation obtained an accuracy of 89.28% and model evaluation using K-fold cross validation obtained an accuracy of 87.43% for training data and 82.66% for test data.
PEMANFAATAN APLIKASI PENDAFTARAN DALAM BAKTI SOSIAL KESEHATAN TZU CHI KE-145 Rachmat, Nur; Indrawan, Indrawan; Petrus, Johannes
FORDICATE Vol 4 No 2 (2025): April 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i2.11335

Abstract

Bakti Sosial Kesehatan Tzu Chi ke-145 adalah kegiatan kemanusiaan yang memberikan layanan kesehatan bagi masyarakat yang membutuhkan. Sebelumnya, pendaftaran peserta dilakukan menggunakan Google Form, yang menyebabkan keterlambatan administrasi, duplikasi data, dan penyebaran informasi yang kurang efektif. Untuk mengatasi masalah ini, dikembangkan aplikasi berbasis web yang mempermudah pendaftaran, verifikasi data, dan koordinasi antara tim kesehatan serta relawan. Hasilnya, proses administrasi menjadi lebih cepat dan tertata. Dari 582 peserta yang mengikuti screening, berdasarkan data di aplikasi, sebanyak 114 peserta katarak, 29 hernia, 2 bibir sumbing, dan 60 bedah minor lolos untuk operasi. Pada hari operasi, 106 peserta katarak, 25 hernia, 2 bibir sumbing, dan 57 bedah minor berhasil menjalani tindakan kesehatan. Aplikasi ini membuat proses pendaftaran dan pelayanan lebih cepat, akurat, dan terorganisir. Sistem ini diharapkan dapat terus digunakan dan dikembangkan untuk kegiatan bakti sosial kesehatan berikutnya agar lebih banyak masyarakat yang terbantu.
Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification Maximilliano, Wesley; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6259

Abstract

Corn leaf disease represents a significant threat to agricultural productivity, capable of causing substantial economic losses in Indonesia. Conventional identification methods, which rely on visual observation by farmers, are frequently subjective, time-consuming, and inaccurate. This study conducts a systematic comparative analysis of two efficient Convolutional Neural Network (CNN) architecture variants, MobileNetV3-Large and MobileNetV3-Small, for the classification of four corn leaf conditions: Gray Leaf Spot, Common Rust, Northern Leaf Blight, and Healthy. The research further evaluates the influence of two prevalent optimizers, Adam and Stochastic Gradient Descent (SGD), to ascertain the most optimal model configuration through hyperparameter tuning. The models were trained and evaluated using a local image dataset from Sampang, Indonesia, comprising 4000 images. The methodology included image preprocessing, data augmentation, and hyperparameter tuning of the learning rate and batch size. The results demonstrate that both architectures achieved exceptionally high accuracy. The principal finding reveals that MobileNetV3-Small unexpectedly outperformed its larger variant, attaining a peak accuracy of 99.5% with the SGD optimizer, a learning rate of 0.01, and a batch size of 32. In comparison, MobileNetV3-Large reached a maximum accuracy of 99.0% under a similar configuration. These findings underscore the considerable potential of lightweight architectures for the development of rapid, accurate, and field-deployable plant disease diagnostic applications on mobile devices using deep learning.
Klasifikasi Spesies Jamur Menggunakan Convolutional Neural Network dengan Arsitektur MobileNetV2 Hakiki, Muhammad Anugrah; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.11077

Abstract

Indonesia has a high biodiversity of fungi, including edible and toxic species. Manual identification is often challenging due to morphological similarities between safe and poisonous species. Therefore, this study evaluates the use of deep learning-based Convolutional Neural Network (CNN) with the MobileNetV2 architecture for mushroom classification. The research method includes collecting a dataset of 1,500 images from 10 mushroom species (5 edible and 5 toxic), preprocessing data by normalizing image size and applying augmentation techniques, and training the model using the Adam optimizer with dropout and early stopping to prevent overfitting. Hyperparameter tuning was performed using grid search on batch size (64, 128, 256), epochs (20, 50, 100), and learning rate (0.1, 0.01, 0.001). The test results show that a combination of batch size 64, epoch 50, and learning rate 0.1 achieved 98% validation accuracy. The final model was tested and achieved 95.33% accuracy, with an average precision, recall, and f1-score of 95%. These results confirm that MobileNetV2 is effective in classifying mushroom species and can assist in more accurately identifying edible and toxic fungi.
Klasifikasi Penyakit Daun Tanaman Jagung Menggunakan Pendekatan Transfer Learning Arsitektur MobileNetV4 Muhammad Naufal Anugrah; Nur Rachmat
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 4 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v4i4.1393

Abstract

Jagung (Zea mays) merupakan komoditas pangan utama, namun produktivitasnya terhambat oleh serangan penyakit daun. Identifikasi penyakit daun jagung secara manual memakan waktu dan bersifat subjektif. Penelitian ini bertujuan mengembangkan model klasifikasi otomatis menggunakan Convolutional Neural Network (CNN) berbasis transfer learning dengan mengevaluasi tiga varian arsitektur MobileNetV4 (Small, Medium, Large) serta membandingkan optimizer Adam dan SGD. Peneliti menggunakan 4000 citra daun jagung dari empat kelas dengan pembagian 80% data pelatihan, 10% data validasi dan 10% data pengujian. Hasil eksperimen menunjukkan bahwa model terbaik diperoleh dari MobileNetV4-Conv-Medium dengan optimizer SGD, yang mencapai akurasi validasi tertinggi 95,25% dan F1-Score 92,00% dengan penggunaan hyperparameter learning rate 0.01, epoch 50 dan batch size 32. Kinerja ini menegaskan potensi MobileNetV4, khususnya varian Medium, dalam mencapai keseimbangan optimal antara efisiensi komputasi dan kinerja klasifikasi, menjadikannya model yang sangat menjanjikan untuk implementasi sebagai sistem mobile dalam pertanian presisi.
Indonesian-Language Spam Email Classification Using Support Vector Machine Rizi, Muhammad Alfa; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7578

Abstract

Spam email remains a significant problem in digital communication, particularly for Indonesian-language emails, due to linguistic complexity, informal writing styles, and similarities between spam and legitimate (ham) messages. These factors often reduce the effectiveness of traditional spam filtering techniques. This study evaluates the performance of the Support Vector Machine (SVM) algorithm for classifying Indonesian spam emails using a combination of Term Frequency–Inverse Document Frequency (TF-IDF) and N-gram features. The proposed approach applies a text preprocessing pipeline, including case folding, text cleaning, tokenization, stopword removal, and stemming, to reduce noise and improve feature representation. Text data are transformed into numerical vectors using TF-IDF with unigram and bigram configurations to capture individual terms and contextual phrase patterns commonly found in spam emails. A linear kernel SVM is used as the classification model, and its performance is evaluated using K-Fold Cross-Validation to ensure robustness and reduce evaluation bias. The model is assessed using accuracy, precision, recall, and F1-score metrics. Experiments are conducted on the Indonesian Email Spam Dataset, consisting of 2,636 emails, with 1,368 spam messages and 1,268 non-spam (ham) messages. Experimental results show that the proposed model achieved an average accuracy of 98.71%, precision of 98.34%, recall of 99.20%, and F1-score of 98.76 across 10-fold cross-validation. This study contributes to the development of an efficient and lightweight spam detection model for Indonesian-language emails and provides empirical evidence that SVM combined with TF-IDF and N-gram features remains a reliable alternative to more complex deep learning approaches for medium-sized text datasets.
Comparison Of Adam and SGD For The Classfication Of Palm Tree Leaf Diseases With ResNet50 Ardi, Ardi al Ghifari; Nur Rachmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7501

Abstract

Plants from the palm tree family (Arecaceae), such as coconut, oil palm, and date palm, play an important role in the economy and food security, especially in Indonesia. However, leaf diseases such as leaf spot disease pose a serious threat that can reduce productivity. Manual disease identification is time-consuming and prone to errors, necessitating an image-based automatic classification system. This study aims to apply the ResNet50 Convolutional Neural Network (CNN) architecture for palm tree leaf disease classification and compare two popular optimization algorithms, Adam and Stochastic Gradient Descent (SGD), in terms of model training accuracy and efficiency. The dataset used is public, covering five classes of leaf images: Healthy, White Scale, Brown Spot, Leaf Smut, and Bacterial Leaf Blight. The research process includes data collection and preprocessing (resizing, normalization, and augmentation), dividing the dataset into three parts, namely training, validation, and testing data using the train/validation/test split approach. This approach provides a fairly representative evaluation of model performance while being computationally efficient. Model training was performed using transfer learning with ResNet50, and performance evaluation was performed using a confusion matrix to obtain accuracy, precision, recall, and F1-score values. The results of the two optimizers were compared to determine their effect on model performance. The experimental results show that the ResNet50 model optimized with Adam achieved a higher test accuracy of 87.23% compared to SGD with 85.96%, while SGD demonstrated more consistent performance between validation and testing phases, indicating better training stability.
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.
Co-Authors Adhytio Mahendra Andrian Wijaya Antonius Wahyu Sudrajat Ardi, Ardi al Ghifari Bobby Jaya Saputra Calvin Oliver Saputra Davin Hansel Winata Devella, Siska Dicky Ryanto Fernandes Elizabeth Fiona Hartono Farisi, Ahmad Fionna Caroline 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 Saputra, Muhammad Redho Ubaidillah Ubaidillah Vannes Wijaya Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes