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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 JATI (Jurnal Mahasiswa Teknik Informatika) 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 Penelitian Teknologi Informasi dan Sains Jurnal Pengabdian Masyarakat Nian Tana
Nur Rachmat
Universitas Multi Data Palembang, Kota Palembang
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Klasifikasi Tingkat Kematangan Buah Kakao Berdasarkan Fitur Warna Menggunakan Algoritma K-Nearest Neighbor Mahendra, Izha; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || 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.v4i1.5485

Abstract

So far, cocoa farmers choose the quality of the maturity level of cocoa pods manually ormake selections based on estimates from these farmers, so that the manual method is very proneto errors in sorting the quality of cocoa pod maturity with various human factors, such as fatigueand doubt. Based on these problems, this study developed an application for classification ofcocoa pods using Hue, Saturation, Value (HSV) color extraction with the classification methodusing K-Nearest Neighbor (KNN) and applying the evaluation results method using the EuclideanDistance, so that in choosing the level of maturity Cocoa pods have the same standard and ahigher level of accuracy with digital processing. Therefore this research was conducted. Theprocess of classification of ripeness into 4 classes, namely: rotten, ripe, unripe and half ripe. Withthe KNN classification method, and the dataset used is 80 databases, as well as 40 testing data.The highest value is at k=1 with 90% accuracy, 90% precision, and 90% recall. The tool used todevelop the system is matlab.
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.
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.
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%. 
Comparison of XGBoost and LightGBM Algorithms in Predicting Heart Disease Fionna Caroline; Nur Rachmat
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7505

Abstract

Heart disease remains a leading cause of mortality worldwide, underscoring the need for early and accurate diagnosis to reduce complications and improve patient outcomes. Recent advances in machine learning have enabled the development of predictive models that assist healthcare professionals in disease detection using patient medical records. This study aims to develop and compare the performance of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) for heart disease prediction. The dataset used in this research was obtained from the UCI Machine Learning Repository and consists of 303 patient records with binary class labels indicating the presence or absence of heart disease. Data preprocessing involved feature standardization using StandardScaler and handling class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was conducted using Stratified K-Fold Cross Validation with K values of 3, 5, and 7 to ensure robust and unbiased performance assessment. Hyperparameter optimization was carried out using RandomizedSearchCV to efficiently identify optimal model configurations. Experimental results indicate that both XGBoost and LightGBM achieved strong classification performance, with accuracy exceeding 80% and AUC values above 0.89. LightGBM demonstrated slightly superior performance in terms of average accuracy, F1-score, and stability across folds, while XGBoost achieved higher precision, reflecting better control of false positives. Overall, both algorithms are effective for heart disease prediction, supporting the potential of machine learning in early disease detection and clinical decision-support systems.
Penggunaan MobileNetV2 untuk Klasifikasi Penyakit Daun Cabai Saputra, Muhammad Redho; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.13562

Abstract

Chili peppers (Capsicum annuum L.) are an important horticultural commodity in Indonesia with high economic value, but they are susceptible to leaf diseases such as leaf spots, curled leaves, yellowing leaves, and whitefly pests. This study aims to classify chili leaf diseases using a MobileNetV2-based Convolutional Neural Network (CNN) architecture utilizing the Depthwise Separable Convolution mechanism for filter decomposition and model complexity reduction. Based on previous studies, MobileNetV2 has been proven to maintain a highly competitive level of accuracy. The dataset used consisted of 6000 images from five categories: healthy, leaf spot, leaf curl, yellowish, and whitefly, which were taken from open sources and equalized in number for each class. The data was divided into training, validation, and testing sets with a ratio of 80:10:10. The training process used depthwise separable convolution, dropout, and Adam and SGD optimization techniques to prevent overfitting. Model evaluation was carried out through 12 scenarios with variations in batch size, dense layer, optimizer, and epoch. The results show the highest accuracy of 98.40% in the scenario with a batch size configuration of 32, a dense layer of 128, a learning rate of 0.001, an Adam optimizer, and 20 epochs. Most scenarios achieved an accuracy above 96%, proving that MobileNetV2 is effective for classifying chili leaf diseases. The contribution of this study is the identification of an optimal and efficient MobileNetV2 parameter configuration for chili leaf disease classification.
Penerapan Algoritma Gradient Boosting dalam Mendiagnosa Penyakit Kucing dan Anjing Vincent; Nur Rachmat
Jurnal Buana Informatika Vol. 16 No. 2 (2025): Jurnal Buana Informatika, Volume 16, Nomor 02, Oktober 2025
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v16i2.12634

Abstract

Royal Canin selaku lembaga riset hewan domestik mengungkapkan bahwa hewan peliharaan di Indonesia jarang sekali melakukan pemeriksaan rutin ke klinik hewan, jika dipersentasekan hanya berada di angka 29,5%. Dengan persentase tersebut, semakin khawatir hewan dapat menularkan penyakit ke manusia atau disebut sebagai zoonosis, jika hewan sama sekali tidak mendapatkan perawatan dan identifikasi dini penyakit yang dialami. Pada penelitian ini menggunakan metode gradient boosting sebagai fokus utama untuk memprediksi penyakit berdasarkan gejala-gejala yang dialami hewan peliharaan. Melalui proses hyperparameter tuning menggunakan gridsearch, diperoleh model terbaik dengan kombinasi parameter: learning_rate 0,05, max_depth 7, min_samples_leaf 1, min_samples_split 2, n_estimators 200, dan subsample 0,9. Dari hasil hyperparameter tuning, model tersebut menunjukkan performa terbaik dengan accuracy 88%, precision 97%, recall 96%, f1-score 96%, dan hamming loss 0,29%. Hasil tersebut menunjukkan bahwa model memiliki kemampuan memprediksi multilabel yang akurat.
Klasifikasi Non-Destruktif Kemanisan Semangka Manohara Menggunakan Transfer Learning VGG-16 Dicky Ryanto Fernandes; Nur Rachmat
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 1 No. 4 (2022): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/81wv2s06

Abstract

Semangka (Citrullus lanatus) merupakan buah tropis populer di Indonesia karena rasanya yang manis dan kandungan airnya yang tinggi. Penentuan tingkat kemanisan masih banyak dilakukan secara destruktif dengan refraktometer, sehingga kurang efisien. Penelitian ini bertujuan mengklasifikasikan tingkat kemanisan semangka Manohara secara non-destruktif berdasarkan ciri fisik luar menggunakan Convolutional Neural Network (CNN) dengan arsitektur VGG-16 dan pendekatan transfer learning. Data dikumpulkan secara mandiri dan dibagi menjadi 80% data latih, 10% validasi, dan 10% uji. Model menggunakan Adam Optimizer dan Softmax sebagai classifier. Hasil terbaik diperoleh pada skenario ke-4 dengan akurasi 67,42%. Namun, model menunjukkan gejala underfitting dan kecenderungan mengklasifikasi ke satu kelas. Penelitian ini menunjukkan potensi awal penggunaan visi komputer dalam seleksi kualitas semangka secara otomatis dan non-destruktif, meskipun masih diperlukan peningkatan akurasi agar dapat diimplementasikan secara praktis di lapangan.
Klasifikasi Penyakit Daun Tomat Menggunakan MobileNetV3 Russel Wijaya; Nur Rachmat
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 2 (2026): Juni: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i2.4230

Abstract

Tomato (Solanum lycopersicum) is a high-value horticultural commodity in Indonesia, yet its cultivation is frequently disrupted by leaf diseases that are difficult to distinguish visually. Diseases such as Bacterial Spot, Early Blight, and Tomato Yellow Leaf Curl Virus often present overlapping visual symptoms, making early and accurate diagnosis a significant challenge for farmers. The manual identification methods currently in use are inefficient and error-prone, ultimately leading to reduced crop yield and quality. The general objective of this study is to develop software capable of automatically classifying tomato leaf diseases. Specifically, this research aims to implement the MobileNetV3 Small architecture based on Convolutional Neural Network (CNN) with ImageNet pre-trained weights to classify 10 types of tomato leaf diseases. The research methodology encompasses dataset collection from Kaggle comprising 10,000 images (1,000 per class), image pre-processing through resizing to 224x224 pixels, and normalization, as well as hyperparameter optimization (optimizer, learning rate, epoch, batch size) via scheduler. Model performance is evaluated using a confusion matrix encompassing accuracy, precision, recall, and F1-score.
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.
Co-Authors Adhytio Mahendra Ahmad Naufal Andrian Wijaya Ardi al Ghifari Ardi 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 Intan Sanu Izha Mahendra Johannes Petrus Michael Lorens Molavi Arman Monica Rizki Ayustin Muhammad Alfa Rizi Muhammad Ezar Al Rivan Muhammad Naufal Anugrah Ricko Andreas Kartono Riski Ariza, Agung Russel Wijaya Saputra, Muhammad Redho Ubaidillah Ubaidillah Vannes Wijaya Vincent Wahyu Sudrajat Wesley Maximilliano Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes