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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
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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.
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.
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