Article Per Year (5 Year)
p-Index From 2021 - 2026
3.291
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal ICT : Information Communication & Technology Tematik : Jurnal Teknologi Informasi Komunikasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Sistim Informasi dan Teknologi AMMA : Jurnal Pengabdian Masyarakat
Dadang Sudrajat
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Pengaruh Augmentasi Data dan Dropout terhadap Generalisasi Model Deteksi Kerusakan Panel Surya Irfan Ali; Rudi Kurniawan; Dadang Sudrajat; Saeful Anwar; Nining Rahaningsih
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Automatic defect detection in photovoltaic (PV) panels is a crucial challenge for maintaining energy efficiency and reliability in renewable power systems. However, the limited availability of labeled datasets and high environmental variability often lead deep learning models to overfit and lose generalization capability. This study investigates the combined effects of data augmentation and dropout regularization on improving the generalization performance of transfer learning-based models for multi-class PV defect classification. Two pretrained architectures, VGG16 and InceptionV3, were fine-tuned using the Faulty Solar Panel dataset comprising six defect categories. Experiments were conducted under three main configurations: (1) baseline without regularization, (2) augmentation only, and (3) combined augmentation–dropout with dropout rates of 0.2, 0.3, and 0.5. Performance evaluation employed accuracy, precision, recall, macro-F1, and confusion matrix analysis for each defect class. The results demonstrate that the combination of data augmentation and moderate dropout (0.3) significantly enhances generalization, achieving 92.10% accuracy and 0.90 macro-F1 with the InceptionV3 architecture. Higher dropout values (0.5) caused slower convergence and decreased accuracy. These findings confirm that balanced integration of augmentation and dropout effectively mitigates overfitting and strengthens model robustness under limited and imbalanced data conditions. The proposed approach provides practical implications for implementing reliable, lightweight, and deployable deep learning-based inspection systems in real-world PV monitoring using edge computing devices.
Integrasi Deep Learning Multimodal Untuk Peramalan Penjualan Toko Menggunakan Keras Functional API Khaerul Anam; Dadang Sudrajat; Saeful Anwar; Rudi Kurniawan
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Store sales forecasting based on historical data has been widely studied; however, most conventional approaches remain limited to single time series data and are less capable of capturing the complex influence of external factors. Existing knowledge suggests that deep learning can improve forecasting accuracy compared to traditional statistical methods, but what remains unclear is the extent to which multimodal integration—combining time series, economic, and categorical data—can enhance predictive performance in a dynamic retail context. This study aims to develop and evaluate a multimodal deep learning model using the Keras Functional API for store sales forecasting. The methodology involves collecting and processing daily transaction data, oil prices, holidays, and store information, followed by preprocessing, feature engineering, normalization, and time-window construction stages. Four architectures were tested—LSTM, 1D CNN, CNN+RNN, and Multiscale CNN—with performance evaluation conducted using Mean Absolute Error (MAE). The results indicate that multimodal integration yields a significant improvement compared to single-source data, with the 1D CNN model achieving the best performance at an MAE of 57,4318. The discussion highlights that integrating external variables such as oil prices and holidays enhances the robustness of predictions, while the main challenges remain in high computational requirements and limited model interpretability. This study concludes that the multimodal deep learning approach provides a scientific contribution by enriching the literature on sales forecasting while offering practical implications for the retail sector in inventory management, promotional planning, and data-driven decision-making.
Arsitektur Ensemble Convolutional Neural Network untuk Klasifikasi Multi Kelas Penyakit Daun Kopi Ade Irma Purnamasari; Dadang Sudrajat; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Coffee leaf disease remains one of the most significant threats to global coffee production, particularly Coffee Leaf Rust (CLR) caused by Hemileia vastatrix. Early and accurate disease detection is essential for maintaining yield stability and ensuring sustainable coffee farming. This study proposes an Ensemble Convolutional Neural Network (CNN) architecture that combines MobileNetV2 and ResNet50 to enhance robustness and generalization in multi-class classification of coffee leaf diseases. The dataset consists of 1,664 images categorized into four classes: miner, nodisease, phoma, and rust, collected from both public repositories and real-field observations. Image preprocessing includes resizing, normalization, and augmentation to increase diversity and reduce overfitting. The ensemble model is trained using the Adam optimizer with a learning rate of 0.0001 and evaluated through accuracy, precision, recall, and F1-score metrics. Results demonstrate that the ensemble CNN outperforms single CNN architectures, achieving an accuracy of 95.6%, precision of 94.4%, and F1-score of 94.2%, even under challenging illumination and noise conditions. Compared to individual models, performance improvement ranges from 2%–4%. The model also maintains higher stability when tested under low-light and noisy images, confirming its robustness in real-world scenarios. This study concludes that ensemble CNN offers a reliable and efficient framework for real-time coffee leaf disease detection and can serve as a foundation for developing intelligent agricultural systems using edge computing.
Analisis Kinerja Algoritma Machine Learning untuk Klasifikasi Prestasi Mahasiswa pada Mata Kuliah Bahasa Inggris Riri Narasati; Dadang Sudrajat; Ahmad Faqih; Indra Wiguna Marthanu; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study analyzes the performance of several machine learning algorithms in classifying student achievement in English language courses. The research focuses on comparing the performance of K-Nearest Neighbors (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM) using the K-Fold Cross Validation approach to evaluate accuracy, F1-score, and fairness. The dataset, consisting of students’ final grades, was processed through data pre-processing and feature scaling. Results show that the KNN model with K=5 achieved the highest accuracy of 100%, followed by Naïve Bayes with 95.59%. Statistical tests indicated a significant performance difference between Random Forest and SVM, while fairness evaluation revealed that Random Forest provided the most balanced error distribution. These findings confirm that KNN and Random Forest algorithms are highly effective for academic performance classification based on numerical data. The study highlights the potential of machine learning to enhance adaptive, objective, and equitable educational evaluation systems.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
Co-Authors Abdul Aziz Abrar Hiswara Ade Irma Purnamasari Ade Rizki Rinaldi agus bahtiar Ahmad Faqih Ahmad Rifai Ajudin Arif Rinaldi Dikananda Arif Rinaldi Dikananda Bani Nurhakim Bani Nurhakim Cep Lukman Rohmat Dian Ade Kurnia Dian Ade Kurnia Fadhil Muhammad Basysyar Fatihanursari Dikananda Gifthera Dwilestari Harry Budi Santoso Hery Widijanto Indra Wiguna Marthanu Irfan Ali Jayawarsa, A.A. Ketut Kaslani Khaerul Anam Khaerul Anam Lana Surlanto Martanto Maulana Sujarwadi Nana Suarna Nining Rahaningsih Nisa Dienwati Nuris Novianti Indah Putri Odi Nurdiawan Othman bin Mohd Raditya Danar Dana Rinovian Rais Riri Narasati Rudi Kurniawan Rudi Kurniawan Saeful Amri Saeful Anwar Salman Alfarizi Sandy Eka Permana Selvi Andini suroso, amat Suteja Syaiful Imanudin Tati Suprapti Ujang Supriatna Umi Hayati Yudhistira Arie Wijaya Zen Munawar