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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) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistim Informasi dan Teknologi AMMA : Jurnal Pengabdian Masyarakat
Dadang Sudrajat
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Bibliometrik Analysis: Signal Preprocessing Techniques for Kualitas Sinyal Electrogram Odi Nurdiawan; Dadang Sudrajat; Fathurrohman; Ade Rizki Rinaldi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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This study explores electroencephalogram (EEG) signal preprocessing techniques used in the early detection and diagnosis of epilepsy, aiming to enhance the quality and reliability of data used in clinical applications. Effective signal preprocessing techniques are crucial for minimizing artifacts and noise, which can obscure critical information in EEG signals. More accurate EEG signal processing allows for the identification of abnormal patterns associated with various neurological conditions, such as epilepsy, which heavily relies on this signal analysis for precise diagnosis. This study conducted a bibliometric analysis using a descriptive approach to identify research trends, geographic distribution, institutional contributions, and key authors in this field. Data was collected from the Scopus database using the keywords "electroencephalogram AND signal AND processing AND epilepsy". The analysis results show a significant increase in the number of publications related to EEG signal preprocessing techniques over the past five years, with major contributions from countries like China, India, and the United States, reflecting the high global interest and focus on this topic. Additionally, deep learning and machine learning techniques emerged as the most dominant methods in this research, indicating future trends in the development of increasingly sophisticated EEG signal processing technologies. The findings also suggest that using techniques such as artificial neural networks, convolutional neural networks (CNN), and deep learning can enhance the accuracy of epilepsy diagnosis and prediction, making a significant contribution to modern clinical practice. Moreover, this study emphasizes the importance of developing and integrating more advanced preprocessing techniques to improve the effectiveness of EEG signal detection and classification, which is expected to enhance diagnostic outcomes and patient management with neurological disorders. This study provides valuable contributions to the development of medical diagnostic technologies, particularly for neurological disorders such as epilepsy, and highlights the need for further research to optimize these techniques for broader clinical application.
Bibliometrik Analysis: Kontruksi Sosial Masyarakat Mengenai Teknologi AI Pada Data Base Scoupus 2014-2024 Nisa Dienwati Nuris; Khaerul Anam; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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This research investigates the social construction of ChatGPT technology in society by identifying and analyzing the factors that influence its adoption and utilization. Through this analysis, we aim to identify recent research trends, gaps, and future research opportunities. The study utilizes data from various international scientific journals indexed by Scopus to explore the application of social construction of technology techniques and their societal impact. The method used in this research is bibliometric analysis to uncover patterns in the study of the social construction of ChatGPT technology in society. The results show that user perceptions of ChatGPT are influenced by digital readiness, technological literacy, as well as perceptions of benefits and risks. Additionally, ChatGPT is closely related to the development of critical skills among students, supporting the enhancement of analytical and critical abilities. The research focus in the field of AI, particularly concerning social and economic impacts, is expanding. This study emphasizes the importance of AI in various aspects of life and its contribution to sustainable development, especially in higher education, where AI technology integration is involved. Educational institutions are encouraged to design policies to support learning and skill development through AI. This research has limitations, particularly in terms of sample size and methodology, which can be addressed in future studies by expanding the scope and methods of the research. Overall, this study enriches the understanding of the impact of AI technology, particularly ChatGPT, in higher education and provides a foundation for further research.
Bibliometrik Analysis: Pembelajaran Speaking Session Menggunakan Instagram Pada Data Base Scopus 2014-2024 Riri Narasati; Ahmad Faqih; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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In today's digital age, social media plays an important role in various aspects of life, including education. This research explores the use of Instagram as a learning tool to improve English speaking skills. Through bibliometric analysis, this study identifies previous research trends and evaluates the effectiveness of Instagram-based learning methods. It also reviews existing literature to understand how Instagram has been used in language learning contexts, as well as identifying existing research gaps. The results show that the use of Instagram in English language learning can increase student motivation and participation. Interaction through video, story and dialog-based content on Instagram proved effective in improving speaking skills. The study also found that the use of project-based tasks on Instagram can help students in developing their confidence and speaking ability. In addition, this study highlights the importance of structured guidance and feedback in maximizing the benefits of learning through Instagram. This research makes a novel contribution to the literature by offering a more in-depth approach to the use of Instagram in English language learning. By exploring effective learning strategies and their impact on students' speaking skills, this study explores the role of Instagram in English language learning.
SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION Bani Nurhakim; Ahmad Rifai; Dian Ade Kurnia; Dadang Sudrajat; Ujang Supriatna
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5992

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.
Evaluasi Pembelajaran AR Sejarah Berbasis SUS, UEQ, TAM Rudi Kurniawan; Dadang Sudrajat; Kaslani; Gifthera Dwilestari; Sandy Eka Permana
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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History education in secondary schools still faces challenges in presenting material that attracts the digital generation’s attention. The Bandung Lautan Api event, a topic rich in local and national values, is often taught using conventional methods that limit student engagement and motivation. This study evaluates the feasibility of Augmented Reality (AR)-based learning media to enhance students’ historical literacy on the Bandung Lautan Api topic. A quantitative approach was applied using three integrated evaluation models: the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Technology Acceptance Model (TAM), involving 100 respondents comprising high school teachers and students. The results indicate that the AR media demonstrates excellent usability (SUS = 87.69), a highly positive user experience across all UEQ dimensions (highest attractiveness = 2.12), and strong technology acceptance (PU = 5.87; PEOU = 5.69; BI = 6.18). Both teachers and students shared consistent perceptions. These findings confirm that the AR media is feasible and capable of creating immersive and interactive learning experiences. Theoretically, this research enriches AR-based learning evaluation literature, while practically, it provides a ready-to-adopt model for integrating AR into history education.
Analisis Data Hasil Laporan Skripsi Berbasis Aspect Based Sentiment Analysis Menggunakan Algoritma K-Means Clustering Nana Suarna; Dadang Sudrajat; Umi Hayati; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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This study discusses the application of Aspect-Based Sentiment Analysis (ABSA) combined with the K-Means Clustering algorithm to analyze student thesis report data. The research scope includes text data processing from VAK (Visual, Auditory, Kinesthetic) learning style questionnaires to identify research aspects and automatically group thesis themes. The objective is to obtain a structured and representative mapping of students’ research themes based on their fields of study. The methodology involves several stages, including text preprocessing, TF-IDF weighting, aspect extraction using ABSA, and clustering with K-Means, validated through the Davies-Bouldin Index (DBI). The dataset consists of 976 textual entries derived from student questionnaire responses. The results indicate that the optimal cluster is achieved at k = 3 with a DBI value of 3.276, forming three main groups: (1) data mining, (2) statistical analysis, and (3) learning technology. The study concludes that the combination of ABSA and K-Means is effective in accurately classifying research themes and provides an analytical foundation for academic decision-making regarding student research trends.
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

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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

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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

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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

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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.
Co-Authors Abrar Hiswara Ade Irma Purnamasari Ade Rizki Rinaldi Adila G Khiqmatiar Muchsin agus bahtiar Ahmad Faqih Ahmad Rifai Ajudin Arif Rinaldi Dikananda Arif Rinaldi Dikananda Bani Nurhakim Bani Nurhakim Cep Lukman Rohmat Denni Pratama Dian Ade Kurnia Dian Ade Kurnia Fadhil Muhammad Basysyar Fatihanursari Dikananda Fatkhan Mubarok Firman Yudatama Gifthera Dwilestari Harry Budi Santoso Hery Widijanto Ikhwal Alfarizi Suherman Indra Wiguna Marthanu Intan Wangi Nur Qibti Irfan Ali Irfan Ali Jayawarsa, A.A. Ketut Kaslani Khaerul Anam Khaerul Anam Lana Surlanto Luthfi Adianto Luthfiyyah Iffah Adella Martanto Maulana Sujarwadi Muhamad Ilham Nur Faqih Muhammad Agastya Muhammad Zamil Farhan Nana Suarna Nining Rahaningsih Nining Rahaningsih Nisa Dienwati Nuris Novianti Indah Putri Odi Nurdiawan Othman bin Mohd Raditya Danar Dana Rinovian Rais Riri Narasati Rosidin Rudi Kurniawan Rudi Kurniawan Saeful Amri Saeful Anwar 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