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All Journal Pro Bisnis Telematika Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT JITK (Jurnal Ilmu Pengetahuan dan Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia
Azhari Shouni Barkah
Universitas AMIKOM Purwokerto
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers Setiyawan, Dillyana Tugas; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4577

Abstract

The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.
Analisis Faktor-Faktor Penerimaan Teknologi dalam Pembelajaran Vokasi: Integrasi Model Technology Acceptance Model dan Theory of Planned Behavior di SMK Ma’arif 1 Kroya Harimato, Bambang; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 8 (2025): JPTI - Agustus 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.772

Abstract

Penerapan teknologi dalam pendidikan vokasi menjadi semakin penting untuk menunjang kesiapan siswa menghadapi era digital dan industri 4.0. Namun, tingkat penerimaan teknologi oleh siswa, khususnya di jurusan Teknik Komputer dan Jaringan (TKJ), masih menunjukkan variasi yang signifikan dan membutuhkan kajian lebih lanjut. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi penerimaan teknologi dalam pembelajaran vokasi di SMK Ma’arif 1 Kroya, dengan mengintegrasikan pendekatan Technology Acceptance Model (TAM) dan Theory of Planned Behavior (TPB). Penelitian ini menggunakan metode kuantitatif dengan teknik analisis Partial Least Squares Structural Equation Modeling (PLS-SEM) melalui perangkat lunak SmartPLS 4. Hasil analisis menunjukkan bahwa PU dan PEU berpengaruh signifikan terhadap sikap siswa terhadap penggunaan teknologi (ATT), sedangkan SN dan PBC berkontribusi langsung terhadap niat penggunaan (BI). Selanjutnya, BI terbukti berpengaruh terhadap penggunaan aktual teknologi dalam pembelajaran (AU). Temuan ini mengindikasikan bahwa strategi peningkatan penerimaan teknologi perlu difokuskan pada penguatan persepsi kegunaan dan kemudahan teknologi, dukungan sosial, serta pemberdayaan kontrol perilaku siswa. Implikasi praktis dari penelitian ini mencakup pengembangan pelatihan teknologi bagi siswa dan guru, serta desain pembelajaran vokasi yang berbasis teknologi secara lebih interaktif dan aplikatif.
Analysis of Technology Adoption Factors in Learning among Vocational Students using UTAUT2 Model Harimanto, Bambang; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4940

Abstract

Technology acceptance in vocational education is a key factor in supporting the effectiveness of teaching and learning processes in the digital era. This study aims to analyze the factors influencing technology acceptance among students of the Computer and Network Engineering (TKJ) Department at SMK Ma'arif 1 Kroya using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model includes the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, Behavioral Intention, and Actual Usage. The results reveal that five key variables—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value—significantly influence Behavioral Intention, while Habit, Facilitating Conditions, and Behavioral Intention directly affect Actual Usage. All constructs in the model meet validity and reliability criteria, and no multicollinearity was detected (VIF < 3.3). The coefficient of determination (R²) values of 0.612 for Behavioral Intention and 0.673 for Actual Usage indicate strong predictive power of the model. These findings confirm the relevance of the UTAUT2 framework for understanding and enhancing technology acceptance in vocational education settings and provide valuable insights for improving technology integration in technical learning environments.
Perbandingan Algoritma Support Vector Machine, Decision Tree, dan Logistic Regresion Pada Analisis Sentimen Ulasan Aplikasi Netflix Ramadani, Nevita Cahaya; Tahyudin, Imam; Shouni Barkah, Azhari
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 2 (2024): Agustus 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i2.2024.110-117

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen terhadap ulasan pengguna Netflix menggunakan algoritma machine learning seperti Support Vector Machine (SVM), Decision Tree dan Logistic Regression. Dataset yang terdiri dari 3000 ulasan pengguna diambil dari Google Play Store dan melalui proses preprocessing teks yang mencakup penghapusan karakter, tokenisasi, penghapusan stopword, stemming, serta penyaringan token pendek. Metode TF-IDF digunakan untuk ekstraksi dan pembobotan fitur dalam analisis. Evaluasi hasil menunjukkan bahwa SVM secara konsisten memberikan akurasi yang lebih tinggi dibandingkan Decision Tree dan Logistic Regression dalam klasifikasi sentimen, dengan SVM mencapai akurasi rata-rata 88.18% dan puncak tertinggi 92.69% dalam K-Fold Cross Validation. Implikasi praktis dari penelitian ini adalah Netflix dapat memanfaatkan analisis sentimen untuk meningkatkan pengalaman pengguna dan pengelolaan layanan lebih baik.
Klasifikasi Spam Bahasa Indonesia dengan IndoBERT dan XLM-RoBERTa: Evaluasi Pooling, Stride, dan Late-Fusion Darmono, Darmono; Saputro, Rujianto Eko; Barkah, Azhari Shouni
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8034

Abstract

Spam detection for Indonesian short messages such as SMS and email remains challenging due to lexical variation, character obfuscation, and class imbalance. This study provides a systematic evaluation to determine the most balanced configuration between accuracy and efficiency for Indonesian spam filtering. We compare two pretrained backbones (IndoBERT and XLM RoBERTa), along with representation strategies (truncation versus chunking), summarization schemes (pooling), and feature fusion approaches. The system follows a feature based design with an emphasis on simplicity, and is assessed using F1 Macro, spam class recall, AUPRC (Area Under the Precision Recall Curve), and efficiency metrics in terms of embedding build time and training latency. Results indicate that IndoBERT achieves superior binary classification performance with high efficiency, while XLM RoBERTa slightly outperforms on AUPRC, making it more suitable for risk ranking scenarios. Truncation combined with mean pooling consistently yields stable results. Although late fusion only provides marginal improvements, it remains relevant as it highlights the potential of domain specific signals to enhance robustness under heavy obfuscation. The final recommendation for production is IndoBERT with truncation, mean pooling, and embedding only. Limitations include the focus on short messages and the lack of evaluation under extreme obfuscation. Future work should explore character level augmentation, cross domain evaluation, and cost sensitive threshold tuning.
MLP Model Optimization for Heart Attack Risk Prediction: A Systematic Literature Review Supriyanto, Heru; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15027

Abstract

Heart disease remains a leading cause of global mortality, making the development of accurate predictive models a clinical priority. While Multilayer Perceptron (MLP) models offer significant potential, their application is hindered by challenges in optimization, data imbalance, and interpretability. This systematic literature review aims to address these issues by synthesizing current research on MLP model optimization for heart disease prediction, focusing on strategies for handling class imbalance and achieving model transparency with SHapley Additive exPlanations (SHAP). Following PRISMA guidelines, a structured search of major scientific databases resulted in the in-depth analysis of 30 peer-reviewed studies. The findings indicate that MLP optimization is increasingly sophisticated, employing automated hyperparameter tuning and novel architectures. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is the predominant data-level solution, though a trend towards advanced algorithm-level techniques is emerging. The application of SHAP has successfully validated models by confirming the importance of known clinical risk factors like age and chest pain type, while also demonstrating potential for new discovery. This review concludes by providing a comprehensive roadmap for researchers, highlighting a critical need for comparative studies on imbalance techniques, deeper applications of explainable AI for local-level analysis, and a stronger focus on validation using large-scale, real-world clinical data to develop truly robust and trustworthy predictive systems.
IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Muhtyas Yugi; Utomo, Fandy Setyo; Barkah, Azhari Shouni
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6982

Abstract

Handwritten digit recognition presents persistent challenges in computer vision due to the high variability in human handwriting styles, which necessitates robust generalization in classification models. This study proposes an advanced data augmentation strategy using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve recognition accuracy on the MNIST dataset. Two architectures are evaluated: a standard Convolutional Neural Network (CNN) and a hybrid model combining CNN for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential pattern modeling. The CycleGAN-based augmentation generates realistic synthetic images that enrich the training data distribution. Experimental results demonstrate that both models benefit from the augmentation, with the CNN-BiLSTM model achieving the highest accuracy of 99.22%, outperforming the CNN model’s 99.01%. The study’s novelty lies in the integration of CycleGAN-generated data with a CNN–BiLSTM architecture, which has been rarely explored in previous works. These findings contribute to the development of more generalized and accurate deep learning models for handwritten digit classification and similar pattern recognition tasks.
Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection Barkah, Azhari Shouni; Selamat, Siti Rahayu; Abidin, Zaheera Zainal; Wahyudi, Rizki
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1041

Abstract

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.
Academic Performance Prediction from Student–VLE Bipartite Interaction Graphs Using Centrality Features A Comparative Study with Classical Classifiers Sumiati, Ai Irma; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15798

Abstract

The rapid growth of digital learning platforms has increased the availability of student academic records and fine-grained interaction logs, creating opportunities for Educational Data Mining (EDM) to support early academic monitoring. However, many predictive models still rely mainly on individual tabular attributes and underutilize relational signals embedded in learning interactions. This study proposes a graph-mining feature approach for predicting student academic performance using a bipartite Student–VLE interaction graph. Centrality measures—degree, weighted degree, HITS hub, PageRank, and eigenvector centrality—are extracted to form a centrality feature set and combined with standard student information features. Using the public OULAD dataset, we compare three supervised classifiers: Random Forest, Support Vector Machine, and XGBoost. Experiments show that adding the centrality feature set consistently and substantially improves performance across all models compared to baseline tabular features. On the test set, XGBoost achieves the strongest results with accuracy 0.842, ROC-AUC 0.922, PR-AUC 0.902, and MCC 0.684, while Random Forest is close behind (accuracy 0.834, ROC-AUC 0.916, PR-AUC 0.894, MCC 0.672). The SVM model also benefits (accuracy 0.800, ROC-AUC 0.869, PR-AUC 0.811, MCC 0.599), confirming the robustness of the graph-derived signal. Scientifically, this study provides empirical evidence that a multi-centrality representation offers more systematic and transferable predictive value than relying on a single graph metric, across multiple classical model families under the same evaluation protocol. These findings indicate that graph-mining centrality features capture complementary structural information about learning engagement that is not represented by tabular attributes alone, and they offer a practical, interpretable enhancement to classic EDM pipelines for academic performance prediction.
Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data Nugroho, Khabib Adi; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4672

Abstract

This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.
Co-Authors Abidin, Uun Abidin, Zaheera Zainal Ajis Solihin, Afit Anggun Purnomo Bagaskoro, Galih Berlilana Berlilana Darmono Fandy Setyo Utomo Harimanto, Bambang Harimato, Bambang Heru Supriyanto Imam Tahyudin Kusreynada, Sabrina Uhti Latif, Imam Sofarudin Melia Dianingrum Muhtyas Yugi Nanjar, Agi Nugroho, Khabib Adi Ramadani, Nevita Cahaya Rizki Wahyudi Rujianto Eko Saputro Selamat, Siti Rahayu Setiyawan, Dillyana Tugas Sumiati, Ai Irma Taqwa Hariguna