Article Per Year (5 Year)
p-Index From 2021 - 2026
5.557
P-Index
This Author published in this journals
All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science Journal of Information Systems and Informatics IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology International Journal for Applied Information Management Jurnal Abdi Masyarakat Indonesia Journal of Social Research Scientific Journal of Informatics Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

Published : 50 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 50 Documents
Search

 1   2   3   4   5 
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.
Enhancing the Robustness of Adaptive Class Activation Mapping (AD-CAM) Against Noisy Facial Expression Data Using Preprocessing and Adaptive Normalization Sugianto, Dwi; Hariguna, Taqwa; Utomo, Fandy Setyo
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1005

Abstract

In real-world computer vision applications, visual data is often corrupted by noise, reducing both the accuracy and interpretability of deep learning models. This study proposes an enhanced AD-CAM framework that integrates noise-aware preprocessing and adaptive normalization to improve robustness in both prediction and visual explanation. Experiments were conducted on the FER2013 facial expression dataset augmented with Gaussian, salt-and-pepper, and speckle noise. Using ResNet-50 as the backbone, the proposed method demonstrated significant gains across multiple evaluation metrics, including Robust Accuracy (RA), Drop Coherence (DC), Area Under Robustness Curve (AURC), and Signal-to-Noise Ratio (SNR). Compared to the baseline, the model achieved over 10% accuracy improvement and up to 0.16 DC reduction under noise. Qualitative visualizations showed that the improved model consistently highlighted semantically relevant facial regions, maintaining interpretability even under severe input degradation. These results support the adoption of noise-aware interpretability frameworks for more reliable and trustworthy deployment in real-world vision systems.
An Empirical Study to Understanding Students Continuance Intention Use of Multimedia Online Learning Hariguna, Taqwa
International Journal for Applied Information Management Vol. 1 No. 2 (2021): Regular Issue: July 2021
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v1i2.10

Abstract

The purpose of this study was to assess students' ongoing intentions towards online multimedia learning such as perceived usefulness, ease of use, and flow experience. The sample of this study was 523 students who used off-campus/online learning resources and examined the content of online learning resources and their multimedia aspects. The Extended of Technology Acceptance Model (TAM) was used to predict students' continuing intentions. The results showed that students' intentions were positively influenced by their perceived usefulness, ease of use, and flow experience. It is suggested that the designer of multimedia online learning should be more specific in determining the target users to receive and cultivate a more positive sustainable intention.
Evaluasi Ensemble Learning untuk Prediksi Nilai Matematika Siswa Sekolah Menengah Asikin, Zaenal; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.858

Abstract

Prediksi dini performa matematika siswa sekolah menengah sangat penting untuk merancang intervensi pendidikan yang lebih adaptif dan efektif sebelum ujian akhir resmi dilaksanakan. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga model machine learning Random Forest (RF), Gradient Boosting Regressor (GBR), dan Multi-Layer Perceptron (MLP) dalam memprediksi nilai matematika siswa di Indonesia, serta mendokumentasikan proses tuning hyperparameter secara sistematis untuk setiap model. Dataset yang digunakan terdiri dari skor matematika, membaca, menulis, serta variabel demografis meliputi jenis kelamin, latar belakang pendidikan orang tua, jenis layanan makan, dan keikutsertaan kursus persiapan. Proses tuning hyperparameter untuk RF dan GBR dilakukan menggunakan RandomizedSearchCV dengan 5-fold cross-validation, menguji rentang nilai untuk jumlah estimator, kedalaman maksimum pohon, dan laju pembelajaran (learning rate). Sedangkan pada Multi-Layer Perceptron, GridSearchCV diterapkan dengan variasi arsitektur hidden_layer_sizes, laju pembelajaran awal (learning_rate_init), dan faktor regularisasi (alpha) pada 5-fold CV. Model diukur menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil eksperimen menunjukkan bahwa GBR memberikan performa terbaik dengan MAE sebesar 11,61 poin, RMSE 15,23 poin, dan R² 0,10. Random Forest menempati urutan kedua (MAE 12,34; RMSE 16,05; R² 0,64), diikuti MLP (MAE 13,10; RMSE 17,20; R² 0,60). Analisis feature importance mengungkap bahwa skor membaca dan menulis bersama-sama menyumbang lebih dari 60 % kontribusi prediksi, sedangkan faktor demografis seperti latar belakang pendidikan orang tua dan keikutsertaan kursus berperan sekunder namun tetap signifikan. Temuan ini mengindikasikan bahwa model ensemble learning tidak hanya unggul dalam akurasi prediksi, tetapi juga memberikan wawasan mendalam tentang variabel kunci yang memengaruhi performa matematika siswa. Implementasi model ini memungkinkan guru dan pihak sekolah untuk mengidentifikasi siswa yang berisiko rendah secara lebih cepat, merancang program remedial atau pengayaan yang tepat sasaran, serta memanfaatkan sumber daya pendidikan secara lebih efisien. Untuk penelitian lanjutan, disarankan penambahan variabel perilaku siswa seperti durasi belajar mandiri dan kehadiran serta eksplorasi model sekuensial (RNN/Transformer) untuk menangkap dinamika pembelajaran dari waktu ke waktu.
Transformasi Portal Data Pemerintah di Indonesia dengan Large Language Model dan Retrieval-Augmented Generation: Tinjauan Pustaka Sistematis Hadie, Agus Nur; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1175

Abstract

Integrasi kecerdasan buatan (Artificial Intelligence/AI) seperti Large Language Model (LLM) dan Retrieval-Augmented Generation (RAG) berpotensi mentransformasi portal data pemerintah, namun implementasinya terhambat oleh kurangnya tinjauan sistematis dan kerangka evaluasi yang spesifik. Penelitian ini bertujuan untuk mengidentifikasi, mengevaluasi, dan mensintesis literatur terkini mengenai metodologi, keberhasilan, dan tantangan integrasi teknologi tersebut melalui tinjauan pustaka sistematis. Metode ini diterapkan dengan pencarian terstruktur pada basis data Google Scholar, Scopus, dan IEEE Xplore, diikuti proses penyaringan bertahap. Hasil tinjauan menunjukkan bahwa teknologi AI terbukti efektif meningkatkan komunikasi pemerintah-warga, efisiensi layanan, dan akurasi pengambilan data, di mana penyesuaian model menjadi faktor penting. Namun, implementasinya masih menghadapi tantangan signifikan terkait tata kelola, kualitas data, dan masalah etis. Hasil penelitian ini menekankan pentingnya pengembangan kerangka kerja tata kelola yang komprehensif untuk memastikan penerapan AI yang akuntabel dan selaras dengan kepentingan publik.
Sentiment and Concern Classification on Metaverse Governance Responses Using Naïve Bayes and Support Vector Machine (SVM) Ben-Othman, Jalel; Hariguna, Taqwa
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.43

Abstract

The rapid advancement of immersive technologies such as the metaverse has introduced new opportunities and challenges for digital governance. Understanding public perception of these technologies is essential for designing governance systems that are transparent, inclusive, and responsive to citizens’ needs. This study analyses public sentiment and concerns regarding the use of metaverse technology in governance by applying two machine learning algorithms: Naïve Bayes and SVM. The dataset, consisting of open-ended survey responses from participants in The Gambia, was pre-processed through tokenization, stopword removal, and TF-IDF vectorization before model implementation. The results indicate that both algorithms can classify sentiment into positive, neutral, and negative categories; however, SVM consistently outperforms Naïve Bayes across all evaluation metrics. The SVM model achieved an accuracy of 88.6 percent and an F1-score of 0.873, demonstrating superior capability in recognizing contextual and semantic nuances within short text responses. In contrast, Naïve Bayes tended to overclassify responses as neutral, reflecting its limitation in capturing word dependencies. These findings confirm that SVM is better suited for sentiment analysis involving complex linguistic expressions and context-dependent opinions. The study contributes to the growing body of research on artificial intelligence in public policy by demonstrating how machine learning can provide deeper insights into citizen perspectives on emerging digital technologies. Such analytical approaches can assist policymakers in identifying public expectations, addressing concerns, and fostering trust in metaverse-based governance systems.
User Experience Analysis of Learning Management System (LMS) SINAU to Support Learning with MERDEKA Flow Using UX Curve Method Yarsasi, Sri; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4579

Abstract

The rapid development of information technology has driven transformation in education, including the use of Learning Management Systems (LMS) to facilitate independent and flexible learning aligned with the Merdeka Curriculum. This study aims to evaluate the user experience (UX) of the Sinau LMS at SMA Negeri 1 Sidareja using the UX Curve method, which tracks changes in user perceptions over time. The research involved 20 grade XII students who had used the LMS for at least three months. Data were collected through initial questionnaires, interviews, UX curve drawings, and final questionnaires, focusing on five main UX aspects: General UX, Attractiveness, Ease of Use, Utility, and Degree of Usage. The analysis of 100 curves revealed that more than half of the respondents experienced a decline in user experience quality, particularly in Ease of Use, General UX, and Degree of Usage, due to issues such as an unattractive interface, navigation challenges, and limited feature relevance. Conversely, a minority showed improved perceptions as they adapted and became more familiar with the system. These findings highlight the need for continuous improvement of the LMS's interface design and features to enhance user satisfaction and learning effectiveness. The study contributes theoretically by demonstrating the application of the UX Curve in educational systems and practically by providing recommendations for refining LMS development to better support the Merdeka Curriculum.
Explainable Aspect-Based Sentiment Analysis with Contrast-Aware IndoBERT for Indonesian Public Service Reviews Jondien, Muhammad Shihab Fathurrahman; Hariguna, Taqwa; Saputra, Dhanar Intan Surya
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9162

Abstract

This study presents an Explainable IndoBERT with Contrast-Aware Attention framework for Aspect-Based Sentiment Analysis (ABSA) on Indonesian public service reviews. The proposed model integrates automated aspect labeling using KeyBERT with a contrast-aware mechanism to handle mixed or opposing sentiments within a single sentence. By leveraging IndoBERT as the base transformer, the system captures context-sensitive sentiment cues while maintaining interpretability through attention-based rationale extraction. Experimental results on the SMSA dataset demonstrate an accuracy of 83.4%, with strong precision in positive and negative sentiment detection. The contrast-aware module improves clause-level understanding, while the attention-based explainability module provides transparent, token-level rationales that align with human judgments at an average rate of 87.7%. Although a modest performance decline occurs compared to non-explainable baselines, the proposed model offers significant gains in semantic transparency, making it suitable for evidence-based policy evaluation and citizen feedback monitoring. This research contributes a practical, interpretable, and linguistically grounded solution for explainable sentiment analysis in low-resource languages, advancing the application of responsible AI in public service analytics.
Usability Evaluation of a School Library OPAC Using Heuristic Evaluation and User Testing Faradina, Faradina; Hariguna, Taqwa; Utomo, Fandy Setyo
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1528

Abstract

This study evaluates the usability of the Online Public Access Catalog (OPAC) at SMK Negeri 1 Purwokerto to address the persistent gap between traditional library information architectures and the modern search behaviors of vocational students within the Kurikulum Merdeka ecosystem. The research aims to solve the problem of "mental model dissonance" that hinders independent information literacy among digital native learners. A hybrid evaluation approach was employed, integrating a Heuristic Evaluation by three experts with empirical User Testing involving students. The study utilized the Think-Aloud protocol and the System Usability Scale (SUS) to capture both performance and perception data. Result: The expert inspection identified 18 significant usability violations, primarily in library technical jargon (H2) and error prevention (H5). Empirical testing revealed a low average Task Success Rate (TSR) of 49.3% and a mean SUS score of 55.0, placing the system in the "Unacceptable" category. These figures confirm that current cataloging logic significantly obstructs retrieval efficiency. The originality of this research lies in the identification of specific dissonance points between vocational students' mental models and bibliographic metadata. It provides a strategic framework for interface restructuring through semantic simplification and department-based navigation, offering a practical model for developing user-centric "smart" library services in vocational education.
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Adi Suryaputra Paramita Al-Rawahna, Ammar Salamh Mujali Anton Satria Prabuwono Asikin, Zaenal Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Ben-Othman, Jalel Berlilana Berlilana Chen, Shih Chih Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fandy Setyo Utomo Faradina, Faradina Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Heru Supriyanto Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Izumi, Calvina Jawad, Haider Mahmood Johan Reimon Batmetan Jondien, Muhammad Shihab Fathurrahman Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nugroho, Khabib Adi Nurwanti, Aulia Othman, Jalel Ben Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Setiawan, Ito Subhan Mahendrasyah, Muhammad Sugianto, Dwi Sumiati, Ai Irma Susanti, Nindi Nofi Syahrizal, Hendrawan Tikaningsih, Ades Tri Wahyuningsih Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding