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All Journal JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Idealis : Indonesia Journal Information System Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Bulletin of Information System Research Jurnal Ilmu Komputer, Teknologi Dan Informasi Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Juni Ismail
Politeknik Bisnis Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Mapping Research Trends of Entropy-Based Weighting and AHP Integration in Multi-Criteria Decision Analysis for Sustainable Development Applications Zulfikar Zulfikar; Juni Ismail; Alfry Aristo Jansen Sinlae; Yanto Saputra; Raja Anan Nasution; Elsy Rahajeng; Mesran Mesran
Bulletin of Information System Research Vol 4 No 1 (2025): December 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v4i1.230

Abstract

The integration of entropy-based weighting and the Analytic Hierarchy Process (AHP) has become an increasingly important strategy for balancing objective and subjective criterion weights in multi-criteria decision-making (MCDM), yet the intellectual structure of this hybrid field remains fragmented and insufficiently mapped. This study aims to systematically chart the global research landscape of entropy-AHP integration in MCDM and to identify its leading contributors, foundational works, and dominant thematic structures. A bibliometric research design guided by the PRISMA protocol was adopted, drawing on 160 English-language documents retrieved from the Scopus database for the period 2001 to 2025. The data were analysed using VOSviewer and Scopus analytical tools to examine annual publication trends, subject-area distribution, leading sources, co-citation networks, and keyword co-occurrence patterns. The results reveal a field that has accelerated sharply since 2021, reaching a peak of thirty-four documents in 2025, with output concentrated in Engineering and Computer Science and disseminated through a diverse ecosystem of energy-oriented journals and conference outlets. Co-citation analysis confirms a theoretical base anchored in the canonical works of Saaty and Zeleny, while keyword mapping shows entropy functioning as a conceptual bridge between expert judgment and data-driven weighting, with TOPSIS emerging as a salient companion technique. The novelty of this study lies in its focused mapping of the entropy–AHP intersection rather than MCDM in general, exposing a loosely integrated thematic structure and a reliance on a narrow citation canon. Its principal contribution is a consolidated knowledge map that clarifies the field's foundations and directs future methodological and interdisciplinary innovation.
Deteksi Phishing URL Menggunakan XGBoost dengan Explainable AI pada Web Page Phishing Detection Dataset Juni Ismail; Raja Anan Nasution; Muhammad Nasri Gea
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3810

Abstract

Phishing attacks distributed through fraudulent URLs remain one of the most damaging cyber threats, including in Indonesia where malicious links spread widely through messaging applications and e-mail. Blacklist-based approaches cannot recognize newly created phishing URLs, while accurate machine learning models are often difficult to interpret. This study aims to compare six machine learning algorithms, namely XGBoost, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes, for phishing website detection based on lexical URL, page content, and external reputation features, while providing model transparency through Explainable Artificial Intelligence (XAI). Experiments were conducted on the Web Page Phishing Detection Dataset containing 11,430 URLs with 87 features and a balanced class distribution. The research stages include exploratory data analysis, feature selection analysis using Chi-Square, Mutual Information, and Recursive Feature Elimination, an 80:20 data split, model training, hyperparameter optimization using RandomizedSearchCV, and interpretation of the best model using SHapley Additive exPlanations (SHAP). The results show that XGBoost delivers the best performance with 96.50% accuracy, 96.26% precision, 96.76% recall, 96.51% F1-score, and an AUC of 0.9943. SHAP analysis identifies google_index, page_rank, and nb_hyperlinks as the most influential features, dominated by external reputation-based features. Under this experimental setting, the findings indicate that an accurate phishing detection model can be equipped with interpretable explanations of its feature contributions. The main contribution of this study is an integrated comparative evaluation that combines six-algorithm benchmarking, leakage-free hyperparameter optimization, and SHAP-based interpretation on a public phishing dataset, offering practical guidance for security analysts.
WEIGHTED LOSS STRATEGY FOR BERT-BASED TWITTER SENTIMENT ANALYSIS WITHOUT SYNTHETIC OVERSAMPLING Timbo Faritcan Siallagan; Riki Winanjaya; Juni Ismail
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.7680

Abstract

The widespread adoption of ChatGPT has generated extensive public discourse across social media, necessitating robust sentiment analysis to understand collective opinions. Traditional approaches frequently employ the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance; however, its effectiveness on short-text data remains an open question. This study develops an optimized sentiment classification model and evaluates whether competitive performance can be achieved without synthetic data augmentation. The methodology encompasses comprehensive Natural Language Processing (NLP) preprocessing and stratified data partitioning to preserve distributional characteristics. A BERT-base architecture is fine-tuned using a class-weighted Cross-Entropy loss combined with weighted random sampling, deliberately avoiding SMOTE-based oversampling. The model is trained with the AdamW optimizer (learning rate: 3 × 10⁻⁵), batch size 32, and mixed-precision training for four epochs. On 198,639 preprocessed tweets, the proposed approach achieves 93.81% accuracy, with weighted precision, recall, and F1-score of 0.9365, 0.9381, and 0.9380 respectively, outperforming the baseline by 1.75 percentage points. Per-class analysis reveals strong performance for negative (F1-score: 0.96) and positive sentiment (F1-score: 0.94), with lower neutral classification (F1-score: 0.89), attributable to the inherent heterogeneity of neutral expressions. The training-validation gap remains below 5%, consistent with adequate regularization. These findings provide empirical evidence that, within the present experimental configuration, a properly optimized weighted loss strategy offers a viable and computationally efficient alternative to synthetic oversampling for BERT-based Twitter sentiment classification. Further controlled ablation studies and statistical validation are needed to establish generalizability.
Co-Authors Alfry Aristo Jansen Sinlae Annisa Shafira Zuhri Elida Madona Siburian Elsy Rahajeng Evi Handayani Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Inggrid Ester Erlinda Simarmata Juliana Rotua Pasaribu Lili Tanti Mesran Mesran Muhammad Nasri Gea Nurhafizah Yazid Pangidoan Adventus Ambarita Pardede, Akim Manaor Hara Prasetya, Hardi Raja Anan Nasution Raja Anan Nasution Raja Anan Nasution Randi Sumitro Renovand Mikael Situmorang Riki Winanjaya Sihombing, Rotua Syaipuddin Tammamah Lubis, Hartati Timbo Faritcan Parlaungan Siallagan Wanayumini Yanto Saputra Zulfikar Zulfikar