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Transaction on Informatics and Data Science
Published by Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto
ISSN : -     EISSN : 30641772     DOI : https://doi.org/10.24090/tids
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Transactions on Informatics and Data Science (TIDS), with ISSN: 3064-1772 (online), is a scientific journal that publishes the latest research in the fields of informatics and data science, focusing on both theoretical advances and practical applications. Published by the Department of Informatics, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto, Purwokerto, this journal serves as a platform for researchers, academics, and practitioners to share new ideas and innovations in data science, artificial intelligence, natural language processing, cloud computing, and information technology applications across various domains. It promotes collaboration and deep knowledge exchange within the scientific community, bridging the gap between theory and practice in the rapidly evolving fields of informatics and data science. Aims Transaction on Informatics and Data Science aims to advance the frontiers of informatics and data science knowledge by publishing high-quality research that encompasses theoretical advancements and practical applications. The journal seeks to contribute significantly to the understanding and developing of innovative approaches, methodologies, and technologies in these domains. Scopes The scope of "Transaction on Informatics and Data Science" covers a wide range of topics related to informatics and data science, including but not limited to: - Data analysis and mining - Artificial intelligence and machine learning - Natural language processing and understanding - Cloud computing and big data technologies - Information retrieval and knowledge management - Data-driven decision-making and predictive modelling - Internet of Things (IoT) and data analytics - Cybersecurity and privacy in data science - Informatics and data science applications in various healthcare, finance, education, and other domains. The journal welcomes original research articles, reviews, case studies, and technical notes that contribute significantly to advancing knowledge and practice in informatics and data science. Submissions should demonstrate novelty, tightness, and relevance to the rapidly evolving landscape of information technology and data-driven decision-making processes.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 4 Documents
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Search results for , issue "vol. 3 no. 2 (2026)" : 4 Documents clear
Analytical Hierarchy Process Approach in Expert Systems to Improve the Accuracy of Network Fault Detection Linda Perdana Wanti; Frisky Agus Umbara; Annisa Romadloni
Transactions on Informatics and Data Science Vol. 3 No. 2 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i2.16250

Abstract

Computer networks play an important role in supporting information systems across sectors such as education, government, industry, and private organizations. The growing reliance on stable, reliable networks makes network disruptions a serious concern. Common issues such as internet disconnections, IP address conflicts, device failures to connect, and DNS errors often require a fast, accurate diagnosis. In practice, however, the handling of these problems is often limited by the number of available technicians and by users lack of technical understanding. This study aims to develop an expert system to automatically monitor and detect network faults. The system applies the Analytical Hierarchy Process (AHP) to assign weights to each symptom through pairwise comparisons, thereby prioritizing and yielding more relevant diagnostic results. The knowledge base was developed using secondary data from previous studies that utilized forward chaining. The system is implemented as a web-based application with a user-friendly interface. System evaluation was conducted using 50 network fault test cases, and the results showed that the proposed system achieved 92% accuracy, 91% precision, 90% recall, and an F1-score of 90.5% based on expert validation. In addition, the AHP consistency test produced a Consistency Ratio (CR) value of 0.08, indicating that the pairwise comparison matrix was logically consistent. These results demonstrate that the proposed system improves the efficiency and effectiveness of network fault diagnosis for both technical and non-technical users.
Enhancing Image Tampering Detection Using ResNet50 with Error Level Analysis and Gamma Correction Hanief Widya Wardhana; Riza Arifudin
Transactions on Informatics and Data Science Vol. 3 No. 2 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i2.16322

Abstract

Image manipulation has become increasingly sophisticated, making the verification of visual authenticity a critical challenge for preventing misinformation, fraud, and other malicious activities. Conventional detection methods often fail to capture subtle traces of tampering, motivating the use of deep learning combined with targeted preprocessing. This study proposes a detection approach that couples the ResNet50 architecture with two complementary preprocessing techniques—Error Level Analysis (ELA) and gamma correction—to sharpen the features associated with tampered regions. ELA exposes discrepancies in JPEG compression levels, while gamma correction enhances the contrast between authentic and manipulated areas; combining them yields a more discriminative feature space than either technique alone. On the CASIA 2.0 dataset (12,614 images), the model attains 82.60% accuracy without preprocessing, 95.16% with ELA, and 78.74% with gamma correction alone. Applying both techniques together produces the highest accuracy of 97.58%, a 14.98 percentage-point improvement over the baseline, because ELA isolates compression-level traces of tampering while gamma correction sharpens the contrast that makes those traces separable. These results demonstrate that carefully selected preprocessing significantly strengthens ResNet50-based image tampering detection.
Quality-Controlled Indonesian Twitter Sentiment Analysis Baseline Using TF-IDF and Classical Machine Learning Approaches Aan Ansori; Raden Kania; Tifani Intan Solihati; Fitri Damyati
Transactions on Informatics and Data Science Vol. 3 No. 2 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i2.16900

Abstract

Social media provides a large volume of informal textual data for examining public sentiment, but Indonesian Twitter content presents substantial challenges due to slang, nonstandard spelling, limited context, and uneven class distribution. This study evaluates a reproducible machine-learning and natural-language-processing pipeline for three-way sentiment classification on a publicly available Indonesian Twitter dataset. The original corpus contained 10,806 manually labeled tweets. Quality control removed duplicate texts and all duplicate texts carrying conflicting labels, yielding 9,948 observations: 2,606 negative, 4,955 neutral, and 2,387 positive. Text was normalized and represented using term frequency-inverse document frequency with unigram and unigram-bigram configurations. Multinomial Naive Bayes, logistic regression, linear support vector machine, and random forest classifiers were evaluated using a stratified 80:20 split, with macro-F1 prioritized due to class imbalance. Multinomial Naive Bayes achieved the highest accuracy at 60.35%, but logistic regression with unigram-bigram features produced the best macro-F1 of 56.22% and a weighted F1 of 58.45%. Five-fold cross-validation produced a mean macro-F1 of 56.59% with low dispersion. Error analysis indicates that negation, mixed sentiment, informal expressions, and context dependence remain major sources of misclassification. These findings establish a transparent classical baseline for Indonesian social-media sentiment analysis.
Robust Multi-Turn-Rate IMM-EKF Algorithm for Maneuvering Target Tracking under Model Mismatch and Measurement Faults Trinh Thi Minh; Vu Quang Luong
Transactions on Informatics and Data Science Vol. 3 No. 2 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i2.16951

Abstract

Reliable maneuvering-target tracking is difficult when the motion-model bank is incomplete and range-bearing measurements contain dropout, bias, burst noise, or impulsive outliers. This study proposes a robust multi-turn-rate interacting multiple-model extended Kalman filter (RMT-IMM-EKF) that combines six motion hypotheses with statistically normalized componentwise innovation clipping. Multi-turn-rate denotes multiple coordinated-turn hypotheses, not different sampling rates; all estimators use  s. The model bank includes constant velocity, coordinated turns at  and  deg/s, and a high-process-noise escape mode. The bounded innovation is used in both state correction and model likelihood. A 120-s programmed trajectory containing straight motion, in-grid/off-grid turns, acceleration, and an S-turn is evaluated in 30 paired Monte Carlo runs with identical truth, measurements, faults, and random seeds across estimators. RMT-IMM-EKF achieves a position RMSE of  m and a velocity RMSE of  m/s. Relative to AIMM-EKF, these errors decrease by 18.64% and 11.25%, respectively, while the proposed estimator wins all paired runs on both metrics; one-sided Wilcoxon tests yield . Condition-wise analysis shows clear gains in nominal, dropout, and impulsive-outlier samples but no uniform advantage during sustained noise bursts or persistent bias. The results support complementary benefits of broader maneuver coverage and bounded residual influence within the tested benchmark, while persistent bias and sustained changes in variance remain limitations.

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