cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yogiek Indra Kurniawan
Contact Email
yogiek@unsoed.ac.id
Phone
+6285640661444
Journal Mail Official
jutif.ft@unsoed.ac.id
Editorial Address
Informatika, Fakultas Teknik Universitas Jenderal Soedirman. Jalan Mayjen Sungkono KM 5, Kecamatan Kalimanah, Kabupaten Purbalingga, Jawa Tengah, Indonesia 53371.
Location
Kab. banyumas,
Jawa tengah
INDONESIA
Jurnal Teknik Informatika (JUTIF)
Published by Universitas Jenderal Soedirman
ISSN : 27233863     EISSN : 27233871     DOI : https://doi.org/10.20884/1.jutif.Year.Vol.No.ID
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,314 Documents
 128   129   130   131   132 
Cost-Efficient Strategies for Production Indonesian Sentiment Analysis: Open-Source Models, Commercial LLMs, and Hybrid Pipelines Ridwan Ilyas; Mochammad Arif Bijaksana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.5.6297

Abstract

Production sentiment analysis for Indonesian must balance accuracy against operating cost, yet prior work benchmarks accuracy in isolation. This study aims to quantify how much accuracy is purchased per unit of monetary cost across three pipeline families, and to test whether hybrid pipelines can approach large language model accuracy at a fraction of the cost. We fine-tune four open-source encoders (IndoBERT-base, IndoBERT-large with Low-Rank Adaptation, IndoBERTweet, and IndoBERT-lite), evaluate four commercial large language model arms (Claude Haiku, Sonnet, and Opus under zero-shot and few-shot prompting), and build three hybrid pipelines—confidence routing, uncertainty cascading, and knowledge distillation—on four Indonesian sentiment datasets (SmSA, NusaX, UGM Twitter, and ID-SMSA). Accuracy is measured by Macro-F1 over three seeds, cost by a per-1,000-prediction model, and differences by McNemar's test with bootstrap confidence intervals. Paying for a large language model is statistically significant on only one of four datasets: on SmSA the best open-source encoder reaches 0.924 against 0.966 for Sonnet, whereas on the noisy UGM corpus the free encoder (0.708) significantly outperforms every paid model (≤0.651, p<0.001). Confidence routing is the consistent winner, reaching 0.947 on SmSA at only US$0.18 per 1,000 predictions—about 98% of full-model accuracy at one-ninth of the cost—and surpassing both pure endpoints on three of four datasets. The contribution is a statistically grounded, reproducible decision framework for selecting sentiment pipelines in low-resource languages that specifies when paying for large language models is justified and when free fine-tuned encoders suffice.
An Explainable Machine Learning Decision Support System for Student Tuition Fee Reduction: Integrating Tree-Based Ensemble Models and SHAP-Based Justification Riyadi Purwanto; Fajar Mahardika; Dwi Novia Prasetyanti; Cahya Vikasari; Rostika Listyaningrum; Raden Bagus Bambang Sumantri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.5.6573

Abstract

Tuition fee reduction (Uang Kuliah Tunggal, UKT) is a key affordability instrument in Indonesian higher education, yet its assessment still relies largely on manual verification, producing decisions that are slow, inconsistent between reviewers, and difficult to justify to applicants. This study develops and evaluates an explainable machine learning decision support system that classifies the eligibility of tuition fee reduction applications and accompanies every prediction with a human-readable justification. Four tree-based ensemble models (Random Forest, XGBoost, CatBoost, and LightGBM) were trained on 5,000 application records from Politeknik Negeri Cilacap (2022–2024) described by socio-economic, academic, and administrative attributes, and benchmarked against a Logistic Regression baseline using hold-out testing, 10-fold cross-validation, and the Friedman test. SHAP (SHapley Additive exPlanations) was then applied to the best-performing model to quantify global feature contributions and generate per-application explanations. XGBoost achieved the highest accuracy (0.822) and F1-score (0.850), while Random Forest obtained the highest ROC-AUC ([≈0.89]); however, all models remained within approximately 2.6 percentage points of accuracy, and the Logistic Regression baseline attained the highest precision (0.838). The SHAP analysis revealed that number of dependents and document completeness dominated the decision function, far exceeding parental income, indicating that the historical decision process encoded an administrative gate alongside genuine economic need. These findings support deploying the system as a human-in-the-loop screening aid rather than an autonomous decision maker, and demonstrate that explainability is essential for accountable financial aid decisions.
Class Imbalance In Student Dropout Prediction: A Prisma-Guided Systematic Review With Narrative Synthesis And Bibliometric Mapping Hendra Effendi; Wendi Usino; Deni Mahdiana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.5.6637

Abstract

Student dropout prediction supports early warning and institutional intervention, but class imbalance can obscure errors affecting students at risk. This review aims to synthesize imbalance-handling strategies and identify gaps in their representation, validation, and evaluation in educational prediction. A PRISMA-guided systematic review of Scopus-indexed studies published from 2020 to 2026 combined narrative synthesis with complementary bibliometric mapping using VOSviewer. A total of 49 studies were included. Conventional resampling was the most frequently coded imbalance-handling theme, while algorithm-level balancing and threshold optimization received less attention. Temporal and sequential approaches represented academic histories and learning activities, and generative methods were explored for student-data augmentation. The included studies did not establish a consistently superior technique across datasets and evaluation protocols. Reporting gaps concerned quantitative class distributions, training-only preprocessing and resampling, advanced minority-class metrics, reproducibility, and validation of synthetic records. The bibliometric map contextualizes these themes without establishing comparative effectiveness. The synthesis supports clearer outcome definitions, conventional baselines, validation matched to the prediction setting, and minority-class evaluation. Where synthetic data are used, fidelity, predictive utility, privacy, and domain consistency should be assessed together. For informatics and computer science, the review contributes a structured account of how data representation, imbalance handling, and validation jointly affect the assessment of predictive systems, helping researchers distinguish credible methodological improvement from gains attributable to incomplete evaluation.
Explainable Ordinal Prediction of Journal Accreditation Tiers from Registry Metadata: Evidence of Content Redundancy Eko Siswanto; Danang Danang
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.5.6468

Abstract

National accreditation tiering of scholarly journals is a labour-intensive administrative process, and indexing platforms increasingly harvest full article metadata in the hope that publication content will support automated screening. We test whether that content is worth harvesting. Using the operational database of a regional journal indexing platform covering 305 journals and 26,210 harvested articles in Central Java, Indonesia, we construct a three-level ordinal accreditation target and predict it with a Frank-Hall ordinal random forest over 36 registry metadata variables, after an explicit audit that removes columns acting as label proxies. The registry model reaches a quadratic weighted kappa (QWK) of 0.549 under 5 × 5 repeated stratified cross-validation, with pooled out-of-fold QWK 0.572 (95% CI 0.456-0.679) and accuracy 0.678 against a majority baseline of 0.384. We then add journal-level representations built from 4,020 article abstracts using three sentence encoders, three pooling schemes, four PCA sizes and two fusion strategies. Abstract content is informative on its own (QWK 0.283) but no configuration met an improvement criterion fixed before the results were inspected (∆QWK ≥ 0.05 and p < 0.05); the largest observed gain was +0.023 (p = 0.17). A power analysis places the minimum detectable effect at 0.176 QWK, so small effects cannot be excluded. SHAP and permutation importance agree that article processing charge disclosure, aim-and-scope length and journal age dominate. For platform designers, the practical implication is that content harvesting is not required for this screening function.

Page 132 of 132 | Total Record : 1314

 128   129   130   131   132 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024 Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023 Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023 Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023 Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023 Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023 Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023 Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022 Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022 Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022 Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022 Vol. 3 No. 1 (2022): JUTIF Volume 3, Number 1, February 2022 Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021 Vol. 2 No. 1 (2021): JUTIF Volume 2, Number 1, June 2021 Vol. 1 No. 2 (2020): JUTIF Volume 1, Number 2, December 2020 Vol. 1 No. 1 (2020): JUTIF Volume 1, Number 1, June 2020 More Issue