cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Usman Ependi
Contact Email
usmanependi@adsii.or.id
Phone
081271103018
Journal Mail Official
usmanependi@adsii.or.id
Editorial Address
Jl AMD, Lr. Tanjung Harapan, Taman Kavling Mandiri Sejahtera B11, Kel. Talang Jambe, Kec. Sukarami, Palembang, Provinsi Sumatera Selatan, 30151
Location
Unknown,
Unknown
INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 47 Documents
 1   2   3   4   5 
Search results for , issue "Vol 8 No 2 (2026): April" : 47 Documents clear
Sequential Requirements Prediction in Synthetic Fintech-Like Backlogs Using an Interpretable Hybrid of Transition Rules and Transformer Models Diana Laily Fithri; Soni Adiyono; Muhammad Arifin
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1469

Abstract

Fintech software development is characterized by rapid product iteration and stringent regulatory requirements, resulting in changing requirements as an interrelated set rather than individual elements. In this research, Sequential Requirements Prediction is proposed as a decision support task in Requirements Engineering, where the time-ordered prefix of completed backlog items is used to predict the next likely canonical requirement type as Top-k ranked output. To mitigate noise and inconsistency in backlog data, an LLM-aided semantic normalization step maps diverse requirement descriptions to a closed set of fintech requirement types. The research compares an interpretable rule-based Markov-1 predictor with Transformer-based sequential predictors under a case-level time-aware split. The proposed method is evaluated on a synthetic fintech-like backlog dataset consisting of 900 cases, 5,252 events, and 18 canonical requirement types. The best-performing model, Transformer + normalization + augmentation (M4), achieved Recall@5 = 0.638889, MRR@5 = 0.536806, and NDCG@5 = 0.566667. These results surpassed the rule-based predictor and non-normalized Transformer model. In addition, augmentation further improved Recall@5 from 0.493056 to 0.527778 in the rare-type subset. These findings suggest the methodological promise of the proposed framework for sequence-aware and compliance-conscious backlog analytics in synthetic fintech-like settings.
Institutional and Individual Drivers of AI Adoption in Higher Education: An Integrative TAM–TOE Model Baiq Sri Mardiani; Ema Utami
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1470

Abstract

The rapid diffusion of artificial intelligence (AI) in higher education necessitates a deeper understanding of both institutional and individual factors influencing its adoption, particularly in developing-country contexts. This study examines the drivers of AI adoption in Indonesian higher education institutions by integrating the Technology Acceptance Model (TAM) and the Technology, Organization, Environment (TOE) framework. Addressing a gap in prior research that often separates individual acceptance from institutional readiness, this study adopts a quantitative survey approach involving 366 academic stakeholders, including lecturers, students, and administrative staff. Data collected between October and December 2025 were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived ease of use strongly influences attitude toward AI, which in turn significantly affects behavioral intention. Perceived usefulness also has a positive, albeit weaker, effect on behavioral intention. At the institutional level, environmental context is found to significantly influence AI readiness, while other contextual factors exhibit limited explanatory power. Several hypothesized relationships, including the effects of AI readiness on perceived usefulness and the moderating roles of digital literacy and top management support, are not supported. These results suggest that AI adoption in higher education is primarily shaped by user-centered factors, while institutional readiness may depend on additional determinants not fully captured in the model. This study provides empirical insights into the role of AI readiness as an intermediate construct within an integrated TAM–TOE framework in higher education.
CultureFun: An Interactive Web-Based Cultural Learning Platform Using a Human-Computer Interaction Approach Syaqilla Maulidia; Najjuan Al Fariz; Andhika Presha Saputra; Muhammad Darwis
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1472

Abstract

This study aimed to develop CultureFun, a user-centered web-based platform for cultural learning among younger generations. The study addressed the limitation of existing digital cultural learning media, which often rely on text-heavy content and provide limited interactivity. User-Centered Design and Design-Based Research were applied to guide needfinding, empathy-based analysis, iterative prototyping, and usability evaluation. Data were collected through semi-structured interviews and direct observation involving twelve participants aged 10–23 years. The final prototype was evaluated using the System Usability Scale. The results showed that CultureFun was perceived as easy to use and engaging, particularly because the platform presented short cultural content supported by images, quizzes, and mini-games. The platform achieved an average usability score of 72.08, indicating good and acceptable usability. This study concludes that integrating user-centered analysis, iterative prototyping, and gamified interaction can support the development of an effective and usable digital cultural learning platform. However, the findings were limited by the small sample size and the focus on usability rather than direct measurement of learning effectiveness. Future studies are recommended to involve more diverse participants and examine long-term cultural knowledge retention.
Post-Quantum Migration in the Financial Sector: A Systematic Review of Readiness, Risks, and Transition Frameworks Hillary Muzenda; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1477

Abstract

Quantum computing threatens the classical cryptographic systems underpinning financial infrastructure, exposing payment platforms, interbank networks, and digital services to Harvest-Now-Decrypt-Later (HNDL) risks. This study systematically reviews quantum threats and post-quantum cryptographic readiness in the financial sector to assess preparedness, identify implementation challenges, and synthesize migration pathways aligned with emerging standards. A PRISMA-guided review of 17 peer-reviewed studies published between 2020 and 2025 was conducted, examining quantum threat models, post-quantum cryptographic schemes, quantum key distribution architectures, and sector-specific deployment barriers. The review finds that lattice-based schemes, especially CRYSTALS-Kyber and CRYSTALS-Dilithium, are the leading candidates for financial adoption, while hybrid cryptographic approaches offer the most feasible transition strategy. However, the current evidence base is predominantly simulation-driven, with limited real-world deployment and validation. The study provides a sector-specific synthesis of quantum threats, post-quantum readiness, and migration pathways in financial systems, and advances an integrated readiness and migration framework based on cross-study thematic analysis.
Analyzing Public Sentiment on the Proposal to Return Regional Head Elections to DPRD on Platform X Using the C4.5 Algorithm Ade Novia Maulana; Wan Moh Yusoff bin Wan Yaacob; Fatima Felawati
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1483

Abstract

This study examines public sentiment among X users toward the proposal to return regional head elections (Pilkada) to an indirect electoral mechanism through the Regional People’s Representative Council (DPRD), using a decision-tree classifier based on the C4.5 approach. A dataset of 4,127 tweets collected via X API v2 between December 2024 and January 2026 was analyzed using a seven-stage text preprocessing pipeline. Sentiment labels were generated through a hybrid lexicon-based approach, followed by manual verification of 500 stratified tweets by two independent annotators, yielding substantial inter-annotator agreement (Cohen’s Kappa = 0.78). TF-IDF was used for feature extraction, and the dataset was divided using an 80:20 stratified train-test split. The classifier achieved 81% accuracy, 82% precision, 79% recall, and an F1-score of 80%, outperforming Naive Bayes (74%) and Support Vector Machine (79%) baselines on the same dataset. The sentiment distribution showed that 45% of tweets were negative, 32% were positive, and 23% were neutral, indicating a predominantly critical response among X users toward the proposal. These findings describe discourse on X during the study period and should not be interpreted as representative of broader public opinion. Overall, the study highlights the potential of machine learning methods for analyzing Indonesian political discourse on social media.
A Lightweight Classical Machine Learning Pipeline for Rice NPK Deficiency Classification Using Hand-Crafted Feature Fusion Dhiyaussalam; Kun Nursyaiful Priyo Pamungkas; Wanvy Arifha Saputra; Ahmad Yusuf
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1486

Abstract

Many high-accuracy deep learning solutions for plant nutrient deficiency remain impractical in resource-limited settings due to computational cost and limited explainability. This study proposes a lightweight classical machine learning pipeline for rice leaf NPK (nitrogen, phosphorus, potassium) deficiency classification on the publicly available Kaggle Nutrient-Deficiency-Symptoms-in-Rice dataset (1,156 images); all results should be interpreted in this dataset context rather than as field-validated performance. The pipeline applies HSV-based leaf segmentation to reduce background influence. It extracts a 126-dimensional feature set combining masked color moments, HSV histograms, vegetation indices, LBP and GLCM texture descriptors, and spatial symptom ratios. Hyperparameters are tuned via RandomizedSearchCV with 5-fold StratifiedKFold and macro-F1 scoring; final evaluation uses a held-out 80/20 stratified test set kept separate throughout tuning. XGBoost achieves the best test performance (accuracy 0.9267; macro-F1 0.9233), followed by SVM-RBF (0.9224; 0.9187) and Random Forest. Feature importance analysis confirms that color moments dominate class separability, with texture and spatial features providing complementary support. The dominant remaining error is phosphorus–potassium confusion. The novelty lies in integrating leaf-focused preprocessing with a structured, low-cost feature representation suitable for mobile or edge deployment.
Time-Series Monitoring of Sentiment Dynamics in Reviews of Four Indonesian E-Wallet Applications Using a Hybrid TF-IDF and Bi-LSTM Framework Noor Latifah; Dias Henandra Eka Putra; Fajar Nugraha
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1488

Abstract

This study proposes a hybrid sentiment analysis framework to examine user perceptions of four Indonesian e-wallet applications using Google Play Store reviews. The framework combines TF-IDF features reduced through Truncated SVD with a Bidirectional Long Short-Term Memory (Bi-LSTM) model within a two-stage evaluation design consisting of holdout classification and external temporal inference. For supervised classification, 20,000 raw reviews were filtered and labeled using a rating-based strategy, resulting in 13,823 labeled reviews. Reviews with ratings of 4–5 stars were assigned to the positive class and 1–2 stars to the negative class; these labels should be interpreted as sentiment proxies rather than fully human-validated ground truth. A second dataset of 24,000 reviews was constructed for balanced cross-application temporal comparison across 2024–2026. On the holdout test set, the proposed model achieved an accuracy of 0.881, with macro-F1 and weighted-F1 scores of 0.881. Under the external temporal setting, DANA remained relatively stable, GoPay improved markedly in 2025 and remained high in 2026, ShopeePay showed a gradual decline, and OVO exhibited the strongest negative trend. These results indicate that the proposed framework is useful not only for supervised sentiment classification but also for structured temporal monitoring across e-wallet platforms.
Data-Driven Traffic for Infrastructure Planning: An LSTM Approach Using Indonesian Road-Vehicle Trends Aria Hendrawan; Nabilah Putri
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1516

Abstract

The rapid growth of motorized vehicles in Indonesia, unmatched by proportional expansion in road infrastructure, has intensified pressure on the national transportation system. This study examines the application of a Long Short-Term Memory (LSTM) model to analyze and forecast the national traffic load ratio, defined as the ratio of total motorized vehicles to total road length. Annual aggregate data from the Indonesian Central Bureau of Statistics (BPS) for the period 2016–2023 were used in the analysis. The results indicate that the model achieved a strong fit on the training data, with RMSE = 0.3652 and MAE = 0.3617, but performed substantially worse on the test data, with RMSE = 1.7585 and MAE = 1.7585. This discrepancy suggests overfitting, largely attributable to the extremely limited sample size. As such, the findings should be interpreted as exploratory rather than as evidence of reliable forecasting performance. Despite these limitations, the model projects a continued upward trend in national infrastructure pressure over the next five years. These findings provide an initial data-driven indication that transportation infrastructure demand in Indonesia is likely to intensify, while also underscoring the need for future research using larger datasets and baseline model comparisons before policy-level application can be justified.
A Multi-Algorithm Approach for Predicting OSCE Exam Passing Status Zulkifli; Panji Bintoro; Fitriana; Muhammad Galih Ramaputra; Hafsah Mukaromah
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1518

Abstract

This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams. Five machine learning algorithms Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (kNN) are assessed experimentally in this study. A dataset of 439 clinical competency data from Aisyah Pringsewu University midwifery students was used to create the model. Eight clinical skill factors were used as input, including baby massage, newborn care, and family planning services. To guarantee result stability, the 5-fold cross-validation approach was used for model validation. According to the test findings, every algorithm performs well, with an accuracy of more than 90%. On this particular dataset, SVM achieved a 100% classification accuracy, whereas Random Forest and SVM showed the most efficacy. With an average validation accuracy of 95%, neural networks also demonstrated excellent performance. This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams.
A Hybrid Certainty Factor–XGBoost Approach for Cyberattack Detection Using the TON_IoT Dataset Adiva Dwi Aprianto; Ratih Hafsarah Maharrani; Indi Cahya Ratna Auliya; Vania Rizky Alifiah
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1519

Abstract

Computer networks are vital to modern organizations, yet growing digital dependence has increased both the frequency and complexity of cyberattacks. To address this challenge, this study proposes an interpretable cyberattack detection framework that combines rule-based reasoning with machine learning through a hybrid Certainty Factor (CF)–XGBoost model. The framework integrates CF confidence scores and XGBoost probability outputs within a meta-learning classifier, enabling strong predictive performance while preserving explainability. Experiments conducted on the TON_IoT dataset using an 80:20 stratified split demonstrate that XGBoost achieved the highest accuracy at 99.61%, followed closely by the hybrid model at 99.42%, whereas the standalone CF model reached 76.31%. Although the hybrid approach produced a slightly lower accuracy than XGBoost alone, it substantially enhanced interpretability by connecting predictions to explicit rule-based reasoning. This makes the proposed framework especially suitable for Security Operations Center (SOC) environments, where transparent decision-making is essential. Overall, the findings suggest that the hybrid CF–XGBoost model offers a practical and explainable solution for cyberattack detection, though further validation on more diverse datasets is necessary before real-world deployment.

Page 1 of 5 | Total Record : 47

 1   2   3   4   5 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 8 No 2 (2026): April Vol 8 No 1 (2026): February Vol 7 No 4 (2025): December Vol 7 No 3 (2025): September Vol 7 No 2 (2025): June Vol 7 No 1 (2025): March Vol 6 No 4 (2024): December Vol 6 No 3 (2024): September Vol 6 No 2 (2024): June Vol 6 No 1 (2024): March Vol 5 No 4 (2023): Journal of Information Systems and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics Vol 5 No 1 (2023): Journal of Information Systems and Informatics Vol 4 No 4 (2022): Journal of Information Systems and Informatics Vol 4 No 3 (2022): Journal of Information Systems and Informatics Vol 4 No 2 (2022): Journal of Information Systems and Informatics Vol 4 No 1 (2022): Journal of Information Systems and Informatics Vol 3 No 4 (2021): Journal of Information Systems and Informatics Vol 3 No 3 (2021): Journal of Information Systems and Informatics Vol 3 No 2 (2021): Journal of Information Systems and Informatics Vol 3 No 1 (2021): Journal of Information Systems and Informatics Vol 2 No 2 (2020): Journal of Information Systems and Informatics Vol 2 No 1 (2020): Journal of Information Systems and Informatics Vol 1 No 2 (2019): Journal of Information Systems and Informatics Vol 1 No 1 (2019): Journal of Information Systems and Informatics More Issue