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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 733 Documents
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Implementation of a Web-Based Media Partnership Registration Information System Using Waterfall Model David Vernando Baridji; Alfiansyah Hasibuan; Medi H. Tinambunan
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.v8i1.1411

Abstract

The Minahasa Regency Department of Communication and Informatics manages public information and cooperation with press media organizations, yet press media partnership registration is still handled manually. This causes processing delays, data duplication, limited traceability, and poor visibility of verification status, increasing administrative workload and reducing service transparency for applicants. This study implements a web-based Press Media Partnership Registration Information System to digitalize and standardize the registration workflow. Development follows the Waterfall model, including requirement analysis, system design, implementation, testing, and refinement to ensure structured deliverables suitable for government environments. The system provides CRUD-based data management, administrator-led verification with status tracking, and automated email notifications for verification outcomes. Functional validation uses Black Box Testing to evaluate input-output behavior against predefined specifications. Test results show that core modules—account registration, login, partnership submission, verification (approve/reject with notes), CRUD operations, session control, and email notification—operate correctly and meet functional requirements. The implemented system is feasible for operational use and improves efficiency, data accuracy, traceability, and transparency in local government press media partnership administration.
Analyzing the Impact of Review Sentiment on Carpentry Product Sales: Evidence from Tokopedia Agung Chandra Kharisma; Muhammad Haykal Alfariz Saputra; Ali Ibrahim; Mira Afrina
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.v8i1.1412

Abstract

The rapid growth of e-commerce in Indonesia has increased the importance of consumer reviews as signals influencing purchasing decisions. This study examines the relationship between review sentiment and sales performance in the carpentry tools category on Tokopedia. Using a 2019 Kaggle dataset consisting of 1,826 reviews across approximately 60 products, we apply an NLP-based pipeline to classify review sentiment into positive, neutral, and negative categories. Sentiment labeling combines rating-based rules and a TF-IDF + Logistic Regression baseline, with additional evaluation using IndoBERT. Product-level metrics—including the proportion of positive sentiment (pos_share), average rating, and units_sold (sales proxy)—are analyzed using descriptive statistics, correlation analysis, and cross-sectional OLS regression. The findings reveal that, in this snapshot dataset, the association between positive sentiment share and log(units_sold + 1) is weak and statistically limited, suggesting that sales variation cannot be explained solely by sentiment polarity or average ratings without considering other commercial factors. These results highlight the importance of incorporating contextual variables and temporal design in future research. Practically, the study suggests that sellers should monitor not only sentiment polarity but also the informational richness of reviews to strengthen reputation management strategies.
A Decision Support System for Assessing High School Students' Soft Skills Using the Analytical Hierarchy Process Yuwono Wisudo Pramono; Berlilana Berlilana; Azhari Shouni Barkah
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.v8i1.1420

Abstract

Assessing students' soft skills in educational settings is often challenging due to the subjectivity and inconsistency inherent in evaluating qualitative traits. This study employs the Analytical Hierarchy Process (AHP) as a decision support tool to provide a more systematic, consistent, and objective method for evaluating students' soft skills. The assessment model is based on four key criteria—critical thinking, communication, collaboration, and creativity—each further broken down into measurable subcriteria. The study was conducted at MA Mu’allimin Sruweng Kebumen, where evaluations were carried out by a guidance and counseling teacher acting as an expert evaluator, using a numerical scale ranging from 1 to 100. Pairwise comparison matrices were developed using Saaty’s fundamental scale to determine the weights for both criteria and subcriteria, followed by consistency testing using the Consistency Ratio (CR). The findings reveal that critical thinking and collaboration were assigned the highest priority weights, with all comparison matrices meeting the acceptable consistency threshold. The resulting global preference values offer a more objective, proportional representation of students’ soft skills achievements. This AHP-based model enables fairer, more consistent evaluations and provides quantitative outputs that can be utilized for student ranking and structured feedback in educational decision-making.
Post-Quantum Cryptography for securing Electronic Health Records in the South African Public Healthcare System Kabelo Given Chuma
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.v8i1.1423

Abstract

The growing dependency on Electronic Health Records (EHRs) has intensified the exposure of sensitive patient information to advanced cybersecurity threats, including those posed by quantum computing technologies. South African public hospitals depend on conventional encryption mechanisms to secure EHRs; however, these methods are susceptible to quantum threats. The study explored quantum-resistant cryptography for securing EHRs in South African healthcare. The study adopted a phenomenological approach, employing semi-structured interviews with 12 ICT specialists, policymakers, health information managers and cybersecurity practitioners. The study established a misalignment between national digital health and cybersecurity strategies and future quantum threats, as they prioritise digital transformation, data security and interoperability. Public hospitals were found to be reliant on conventional encryption methods, resulting in structural lock-in and impeding adaptability of post-quantum cryptography. Although stakeholders demonstrated awareness of quantum threats, organisational readiness remains constrained by technical, institutional and capacity barriers. It is concluded that South African public healthcare system remains behind towards post-quantum security transformation. The study recommends the development of a roadmap for post-quantum cryptographic migration, system modernisations and capacity building to strengthen the security of EHRs. The findings provide evidence-based guidance for policymakers to strengthen digital health security and resilience of EHRs in public healthcare systems.
Spatio-Temporal Graph Neural Network for Solar Irradiance Prediction: A Case Study in Nganjuk, Indonesia Agung Wilis Nurcahyo; Bambang Purnomosidi Dwi Putranto
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.v8i1.1424

Abstract

Solar energy utilization in tropical regions is strongly influenced by the accuracy of solar irradiance estimation, which is affected by temporal variability and spatial atmospheric interactions. Conventional forecasting approaches commonly rely on single-station time-series models, limiting their ability to capture regional dependencies. This study proposes a spatio-temporal modeling framework based on Graph Neural Networks (GNN) to estimate solar irradiance by explicitly incorporating spatial relationships among observation sites. The study focuses on Sawahan Subdistrict, Nganjuk Regency, Indonesia, using solar irradiance data collected from five Automatic Weather Stations (AWS) operated by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) during 2024. Each station is represented as a graph node, with spatial connections constructed based on geographical distance, while temporal dependencies are modeled using Long Short-Term Memory (LSTM). Experimental results show that the proposed model achieves a Mean Absolute Error (MAE) of 102.64 W/m², a Root Mean Squared Error (RMSE) of 166.76 W/m², and an R² value of 0.6446 for the target location. These findings demonstrate that GNN-based spatial aggregation improves estimation stability and accuracy, providing practical support for localized solar energy assessment in tropical regions.
Integrating EUCS and TAM to Evaluate DAPODIK User Satisfaction and Use in Central Lombok Melyana Febrian; Khairul Imtihan; Sofiansyah Fadli
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.v8i1.1426

Abstract

This study evaluates user satisfaction and post-adoption behavior toward the Data Pokok Pendidikan (DAPODIK) Information System by integrating the End-User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) within a mandatory public education system context. Although DAPODIK has been nationally enforced and widely implemented in Indonesia, empirical evidence explaining how system quality translates into user satisfaction, technology acceptance, and actual use remains limited, particularly at the local government level. A quantitative survey-based approach was employed involving 255 active DAPODIK users in Central Lombok Regency, Indonesia, collected between December 2025 and January 2026 using a five-point Likert-scale questionnaire. Data were analyzed using PLS-SEM, encompassing measurement model evaluation, structural model assessment, hypothesis testing, and moderation analysis of digital literacy. The findings indicate that format, ease of use, and timeliness significantly influence user satisfaction, whereas content and accuracy do not show significant effects. User satisfaction has a strong positive effect on perceived usefulness and perceived ease of use, while behavioral intention significantly predicts actual system use. The moderation analysis further reveals that digital literacy strengthens the relationship between technology acceptance and users’ attitudes toward system use. From a practical perspective, the results suggest that policymakers should prioritize interface format, usability simplification, and system responsiveness during data collection cycles to enhance user satisfaction and sustained system use.
A Hybrid Machine Learning and Signature-Based Approach for Detecting Network Pivoting in BYOD Environments Nassor Suleiman Amour; Judith Leo; Mussa Ally Dida
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.v8i1.1428

Abstract

This study addresses the challenge of detecting network pivoting, a lateral movement technique that is difficult to identify in insider and BYOD environments because malicious transitions can resemble normal internal activity. The objective was to improve detection of both known and unknown pivoting behaviours while supporting practical triage in resource-constrained institutions. A hybrid detection framework was developed that fuses Snort signature alerts with machine learning classification and unsupervised anomaly detection using behavioural features derived from BYOD-like network traffic. The approach was evaluated in a controlled testbed and supported by organisational survey findings on awareness and monitoring practice. Results show the hybrid system achieved 96.2% classification accuracy with a 4.5% false positive rate when distinguishing normal traffic, suspicious activity, and pivoting attacks. Compared with signature-only and machine-learning-only baselines, the hybrid design detected simulated pivoting attempts earlier and more consistently. User acceptance testing also reported strong satisfaction with the integrated dashboard for monitoring, filtering, and reporting. The key contribution is a unified, dashboard-oriented fusion of signature and behavioural evidence that strengthens early lateral movement detection and reduces manual correlation effort.
Small Language Models for Drug-Drug Interaction Extraction from Biomedical Text: A Systematic Literature Review Fortunate Mutanda; Belinda Ndlovu
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.v8i1.1430

Abstract

Drug–drug interaction (DDI) extraction from biomedical text is central to pharmacovigilance but remains challenging in resource-constrained clinical environments. While large language models have shown promise, their computational cost and deployment complexity limit practical adoption. This study systematically reviews the role of small language models (SLMs) for DDI extraction and examines their effectiveness, efficiency, and deployability. A systematic literature review was conducted following PRISMA guidelines, covering empirical studies published between 2020 and 2025 in PubMed, IEEE Xplore, ACM and  SpringerLink. Eligible studies were analysed with respect to model architectures, datasets, evaluation metrics, and deployment considerations. Quality assessment was applied to ensure methodological robustness. The synthesis indicates that SLM-based approaches, including CNN-, LSTM-, and lightweight transformer models, can achieve competitive F1-scores on benchmark DDI datasets while requiring substantially fewer computational resources than large language models. However, performance varies across datasets, and real-world clinical evaluations remain limited. These findings support the feasibility of deploying SLM-based DDI extraction systems in resource-constrained clinical and pharmacovigilance settings and provide a baseline for future benchmarking and comparative research in clinical natural language processing.
Towards Cloud-Based Electronic Health Records in Healthcare Systems: Security, Scalability, and Migration Strategies: A Systematic Literature Review Musawenkosi Moyo; Belinda Ndlovu
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.v8i1.1431

Abstract

Cloud-based Electronic Health Records (EHRs) are being adopted rapidly worldwide, but implementation still encounters recurring obstacles in security assurance, elastic scalability, and migration readiness. Prior reviews often treat these issues separately, leaving limited practical guidance for organizations planning end-to-end deployment. This study synthesizes recent evidence on cloud EHR adoption by examining how security controls, scalability claims, and migration strategies interact in real implementation contexts. A systematic literature review following PRISMA guidelines was conducted across ACM Digital Library, PubMed, IEEE Xplore, and ScienceDirect, covering peer-reviewed studies published from 2021 to 2025. Results show that the literature is technically mature in proposing encryption, access control, auditing, and performance optimization, and frequently reports scalability advantages. In contrast, evidence on complete migration pathways—data mapping, interoperability, validation, cutover planning, and post-migration assurance—remains sparse, with many studies relying on simulations rather than longitudinal deployments. The review also identifies geographic concentration in high-income settings, limiting generalizability to resource-constrained health systems. By integrating security, scalability, and migration readiness within a socio-technical, implementation-oriented perspective, this review provides actionable directions for secure and scalable cloud EHR transitions.
Multimodal Implicit Sentiment Analysis for Tourism Development: A Systematic Literature Review Yoannes Romando Sipayung; Mochamad Agung Wibowo; Ridwan Sanjaya
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.v8i1.1436

Abstract

This study aims to examine the application of multimodal approaches in implicit sentiment detection within the tourism sector to support data-driven digital development strategies. This review identifies prevailing trends, methodologies, datasets, and scientific novelties in multimodal sentiment analysis capable of capturing hidden emotions, such as sarcasm and ambiguity, in tourist reviews. Using a systematic literature review approach, ten core studies published between 2020 and 2025 were analyzed to identify prevailing research trends, dominant methodological frameworks, commonly used datasets, and emerging scientific contributions. Results demonstrate that multimodal deep learning models—particularly those employing attention-based fusion and contrastive learning—consistently outperform unimodal approaches in recognizing nuanced tourist emotions that are not explicitly stated in text. Despite these advances, the review reveals a significant gap in tourism-specific and Indonesian-context studies, as well as an overreliance on general-purpose social media datasets. This review provides a conceptual and methodological foundation for implementing multimodal implicit sentiment analysis in tourism decision-making systems, enabling destination managers and policymakers to develop early warning mechanisms for tourist dissatisfaction, enhance destination quality assessment, and support more targeted and sustainable tourism development strategies.

Page 72 of 74 | Total Record : 733

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