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 733 Documents
 70   71   72   73   74 
The Impact of Team Dynamics on Software Quality and Productivity: Evidence from South Africa Tshipuke Vhahangwele; Bassey Isong; Adnan Abu Mahfouz
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.v8i1.1438

Abstract

Software quality and productivity are influenced not only by technical practices but also by the social dynamics within development teams. This study investigates the combined effect of team dynamics, including trust, communication, collaboration, diversity, and conflict resolution, and software development practices on project outcomes. A mixed-methods design combined regression, Spearman’s Rho, and thematic analysis of survey data from 124 South African software professionals. The findings indicate that trust is the strongest positive predictor of software quality and productivity, while communication effectiveness and the use of collaboration tools also improve software outcomes. Equally, unstructured collaboration, excessive planning meetings, and poorly managed communication channels negatively affect performance. Diversity and effective conflict resolution were positively associated with productivity and efficiency. Thematic analysis corroborated these findings, illustrating how unclear communication, low trust, and dysfunctional collaboration lead to delays, rework, and lower quality. The study confirms that successful software outcomes emerge from the alignment of social and technical subsystems, highlighting the critical role of team dynamics in realising the full potential of software development practices. It contributes empirical evidence from an understudied developing-country context and proposes a socio-technical framework to enhance software quality and productivity.
Application of the Key Performance Indicator Method in an Employee Information System Eva Putri Rosanti; Noor Latifah; Fajar Nugraha
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.v8i1.1439

Abstract

The rapid development of information technology has significantly encouraged the integration of information systems in human resource management to enhance efficiency, effectiveness, and objectivity. However, performance appraisal systems that lack standardized indicators can lead to subjectivity and inconsistency, impacting employee productivity and managerial decision-making. This study proposes a web-based Personnel Management Information System (PMIS) that integrates Key Performance Indicators (KPIs) to provide an objective and measurable performance evaluation system. The system design incorporates KPIs, weights, and targets, supported by a structured, transparent process for performance assessments. The system was implemented at PT Kebon Agung Trangkil, a sugar industry company, to improve employee performance evaluations and managerial decision-making. This research adopts the Waterfall system development method and includes a User Acceptance Test (UAT) with 15 respondents, achieving an 88% acceptance rate. The results indicate that the developed system improves assessment efficiency, reduces subjectivity, and supports more transparent decision-making. The study concludes with recommendations for expanding the system’s capabilities and improving KPI validation through formal methods.
Centroid Optimization of K-Means Using Ant Colony Optimization for Culinary MSME Clustering Muhammad Fharahbi Fachri; Lisna Zahrotun
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.v8i1.1443

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are economic activities conducted by individuals or groups, particularly in the culinary sector. The rapid expansion of culinary MSMEs, especially in tourism-oriented regions such as the Special Region of Yogyakarta, necessitates effective data clustering to systematically analyze their characteristics. High-quality clustering plays a crucial role in supporting informed decision-making, including business development planning, MSME assistance programs, and the formulation of well-targeted policies. This study applies the K-Means algorithm to cluster culinary MSME data; however, its performance is sensitive to centroid initialization, which may result in suboptimal clustering outcomes. To address this limitation, Ant Colony Optimization (ACO) is employed as a centroid optimization approach. ACO is a metaheuristic algorithm inspired by the foraging behavior of ant colonies, where pheromone trails guide the search toward optimal solutions. The results indicate that the integration of ACO enhances clustering performance compared to K-Means. The silhouette scores obtained are 0.88 and 0.89 for two clusters, 0.80 and 0.86 for three clusters, and 0.80 and 0.92 for four clusters for K-Means and ACO-optimized K-Means, respectively. These findings demonstrate that ACO effectively improves centroid initialization, with four clusters identified as the optimal configuration.
Modeling EMIS Adoption with PLS-SEM: Integrating the Government Adoption Model and DeLone–McLean IS Success Model Mardiyanto Mardiyanto; Berlilana Berlilana; Purwadi Purwadi
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.v8i1.1445

Abstract

This study explores the key factors influencing the adoption of the Education Management Information System (EMIS) within Indonesia's Ministry of Religious Affairs (Kemenag), which is vital for managing data and distributing Teacher Professional Allowances (TPG). Data inconsistencies have been a significant challenge, leading to delays in TPG disbursement. To understand the determinants of EMIS adoption, this study integrates the Government Adoption Model (GAM) and DeLone & McLean’s (D&M) Information Systems Success Model. A quantitative approach was used, collecting data from 328 valid responses from MTsN teachers in Kebumen Regency, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Perceived Uncertainty (PU), Perceived Security (PSC), and Perceived Privacy (PP) positively contribute to Perceived Trust (PT). Additionally, Information Quality (IQ) emerged as the strongest predictor of EMIS adoption, followed by System Quality (SYQ), Service Responsiveness (PSR), and Trust. The study emphasizes that improving data accuracy (IQ), ensuring system reliability (SYQ), and strengthening security measures (PSC) are critical for accelerating EMIS adoption. The findings offer practical implications for Kemenag to optimize the implementation of EMIS, ultimately improving the efficiency and timeliness of TPG disbursements for educators.
Cyberbullying Detection in Indonesian TikTok Comments Using IndoBERT with Fairness Evaluation Hanik Dewi Jayanti; Abdul Rohman
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.v8i1.1448

Abstract

This study investigates automated cyberbullying detection on TikTok within the Indonesian digital context, where high social media usage among children and adolescents demands scalable and consistent content moderation. We propose an IndoBERT-based framework for detecting and classifying cyberbullying in Indonesian-language TikTok comments, incorporating algorithmic fairness considerations. A dataset of 2,122 TikTok comments was collected from a publicly available Kaggle repository and divided into training, validation, and testing sets using a 70:15:15 stratified sampling ratio. The IndoBERT-base-p1 model was fine-tuned with the PyTorch and HuggingFace frameworks, optimizing hyperparameters like the AdamW optimizer and learning rate scheduling. Experimental results show that the model achieved an accuracy of 70.66% and a ROC-AUC score of 0.7969, demonstrating solid discriminative power. With a macro F1-score of 0.7066 and a cyberbullying recall of 0.7170, the model shows balanced performance in identifying harmful content. A key contribution of this study is a fairness evaluation framework that reveals an accuracy gap of 2.08% and an equal opportunity gap of 0.0208, indicating overall fairness. However, demographic parity remains a concern. This system, supporting content triage combined with human review, enhances moderation workflows by filtering non-cyberbullying cases while flagging potentially harmful content for human oversight.
Feature Selection vs Dimensionality Reduction for Steam Game Metadata Classification: An Ensemble Learning Study Ferdi Setyo Handika; Lili Dwi Yulianto; Septi Andryana
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.v8i1.1456

Abstract

Optimizing noisy Steam game metadata is essential for accurate binary classification. This study compares feature selection (MI) and dimensionality reduction (PCA, LDA) using a dataset of 55,144 Steam reviews and four ensemble algorithms, evaluated through Stratified 5-Fold Cross-Validation. The results show that the 125-feature baseline achieved the highest accuracy of 0.7728 with CatBoost. Feature selection (FS_10) maintained competitive performance with an accuracy of 0.7449, while LDA, after optimization, achieved 0.7281. In contrast, PCA significantly hindered performance (0.6963) due to the inability of linear transformations to preserve the discriminative power of one-hot encoded categorical features, which ensemble models handle better in their original form. These findings highlight the importance of preserving original features, especially in low-to-medium dimensional metadata, where feature selection outperforms dimensionality reduction in maintaining predictive integrity. High accuracy is crucial for developers to track product reception and for platforms to improve recommendation systems that influence user purchasing decisions. The study concludes that for Steam game metadata, strategic feature selection is superior to dimensionality reduction for maintaining classification performance.
Integrating SERVQUAL and ECM to Explain E-Wallet Satisfaction and Continuance in Semi-Urban and Rural Indonesia Juli Muslim Ihsan; Khairul Imtihan; Muhammad Fauzi Zulkarnaen
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.v8i1.1458

Abstract

This study aims to analyze user satisfaction and continuance intention toward mobile payment applications in semi-urban and rural Indonesia by integrating the SERVQUAL framework and the Expectation–Confirmation Model (ECM). Although e-wallet adoption has increased rapidly, empirical evidence on post-adoption behavior in non-urban contexts remains limited. This study addresses this gap by examining how service quality, expectation, confirmation, and digital literacy shape satisfaction and continued usage. Using a quantitative survey, data were collected from 212 active DANA users in Central Lombok Regency during December 2025–January 2026 and analyzed using PLS-SEM with SmartPLS. The results show that among the five SERVQUAL dimensions, only empathy has a significant positive effect on user satisfaction. Expectation and confirmation significantly influence satisfaction, whereas perceived usefulness does not directly affect either satisfaction or continuance intention. User satisfaction and digital literacy significantly predict continuance intention, while the moderating effect of digital literacy is not supported. From a practical perspective, these findings indicate that providers should prioritize user assistance, communication, and expectation management rather than interface or technical attributes when serving semi-urban and rural markets. This study demonstrates that expectation fulfillment and relational-based service quality are more decisive for sustaining e-wallet usage than technical features in non-urban settings.
Performance Comparison of Random Forest, XGBoost, and SVM for Flood Risk Prediction Using BNPB GIS Data Muhammad Amanulloh Mz; Oky Dwi Nurhayati; Jatmiko Endro Suseno
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.v8i1.1461

Abstract

This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—for predicting flood risk using spatial and non-spatial data from BNPB GIS. The analysis focuses on disaster records from January 3 to 15, 2026, with district-city as the spatial unit of observation. Following data cleaning, exploratory analysis, and feature preparation, the models were evaluated using ROC-AUC, PR-AUC, F1-Score, Precision, Recall, and Accuracy. XGBoost demonstrated the highest ROC-AUC (0.675), indicating strong overall performance in distinguishing flood from non-flood events. Random Forest achieved the highest Recall (0.947), showing superior sensitivity in detecting flood events, while SVM exhibited fluctuating performance with a lower ROC-AUC (0.496). Visualizations of model behavior and spatial flood patterns were provided to support model interpretability. The study’s results suggest that ensemble models, particularly XGBoost and Random Forest, can significantly enhance flood risk prediction, improving the accuracy and sensitivity of early warning systems. These findings contribute to the development of more effective data-driven flood mitigation strategies in Indonesia, enabling better disaster preparedness and response.
AI and Digital Transformation Trends: A Systematic Review with Multi-Criteria Analysis Soni Adiyono; Muhammad Arifin
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.v8i1.1462

Abstract

This research investigates the integration of Artificial Intelligence (AI) and Digital Transformation (DT) as critical enablers of Industry 4.0, highlighting their combined influence in reshaping industrial processes and enhancing operational efficiency. AI technologies, including machine learning, natural language processing, and computer vision, are driving advancements in automation, real-time decision-making, and personalized services across various industries, such as manufacturing, healthcare, and logistics. DT involves the widespread adoption of digital technologies that transform business models, stakeholder interactions, and organizational structures, working synergistically with AI to foster innovation. While existing literature often examines AI and DT in isolation, this study addresses the gap by employing a Systematic Literature Review (SLR) and Multi-Criteria Analysis (MCA) methodology to evaluate research based on academic impact, practical relevance, and sectoral readiness. The analysis reveals emerging trends such as predictive analytics, autonomous systems, and smart manufacturing, with industries like healthcare and retail showing strong adoption, while real estate and legal services remain underexplored. The research examines 46,936 Scopus records and selects 32 studies for analysis. The MCA results underscore the importance of aligning academic research with industrial needs and fostering cross-sector collaboration. Ultimately, this study bridges the gap between theory and practice, offering valuable insights for policymakers, scholars, and practitioners to strengthen competitive advantage in the digital era.
A Layered Digital Investigation Framework for Internet of Things (IoT) Forensics: A Smart Home Camera Case Study Desylo Santicho; Ahmad Luthfi; Tito Yuwono
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.v8i1.1468

Abstract

The rapid adoption of Internet of Things (IoT) technology in various sectors, such as smart homes, healthcare, and transportation, has provided significant efficiency. Nevertheless, many IoT devices are developed without a serious review of security standards, and forensic readiness consideration. As a result, these IoT devices are vulnerable to cyber-attacks that can potentially lead to malware attacks, and system manipulation. This study aims to propose and validate a digital forensic investigation framework in the IoT ecosystem. The framework layers designed in this study consist of the device layer, network layer, and cloud layer. Validation is carried out through a simulated crime scenario recorded by the Mi 360° smart home camera. Meanwhile, the analysis phase focuses on data source artifacts from the device layer, video metadata, technical attributes, and cryptographic integrity verification using hash values (MD5 and SHA-1) documented in the Chain of Custody (CoC) method. The experimental results of this study indicate that digital evidence artifacts sourced from across layers have reliable temporal and structural consistency in reconstructing the chronology of events. This framework successfully correlated artifacts across three layers to reconstruct a complete event timeline, demonstrating its practical validity in distributed IoT forensic investigation.

Page 73 of 74 | Total Record : 733

 70   71   72   73   74 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue 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