Journal of Advances in Information and Industrial Technology

Digital Transformation in the Maritime Supply Chain: Systematic Literature Review

2026-05-15T02:38:09+00:00

The maritime sector plays a vital role in global trade and increasingly requires digital transformation to address operational complexity, logistics disruptions, and supply chain resilience challenges. This study aims to analyze digital transformation technologies in maritime supply chains through a Systematic Literature Review (SLR). The review followed the PRISMA protocol, including identification, screening, eligibility, and inclusion stages. Literature searches were conducted using Scopus, ScienceDirect, and Google Scholar with keywords related to digital transformation, maritime supply chains, smart ports, blockchain, and IoT. From 35 identified articles, 20 peer-reviewed studies published between 2020 and 2026 were selected after screening and full-text evaluation. The findings indicate that technologies such as IoT, AI, Blockchain, Big Data, ERP systems, and smart port technologies improve operational efficiency, transparency, real-time monitoring, and supply chain resilience. However, successful implementation depends on infrastructure readiness, cybersecurity, policy support, interoperability, and human resource capabilities.

A Bibliometric-Systematic Review of Digital Transformation in Indonesia’s Economic Development

2026-05-12T07:51:10+00:00

Digital transformation has become a key driver of technological innovation, economic restructuring, and institutional change in Indonesia, yet systematic synthesis of its scholarly development remains limited. This study addresses this gap by applying an integrated bibliometric and systematic literature review (SLR) approach to examine the intellectual structure, thematic evolution, and research dynamics of digital transformation scholarship in Indonesia from 2017 to 2025. Using Scopus-indexed publications, centrality-based bibliometric analysis identifies eleven major research clusters shaping the field. Among these, digital transformation emerges as the most structurally influential cluster, exhibiting the highest betweenness, closeness, and PageRank centrality, and functioning as an intellectual hub linking MSMEs, sustainability, circular economy, smart cities, environmental economics, and organizational transformation. Intermediate clusters, including digital economy, economics, MSMEs, and circular economy, reinforce the core discourse, reflecting growing academic attention to Industry 4.0, artificial intelligence, cybersecurity, and sustainable development. Smaller clusters, such as fintech, digitization, and developing countries, represent emerging research trajectories focused on governance, resilience, and digital inclusion. The SLR component complements the bibliometric mapping by synthesizing theoretical contributions, methodological trends, and research gaps within key thematic clusters, thereby enhancing conceptual coherence. The novelty of this study lies in its integrative mixed-method design, offering the most comprehensive mapping of Indonesia’s digital transformation literature to date. The findings provide strategic directions for future interdisciplinary research and generate policy-relevant insights to support inclusive and sustainable digital transformation aligned with national and global development agendas.

Success Factors of Knowledge Management Systems in Technology-Based Organizations: A Systematic Literature Review

2026-05-15T02:42:59+00:00

Knowledge Management Systems (KMS) play a strategic role in enhancing organizational performance, innovation, and competitiveness in technology-based organizations. This study aims to identify critical success factors, implementation challenges, organizational impacts, and emerging trends of KMS through a Systematic Literature Review (SLR) combined with bibliometric analysis. This review follows the PRISMA framework using the Scopus database as the primary source. A total of 3,373 records were initially identified from Scopus, resulting in 55 eligible journal articles and 15 selected studies for in-depth qualitative analysis. Bibliometric analysis was conducted using VOSviewer to examine publication trends, keyword co-occurrence, and thematic research clusters. The findings indicate that KMS success is strongly influenced by strategic alignment, top management support, organizational culture, employee engagement, technology infrastructure, and knowledge quality. The study also reveals that recent KMS research increasingly emphasizes socio-technical integration, user-centric approaches, cloud-based systems, and sector-specific implementation frameworks. In contrast to previous KMS reviews that primarily focused on the technological dimension, this study provides an integrated perspective by combining bibliometric mapping and thematic synthesis to highlight the interactions between organizational, technological, and human factors in technology-based organizations.

The Influence of System Quality, Information Quality and Service Quality on E-Learning User Satisfaction at UIN Walisongo Semarang

2025-11-14T02:42:27+00:00

State Islamic University (UIN) Walisongo Semarang uses the e-learning system as an online learning support, but students still face obstacles such as system errors, slow access, and data that is not updated. This study aims to analyze the influence of system quality, information quality, and service quality on user satisfaction using the DeLone and McLean models. The quantitative method was used through the distribution of questionnaires to 97 students of the 2020 cohort, and the data were analyzed using SmartPLS 3. The results showed that the quality of the system had no significant effect (t = 0.658; p > 0.05), the quality of service was also insignificant (t = 1.539; p > 0.05), while the quality of information had a significant effect on user satisfaction (t = 2.853; p < 0.05). This research contributes to the understanding of the factors that determine the success of e-learning, as well as provides practical implications in the form of the need to improve system stability, improve information features, and strengthen technical services to improve the student learning experience.

Business Process Improvement Approach in Vehicle Facility Application System: A Case Study at PT Unggul Lancar Sejahtera (ULS)

2025-11-10T03:39:17+00:00

Deadlock incidents in the vehicle facility application system at PT Unggul Lancar Sejahtera (ULS) caused significant operational delays, with cycle time efficiency as low as 2.02% and frequent manual interventions. This study addresses the problem using the Business Process Improvement (BPI) approach to redesign workflows and eliminate bottlenecks. The proposed solution integrates BPI analysis with the deployment of a Hangfire-based workflow engine, enabling automatic retry mechanisms and process monitoring. The methodology involved identifying NonValue Added (NVA) activities, restructuring approval flows, and implementing automation to improve transparency and reduce downtime. The research contributes a practical model for process improvement that combines structured BPI analysis with modern workflow orchestration tools. Results show a substantial improvement, with deadlock-handling time reduced from 120 minutes to 0.32 minutes and cycle-time efficiency increasing to 93.99%. Compared to existing manual resolution methods, the proposed approach offers higher reliability, faster recovery, and enhanced visibility, providing a replicable framework for organizations facing similar workflow inefficiencies.

Integrating Statistical Process Control and Failure Mode and Effects Analysis to Reduce Product Defects in Oil Filter Manufacturing (Case Study: CV XYZ)

2026-05-06T06:07:04+00:00

CV XYZ is a small-to-medium-sized manufacturing company struggling with a high rate of product defects. This study integrates Statistical Process Control (SPC) and Failure Modes and Effects Analysis (FMEA) to analyze and improve the quality performance of the oil filter production process at CV XYZ. Several SPC tools were applied, including a check sheet, Pareto analysis, P-control chart, and root cause analysis using a fishbone diagram. The findings revealed that deformation was the most frequent defect type, accounting for 152 units (23%) out of 675 total defects recorded over five months. Root cause analysis identified multiple contributors to deformation defects, which were then evaluated using FMEA based on three parameters: severity, occurrence, and detection. The Risk Priority Number (RPN) calculations determined that excessive product stacking was the primary cause, followed by non-compliance with standard operating procedures (SOPs), a poorly organized workstation, and inadequate lighting and visibility. Based on these findings, several corrective actions were recommended, including improving SOPs for the pressing process, scheduling regular SOP training, and implementing standardized stacking procedures aligned with production output. These measures are projected to reduce total nonconformities by approximately 6.9%. The key contribution of this study is providing a practical quality improvement framework for SME manufacturers with limited quality management resources and analytical capabilities. The integrated SPC-FMEA approach enables data-driven decision-making without requiring extensive expertise. Nevertheless, real-world implementation remains necessary to validate the projected outcomes.

Stock Price Prediction and Loss Risk Analysis of PT Sawit Sumbermas Sarana Tbk Using a Hybrid TCN-GAN Model

2026-05-09T15:21:24+00:00

The Crude Palm Oil (CPO) industry is a strategic sector for the Indonesian economy. Yet, stock prices of companies in this sector tend to be highly volatile due to global market dynamics and export policies, increasing investment risk. Conventional models, such as ARIMA, rely on linearity assumptions that limit their ability to capture nonlinear dynamics, while deep learning models, such as RNN, GRU, and LSTM, still suffer from vanishing-gradient problems. Therefore, this study proposes a hybrid Temporal Convolutional Network–Generative Adversarial Network (TCN-GAN) model for stock price prediction and investment risk analysis using the Value-at-Risk (VaR) method with Historical Simulation. The TCN-GAN combines TCN's ability to capture long-term temporal patterns with the adversarial mechanism of GAN to improve prediction accuracy. The data consist of daily closing prices of PT Sawit Sumbermas Sarana Tbk (SSMS.JK) from Yahoo Finance, covering January 1, 2020, to September 30, 2025. A sensitivity analysis on sliding window lengths of 10, 20, and 30 days was conducted to validate model robustness, with window 20 identified as optimal. The TCN-GAN model significantly outperforms the ARIMA baseline, which yielded a MAPE of 18.12% and RMSE of 368.68, by achieving a MAPE of 3.22% and RMSE of 84.23. The model was further used to predict stock prices for the next five periods, yielding an average of IDR 1,647.82. The VaR analysis at a 95% confidence level with a five-day holding period indicates a maximum potential loss of IDR 146,204.

Econometric Analysis of Poverty and Economic Welfare as Predictors of Criminal Activity in Indonesia

2026-05-10T15:09:33+00:00

Criminal activity is defined as a violation of legal mandates and societal standards that triggers communal instability and economic detriment. To address this challenge, identifying the core determinants of such behavior is essential, which this study achieves through a multiple regression methodology. The empirical results demonstrate that Gross Domestic Product (GDP), poverty demographics, per capita spending, and the Human Development Index (HDI) collectively exert a significant influence on national crime rates. This integrated model accounts for 0,775 of the observed variances, as evidenced by the value. Individually, higher levels of GDP, poverty, and HDI correlate with increased criminality, whereas improved per capita expenditure serves as a restrictive factor. These outcomes underscore the necessity of prioritizing welfare enhancement and poverty alleviation within national crime prevention frameworks.

Analysis of Occupational Safety and Health Risks in Scaffolding Work Using HIRADC and JSA Methods at PT. XYZ

2026-05-11T06:11:35+00:00

This study aims to analyze occupational safety and health (K3) risks in scaffolding work at PT. XYZ is using the Hazard Identification, Risk Assessment, and Determining Control (HIRADC) and Job Safety Analysis (JSA) methods. The study used a quantitative descriptive approach with field observation methods, structured interviews, and documentation of 11 respondents during the period August to December 2025. The results of the hazard identification showed that there were 17 potential hazards classified into 13 risk items. Risk assessment using a risk matrix of 5×5 showed that there were 2 high risks (R=15) and 11 moderate risks. The highest risk comes from work at height on the installation and painting of scaffolding. After the implementation of hierarchical-based controls, all high risks were successfully downgraded to medium risk, with an average reduction in risk values by 40%. The JSA analysis produced 37 safe work steps that can be used as the basis for the preparation of SOPs and safety briefings in the field. The results of the study show that the integration of HIRADC and JSA methods is able to increase the effectiveness of comprehensive risk identification and control in construction work.

NASA-TLX Mental Workload Analysis of Agrarian Reform Access Field Staff at Kantor Pertanahan X

2026-05-11T05:45:51+00:00

Agrarian Reform Access activities require field staff to integrate field and administrative responsibilities, including data collection, field verification, community coordination, data processing, mentoring, and report preparation. In public land administration, this integrated role requires a workload evaluation approach that can describe both the overall workload level and its dominant dimensions. This study applied the NASA Task Load Index (NASA-TLX) as a practical diagnostic method to assess the perceived mental workload of field staff at Kantor Pertanahan X and to formulate dimension-based improvement directions. A descriptive quantitative survey was conducted using a total sampling of all eight field staff members. The results showed an average NASA-TLX score of 89.88, indicating a high level of perceived mental workload; all respondents were classified in the high workload category, with scores ranging from 82.00 to 99.33. The dominant dimensions were Performance, Mental Demand, and Effort, suggesting that respondents perceived their workload most prominently in relation to program target achievement, cognitive processing of field and administrative data, and the effort required to complete interconnected tasks. These findings should be interpreted as descriptive perceived workload patterns rather than causal relationships. Compared with general workload evaluation approaches, NASA-TLX offers the advantage of identifying both aggregate workload and dimension-level profiles. The findings support targeted improvements in task distribution, work scheduling, reporting formats, technical support, and internal coordination in Agrarian Reform Access activities.

Application of Naïve Bayes Method for Assessing Student Performance

2026-05-11T04:12:16+00:00

Every school has pupils with varying levels of achievement. These differences in achievement can be influenced by several factors, such as the parents’ level of education and the pupils’ readiness for examinations. Furthermore, they can also be influenced by pupils’ abilities in mathematics, writing, and reading. The aim of this study is to classify student performance so that the performance of students at an adequate level or below average can be improved. The method used in this study is Naïve Bayes as a classification method. There are 150 training data points and 50 test data points. Five metrics were evaluated: precision at 94.4%, recall at 94.4%, specificity at 50%, accuracy at 90%, and the F1 score at 94%. This indicates that the model performs well in providing accurate positive predictions. Furthermore, the model is capable of detecting the majority of positive cases effectively.

Bayesian Spatio-Temporal Conditional Autoregressive Modeling of Stunting Risk Factors in East Java

2026-05-12T08:01:35+00:00

This study analyzes stunting cases in East Java Province using district/city-level panel data covering the period 2022-2024. The data were obtained from the Indonesian Nutritional Status Survey (SSGI), the Indonesian Health Survey (SKI), and official statistical sources, consisting of stunting cases and several health, socioeconomic, and environmental indicators across 38 districts and municipalities. The study applies a Bayesian Spatio-Temporal Conditional Autoregressive (BST-CAR) model with the Integrated Nested Laplace Approximation (INLA) approach to account for spatial dependence among neighboring regions and temporal variation over time. The results show that stunting cases in East Java exhibit significant spatial and temporal dependence, supported by significant positive spatial autocorrelation across all observation years. Model evaluation yields a Deviance Information Criterion (DIC) value of 1477,267 and a Watanabe-Akaike Information Criterion (WAIC) value of 1442,479. The estimation results indicate that all examined covariates, including low birth weight, complete basic immunization, exclusive breastfeeding, proportion of poor population, access to improved drinking water, and access to improved sanitation, are statistically significant in explaining variations in stunting cases after controlling for spatial and temporal effects. Relative risk mapping reveals clear spatial heterogeneity, with higher-risk clusters concentrated in districts such as Jember, Lumajang, and Probolinggo, while lower-risk areas are mainly observed in urban regions such as Surabaya, Mojokerto, and Madiun. Overall, the findings suggest that stunting distribution in East Java is shaped by both spatial and temporal structures, highlighting the importance of geographically targeted intervention strategies at the district/city level.

Revisit Intention in Trend Driven Casual Dining: A Stimulus-Organism-Response Approach in Trendy Noodle Restaurants

2026-05-13T10:00:06+00:00

As trendy noodle restaurants continue to proliferate in Indonesia, competition in this industry has become increasingly fierce, making it all the more important to understand the factors that drive customer satisfaction and the intention to return. This study focuses on modern casual dining restaurants characterized by customizable noodle menus, distinctive food presentation, and a socially oriented dining experience.
Using the Stimulus-Organism-Response (SOR) model, this study examines three external stimuli (price, food quality, and restaurant ambiance) and their influence on customer satisfaction (organism) and intention to revisit (response). A quantitative research design employing Covariance-Based Structural Equation Modeling (CB-SEM) was used, with data collected from 243 respondents who had previously dined at trendy noodle restaurants in Indonesia. The results indicate that food quality is the strongest predictor of customer satisfaction, while restaurant ambiance and customer satisfaction are significant predictors of revisit intention. Interestingly, price had no significant effect on either customer satisfaction or intention to revisit, indicating that customers of trendy casual restaurants are more sensitive to quality and experience than to price. These results suggest that in an experience-oriented dining environment, customers prioritize food quality and atmosphere over price.

Implementation of the Semiparametric Geographically Weighted Logistic Regression (GWLRS) Model for Predicting Poverty Depth Index in Central Java Province

2026-05-27T06:05:59+00:00

Poverty in Central Java Province remains a significant multidimensional issue, characterized by socio-economic disparities across regencies and municipalities and high rates of school dropouts among children. This study aims to evaluate the influence of socio-economic variables on poverty depth (P1 Index) at both local and global levels. The approach employed is the Geographically Weighted Logistic Regression Semiparametric (GWLRS), which integrates local and global effects. The model uses two types of spatial weights: Adaptive Gaussian Kernel and Queen Contiguity, with predictor variables including Dependency Ratio, Minimum Regional Wage (UMK), Number of Industries, Open Unemployment Rate (TPT), Adequate Housing, and Sanitation. Parameter estimation was conducted using Maximum Likelihood. The results indicate that the Dependency Ratio, Adequate Housing, and Sanitation are locally significant in 2–5 regions. Local coefficients for the Dependency Ratio range from 0.42 to 11.57 (mean 3.33), Adequate Housing from -15.808 to -2.371 (mean
-5.95), and Sanitation from 1.86 to 17.27 (mean 5.71). The model correctly predicts 31 out of 35 cases, yielding an accuracy of 91.4%. The Number of Industries, UMK, and TPT are not globally significant, indicating that their effects are more stable across regions. In conclusion, the GWLRS model effectively captures the spatial heterogeneity of poverty determinants and provides quantitative insights that can support more targeted, location-based poverty alleviation policies in Central Java Province.