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M. Khalil Gibran
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jitcos@multimediatekno.org
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JITCoS : Journal of Information Technology and Computer System
Published by CV. Multimedia Teknologi Kreatif
ISSN : -     EISSN : 31096182     DOI : https://doi.org/10.65230/jitcos
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
JITCoS: Journal of Information Technology and Computer System is a peer-reviewed scholarly journal that aims to advance the theory and practice of information technology and computer systems. The journal seeks high-quality contributions from researchers, academics, and industry professionals that enrich the body of knowledge and deliver practical insights. The journal welcomes original articles, comprehensive reviews, and practical case studies in, but not limited to, the following areas: Information systems development and IT governance, Web and mobile application engineering, Big data analytics, data mining, and data science, Cybersecurity, digital forensics and privacy, Digital transformation, E-Government, and Smart Cities, Cloud and edge computing technologies, Geographic Information Systems (GIS), Decision Support Systems (DSS) and business intelligence, Computer architecture and hardware acceleration, Networking protocols and distributed systems, Embedded systems and Internet of Things (IoT), Operating systems and kernel-level development, Parallel, grid, and cloud-based computation, Control systems, robotics, and, intelligent automation, Artificial Intelligence (AI) and Machine Learning (ML). JITCoS encourages interdisciplinary approaches that merge engineering, computing, and data-driven insights to tackle contemporary challenges and foster innovation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 26 Documents
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Solvency Resilience Analysis Using Dynamic Financial Analysis:A Simulation of Adaptive Asset Allocation at PT Asuransi Bina Dana Arta Tbk Aurellia Aknesia Hendrawan; Sindi Pratika Siwi; Fikri Haikal; Putri Najwa Minadhifa
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.75

Abstract

This research aims to evaluate the solvency resilience of PT Asuransi Bina Dana Arta Tbk (ABDA) against catastrophic shocks and investment market volatility through a System Dynamics simulation approach within a Dynamic Financial Analysis (DFA) framework, addressing the limitations of prior static methods that fail to capture dynamic management responses. Utilizing Q1 2025 financial data projected over a 60-month horizon, this study tests a double stress test scenario comprising a 350% surge in claims and a simultaneous stock market correction to measure the effectiveness of adaptive asset allocation policies. The simulation results demonstrate that the company's capital structure is highly robust, with the Risk-Based Capital (RBC) ratio remaining above 200% under normal conditions, and despite experiencing severe pressure in the crisis scenario, the lowest solvency level (nadir point) reached was 150.64%, which remains significantly above the Financial Services Authority (OJK) regulatory threshold of 120%. These findings conclude that the company possesses resilient fundamentals, where the implementation of an automated de-risking mechanism proved to be a crucial factor in preventing insolvency and mitigating capital erosion during economic turbulence, suggesting that dynamic approaches should be adopted as a new standard in insurance risk management.
Impact of Social Restrictions on ISPA Dynamics in Tasikmalaya City (2018-2023): A Counterfactual SIRS Model Analysis Anggi Jelita Sitepu; Putri Nabawy; M Hasan Wijaya
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.77

Abstract

The COVID-19 pandemic and the implementation of Non-Pharmaceutical Interventions (NPIs), such as Large-Scale Social Restrictions (PSBB), have drastically altered the transmission landscape of endemic respiratory diseases. This study aims to quantitatively evaluate the impact of social restrictions on the incidence of Acute Respiratory Infection (ISPA) in Tasikmalaya City and to elucidate the mechanisms driving the post-pandemic case resurgence. A dynamic SIRS (Susceptible-Infected-Recovered-Susceptible) mathematical model was constructed and calibrated using historical time-series data from 2018 to 2023, incorporating the biological factor of waning immunity. To measure policy effectiveness, a counterfactual analysis was performed by comparing the factual simulation (with interventions) against a hypothetical no-intervention scenario. The results demonstrate that the model achieved a high goodness-of-fit, accurately replicating the sharp decline in cases during the 2020-2021 restriction period and the significant "rebound" to pre-pandemic levels in 2023. The counterfactual analysis estimates that social restrictions prevented approximately 50,000 to 60,000 potential ISPA cases cumulatively over the two-year period. The study concludes that while NPIs were highly effective in suppressing transmission, the subsequent resurgence was a predictable mathematical consequence of "immunity debt" the accumulation of susceptible individuals due to prolonged lack of pathogen exposure. These findings underscore the necessity for anticipatory surveillance and targeted interventions during the transition from pandemic to endemic phases.
K-Means-Based Deterministic Simulation of Regional Budget Redistribution Policy Alfani Azhard; Rifandi Mahendra; Alfira Syah Kadri; Aldi Kurniawan
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.79

Abstract

Fiscal disparity remains a critical challenge in Indonesia's decentralized governance, characterized by an extreme gap in financial capacity between economic growth centers and hundreds of other regions. This study aims to map these inequality patterns and design an objective budget redistribution policy simulation model. Using the State Revenue and Expenditure Budget (APBN) data for the Fiscal Year 2024, this research integrates Data Mining and Deterministic Simulation approaches. The K-Means Clustering algorithm, validated using the Elbow Method, was employed to segment districts/cities, revealing a heavily right-skewed distribution where Central Jakarta acts as an extreme outlier. To address the limitation of static mapping, a Python-based simulation model was developed to test multiple cross-subsidy scenarios (2.5%, 5%, and 7.5%). The results demonstrate that a 5% budget redistribution from the High Cluster significantly elevates the fiscal capacity of lagging regions, providing an estimated additional subsidy of IDR 1.1 Trillion per region. Sensitivity analysis confirms that even a conservative 2.5% cut yields substantial impact without destabilizing donor regions. This study concludes that integrating clustering and What-If Analysis serves as an effective Decision Support System (DSS) for formulating equitable fiscal policies, though implementation must consider political constraints and regional absorption capacity.
Application of Deterministic Simulation Modeling for Capital Structure Robustness Testing: A Case Study in the Capital-Intensive Industry (AGII) Legiman Samosir; Haikal Habibi Siregar
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.80

Abstract

In the capital-intensive sector, the strategic management of capital structure is often constrained by cost rigidity, where high fixed costs magnify the impact of demand volatility on corporate solvency. This study addresses the methodological gap in static financial analysis by applying Deterministic Simulation Modeling to conduct a Robustness Test on PT Aneka Gas Industri Tbk (AGII). The primary objective is to measure the structural resilience of the firm against operational shocks specifically demand fluctuations attributed to the bullwhip effect and to evaluate the efficacy of refinancing as a risk mitigation strategy. Using audited financial data, the simulation executes specific "what-if" scenarios to project changes in Net Income, Interest Coverage Ratio (ICR), and Financial Distress probability based on the Springate S-Score. The empirical results reveal a significant structural vulnerability: a moderate revenue decline of 5% plunges the baseline performance into a net loss of IDR 5,027 million, confirming the detrimental impact of high operating leverage. However, the simulation demonstrates that a strategic refinancing intervention, reducing the cost of debt from 8.1% to 6.75%, effectively transforms the company’s risk profile from fragile to robust. This optimization not only maintains profitability under stress but also significantly lowers the Break-Even Point (BEP), thereby widening the Margin of Safety. The study concludes that deterministic simulation offers a superior, forward-looking framework for management to identify financial "breaking points" and implement proactive liability management to ensure sustainability amidst economic uncertainty.
Employee Performance Classification Using K-Nearest Neighbor and Random Forest with Work Behavior Scenario Simulation Hafiz Aryanda; Alwi Syahputra; Muhammad Farhan; Ade Aulia Dharma
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.83

Abstract

Employee performance evaluation is a critical aspect of human resource management, directly influencing productivity, promotion decisions, and career development planning. Traditional approaches often fail to capture hidden patterns in complex employee data, making machine learning (ML) a promising solution for more accurate and objective classification. This study aims to model and compare two ML algorithms, K-Nearest Neighbor (KNN) and Random Forest, for employee performance classification, followed by scenario-based simulation using the best-performing model. The research employed a quantitative computational approach with a dataset of 100,000 employee records and 20 features. Preprocessing steps included feature selection, binarization of performance scores, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), and feature engineering to enrich data representation. The dataset was split into 80% training and 20% testing. KNN was first built as a baseline model, then optimized through hyperparameter tuning using GridSearchCV with cross-validation. Random Forest was implemented with 100 decision trees and bootstrap sampling to enhance accuracy and stability. Results show that the tuned KNN model achieved an accuracy of 70.88%, improving from 63.85% baseline. However, Random Forest outperformed KNN significantly, reaching 97.17% accuracy with lower error rates. Scenario simulations using Random Forest demonstrated practical applicability in predicting employee performance based on work behavior profiles. In conclusion, Random Forest provides a more robust and reliable model for employee performance classification compared to KNN. The study contributes by integrating algorithm comparison with simulation design, offering actionable insights for human resource decision-making. Future research may explore additional ensemble methods and real-time evaluation frameworks.
Web-Based Employee Leave Information System Design Using the Waterfall Method at the Deli Serdang District Agricultural Office Risky Ananta Pradana; Muhammad Nabhan Akbar Marpaung
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.94

Abstract

This study aims to address issues in employee leave data management at the Deli Serdang District Agricultural Office, where leave records were previously processed manually using separate documents, resulting in data duplication, inaccuracies, and delays in preparing reports. To solve this problem, a web-based Employee Leave Record Management System was developed using the Waterfall model through stages of requirements analysis, system design, implementation, testing, and maintenance. System evaluation was conducted using Black-Box testing to verify functional performance. The system supports structured recording of employee leave information, employee profile data such as position and department, and provides real-time reporting features to support administrative decision-making. The application was developed using the CodeIgniter-based MVC architecture and MySQL database, which enhances data accuracy, information accessibility, and administrative efficiency. The results demonstrate that the system successfully minimizes recording errors and improves the effectiveness of leave data management. Future development opportunities include integrating automated online leave request and approval workflows, mobile access features, and notification services to maximize usability and system automation.

Page 3 of 3 | Total Record : 26

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