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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol 19, No 1: February (2026)" : 7 Documents clear
Performance Analysis of Ensemble Learning Models in Heart Failure Prediction: Random Forest, AdaBoost, and XGBoost Beny, Beny; Yani, Herti; Yupu, Gangga Ramadhan Putra
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3218

Abstract

Heart failure remains a major global health challenge, and early prediction is essential for improving patient outcomes. This study evaluates three ensemble learning methods, namely Random Forest, AdaBoost, and XGBoost, using the Heart Failure Prediction dataset containing 918 patient records from Kaggle. A quantitative experimental design was applied, including preprocessing with KNN imputation, model development, and evaluation using 10-Fold Cross Validation. Performance was assessed through accuracy, precision, recall, F1-score, and AUC-ROC. Random Forest achieved the highest accuracy (0.868), recall (0.907), F1-score (0.884), and AUC-ROC (0.922), while AdaBoost produced the highest precision (0.874). Although the models showed generally similar performance patterns, statistical tests revealed notable distinctions: RF vs. XGB exhibited significant differences in Recall (p = 0.011) and F1-score (p = 0.016), and the Friedman test identified a significant difference in Recall (p = 0.034) across the three models. Feature importance analysis showed that the models consistently emphasized clinically relevant variables, with ST-segment slope, Oldpeak, and exercise-induced angina appearing among the most influential predictors. These features align with recent cardiovascular evidence identifying exercise ECG indicators and stress-response variables as strong predictors of cardiac risk. Overall, the results suggest that recall-related behaviour is the main performance differentiator among the ensemble models, with Random Forest providing a modest advantage in identifying true heart failure cases. The study is limited by its reliance on a single dataset and a relatively small sample size, which may restrict the generalizability of the findings.
Portfolio Risk Assessment Using VaR and CVaR: A Comparative Study of Variance–Covariance Method and Monte Carlo Simulation Supandi, Epha Diana; Oktavia, Atika
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3120

Abstract

This study examines portfolio risk in Indonesia’s energy sector by applying Value at Risk (VaR) and Conditional Value at Risk (CVaR) under the Variance–Covariance and Monte Carlo Simulation approaches. The analysis focuses on ten stocks from the oil and gas as well as coal subsectors listed on the Indonesia Stock Exchange (IDX), using monthly closing price data from January 2020 to December 2024. A Weighted Scoring Method (WSM) is first employed to select stocks with superior fundamentals and liquidity, based on market capitalization, return on equity, debt-to-equity ratio, net profit margin, trading volume, and dividend yield. An optimal portfolio is then constructed using the Maximum Sharpe Ratio (MSR) framework, resulting in a portfolio dominated by PTBA, MEDC, and MBAP. Portfolio risk is subsequently estimated using VaR and CVaR at the 95% and 99% confidence levels under both the Variance–Covariance and Monte Carlo approaches. The empirical results indicate that CVaR consistently produces higher risk estimates than VaR, highlighting its superior ability to capture tail risk. Furthermore, the Variance–Covariance method yields slightly more conservative CVaR estimates compared to Monte Carlo Simulation, which is attributed to the near-normal distribution of portfolio returns during the observation period. Model validity is confirmed through backtesting using the Kupiec test, which shows that the VaR estimates satisfy statistical adequacy criteria. Overall, the findings suggest that while the Variance–Covariance approach remains effective under normality assumptions, Monte Carlo Simulation offers greater flexibility in modeling extreme market conditions. This study contributes to the literature by providing empirical evidence on comparative risk estimation methods in Indonesia’s highly volatile energy sector.
Performance Analysis of the Fuzzing Method in Detecting API Vulnerabilities in Mobile Healthcare Application X Based on OWASP API Security Top 10 Hakim, Muhammad Ikhwanul; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto; Herteno, Rudy; Saputro, Setyo Wahyu
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3149

Abstract

Traditional perimeter security measures, such as Web Application Firewalls (WAFs) and static analysis, often fail to detect logic-based vulnerabilities in healthcare Application Programming Interfaces (APIs), creating significant risks for patient data confidentiality. Addressing the scarcity of empirical performance evaluations in this domain, this study employs a grey-box controlled experimental design to assess the effectiveness of automated HTTP fuzzing against a production-grade mobile health application ("Application X"). Using the FFUF tool configured with sequential identifier injection, status-code filtering, and hidden-field probing, the experiment tested 33 endpoints against the OWASP API Security Top 10 2023 benchmarks. To ensure data reliability, a rigorous multi-step validation protocol including replay testing and environmental noise elimination was applied to filter false positives. The results identified 88 distinct vulnerabilities distributed across six categories, with a critical dominance of Security Misconfiguration (API8) and Broken Object Property Level Authorization (API3). Analytically, the high prevalence of API3 reveals a systemic failure in backend serialization, where sensitive fields  including password hashes and internal administrative flags were exposed due to the absence of Data Transfer Objects (DTOs), contradicting the assumption of secure client-side filtering. Limitations of this study include the restriction to a single patient-role perspective and the exclusion of third-party integrations. The study concludes that automated fuzzing is superior to static analysis in detecting runtime data leakage and recommends mandatory Server-Side Output Filtering through explicit DTOs as a critical standard for secure health API development and data privacy compliance.
Fairness Auditing and Bias Mitigation in Aspect-Based Sentiment Models for Indonesian Public Services Jondien, Muhammad Shihab Fathurrahman; Hariguna, Taqwa; Saputra, Dhanar Intan Surya
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3269

Abstract

This study presents a comprehensive fairness audit and bias mitigation framework for Indonesian sentiment analysis using the SmSA IndoNLU dataset and the IndoBERT language model. The research investigates demographic and linguistic fairness by evaluating model performance across gender and regional groups and introduces an aspect-based extension to assess semantic fairness using an ABSA-style input formulation. Fairness metrics such as ΔF1, Demographic Parity Difference (DPD), and Equality of Opportunity were employed to quantify disparities in model behavior. The baseline IndoBERT model achieved strong overall accuracy (0.942) and macro-F1 (0.927) but exhibited significant regional bias, particularly toward Eastern and Sumatran dialects. A re-weighting strategy effectively reduced the regional F1 disparity by 59 percent with minimal accuracy loss, demonstrating the viability of loss-based fairness mitigation. The ABSA-style IndoBERT further improved fairness consistency across dialectal and aspect categories, achieving a macro-F1 of 0.930. Despite these improvements, aspect-level imbalances persisted, indicating that fairness challenges extend beyond demographic representation to semantic coverage. This work contributes an empirical and methodological foundation for ethical NLP evaluation in Bahasa Indonesia, emphasizing fairness auditing, bias mitigation, and responsible deployment of language models in low-resource and linguistically diverse settings.
Comparative Analysis of UFW and CSF Using the SEPER Framework Kurniawan, Arif; Yusuf, Muhamad; Prasetio, Agung Budi
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3240

Abstract

This study presents a comparative evaluation of two widely used Linux-based firewall solutions, Uncomplicated Firewall (UFW) and ConfigServer Security & Firewall (CSF), using the SEPER framework, which encompasses Security, Performance, Effectiveness, and Reliability dimensions. While previous studies have examined Linux firewall configurations individually, systematic comparisons that apply a structured evaluation framework such as SEPER remain limited. The experiments were conducted on Ubuntu Server using an intra-host virtualized environment consisting of multiple virtual machines. Network performance was evaluated using throughput and latency measurements, while security effectiveness was assessed through port scanning, SSH brute-force simulations, and mild SYN flood scenarios. System reliability was analyzed based on CPU and memory utilization. The results indicate that UFW and CSF exhibit comparable network performance, with throughput differences remaining below 5%, suggesting no statistically significant performance advantage for either firewall. UFW demonstrates slightly lower resource overhead, whereas CSF provides stronger automated brute-force mitigation through its integrated Login Failure Daemon (LFD), at the cost of modestly higher resource usage. Mild SYN flood tests produced similar outcomes across all configurations, largely influenced by Linux kernel-level mechanisms. Overall, this study highlights a trade-off between resource efficiency and advanced security automation. By applying the SEPER framework, the findings provide balanced and practical guidance for Linux administrators in selecting firewall solutions based on deployment priorities rather than isolated performance metrics.
USB Breakout-Controlled Modular CNC System for Affordable Smart Manufacturing Solutions Artono, Budi; Winarno, Basuki; Kusbandono, Hendrik; Anata, Frian Adi; Gupta, Shashi Kant
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3198

Abstract

The primary objective of this study is to design, develop, and experimentally validate a low-cost modular CNC system controlled via a USB Breakout Controller as an affordable machining solution for small-scale wood manufacturing. The research employed an experimental approach involving the fabrication of an 18 mm plywood test specimen under three feed-rate settings (40%, 50%, and 70%) to evaluate dimensional accuracy, machining time, and surface quality. The CNC prototype was constructed using stepper-driven linear axes, a 500 W spindle, and Mach3-based motion control. Validation procedures included repeated measurements of machined features using a ruler, surface quality inspection, and comparison of actual versus nominal dimensions. The results demonstrate that the system achieved dimensional deviations between 0.32 mm and 0.58 mm across trials, with the optimal performance observed at a 50% feed rate, which produced the lowest mean error (0.32 mm), smoothest surface finish, and efficient machining time (54 seconds). These quantitative findings confirm that the proposed system delivers machining accuracy comparable to entry-level commercial CNC routers while significantly reducing system complexity and cost. The study’s novelty lies in demonstrating the effectiveness of a USB-based modular controller architecture for precision wood machining—an area where low-cost systems typically suffer from poor stability and inconsistent performance. This research concludes that the developed CNC system is technically viable, repeatable, and suitable for vocational education and small-to-medium wood fabrication industries requiring affordable digital manufacturing solutions.
A Clustering-Popularity-based Model for Cold-Start Recommendations using User Attributes and Item Ratings Ifada, Noor; Nikmat, Moh; Suristiar, Weni Pratiwi; Sophan, Mochammad Kautsar
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3202

Abstract

Recommendation systems face a major challenge known as the cold-start problem, which occurs when the system lacks sufficient user interaction data. Thus, there is no basis for a recommendation. Existing approaches, such as co-clustering methods and non-personalized popularity models, often struggle to effectively combine heterogeneous user and item data (categorical user attributes and numerical item ratings) or to capture latent group-level preferences. To bridge this gap, we propose a new clustering-popularity-based model that independently groups users and items using two separate algorithms and integrates them through a popularity measure. Users are clustered using K-Modes based on demographic attributes, while items are clustered separately using either K-Means or Fuzzy C-Means (FCM) based on rating patterns. A rating-aware popularity score is then computed within each item cluster. To generate recommendations for new users, we assign them to the appropriate user demographic clusters and suggest items from the most popular clusters. Experiments on the MovieLens 100K dataset show that the FCM-based variant, ClusterPopRec-FCM, consistently outperforms both a K-Means-based variant (ClusterPopRec-KMeans) and the traditional item-based baseline across all cold-start scenarios (extreme, moderate, and non-cold-start scenarios). In the extreme cold-start scenario, ClusterPopRec-FCM achieved Precision@5=54.65 and DCG@5=1.66, which in comparison to the baseline represents percentage increases of 149.7% and 110.1% respectively, with statistical significance  < 0.001. These results show the benefit of soft clustering (FCM) in capturing nuanced item relationships and demonstrate the effectiveness of hybrid models that combine demographic clustering with in-cluster popularity scores. This work offer a effective solution for cold-start scenarios and heterogeneity, allowing advancement in recommendation systems research.

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