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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 642 Documents
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Gamification-Based Redesign of Sekawan V3 Using User-Centered Design and Usability Evaluation Sopian, Fadhlan Putera; Ratnasari, Chanifah Indah
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3774

Abstract

Higher education institutions expect thesis administration systems to support students not only functionally but also through clear workflow guidance and sustained engagement. In Sekawan V3, preliminary observation and an initial user interview identified issues related to limited progress visibility, fragmented workflow, unclear status feedback, and low motivational support during the undergraduate thesis process. This study aimed to redesign Sekawan V3 by integrating gamification elements based on the Octalysis framework into its interface while grounding the redesign in a user-centered design approach. The novelty of this study lies in applying gamification to a procedural academic administrative workflow to improve progress awareness and user motivation in thesis administration. The study consisted of four main stages: understanding the context of use, specifying user requirements, producing design solutions, and evaluating the resulting prototype. A high-fidelity prototype was developed in Figma by redesigning the main thesis administration features, including Dashboard, Files, Logbook, Progress Check, and Leaderboard. The gamification system was implemented through progress indicators, levels, badges, and task guidance. A usability evaluation was conducted through online unmoderated testing with 26 respondents (17 active students and 9 alumni), followed by the System Usability Scale questionnaire. The results showed an overall SUS score of 72.79, categorized as an acceptable usability level with a good user experience rating. These findings suggest that the gamification-based redesign can improve progress awareness, workflow clarity, and motivational support in thesis administration, while still requiring further refinement and full backend implementation.
Uncovering Hidden Security Risks in Government Web Portals Using Penetration Testing and Attack Modeling Salsabila, Belia Putri; Endah Wahanani, Henni; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3776

Abstract

Government web portals that consolidate public services and process personally identifiable data are prime targets for cyber adversaries. However, many existing assessments rely on single-framework methodologies that provide limited adversarial context and insufficient prioritization guidance. This study evaluates the security posture of System X, a public-facing government portal in Indonesia, using a grey-box penetration testing approach that integrates OWASP Top 10:2021, CVSS v3.1, and MITRE ATT&CK. Automated scanning using OWASP ZAP and Nessus identified 12 potential vulnerabilities, which were subsequently validated through manual testing using Burp Suite, cURL, SQLmap, and browser developer tools. The validation process confirmed nine True Positives and three False Positives, resulting in a 25% false positive rate, consistent with prior studies on government web applications. The identified vulnerabilities fall within Broken Access Control, Security Misconfiguration, and Identification and Authentication Failures, with CVSS Base Scores ranging from 4.2 to 6.1. Unlike traditional severity-based assessments, the integration of MITRE ATT&CK enables adversarial behavior mapping and reveals dependency relationships between vulnerabilities. For example, a single Content Security Policy (CSP) misconfiguration was found to enable multiple attack techniques (T1059.007), demonstrating that addressing one root cause can mitigate several related vulnerabilities simultaneously. This integrated approach enhances vulnerability prioritization by providing both severity and attacker-context insights, offering more actionable remediation strategies compared to single-framework methods. The findings contribute to improving practical security assessment methodologies for government systems and support evidence-based cybersecurity decision-making.
Decision Support System for Selecting Volleyball Starting Players Using the AHP and SAW Methods Yanah, Septi; Purbaratri, Winny; Purwaningsih, Mardiana; Tachyar, Nani Krisnawaty; Akmaliyah, Yasmin
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3783

Abstract

This study develops a decision support system to enhance the objectivity and reliability of selecting starting volleyball players, particularly for the spiker position, where traditional selection processes are often subjective and inconsistent. The research addresses the limitation of single-method decision models by integrating the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) into a unified multi-criteria decision-making framework. AHP is employed to derive consistent and structured criterion weights, while SAW is used to generate a transparent ranking of player alternatives. Seven evaluation criteria were used, including passing, service, smash, block, teamwork, body mass index (BMI), and speed. Data were collected through structured observations and expert evaluations involving the team coach and founder. The results indicate that the smash criterion has the highest weight (0.4138), confirming its dominant role in spiker performance. The final ranking shows that Neng Irma Sukmayani achieved the highest score (0.9678), followed by Siti Karlina (0.9602) and Nova Amelia Putri (0.9040). Compared to subjective selection approaches, the proposed system provides a measurable and reproducible evaluation process, improving decision transparency and consistency. The integration of AHP and SAW contributes by reducing weighting bias while maintaining computational simplicity in ranking. The system was implemented using PHP and MySQL and validated through black-box testing, demonstrating stable functionality across all features. This study contributes both theoretically, by strengthening hybrid MCDM applications in sports analytics, and practically, by providing a scalable decision support model for athlete selection.
Enhancing Brain Tumor Prediction Accuracy through Advanced Convolutional Neural Networks: A Methodological Approach Sutrisno, Sutrisno; Jupron, Jupron
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3784

Abstract

Timely and accurate diagnosis of brain tumors remains a significant challenge in neuro-oncology due to the heterogeneous nature of tumor characteristics and their substantial impact on patient prognosis and treatment outcomes. Conventional diagnostic methods, particularly manual interpretation of medical imaging, often exhibit limited sensitivity and specificity, leading to delayed diagnoses and suboptimal clinical decisions. To address these limitations, this study proposes a tailored Convolutional Neural Network (CNN) framework that leverages hierarchical feature extraction to capture subtle spatial patterns in brain MRI images, offering advantages over traditional machine learning approaches that rely on handcrafted features. This study aims to develop and validate the proposed model to improve the accuracy and efficiency of brain tumor prediction using annotated MRI data. The dataset was systematically preprocessed, augmented, and partitioned into training and testing subsets to ensure reliable evaluation. The proposed CNN architecture introduces a streamlined feature extraction–classification pipeline designed to balance computational efficiency with discriminative capability, making it suitable for limited medical datasets. Experimental results demonstrate that the model achieves an overall classification accuracy of 86.27%, with balanced sensitivity and specificity, representing a measurable improvement over conventional diagnostic workflows and baseline approaches reported in related studies. From a clinical perspective, the model supports early detection by reducing false-negative and false-positive rates, thereby enhancing diagnostic consistency and enabling more timely clinical intervention. These findings highlight the potential of CNN-based systems as fast, accurate, and non-invasive decision-support tools, supporting the integration of artificial intelligence into medical imaging and clinical diagnostic workflows.
Analysis of Pesona Dukcapil Application Service Quality on Population Administration Efficiency Using SERVQUAL Fiteri, Fiteri; Kiswanto, Kiswanto
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3795

Abstract

The digitalization of population administration services through e-government platforms has become a strategic priority; however, the alignment between user expectations and actual service performance remains insufficiently examined in developing country contexts. This study aims to evaluate the service quality of the Pesona Dukcapil web-based application using the SERVQUAL model and to explicitly examine its linkage to perceived administrative efficiency, operationalized through user-reported indicators of time–cost savings and service acceleration. A quantitative survey design was employed, utilizing a structured questionnaire with a five-point Likert scale administered to 44 purposively selected users, followed by gap analysis across ten indicators mapped to five SERVQUAL dimensions. The findings reveal consistently negative gap scores, with an overall average of -0.61; responsiveness exhibits the largest gap (-0.70), with response speed as the most critical deficit (-0.75), while tangibles show the smallest gap (-0.50). These results indicate systemic discrepancies between expectations and perceived performance. The study advances the literature by demonstrating that deficiencies in responsiveness and empathy dimensions are directly associated with reduced perceived administrative efficiency, thereby extending SERVQUAL beyond satisfaction-oriented evaluation toward operational outcome relevance. However, the cross-sectional design and limited sample size constrain the generalizability of the findings, suggesting cautious interpretation. The results provide a prioritized framework for improving digital public services, emphasizing system responsiveness, interface usability, and information clarity as key determinants of both service quality and administrative efficiency.
Analysis of Information and Communication Technology Influence on Poverty Levels Using Analytical Hierarchy Process Megie, Putri x; Kiswanto, Kiswanto
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3796

Abstract

The role of Information and Communication Technology (ICT) in driving economic growth and reducing poverty has attracted increasing global attention. Yet, limited research has systematically prioritised ICT indicators that influence poverty at the regency level in developing countries. In this study, poverty is operationalised as a multidimensional socioeconomic condition reflected through regional deprivation indicators reported by official statistics, serving as the dependent construct influenced by ICT development factors. This study aims to analyse the influence of ICT on poverty levels in Bangka Regency by determining the priority weights for four ICT indicators—network infrastructure, community ICT access, digital literacy, and ICT utilisation in economic sectors—using the Analytical Hierarchy Process (AHP). A quantitative approach was employed, collecting pairwise comparison data from 100 respondents comprising local government officials, academics, and practitioners. The AHP analysis, validated with a Consistency Ratio (CR) of 0.05, revealed that network infrastructure received the highest priority weight (0.36), followed by ICT utilisation in economic sectors (0.29), community ICT access (0.20), and digital literacy (0.15). Methodologically, this study advances existing ICT–poverty research by providing a structured multi-criteria prioritisation framework that complements conventional econometric approaches through expert-based weighting of interrelated ICT dimensions. However, the findings are subject to limitations related to reliance on expert judgment and the absence of direct empirical modelling of poverty outcomes. These results highlight that infrastructure and productive ICT use are the most influential drivers, offering actionable guidance for evidence-based policy design and resource allocation in local governance contexts for sustainable poverty alleviation.
Web-Based Waste Management Information System Using the Waterfall Method Noviadih, Muhamad; Muiz, Adam; Sunandar, Dede
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3804

Abstract

The rapid growth of urbanization and industrial activities has significantly increased waste generation, posing complex environmental and operational challenges when information management remains fragmented across institutions. This study aims to design and implement a web-based waste management information system to support integrated waste handling processes at the Environmental Agency of Tangerang Selatan. The system was developed using the Waterfall method, encompassing requirement analysis, system design, implementation, testing, and maintenance, with data collected through observation, stakeholder interviews, and literature review. UML modeling was employed to formalize system architecture, and the application was implemented using Laravel (PHP) and MySQL. The proposed system introduces a unified platform that integrates citizen reporting, institutional workflow management, and public information dissemination, addressing limitations of prior web-based systems that are typically function-specific and lack cross-stakeholder integration. The system supports waste reporting, processing, and monitoring within a closed-loop workflow, enhancing traceability and coordination. Quantitative evaluation through functional testing achieved a 100% success rate across all core modules, while preliminary operational observations indicate reduced reporting delays and improved data accessibility compared to prior manual processes. The system also enhances public engagement through structured reporting and educational content delivery. These findings demonstrate that the system provides measurable improvements in operational efficiency and stakeholder coordination, offering a scalable and cost-effective digital governance solution for municipal waste management in resource-constrained environments.
Web-Based Expert System for Common Disease Diagnosis Using Forward Chaining Dede Sunandar; Muiz, Adam; Mutaqim, Zainul
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3814

Abstract

The increasing number of patients seeking medical consultation at primary healthcare facilities often leads to prolonged waiting times and delayed preliminary diagnosis, particularly for common diseases with overlapping symptoms. At Puskesmas Karanganyar, diagnostic procedures remain largely dependent on direct consultation, limiting service efficiency under high patient volumes. This study aims to design and implement a web-based expert system to support early diagnosis by simulating clinical reasoning. The system employs a rule-based inference mechanism using Forward Chaining to identify potential diseases from patient-selected symptoms, while the Certainty Factor method is integrated to quantify diagnostic confidence. Unlike prior rule-based diagnostic systems, this study contributes an integrated multi-disease diagnostic framework tailored to primary healthcare workflows, combining transparent rule traceability with graded confidence representation to enhance interpretability and practical usability. Knowledge acquisition was conducted through expert interviews and literature review, resulting in a knowledge base of ten diseases and forty symptoms. The system was implemented using PHP and MySQL and is accessible across devices. Empirical evaluation through structured functional testing and user-oriented validation indicates that the system achieves consistent diagnostic outputs with 100% functional success across tested scenarios, average response time below 2 seconds, and positive usability feedback from healthcare staff, demonstrating operational reliability in real-world settings. The findings suggest that the proposed system provides fast, consistent, and informative preliminary diagnoses, supporting more efficient decision-making in primary healthcare services.
Analyzing Cognitive Determinants of Internet Outcome Diversity using SEM and K-Means Clustering Denandro, Nathanael; Aryanto, Joko
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3815

Abstract

While the digital divide has traditionally been examined through socioeconomic and infrastructural lenses, this study explicitly prioritizes the causal role of cognitive intelligence (IQ) as a primary determinant of third-level digital inequality, focusing on how individuals convert access into diverse internet outcomes. Using a quantitative cross-sectional design with 132 respondents in Indonesia, the analysis applies Covariance-Based Structural Equation Modeling (CB-SEM) as the principal analytical approach to estimate direct and mediated relationships among cognitive intelligence, material access, digital skills, and outcome diversity, complemented by K-Means clustering to reveal heterogeneity in user profiles rather than to construct a predictive model. The SEM results indicate that IQ significantly influences digital skills (β = 0.47, p < 0.01) and indirectly affects outcome diversity (β = 0.38, p < 0.01), while digital skills emerge as the strongest predictor of outcome diversity (β = 0.63, p < 0.01), confirming their central mediating role. These findings operationalize the integration of cognitive capacity into third-level digital divide models by demonstrating that internal cognitive resources systematically condition the conversion of access into outcomes, extending beyond conventional resource-based explanations. The clustering analysis identifies four distinct user segments, including a Resource-Limited Active group that achieves high proficiency despite constrained socioeconomic resources, indicating alternative learning pathways. The combined analytical strategy provides complementary insights by linking structural causality with user heterogeneity, which cannot be captured by single-method approaches. These results suggest that effective digital inclusion policies must incorporate cognitively adaptive strategies alongside infrastructure development
Comparative Evaluation of Random Forest and Support Vector Machine for Interpretable Breast Cancer Prediction Gultom, Herwis; Kristianto, Indra
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3821

Abstract

Breast cancer prediction requires classification models that are not only accurate but also clinically meaningful in minimizing missed malignant cases. This study addresses the research question of whether Random Forest (RF) and Support Vector Machine (SVM) differ meaningfully in sensitivity-oriented breast cancer classification when evaluated under a consistent empirical benchmarking framework. Using the Breast Cancer Wisconsin Diagnostic Dataset from Kaggle, comprising 569 instances and 30 numerical diagnostic features, the study implemented a supervised machine learning workflow involving data cleaning, label encoding, StandardScaler-based feature standardization, stratified 80:20 train–test partitioning, model training, and hyperparameter optimization. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and Area Under the Curve (AUC). The SVM model achieved 97.36% accuracy, 100% precision, 92.85% recall, 96.29% F1-score, and 99.54% AUC, whereas RF achieved 96.49% accuracy, 100% precision, 90.47% recall, 95.00% F1-score, and 99.60% AUC. The primary contribution is therefore positioned as empirical benchmarking rather than a new explainable AI framework. SVM produced fewer false negatives, indicating stronger sensitivity for malignant-case detection at the selected decision threshold, while RF provided complementary feature-importance evidence for identifying influential diagnostic variables. These findings clarify the trade-off between sensitivity-driven predictive reliability and model-specific interpretability, suggesting that SVM is preferable for reducing missed malignant cases, whereas RF remains useful when transparent feature-level insight is required.

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