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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 106 Documents
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Search results for , issue "Vol. 8 No. 3 (2026): bit-Tech" : 106 Documents clear
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.
Sentiment Analysis of the Merah Putih Movie Using Naïve Bayes and Support Vector Machine Sancoko, Sulistyo Dwi; Nafiah, Ulfah; Manda, Yudit; Mukti, Novera Sari
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.v8i3.3823

Abstract

Public engagement on YouTube provides a valuable source for examining audience responses to film productions; however, sentiment classification of Indonesian-language comments remains methodologically challenging due to informal expressions, noisy text, and imbalanced class distributions. This study evaluates the robustness of a classical machine learning pipeline for sentiment classification of YouTube comments on the trailer of the Indonesian animated film Merah Putih: One for All. A total of 5,469 comments were collected using the YouTube Data API v3. After preprocessing and lexicon-based pseudo-labeling, 5,192 comments were retained, consisting of 4,006 negative and 1,186 positive instances. Text features were represented using TF-IDF, while SMOTE was applied only to the training set after a stratified 80:20 split to prevent data leakage. Two classifiers were compared under identical experimental conditions: Multinomial Naïve Bayes and linear Support Vector Machine. The SVM model achieved 81.59% accuracy, 83% precision, 82% recall, and 82% F1-score on the original held-out test set, outperforming Naïve Bayes, which obtained 76.82% accuracy. The findings suggest that margin-based classification is more suitable than probabilistic classification for sparse, high-dimensional Indonesian YouTube comments, particularly when feature independence assumptions are likely violated. The study contributes a leakage-controlled evaluation of classical sentiment classification under imbalanced social-media conditions and highlights the methodological implications of pseudo-labeling and synthetic oversampling in Indonesian film-related opinion mining.
Text-Based Sentiment Analysis of Online Reviews: Evidence from Indonesia’s Muslim Women’s Fashion Sector Nurcahyanie, Yunia Dwie; Saraswati, Sabrina Nur
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia’s Muslim women’s fashion market has expanded rapidly alongside e-commerce growth, generating massive volumes of online product reviews (OPRs) that remain underutilized for systematic product development. This study addresses a gap in the literature: while sentiment analysis can classify review polarity, term-level classification alone cannot translate consumer feedback into actionable design attributes for fashion products, a domain where tacit knowledge, material properties, and aesthetic judgment are central. A two-layer hybrid approach is proposed that combines computational sentiment extraction with expert semantic translation. In the first layer, 2,050 OPRs from three Indonesian Muslim fashion brands on Shopee were preprocessed and classified using a maximum entropy (MaxEnt) model, achieving 84.11% accuracy, 90.09% precision, and an F1 score of 89.95% on test data. In the second layer, ten experienced designers interpreted the MaxEnt output through structured interviews, translating raw sentiment features into design-relevant categories. Positive sentiment features clustered around product quality, material comfort, and design authenticity, while negative features concentrated on product-image discrepancies, poor fabric quality, sizing mismatches, and color inaccuracy. Designer interpretation uncovered semantic dimensions invisible to the classifier, yielding eight major product performance categories. This study contributes methodologically by demonstrating the necessity of a human-in-the-loop expert validation layer for sentiment-based consumer insight extraction in design-intensive domains, and practically by providing a framework for converting OPR data into product development inputs.
Stock Control-Based Personal Protective Equipment Inventory System for Work Safety Santika, Komang Yuli; Putri, Putu Chrisdayanti Suada; Sridyantari, Luh Verra; Anggara, Komang Drei Bayu; Permana, Putu Adi Guna; Mahajaya, Nyoman Sarasuartha
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.v8i3.3842

Abstract

Effective management of Personal Protective Equipment (PPE) is crucial for ensuring worker safety in hazardous environments. However, manual PPE management often leads to issues, such as discrepancies between recorded stock and actual quantities and challenges in tracking tool distribution. As a result, there is a need for an inventory system that can effectively monitor and control PPE stock levels. This research aims to design a web-based inventory system capable of managing PPE with integrated stock control using the Economic Order Quantity (EOQ), Safety Stock, and Reorder Point (ROP) methods. The methodology used in this study is a Research and Development (R&D) approach with a prototyping model. The research includes the design of data structures and system process flows, as well as the implementation of key features, including a dashboard, inventory management, and PPE distribution. The data used consists of employee data, supplier information, PPE types, and existing stock, all of which are analyzed to support the stock control system. The results show that the developed system can record and monitor PPE stock levels more systematically than manual methods. Additionally, the system allows for accurate monitoring of PPE distribution to employees. In conclusion, the web-based PPE inventory system can significantly improve stock management efficiency and support workplace safety by ensuring the timely availability of appropriate PPE. Future research is recommended to test the system with real operational data and assess its impact on managing occupational safety risks.
Analysis of Scrum Project Management Maturity in Software Development Anggara, Komang Dreibayu; Permana, Putu Adiguna; Santika, Komang Yuli; Pradhana, Adhi Agam
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.v8i3.3853

Abstract

The increasing need for adaptive and responsive software development has encouraged organizations to adopt Scrum as an Agile project management framework. However, the formal use of Scrum does not always indicate that its practices have been implemented consistently or maturely across projects. Software House XYZ has implemented Scrum since 2017, but no formal maturity assessment had previously been conducted. This study aims to analyze the maturity level of Scrum project management in software development projects and identify Key Process Areas that require improvement. A quantitative descriptive approach was employed using the Scrum Maturity Model and Agile Maturity Model. The study assessed three Scrum-based projects, namely Project A, Project B, and Project C. Scrum Masters were selected through purposive sampling as respondents. Data were collected using a validated 66-item questionnaire with four response options: Yes, Partially, No, and Not Applicable. The achievement level of each Key Process Area was calculated using the Agile Maturity Model scoring formula. The findings show that Basic Scrum Management, Software Requirements Engineering, Iteration Management, and Performance Management were categorized as Fully Achieved. Customer Relationship Management was categorized as Largely Achieved, while Standardized Project Management was categorized as Partially Achieved. Overall, Software House XYZ's Scrum project management maturity was Level 3: Defined. This study contributes empirical evidence by conducting a project-level Scrum maturity assessment in a real software development organization, demonstrating how long-term Scrum adoption can still reveal inconsistencies in project standardization, Product Owner involvement, backlog management, and metric-based decision-making.

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