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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 648 Documents
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Sentiment Analysis of the Merah Putih Movie Using Naïve Bayes and Support Vector Machine Sulistyo Dwi Sancoko; Ulfah Nafiah; Yudit Manda; Novera Sari Mukti
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 Yunia Dwie Nurcahyanie; Sabrina Nur Saraswati
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 Komang Yuli Santika; Putu Chrisdayanti Suada Putri; Luh Verra Sridyantari; Komang Drei Bayu Anggara; Putu Adi Guna Permana; Nyoman Sarasuartha Mahajaya
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 Komang Dreibayu Anggara; Putu Adiguna Permana; Komang Yuli Santika; Adhi Agam Pradhana
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.
Ontology-Based Semantic Web Model for Cervical Cancer Information Retrieval Sri Rezeki Candra Nursari; Setyawan Widyarto; Amir Murtako; Febri Maspiyanti
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.v8i3.3916

Abstract

Ontology and Semantic Web technologies have become important approaches for improving the accessibility, integration, and semantic accuracy of medical information, particularly in supporting early awareness and information retrieval related to cervical cancer. This study proposes a hybrid ontology and Semantic Web model to enhance cervical cancer information retrieval by transforming heterogeneous web-based health information into structured and machine-interpretable knowledge. The research was conducted through several stages, including data collection using purposive sampling, preprocessing, data cleaning, labelling, ontology modelling, and Semantic Web implementation. A total of 645 data records were collected from 62 web sources and organized into eight main domain features: symptoms, affected organs, maintenance, treatment, characteristic features, causes, prevention, and types of cervical cancer. The proposed system adopts a layered Semantic Web architecture consisting of XML, RDF, OWL, and logic layers. The XML layer represents the data structure, the RDF layer defines semantic relationships, and the OWL-based ontology layer models domain knowledge and rules. In contrast, the logic layer enables reasoning and knowledge inference. In addition, heuristic-based mapping is applied to connect relational database schemas with ontology models to support semantic interoperability. The results show that the proposed model can represent cervical cancer knowledge more systematically and improve semantic search capabilities in healthcare information systems. Therefore, this study contributes to the development of intelligent, interoperable medical information retrieval systems to support cervical cancer education, prevention, and early detection.
Development of an Online Multiplayer Bentengan Game to Support Teamwork Learning Adinda Tzalia Ramadhanti Bukian; Chanifah Indah Ratnasari; Resnia Novitasari
bit-Tech Vol. 9 No. 1 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v9i1.4010

Abstract

Teamwork is an essential social skill that should be practiced through direct interaction from an early age. However, many educational games remain individually oriented and provide limited opportunities for children to communicate, coordinate, and cooperate toward shared goals. This study developed Zona Penjaga, an online multiplayer educational game that adapts the traditional Bentengan game to facilitate teamwork-oriented learning among elementary school students. The game was developed using the ADDIE model and implemented with Unity, Photon Unity Networking for real-time multiplayer interaction, and Firebase for gameplay data storage. The evaluation involved 15 fourth-grade male students aged 9–10 years from one school in a one-day implementation without a control group. Data were collected through pretest-posttest questionnaires, gameplay observation, black box testing, and the System Usability Scale. All tested feature groups functioned as expected, indicating technical feasibility. The Wilcoxon signed-rank test showed a significant increase in posttest teamwork scores (Z = −3.078, p = 0.002); however, the low N-Gain value (0.21) indicates limited improvement. Observed cooperative behavior during gameplay was high, with a mean score of 3.61 out of 4.00, but this finding reflects short-term teamwork tendencies and in-session behavior rather than long-term skill development. Usability was moderate (SUS = 63.33), and 66.7% of participants scored below 68, indicating the need for substantial interface and onboarding refinement before broader classroom use. Overall, Zona Penjaga is technically promising, but its effectiveness and usability require validation through larger, more diverse, controlled, and longitudinal studies.
Performance Comparison of RS-256 and ES-256 Algorithms for JWT-Based Digital Signatures in REST-API Security Amelia Hani; Yiyi Supendi
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.v8i3.4025

Abstract

Representational State Transfer (REST) is widely adopted for web-based client–server communication, but its stateless design requires additional mechanisms to ensure authentication, authorization, and message integrity. JSON Web Token (JWT) addresses these requirements by embedding digitally signed claims that can be verified by REST-API services. This study compares the performance of two asymmetric JWT signing algorithms, RS-256 and ES-256, to determine their suitability for REST-API security under different deployment environments. A comparative experimental approach was applied by implementing both algorithms in a REST-API application and testing token generation and token verification processes on a cloud virtual machine and a physical machine. Apache JMeter was used to conduct scalability testing with 100 to 1,000 request samples. Four performance metrics were evaluated: response time, throughput, CPU usage, and memory usage. The results show that ES-256 consistently outperformed RS-256 in token generation across both environments, with better response time, throughput, CPU efficiency, and memory consumption. During token verification, ES-256 achieved better response time and throughput, while RS-256 showed lower CPU and memory usage. These findings indicate that ES-256 is generally more suitable for REST-API systems prioritizing fast token generation and high throughput, whereas RS-256 remains relevant when verification efficiency in computational resource usage is the primary concern. The study provides guidance for selecting JWT signing algorithms in scalable API security implementations and distributed services.
Spice Image Classification Based on Content-Based Image Retrieval Meidy Fajar Wahyu; Lely Panca Andriyanto; Amin Hidayat; Achmad Sehan; Eko Sutono
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.v8i3.4149

Abstract

Indonesia possesses exceptional spice biodiversity, yet public familiarity with the original visual characteristics of many spices is declining because of packaged processing, reduced direct exposure, and changing food-consumption patterns. This study develops RempahID, a web-based spice identification system that integrates Content-Based Image Retrieval (CBIR) with machine-learning classification. The study addresses the limited availability of Indonesian spice recognition systems that simultaneously provide class predictions and visually similar reference images for user verification. The system uses a dataset comprising ten major spice categories, including ginger, turmeric, galangal, aromatic ginger, cinnamon, cloves, nutmeg, coriander, candlenut, and star anise. Each image is preprocessed through resizing, normalization, noise reduction, and Otsu-based segmentation. Visual representation combines 24 Hue-Saturation-Value color histogram features, four Gray-Level Co-occurrence Matrix texture descriptors, and seven Hu Moment shape features, producing a 35-dimensional feature vector. Euclidean Distance is employed to rank visually similar database images, while K-Nearest Neighbors, Support Vector Machine, and Random Forest are compared for classification. Performance is evaluated using accuracy, precision, recall, and F1-score. The Support Vector Machine with a radial basis function kernel achieved the best result, with 92.1% accuracy, 0.91 precision, 0.92 recall, and a 0.91 F1-score. Retrieved reference images also supported transparent visual comparison rather than presenting an isolated predicted label alone. These findings demonstrate that integrating complementary color, texture, and shape descriptors within a CBIR framework provides an effective and interpretable approach for Indonesian spice identification.

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