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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 114 Documents
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Search results for , issue "Vol. 8 No. 1 (2025): bit-Tech" : 114 Documents clear
Adoption of ShopeePay Among Indonesian Gen Z Women: A UTAUT-Based Evaluation Anidew, Nadilla; Arista Pratama; Asif Faroqi
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2840

Abstract

This study investigates factors influencing the adoption and utilization of ShopeePay, a digital wallet integrated within the Shopee e-commerce platform, among Generation Z women in Indonesia. Given this demographic group's active engagement in digital transactions, identifying key behavioral factors underlying their adoption of mobile payment technologies is essential. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this research examines core determinants of behavioral intention toward ShopeePay, additionally incorporating trust, perceived security, and network externalities into the analytical framework. Employing a quantitative research methodology, the study utilized an online structured survey involving 160 female respondents aged between 18 and 27 years who actively use ShopeePay. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS version 4.1.0.9. Findings demonstrate that performance expectancy, social influence, and perceived security significantly and positively affect behavioral intention to adopt ShopeePay. Trust indirectly influences intention through its impact on performance expectancy. Interestingly, effort expectancy and facilitating conditions did not demonstrate significant direct effects on behavioral intention. These results underscore that Gen Z female users prioritize application performance, social recommendations, and perceived safety when selecting digital payment solutions. Practically, these insights inform e-wallet developers and marketing professionals in designing targeted functionalities and marketing strategies aligned with user expectations and digital habits. Moreover, this study contributes to existing academic literature on mobile payment adoption and offers strategic implications for enhancing user acceptance, particularly among young female consumers in Indonesia.
Implementation of IOT-based Motorcycle Security System with Cut Off Engine and Mobile Application Insany, Gina Purnama; Alawiyah, Sindi Aulia; Somantri; Septian, Deni Ramdan; Iyusmani
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2841

Abstract

The increasing rate of motorcycle theft highlights the limitations of conventional security systems such as mechanical alarms and double locks, which often fail to provide proactive protection. This research proposes an Internet of Things (IoT)-based motorcycle security system integrating an engine cut-off feature, GPS tracking, vibration detection, and real-time notifications via a custom mobile application. Unlike previous solutions that commonly rely on SMS gateways or third-party services, this system leverages the Wemos D1 Mini microcontroller and Firebase Realtime Database to enable high-speed, two-way communication between the vehicle and the user. The system allows real-time vehicle location monitoring, remote engine control, and immediate detection of suspicious activities through the SW-420 vibration sensor connected to an audible buzzer alarm. The Android application, developed independently using the Kodular platform, not only provides digital vehicle location mapping but also enables quick engine deactivation and emergency alerts within seconds. Laboratory and field tests confirm a response time of less than one second for the engine cut-off function and accurate GPS tracking with an average deviation of under 10 meters. The primary innovation of this study lies in the full integration of IoT components, mobile interfaces, and cloud databases into a single platform without external dependencies, thereby enhancing efficiency, reliability, and system flexibility. The results demonstrate the system’s potential to deliver adaptive and modular vehicle security solutions, with opportunities for future enhancements such as geofencing, biometric authentication, and hybrid connectivity for improved resilience.
Building a School Guest Book Information System Using the Waterfall Method Subhana, Muhammad
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2847

Abstract

The increasing volume of data on human activities has resulted in a growing number of visitors to schools, particularly in the western part of Rangkasbitung, Lebak, Banten. Prospective parents visit the RA (Raudhatul Athfal) institution to enroll their children in school, pay monthly administration fees, and engage in educational activities with various general visitors. This influx poses several challenges for school administration and security, especially since visit records are still maintained manually, leading to data loss, difficulties in accessing visit histories, and limitations in monitoring guests, including parents and vendors. This issue impacts the transparency of school activity records. To address this, the research proposes a web-based school booking system to record visitor data based on the purpose of the visit, thus allowing for more effective data access and preliminary analysis in the event of an incident. This system is developed using the waterfall method, which is chosen for its structured concept, suitable for small to medium-sized school environments. Each phase consists of needs analysis, design, implementation, testing, and maintenance, carried out sequentially. Evaluation using a Likert scale from 104 respondents (determined by Slovin's formula) showed the Daily Report feature scored highest at 3.81, while the Weekly Report was lowest at 3.04. Maintenance tests found input validation and weekly report printing less smooth (2.84 and 2.69). Overall, the system effectively supports visitor monitoring with accessible features.
Comparative Analysis of Machine Learning Algorithms for Predicting LQ45 Stock Index Prices Hidayat, Amin; Ade Putra Prima Suhendri
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2853

Abstract

An essential metric for assessing the success of the country's capital markets is the LQ45 index, which is made up of 45 stocks with the biggest market capitalization and liquidity on thb e Indonesia Stock Exchange. Stock price prediction, particularly in volatile markets, remains complex challenge that benefits from advanced analytical approaches. While machine learning (ML) techniques have demonstrated significant promise in financial forecasting, comprehensive comparative evaluations across multiple algorithms and preprocessing strategies remain limited. In order to evaluate the predictive performance of nine machine learning algorithms Random Forest, Decision Tree, AdaBoost, Support Vector Classifier (SVC), XGBoost, Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN) in predicting the direction of movements of the LQ45 index, this study presents a structured comparative framework. The models are trained using a 10-year historical dataset, incorporating both continuous and binary representations of technical indicators. Three data preprocessing approaches are explored: raw trading data, unsmoothed indicators, and smoothed indicators. Accuracy, precision, recall, F1-score, and ROC AUC are all important factors in model evaluation. The findings show that when applied to continuous data with smoothed technical indications, Random Forest and XGBoost produce the best prediction results. For binary classification tasks, Naive Bayes emerges as the most effective model. These results demonstrate how important data representation and preprocessing in particular, smoothing are to enhancing the accuracy and robustness of models.  Research aids in the creation of trustworthy, data-driven stock prediction tools that are suited for developing markets.  Financial analysts, portfolio managers, and algorithmic traders looking to improve investment strategies through well-informed model selection and preprocessing design can benefit from the findings.
Personalized Skincare Recommendation System Based on Ontology and User Preferences Jannatin, Asista Ainun; Ratnasari, Chanifah Indah
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2857

Abstract

Personalized skincare product selection remains a complex but critically important challenge, as tailoring recommendations to individual skin profiles directly enhances treatment efficacy and fosters consumer trust. Traditional systems, such as content-based and collaborative-filtering, often fail to capture semantic interactions among skin types, concerns, and ingredients. To address these limitations, we propose an innovative ontology-based skincare recommendation system that integrates structured dermatological knowledge with semantic reasoning. Leveraging the Methontology framework, we developed an ontology composed of twelve core classes such as Product, Ingredient, Skin Type, and Skin Concern and more than twenty-five object properties to model interrelated concepts. The knowledge base was populated via web scraping from three prominent platforms (Sociolla, Beautyhaul, Skinsort), yielding over 3,800 products and 28,000 ingredients. We augmented this dataset with dermatological literature to ensure clinical validity. The architecture employs Apache Jena Fuseki and SPARQL for inference, with a React-Node.js web interface. Users input skin type, concerns, and sensitivities, which are translated into RDF triples and processed through semantic rules to generate personalized recommendations. An evaluation based on the Technology Acceptance Model (TAM) assessed Perceived Usefulness and Ease of Use. Ten diverse respondents rated the system with an average score of 4.5 out of 5 (SD=0.3) and endorsed the relevance of recommendations with a score of 4.8. Our findings demonstrate that semantic technologies can significantly enhance personalization and transparency in skincare solutions. This work lays a robust foundation for future innovations in beauty technology, clinical decision support, and consumer health platforms.
Evaluation of Student UX on E-Learning Web Using User Experience Questionnaire (UEQ) Darmawan, Akhmad Nabil; Faroqi, Asif; Pratama, Arista
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2860

Abstract

User Experience (UX) in E-Learning systems plays a critical role in fostering student engagement and improving learning effectiveness. Evaluating UX is essential to address challenges such as reduced learning motivation, poor navigation, and low interface appeal, all of which can hinder educational outcomes. This study aims to evaluate the UX of students using the E-Learning system at SMP Muhammadiyah 17 Surabaya by employing the User Experience Questionnaire (UEQ), which measures six dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. A total of 180 students were selected using Slovin’s formula. Data were analyzed using the UEQ Data Analysis Tool (v10) through descriptive statistics and reliability testing (Cronbach’s alpha > 0.70). The results indicate that the dimensions of Attractiveness (M = 1.43), Efficiency (M = 1.35), Dependability (M = 1.43), and Stimulation (M = 1.34) received high scores and exceeded the average benchmark, indicating that the system is engaging, efficient, reliable, and motivating for users. The Perspicuity dimension (M = 1.42) was rated moderately high, though improvements are needed to better support the understanding of complex materials. In contrast, the Novelty dimension (M = 0.28) scored low, suggesting a lack of innovation and freshness in the interface. These findings highlight the pedagogical significance of UX in enhancing student engagement and online learning effectiveness. Developing more innovative features such as gamified elements or adaptive feedback is recommended to increase the appeal and sustained use of the E-Learning system in alignment with educational goals.
Thesis Title Similarity Detection System Using Levenshtein Distance and Cosine Similarity Dede Sunandar; Adam Muiz
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2864

Abstract

The manual verification process of thesis titles in higher education institutions is often time-consuming and prone to oversight, making it difficult to ensure the uniqueness of each student’s work. This poses serious academic risks, as undetected similarities in thesis titles can lead to unintended plagiarism, compromise academic integrity, and undermine the credibility of educational institutions. In a broader sense, repeated or overlapping research topics also reflect a lack of innovation and weaken the scientific contribution of academic programs. To address this issue, an automated detection system is needed to efficiently identify similarities between thesis titles. This study aims to develop a web-based thesis title similarity detection system that integrates Levenshtein Distance and Cosine Similarity algorithms. The system was developed using the Waterfall model, involving stages of requirements analysis, design, implementation, and evaluation. Functional features such as login, title data management, old spelling normalization, and real-time similarity detection were implemented. The results show that the combination of both algorithms effectively detects similarities in character and semantic aspects. The inclusion of an old spelling normalization feature significantly improves detection accuracy by aligning historical and modern word forms prior to analysis. In conclusion, the developed system not only supports a faster and more objective title verification process but also contributes to the prevention of academic plagiarism and promotes integrity in higher education environments.
Personality Prediction Based on Video Using Transfer Learning DeepID Model Pradana, Handika Dio; Nudin, Salamun Rohman
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2866

Abstract

This research presents an automatic personality prediction system based on the Big Five model openness, conscientiousness, extraversion, agreeableness, and neuroticism by leveraging transfer learning on the DeepID architecture. Video input is first processed with the MTCNN algorithm for robust facial region detection under varying lighting and poses. Extracted features are fed into a modified DeepID model, pre-trained on large-scale face-recognition datasets, to perform spatial encoding. To capture temporal dynamics, Long Short-Term Memory (LSTM) networks model frame-to-frame changes in expression. Training and validation use the ChaLearn LAP dataset of approximately 10,000 annotated videos. Experimental results demonstrate 88.6% overall accuracy, with an average precision of 87.2%, recall of 86.5%, and F1-score of 86.8%, confirming the model’s balanced performance across classes. A minimum loss of 11.3% further underscores effective convergence. The complete pipeline is deployed via Flask, enabling real-time, web-based integration. Beyond academic novelty, this system holds promise for practical applications: in recruitment, it can offer unbiased, rapid personality screening; in mental-health contexts, it may assist clinicians by flagging behavioral cues non-invasively; and in human–computer interaction, adaptive interfaces could personalize responses based on users’ inferred traits. By combining transfer learning with temporal modeling, our approach delivers a scalable, non-invasive tool for automated psychological assessment through visual data, paving the way for ethical, real-time personality analytics in diverse domains.
Evaluating Digital Learning Media: Impact of Synchronous and Asynchronous Approaches in IT Harleni, Harleni; Herayono, Andhika; Rizal, Fahmi; Ambiyar; Abdullah, Rijal
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2884

Abstract

This study critically evaluates the effectiveness and relevance of synchronous and asynchronous learning media using a meta-analysis approach, aiming to provide a deeper understanding of their role in the rapidly evolving post-pandemic educational landscape. The unprecedented acceleration of online learning due to COVID-19 has highlighted the need for rigorous evaluation of digital media to ensure that educational delivery remains effective, equitable, and sustainable. Synchronous learning, characterized by real-time interaction, and asynchronous learning, which allows flexible and self-paced engagement, have become indispensable components of modern education. To conduct this analysis, 26 peer-reviewed articles were systematically selected and analyzed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, with studies categorized by research type, education level, and methodology to ensure quality and relevance. The findings indicate that both synchronous and asynchronous media significantly improve learning effectiveness, particularly by enhancing student engagement, accessibility, and adaptability to diverse learning needs. For educators, these results highlight the necessity of designing blended learning strategies that balance real-time interaction with flexible content access. For students, the combined use of these media fosters personalized learning experiences, supporting both collaboration and autonomy. At the institutional level, the findings emphasize the importance of investing in robust digital infrastructures and pedagogical innovations to remain responsive to future disruptions. Funnel plot analysis confirms minimal publication, reinforcing the reliability of the findings. This study provides actionable recommendations for educators and policymakers to strategically integrate synchronous and asynchronous learning methods, shaping more inclusive and resilient educational framework for the digital era.
Designing a Real-Time TOXMAP Backend Based on FastAPI and Firebase for B3 Waste Lubis, Gading Aurelia Nabila; Purnamasari, Rita; Saleh, Khaerudin
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2888

Abstract

Household waste classified as Hazardous and Toxic Waste (Bahan Berbahaya dan Beracun—B3) poses serious risks to health and the environment if not managed properly. Mismanagement can result in groundwater contamination, soil degradation, and long-term exposure to carcinogens. To address this issue, TOXMAP was developed as a mobile-based system that integrates real-time image classification with location-based disposal guidance. This paper discusses the development of the TOXMAP backend using a FastAPI server to process image input and classify waste using a pre-trained Support Vector Machine (SVM) model. Firebase supports user authentication, image storage, and retrieval of nearby dropbox locations. The Flutter-based frontend enables cross-platform access and supports real-time camera input. Load and integration tests show that the system responds in under one second with good classification accuracy and high user responsiveness. The system architecture effectively combines machine learning inference, cloud-based data handling, and mobile accessibility. FastAPI, Firebase, and SVM were selected to ensure lightweight, responsive, and accurate performance. Testing confirmed strong system stability and efficient computation during iterative use. The SVM model offers a balance between prediction accuracy and resource efficiency. By providing accurate classification and practical location guidance, the TOXMAP system enhances environmental awareness and promotes responsible disposal behavior. This architecture presents a scalable, lightweight, and accessible solution to support better household hazardous waste management and sustainable behavioral change.

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