cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Budi Hermawan
Contact Email
-
Phone
+62081703408296
Journal Mail Official
info@kdi.or.id
Editorial Address
Jl. Flamboyan 2 Blok B3 No. 26 Griya Sangiang Mas - Tangerang 15132
Location
Kab. tangerang,
Banten
INDONESIA
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 370 Documents
 33   34   35   36   37 
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.
Development of an Android Application for Recipe Management Using Flutter and API Sutono, Eko; Muiz, Adam; Pratama, Muhammad Hafiz
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.v8i1.2890

Abstract

The advancement of digital technology encourages micro, small, and medium enterprises (MSMEs) to adapt their operational systems, including food recipe management. Many culinary businesses still rely on manual methods, which are prone to data loss, ingredient inconsistency, and inefficiencies. This condition often leads to increased operational costs, food waste, and inconsistency in product quality, which ultimately affects customer satisfaction and business sustainability. Digital solutions, especially those that support real-time recipe standardization, have the potential to streamline operations, minimize errors, and improve production consistency. This study aims to design and develop an Android-based food recipe management application using Flutter and REST API to support the digital transformation of MSMEs in the culinary sector. The Research and Development (R&D) approach with a prototyping model was applied, involving iterative stages: literature review, needs analysis, interface design using Figma, implementation using Flutter and REST API, and Black Box Testing involving real users for functional evaluation. The results show that all core features login, recipe search, categorization, and recipe data management (add, edit, delete) functioned properly as intended. User feedback indicated increased operational efficiency, reduced manual effort, and improved consistency in recipe handling, particularly in onboarding new staff and ensuring product uniformity. In conclusion, the developed application contributes practically to improving recipe management efficiency and supporting the digital transformation of MSMEs. This system also lays the groundwork for future development of AI-powered features, automatic nutrition analysis, and cross-platform expansion to iOS and web.
Design and Development of a Counseling Service System Using Extreme Programming Methodology Nobrian, Ikhsan; Nurlaili, Afina Lina; Aditiawan, Firza Prima
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.v8i1.2928

Abstract

This study addresses the inefficiency and error-prone nature of manual counseling and student violation point recording processes in schools, which often result in delays and inaccuracies. To overcome these challenges, we propose the development of a digital guidance and counseling service system designed to improve data management and enhance service accessibility for school administrators and counselors. The innovation lies in the creation of an integrated, browser-accessible application built using the MERN (MongoDB, Express.js, React, Node.js) stack, which ensures robust functionality and scalability. By applying modern development and testing methodologies, the system is designed to be both reliable and user-friendly. The core objective of this system is to streamline processes such as counseling appointment scheduling, alumni tracking, certificate submission, and student behavior reporting. It was developed using the Extreme Programming (XP) methodology, which encourages flexibility and iterative planning through close collaboration with end users. White Box Testing techniques, including cyclomatic complexity analysis and independent path testing, were employed to validate the system's internal logic. The system’s usability was assessed using the System Usability Scale (SUS), achieving an excellent score of 93.25, indicating high user satisfaction. Furthermore, the Lighthouse performance test yielded a perfect score of 100, confirming the system's high responsiveness. These results demonstrate that the developed system significantly enhances the efficiency, accuracy, and accessibility of guidance services, reduces administrative burdens, and enables better monitoring of student development, making it ideal for deployment in real-world school environments.
Development of Digital Information Systems for Operational Efficiency of Savings and Loan Cooperatives Saputra, Heru; Stephane, Ilfa; Putri, Nency Extise; Edison, Elisa Daniati; Yanto, Gusrino
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.v8i1.2931

Abstract

Savings and loan cooperatives are essential for advancing financial inclusion, especially in rural communities. However, many cooperatives still rely on manual or semi digital systems such as spreadsheets to manage member data and financial transactions. This condition results in low efficiency, delayed reporting, limited transparency, and frequent recording errors. This study aims to design and implement a web based cooperative information system to address operational inefficiencies at Korong Gadang Cooperative in West Sumatra, Indonesia, which serves over 120 active members. The system was developed using the Rapid Application Development (RAD) method, which emphasizes iterative prototyping and user involvement to accelerate development. The process consisted of three stages: requirements planning, system design and construction, and implementation. Modules include member management, savings and loan transactions, financial reporting, and an interactive dashboard. Black Box Testing was used to validate functionality, and user feedback was collected from 12 cooperative staff through questionnaires. The results show significant improvements in performance. Report preparation time decreased from 3–5 days to just 1 day. The system also enhanced data accuracy and transparency, enabling members and staff to access transaction information in real time. In conclusion, the web based cooperative information system developed with the RAD method has proven effective in improving efficiency and accountability. The system can be adopted by similar small-scale cooperatives with basic digital infrastructure. Future development may include mobile access, integration with payment systems, and analytical features to support data driven decisions.
Optimizing Book Genre Classification through AI on a Web Platform Dermawan, Fariz; Latifah, Noor
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.v8i1.3001

Abstract

In the rapidly evolving digital era, the exponential growth of online book collections poses challenges in efficiently classifying literature according to genre. Manual classification methods are often time-consuming, subjective, and inconsistent, necessitating the adoption of advanced, automated approaches. This study aims to develop and implement an Artificial Intelligence (AI)-based genre classification system integrated into a web platform to enhance the accuracy, efficiency, and user experience in book discovery. Leveraging Machine Learning (ML) algorithms—particularly Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Deep Learning—alongside Natural Language Processing (NLP) techniques such as tokenization, stemming, and TF-IDF, the system analyzes book descriptions and synopses to determine the most appropriate genre. The research follows a qualitative and literature study approach, utilizing a dataset sourced from Kaggle, with preprocessing steps to remove noise and convert text into numerical representations. Experimental results demonstrate that the SVM model achieved the highest accuracy, precision, recall, and F1-score compared to other tested algorithms, effectively handling high-dimensional and non-linear data. The developed web application features an interactive dashboard, real-time classification, and a hybrid recommendation system. This work confirms the feasibility and advantages of AI-driven genre classification for large-scale digital libraries and online bookstores. While limitations such as data imbalance and overlapping genre semantics remain, the findings provide a strong foundation for future research employing larger, more diverse datasets and advanced deep learning architectures to further improve classification performance.

Page 37 of 37 | Total Record : 370

 33   34   35   36   37 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2025): bit-Tech Vol. 7 No. 3 (2025): bit-Tech Vol. 7 No. 2 (2024): bit-Tech Vol. 7 No. 1 (2024): bit-Tech Vol. 6 No. 3 (2024): bit-Tech Vol. 6 No. 2 (2023): bit-Tech Vol. 6 No. 1 (2023): bit-Tech Vol. 5 No. 3 (2023): bit-Tech Vol. 5 No. 2 (2022): bit-Tech Vol. 5 No. 1 (2022): bit-Tech Vol. 4 No. 3 (2022): bit-Tech Vol. 4 No. 2 (2021): bit-Tech Vol. 4 No. 1 (2021): bit-Tech Vol. 3 No. 3 (2021): Remote Delivery Vol. 3 No. 2 (2020): Datamining Vol. 3 No. 1 (2020): Distance Learning Vol. 2 No. 3 (2020): Pandemik ICT Vol. 1 No. 3 (2019): Learning Synchronous and Asynchronous in the Industry 4.0 Vol. 2 No. 2 (2019): Support System Vol. 2 No. 1 (2019): Data Mining and Green Technology Vol. 1 No. 2 (2018): Data and Information Quality Vol. 1 No. 1 (2018): First Impression on Computing More Issue