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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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Application of Naïve Bayes for Sentiment Analysis of Shopee App User Comments Muhammad Dwiky Candra Fardani; Esti Wijayanti; Ahmad Abdul Chamid
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2854

Abstract

The growth of e-commerce has transformed consumer behavior, with Shopee emerging as one of the leading platforms in Southeast Asia and particularly dominant in Indonesia. Millions of user reviews on the Google Play Store capture diverse experiences, yet their unstructured nature hinders efficient extraction of actionable insights. This study addresses the challenge by developing an automated sentiment analysis system for Shopee user reviews, focusing on the effective use of the Naïve Bayes algorithm for Indonesian-language data. While Naïve Bayes is widely applied in text classification, this research distinguishes itself by integrating rigorous preprocessing tailored to colloquial and context-specific Indonesian app reviews, coupled with TF-IDF weighting, to enhance classification performance. A dataset of 4,000 reviews was collected via web scraping, labeled automatically based on user ratings, and split into 80% training and 20% testing subsets. Preprocessing included cleaning, case folding, tokenization, and stemming to standardize textual input. The proposed model achieved an accuracy of 83%, precision of 81%, recall of 90%, and F1-score of 85%, indicating strong performance despite class imbalance and the prevalence of ambiguous or sarcastic expressions. The results demonstrate that a lightweight probabilistic classifier, when combined with domain-specific preprocessing, can yield competitive accuracy while maintaining computational efficiency. This study contributes to sentiment analysis research in underrepresented linguistic contexts and offers a practical framework for e-commerce platforms to systematically interpret large-scale user feedback, prioritize feature improvements, and enhance customer satisfaction strategies.
Classification of Wind Instrument Sound Arrangements Using Recurrent Neural Network (RNN) Method Dwinggrit Oktaviani Putri; Dwi Arman Prasetya; Wahyu Syaifullah J.S
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2879

Abstract

Wind instruments such as saxophone, clarinet, trumpet, and others possess unique characteristics in terms of frequency, amplitude, and waveform, serving as distinct acoustic signatures for each instrument. In the digital era, the classification of musical instruments for composition and arrangement is often still carried out manually, which can lead to inefficiencies and potential errors in the music production workflow. Automating this process can significantly enhance the speed, accuracy, and consistency of instrument sound classification, especially for large-scale or real-time applications. This study aims to develop a classification system for wind instrument sounds using the Recurrent Neural Network (RNN) method, leveraging acoustic features such as Mel-Frequency Cepstral Coefficients (MFCC) and spectrograms. The RNN architectures employed in this research are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), chosen for their ability to capture temporal dependencies in sequential data such as audio signals, making them well-suited for sound-based classification tasks. The dataset consists of 1,200 audio files (.wav) comprising four wind instrument classes: trumpet, baritone, mellophone, and tuba, with 300 samples each. Experimental results show that the LSTM model achieved an accuracy of 95%, while the GRU model reached 99%. Compared to previous studies that reported lower accuracy or focused on broader instrument categories, this research demonstrates a significant improvement in wind instrument classification. The results highlight the effectiveness of RNN-based models in learning the temporal dynamics of audio signals, offering a reliable solution for automated instrument classification in digital music systems.
Application of NLP and Rasa for Intent Classification in Durga Historical Texts Dea Puspita; Eka Dyar Wahyuni; Tri Lathif Mardi Suryanto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2887

Abstract

This research aims to develop a chatbot based on Natural Language Processing (NLP) using the Rasa framework and the DIETClassifier model to classify user intent from historical data about Dewi Durga within the Indonesian cultural context. Dewi Durga’s stories play a significant role in preserving Indonesian heritage, especially as younger generations increasingly disengage from traditional knowledge. This study highlights the importance of digital preservation as a strategy to keep these narratives accessible and relevant. The chatbot is designed to provide interactive, educational conversations about Durga mythology, helping users understand Hindu cultural values through an intuitive and accessible digital platform. The research methodology involves several key stages: data cleaning and preparation, intent labeling, splitting data into training and testing sets, and evaluating model performance using a Confusion Matrix. Metrics such as accuracy, precision, recall, and F1-score are used to assess classification performance. The DIETClassifier model achieved strong results, with an accuracy of 0.94, precision of 0.90, recall of 0.93, and an F1-score of 0.91, indicating high effectiveness in intent classification. Following model training and evaluation, the chatbot was deployed using Flask, allowing real-time user interaction through a responsive web interface. This project contributes to the use of NLP-based chatbot technology in cultural preservation, specifically focusing on Dewi Durga’s mythology in Javanese and Balinese traditions. By digitizing these stories, the study aims to prevent cultural erosion and promote broader engagement with heritage. The approach may also be applied to other domains that require deep cultural understanding.
Analyzing Mobile Banking User Experience Using the Modified HEART Metrics Framework Miftah Rahmaddani; Anita Wulansari; Rafika Rahmawati
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2898

Abstract

The rapid development of mobile banking services in Indonesia has facilitated digital financial transactions for the public. BLU by BCA Digital, a fully branchless banking innovation, has experienced significant user growth, yet continues to receive complaints regarding technical issues and unintuitive features raising concerns about the quality of its user experience (UX). This study analyzes the UX of the BLU application using a modified HEART Metrics framework, which includes five dimensions Happiness, Engagement, Adoption, Retention, and Task Success and incorporates an additional variable, Intention to Reuse, to better capture long-term user behavior. The integration of this variable, grounded in the Theory of Planned Behavior, enhances the framework by linking user satisfaction with behavioral intention. Data were collected from 476 active users through an online survey and analyzed using the Structural Equation Modeling Partial Least Square (SEM-PLS) method via SmartPLS 4. The results show that Happiness, Engagement, Adoption, and Task Success have a significant positive effect on users’ intention to reuse the application, while Retention does not. This suggests that task efficiency and emotional satisfaction play a more crucial role in sustaining user engagement than habitual usage alone. Additionally, Adoption was rated at the lower threshold of the “Good” category in the Goal-Signal-Metrics (GSM) analysis, indicating a need for better onboarding and first-time user experience. These findings offer theoretical contributions to UX measurement and provide practical insights for developers to enhance mobile banking applications and foster long-term user loyalty.
Development of an Automatic Clothesline System Based on Weather Sensors and Telegram Notification Hanifan Budi Kustiawan; Heri Sudibyo; Dwi Winarti
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2900

Abstract

Unpredictable weather conditions often cause outdoor laundry to become wet again, requiring constant manual monitoring. This study presents the design and development of an Internet of Things (IoT)-based automatic clothes drying system that responds to real-time environmental changes and offers remote control via the Telegram application. The system employs an ESP32 microcontroller integrated with a rain sensor, a DHT22 humidity sensor, and an LDR light sensor. Sensor data is processed in real time using predefined threshold values: when rain is detected, humidity exceeds 26%, or light intensity rises above 3500 lux, the ESP32 triggers a servo motor to retract the clothesline. Conversely, in dry and bright conditions (humidity < 25%, light < 3400 lux), the system extends the clothesline. A servo motor is used as the actuator to retract or extend the drying rack. A Telegram Bot is also integrated to deliver real-time notifications and accept manual commands such as /masuk, /keluar, and /status. The development method follows a prototyping approach that includes requirement analysis, hardware-software integration, and system evaluation. Experimental results show that the system achieved over 95% accuracy in detecting environmental changes and responded within 2–3 seconds to commands and sensor triggers. The combination of sensor automation and interactive Telegram control demonstrates reliability and flexibility in smart-home applications. In conclusion, the proposed system provides an effective and user-friendly solution for managing outdoor laundry in variable weather conditions, with promising potential for future enhancements in IoT-based household automation.
Sentiment Analysis of YouTube Comments on Free Lunch Program Using Machine Learning Bagus Satrio Pringgodani; Aji Supriyanto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2908

Abstract

In the digital era, social media has become a primary platform for the public to express opinions, including reactions to governmental initiatives such as Indonesia's "Free Lunch" program. This study aims to systematically analyze public sentiment toward the program by leveraging YouTube comment data, providing a data-driven perspective on public perception. Comment data were automatically retrieved using the YouTube Data API v3 and underwent comprehensive text preprocessing, including data cleaning, case folding, normalization, stopword removal, and stemming. The preprocessed text data were classified into positive, negative, and neutral sentiments using two machine learning algorithms: K-Nearest Neighbor (KNN) and Naïve Bayes. Algorithm performance was systematically evaluated using a confusion matrix and standard classification metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrated that the Naïve Bayes classifier achieved higher precision (66%), recall (66%), and accuracy (66%), outperforming KNN in classifying sentiments within imbalanced datasets. Conversely, KNN showed more stable yet lower accuracy (39%) performance when sentiment distribution was relatively balanced. This study highlights the importance of thorough preprocessing and careful algorithm selection to improve sentiment classification accuracy from informal, user-generated content, especially within the Indonesian language context. The findings provide critical insights for policymakers, emphasizing the value of machine learning as a robust, empirical approach to evaluating public opinion.
Development of a Procurement Information System for Medical Service Providers Almas Agung Firdaus; Abdul Rezha Efrat Najaf; Tri Luhur Indayanti Sugata
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2909

Abstract

Procurement processes in healthcare institutions often face challenges such as delays, lack of documentation, and limited transparency in vendor selection. These issues can negatively impact the operational efficiency and availability of medical logistics. This study aims to develop a web-based procurement information system that addresses these problems through a structured digital approach. The research was conducted using a case study at Sekata Medical Center Clinic in Samarinda. The system was developed using the Waterfall model, which includes five stages: requirements analysis, system design, implementation, testing, and maintenance. The development was carried out using PHP as the programming language and MySQL as the database, supporting multiple user roles including admin, department, division, and vendor. The system implementation includes key features such as item request submission, vendor selection, invoice processing, and goods verification. Testing using the Black Box method confirmed that all functionalities performed correctly according to the defined specifications, with accurate error handling and validation processes. The results demonstrate that the system enhances the procurement process by providing structured workflows, real-time documentation, and transparency across all stages. In conclusion, this research contributes to improving procurement efficiency, data accuracy, and accountability in medical service providers. It also lays the groundwork for future development, such as integration with real-time reporting, enhanced data security, and broader deployment in other healthcare institutions.
Genre-Based Anime Recommendation System Using KNN with Fanbase Bias Detection Muhamad Rizky Fauzi; Imam Sanjaya; Ivana Lucia Kharisma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2917

Abstract

The rapid growth of the anime industry presents a challenge for users, especially newcomers, in finding content that matches their personal preferences. To address this issue, this study proposes a genre-based anime recommendation system using a Content-Based Filtering approach, incorporating Term Frequency-Inverse Document Frequency (TF-IDF), the K-Nearest Neighbor (KNN) algorithm, and fanbase bias detection. This system transforms genre information into numerical vectors using TF-IDF, allowing for precise similarity calculations between anime titles based on genre relevance. KNN is used with cosine similarity to identify the top five most similar anime to a given input. A key novelty of this study is the implementation of a fanbase bias detection mechanism that filters out anime with high ratings but very low member counts, which often distort overall ratings due to a small but passionate fanbase. This filtering process ensures that the recommendation output better reflects general audience preferences. The dataset, sourced from MyAnimeList via Kaggle, includes 12,294 entries and underwent extensive preprocessing, including missing value removal, duplicate elimination, and statistical thresholding for bias detection. Evaluation of the system was performed using accuracy, precision, recall, and F1-score, with results showing strong performance (F1-score of 91.94%). Additionally, 5-fold cross-validation confirmed the consistency of the model. Designed for general anime viewers, the system is implemented using the Streamlit framework to provide an accessible and interactive web-based interface. This study demonstrates that the combination of content-based techniques and fanbase bias filtering significantly enhances recommendation quality, offering a novel and practical solution for anime discovery
Interactive Webgis for Mapping and Monitoring Urban Drainage Systems Muhamad Adam; Somantri Somantri; Imam Sanjaya
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2918

Abstract

The development of infrastructure and changes in land use in urban areas increase surface runoff that cannot be properly managed, posing a flood risk if the drainage channel capacity is inadequate. To address the lack of an integrated information system for drainage monitoring, this study developed a WebGIS system based on Laravel, QGIS, and LeafletJS using the Rapid Application Development (RAD) approach. The system is designed to support the Department of Public Works and Spatial Planning (DPUTR) of Sukabumi City in monitoring and mapping the distribution and condition of drainage channels interactively and in real-time. The WebGIS integrates spatial data (Linestring geometry) and non-spatial data (condition attributes, length, and road location), and provides features such as search, condition filters, and elevation contour layers. System testing was conducted using the Black-box method and Lighthouse tools to assess functionality and performance. The results showed scores of 91 for accessibility, 79 for performance (mobile), 72 for best practices, and 92 for SEO, indicating a user-friendly interface that complies with web development standards. The system is considered effective in improving drainage infrastructure management, supporting spatial-based decision-making, and enhancing public information transparency. Therefore, this system serves as a replicable WebGIS model for other cities facing similar drainage management challenges.
Analysis of Factors Influencing E-Book Acceptance Using Technology Acceptance Model (TAM) Indy Millenio Diez Sutanto; Asif Faroqi; Siti Mukaromah
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2919

Abstract

Reading remains a fundamental cognitive activity, even in the digital era. E-books offer an accessible and flexible alternative to printed materials, especially in academic settings. However, their acceptance among students varies significantly. This study aims to investigate the key factors influencing students’ acceptance of e-books using the Technology Acceptance Model (TAM). Specifically, it examines the effects of perceived usefulness, perceived ease of use, attitude toward e-books, innovativeness, and perceived risk on behavioral intention. A quantitative method was employed by distributing structured questionnaires to 400 students at UPN "Veteran" Jawa Timur. Data analysis was conducted using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The findings show that perceived usefulness and attitude toward e-books are the strongest predictors of e-book acceptance. Perceived ease of use and innovativeness also have a significant positive impact, whereas perceived risk shows no meaningful influence. These results suggest that students are primarily motivated by the benefits and positive experiences associated with e-book usage, rather than concerns over potential drawbacks. The practical implications of this study are substantial for higher education institutions. By enhancing the perceived usefulness and usability of digital resources, universities can foster greater adoption of e-books. Moreover, integrating student-centered design and promoting digital innovativeness can support more effective implementation of e-learning policies. This research provides actionable insights for educators and policymakers seeking to optimize digital learning strategies in academic environments.

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