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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 370 Documents
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Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification Perhan, Akbar Ilham; Yustiana, Indra; Sanjaya, Imam
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.v8i1.2755

Abstract

The distribution of social assistance in Indonesia often faces challenges in accuracy, where individuals who are financially capable still receive aid, while those truly in need are excluded. To address this issue, this study applies a Machine Learning approach using the C4.5 Decision Tree algorithm to classify the eligibility of recipients in Bojonggenteng Village. This algorithm was chosen because it is easy to interpret, performs well, and is suitable for categorical data. The main objective of the study is to develop a classification model that enhances the objectivity and accuracy in determining aid recipients, ensuring that assistance is directed to those who truly need it. The research process involves several stages, including problem identification, literature review, data collection, preprocessing, classification, and model evaluation. A total of 904 records from the 2023 BPNT and PBI-JK programs were obtained in collaboration with the local village authorities. The classification process was conducted using RapidMiner, which allows for visual data processing and model building without requiring programming. The model evaluation was carried out using a confusion matrix, yielding an accuracy of 98.90%, precision of 100%, recall of 97.60%, and an AUC score of 0.988. These results indicate that the C4.5 algorithm is effective for prediction tasks and can be a valuable tool in supporting fair and data-driven decision-making in social assistance programs. This study concludes that the application of Machine Learning in this context improves the fairness and transparency of aid distribution and recommends future research to involve larger datasets for broader implementation.
Platform An E-Commerce Platform for Coffee MSMEs: System Design and Basic Features Hesti, Emilia; Kaila, Afifah Syifah; Handayani, Ade Silvia; Novianti, Leni; Rakhman, M Arief
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.v8i1.2756

Abstract

Digitalization of Micro, Small, and Medium Enterprises (MSMEs) has emerged as a strategic necessity in the era of digital transformation. However, many coffee-based MSMEs in Indonesia continue to rely on third-party marketplace platforms that limit autonomy over customer data, pricing control, and brand personalization. This study aims to address these constraints by designing and developing an independent, web-based e-commerce system that aligns with the specific operational needs of coffee MSMEs particularly those seeking low-cost, user-friendly solutions that enable direct customer engagement and reduce commission-based dependencies. The system was developed using Laravel for the backend and Vite.js for the frontend, adhering to the sequential stages of the waterfall model: requirements analysis, system design, implementation, and testing. Key features include product catalog management, shopping cart functionality, manual payment upload, and product review integration. Black-box testing confirmed that all features operated without critical errors under typical usage conditions. Usability testing conducted with five MSME users resulted in an average satisfaction score of 4.23 out of 5 (83%), with high ratings for ease of navigation and interface responsiveness. Performance metrics, including average page load time (<=3 seconds), device compatibility, and user flow scalability, met expected standards. Although the current system employs manual payment validation, future enhancements will focus on integrating secure payment gateways, real-time analytics dashboards, and modular APIs. In summary, the platform offers a practical and scalable e-commerce solution tailored to the autonomy and contextual demands of Indonesia's coffee MSMEs.
Development of a Web-Based Internship Registration System to Improve Administrative Efficiency Apriyanti, Hany Nur; Briyanti, Ika Nova; Hakim, Zainul; Suparman
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.v8i1.2761

Abstract

The rapid advancement of information technology in the era of globalization has significantly impacted sectors such as communication, education, economy, and culture. Information is now easily accessible, overcoming traditional barriers of distance and time. In computer science, computers have become essential tools for meeting information needs in personal, educational, business, and governmental settings due to their ability to process data rapidly and accurately. In education, particularly in vocational schools, there is a need for an information system to streamline access to data, especially related to internship (PKL) activities. This research focuses on developing a web-based internship registration system at SMK Persada Pasarkemis to address issues like document loss, delays in data entry, and errors in manual processes. The system aims to improve registration efficiency by allowing users to upload documents, track registration status in real-time, and enhance transparency and data access for teachers. This study seeks to implement a computer-based system that optimizes PKL data management, reducing administrative bottlenecks and ensuring accurate data processing. The expected outcome is a more organized and efficient PKL registration process. Future developments include integrating the system with other information systems and adding features based on user feedback. The software development process follows the Software Development Life Cycle (SDLC) using the Waterfall model, chosen for its structured, sequential approach. This methodology ensures that each phase design, requirements analysis, testing, implementation, and maintenance is thoroughly completed before moving to the next, ensuring a reliable, well-documented system that meets user needs.
Sentiment Classification of Customer Reviews in the Fast-Food Industry Using the Naïve Bayes Algorithm Rukmana, Diding; Putri, Aliya Namira; Karim, Abdul; ZA, Makmun
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.v8i1.2766

Abstract

In the digital era, online reviews have become a significant source of information, influencing consumer perceptions and purchasing decisions, particularly in the fast-food industry. This research focuses on classifying customer sentiment towards A&W restaurants based on online reviews using the Naïve Bayes algorithm. The objective of this study is to analyze customer feedback to understand their perceptions of A&W’s services and products. The research follows the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, which involves six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data was collected from Google Reviews of the A&W Palem Semi branch, consisting of 200 customer reviews, which were preprocessed to remove irrelevant content and prepare the data for analysis. The Naïve Bayes algorithm was applied to classify the sentiments into three categories: positive, negative, and neutral. The model achieved an overall accuracy of 83%. However, the results revealed a significant class imbalance, with most reviews labeled as neutral. While the model performed well in identifying neutral sentiment (precision 0.89, recall 0.97, F1-score 0.93), it failed to classify positive and negative sentiments accurately, as both achieved precision, recall, and F1-scores of 0.00. This demonstrates that the data imbalance severely impacted the model’s ability to detect minority sentiment classes. The research concludes that while Naïve Bayes offers useful insights into customer sentiment, improvements are necessary, including applying data balancing techniques or exploring alternative algorithms such as SVM or Random Forest to enhance classification performance across all sentiment categories.
Implementation of Technique for Order Preference by Similarity to Ideal Solution for Selecting Content Kharisma, Ivana Lucia; Yustiana, Indra; Zahra, Falya Amrina
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.v8i1.2768

Abstract

This study addresses the challenge faced by the Sukabumi Creative Hub Instagram team in identifying the most engaging content by proposing a web-based Decision Support System (DSS) utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Instagram, as a dominant social media platform in Indonesia, serves as a vital tool for promoting local creative industries, yet current content evaluation lacks systematic analysis. The system developed ranks 62 content items based on three engagement metrics—likes, views, and shares—weighted at 5, 3, and 1 respectively. Data were processed using Microsoft Excel and visualized through an Input-Process-Output (IPO) model. The results show that “Rekap Merangkum Sukabumi” achieved the highest relative closeness (RC = 0.8793), demonstrating TOPSIS’s effectiveness in ranking content based on proximity to ideal engagement levels. Compared to previous studies that applied TOPSIS in different contexts, this research offers a novel contribution by applying it to localized social media content, filling a gap in digital content analytics literature. Despite limitations such as subjective weighting, platform specificity, and manual calculations, the system offers a replicable, structured approach to content evaluation, with implications for improved social media strategy and future research in automated, cross-platform DSS applications. Ultimately, this study bridges practical needs in creative content management with theoretical development in decision support systems for digital engagement analysis.
Multimodal Detection of Covert Online Gambling Advertisements Using Faster R-CNN and Tr-OCR Maldini, Andry Syva; Saputra, Wahyu Syaifullah Jauharis; Prasetya, Dwi Arman
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.v8i1.2769

Abstract

The increasing prevalence of online gambling advertisements on social media has led to the use of covert strategies, such as embedding visual watermarks and employing euphemistic language, to bypass traditional detection methods, rendering manual moderation ineffective. This study proposes an AI-based automated detection system designed to identify both explicit and obfuscated gambling content. The system operates in three stages: (1) Object detection: Faster R-CNN, using a ResNet-50 backbone and Feature Pyramid Network (FPN), detects gambling-related visual elements, such as watermarks and logos; (2) Text extraction: A Transformer-based Optical Character Recognition (TrOCR) model is employed to extract textual content from images and video frames, even in the presence of visual distortions; and (3) Text classification: A BERT-based Natural Language Processing (NLP) model is used to identify gambling-related language within the extracted text. The dataset, manually collected and annotated, was augmented with Roboflow to improve model robustness and generalization. Experimental results show that the Faster R-CNN model achieved an average precision of 98.1%, TrOCR demonstrated a Character Error Rate (CER) of 4.6% and a Word Error Rate (WER) of 29%, while the BERT classifier reached an impressive 99% accuracy with high precision and recall. The system was integrated into a Flask-based web application that allows real-time analysis of both image and video inputs. This system presents strong potential to support automated content moderation and curb the spread of online gambling advertisements on digital platforms, contributing to safer online spaces.
Decision Support System for Selecting the Best Employee Using the Simple Additive Weighting Method Galih Adi Nugraha; Wiji Lestari; Agustina Srirahayu
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.v8i1.2788

Abstract

Employee performance appraisal is a crucial aspect of human resource management, as it influences strategic decisions such as promotions, rotations, and incentives. However, manual evaluations are often prone to subjectivity and inefficiencies in terms of time and effort. This study aims to design and implement a decision support system (DSS) using the Simple Additive Weighting (SAW) method to determine the best employee objectively and measurably. The research adopts a software engineering approach with the waterfall model through stages of requirement analysis, system design, implementation, testing, and maintenance. The developed system is web-based and incorporates five key criteria: productivity, loyalty, work attitude, team contribution, and innovation. The testing results indicate that the system can process employee data, compute preference values, and display final rankings accurately and consistently with manual calculations. The system is also equipped with result export features and a user-friendly interface that facilitates the evaluation process. This study contributes a digital tool that reduces subjectivity in performance assessments and improves HR operational efficiency. In conclusion, the implementation of the SAW method in a web-based system is proven effective for supporting multi-criteria decision-making in selecting the best employee and is suitable for dynamic work environments.
Decision Support System for Selecting Santri Organization Leaders Using AHP and TOPSIS Nesti, Latifa Yulfitra; Firmansyah, Rivaldy; Iqbal, Muchamad; Arifin, Ahmad
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.v8i1.2790

Abstract

The election of the head of the student organization plays a vital role in the leadership regeneration process within Islamic boarding schools (pesantren). However, this process is often conducted manually and subjectively, leading to potential bias, limited transparency, and a lack of standardized documentation. To address this issue, this study aims to develop a web-based Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to facilitate a fair, measurable, and value-aligned selection process. The selection criteria were identified through interviews and observations, covering five main aspects: morality, discipline, leadership, academics, and socialization. AHP was applied to calculate the priority weights of the criteria, while TOPSIS was used to rank five alternative candidates based on their proximity to the ideal leader profile. The results show that morality emerged as the highest-weighted criterion (0.4672), reflecting the pesantren’s emphasis on personal integrity. Candidate Ziyan Kamil achieved the top preference score of 0.754, indicating the closest alignment with the ideal candidate profile. System functionality was validated using black-box testing, confirming successful implementation of all core features. In conclusion, the developed DSS supports a more transparent, objective, and accountable selection process. It contributes not only to digital transformation in Islamic educational institutions but also serves as a replicable model for participatory and value-based governance in other pesantren environments.
Measuring e-Filing Adoption as an e-Government Service Using the Technology Acceptance Model Putri, Syifa Aliya; 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.v8i1.2804

Abstract

The digital transformation of public services in Indonesia, as mandated by Presidential Regulation No. 95 of 2018 on SPBE (Electronic-Based Government System), has led to the development of e-Filing—an electronic tax return reporting system that allows taxpayers to submit their Annual Tax Return (SPT) online. However, adoption among individual taxpayers remains uneven. This study investigates the factors influencing the acceptance and use of e-Filing among individual taxpayers registered at KPP Pratama Banjarmasin using the Technology Acceptance Model (TAM). Employing a quantitative explanatory approach, data were collected from 100 purposively selected respondents through structured questionnaires and analyzed using PLS-SEM. The findings reveal that perceived ease of use significantly influences users’ attitudes, and positive attitudes, in turn, strongly predict the intention to continue using e-Filing. However, perceived usefulness shows no significant effect on either user attitudes or usage intentions, highlighting a key divergence from core TAM assumptions. Moreover, intentions to use the system significantly influence actual usage, while ease of use and usefulness do not directly drive usage intentions. This study contributes uniquely by identifying a gap between perceived system benefits and actual behavioral intent, especially in the context of infrequent or assisted use among taxpayers. It recommends broader research that includes varied demographic groups and adopts extended models like UTAUT to explore external influences such as digital literacy, policy enforcement, and user support.
Comparative Analysis of K-Means and Gaussian Mixture Model in Clustering Global CO2 Emissions Paramarta, Valentinus; Rahman, Alrafiful; Priska, Lely; Roken Gurning, Rizon; Ayu Purwati, Widya
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.v8i1.2805

Abstract

As global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses.

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