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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
Development of a Web-Based Extracurricular Information System Using the Waterfall Model Dos Santos, Savio Nelinhos; Hanifah Permatasari; Eko Purwanto
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.v8i1.2746

Abstract

Extracurricular activities are an important part of the holistic development of students which includes the formation of character, interests, talents, and social skills. However, many educational institutions still manage these activities manually through spreadsheets and printed documents, resulting in data duplication, information delays, and low transparency. This research aims to design and develop a web-based extracurricular information system to support the administration, monitoring, and evaluation of activities in an efficient and structured manner. The development of the system uses the Waterfall model chosen for its sequential and well-documented workflow, suitable for systems with predefined needs from the outset. The development stages include needs analysis, system design using UML, implementation with Laravel 9 and MySQL, as well as testing through the Black Box method and direct evaluation by the end user. The results of the Black Box test show that all system features are working according to specifications. Additionally, feedback from users shows a high level of satisfaction with the ease of use, navigation, and relevance of the features provided. This system successfully overcomes administrative challenges in managing extracurricular activities and is able to improve operational efficiency and user engagement. With the support of features such as automatic periodic assessments, PDF reports, notifications to parents, as well as a multilingual interface, the system has the potential to be more widely adopted by educational institutions that have similar contexts.
Analysis of Frequency Spectrum in Digital Image Transmission Using Orthogonal Frequency Multiplexing Sarjana, Sarjana; Handayani, Ade Silvia; Wahyuni, Devi
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.v8i1.2749

Abstract

Accurate and efficient data transmission is increasingly essential due to the growing reliance on digital communication, particularly for multimedia content such as images. Orthogonal Frequency Division Multiplexing (OFDM) provides high bandwidth efficiency and strong noise resilience by transmitting data over multiple orthogonal subcarriers. Despite its advantages, limited studies have explored how modulation schemes influence frequency-domain characteristics during image transmission under different noise conditions. This study addresses that gap by evaluating digital image transmission through OFDM using Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation. The objective is to compare spectral performance across various signal-to-noise ratio (SNR) levels. A grayscale image is converted into a binary stream, modulated using BPSK and QPSK, and processed through an OFDM system with 512 subcarriers and a 25% cyclic prefix. The signals are transmitted through an Additive White Gaussian Noise (AWGN) channel at SNR values of 0 dB, 5 dB, and 10 dB. Power Spectral Density (PSD) is measured using the Welch method with a Hamming window, 50% overlap, and 1024-point Fast Fourier Transform (FFT). The results show that increasing SNR improves spectral sharpness, reduces the noise floor, and enhances symmetry. BPSK offers better performance in noisy conditions, while QPSK is more efficient in high-SNR environments. These findings provide practical insight for optimizing modulation choices in OFDM-based image transmission systems where spectral efficiency and noise robustness must be balanced.
Business Optimization Through Implementation of Cost of Production Pricing System Using Bill of Material Method Ananta, Yoga Sofian; Sabilla, Alzena Dona; Akbar, Agus Subhan
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.v8i1.2750

Abstract

The furniture industry often struggles with setting accurate selling prices due to the lack of structured and precise cost calculation systems. Many furniture entrepreneurs still rely on traditional methods that base prices on market trends, without accounting for underlying production costs. This often leads to prices that are too low, eroding profit margins, or too high, making it difficult to stay competitive. This study introduces a novel Cost of Goods Manufactured (COGM) calculation system, integrated with the Bill of Materials (BOM) method, to advance current pricing systems in the furniture industry. By incorporating key cost elements such as raw materials, labor, and the impact of waste and rework, the system enhances the accuracy of COGM calculations. Additionally, it offers greater transparency by providing a detailed breakdown of each cost component, allowing entrepreneurs to better understand their production expenses. The implementation of this system led to a 15% improvement in pricing accuracy, significantly reducing pricing errors and optimizing production costs. Furthermore, businesses reported a 20% increase in competitiveness due to more informed pricing strategies. This research demonstrates that integrating COGM with BOM not only improves production efficiency but also strengthens pricing strategies, contributing to long-term profitability. It highlights the role of cost transparency in driving sustainable growth, particularly for small and medium-sized furniture enterprises.
Design and Construction of a Website-Based Water Apple Ordering and Management System Karimah; Murti, Alif Catur; Nindyasari, Ratih
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.v8i1.2753

Abstract

This study aims to design and implement a website-based ordering and management system specifically for water apple commodities in Betokan Village, Demak. Farmers in this area previously relied on manual stock and transaction recording, which led to frequent issues such as data inaccuracy, difficulties in inventory tracking, and inefficiencies in sales operations. To address these challenges, a digital system was developed using the Waterfall method, comprising five stages: requirements analysis, system design, implementation, testing, and maintenance. The platform was built using the PHP programming language with the Laravel framework and a MySQL database. The system includes key features such as real-time stock updates, online ordering, transaction recording, and automated report generation. Evaluation of the system was conducted using black-box testing on eight core functions, of which 87.5% passed as expected. Post-implementation results showed a 60% reduction in inventory-related errors and a notable decrease in administrative workload. The system was also piloted with a sample group of local users, and feedback indicated increased efficiency in stock monitoring and transaction processing. This research contributes significantly to the digital transformation of local agricultural communities by offering a practical, scalable solution that improves business operations and customer service. Moreover, it enhances the ability of rural farmers to enter the digital market ecosystem and expand their market reach. The system demonstrates how localized digital tools can bridge gaps in rural agribusiness, increase productivity, and promote economic resilience through technology adoption.
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.

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