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Buana Information Technology and Computer Sciences (BIT and CS)
Published by Universitas Buana Perjuangan Karawang
ISSN : 27152448     EISSN : 27157199     DOI : https://doi.org/10.36805/bit-cs
Core Subject : Science,
Computer Science & IT
Buana Information Technology and Computer Science (BIT and CS) is a journal focusing on new technologies that handle IT research and management - including strategy, change, infrastructure, human resources, information system development and implementation, technology development, future technology, policies and national standards and articles that advance understanding and application of research approaches and methods. This journal publishes works from disciplinary, theoretical and methodological perspectives. It was designed to be read by researchers, scholars, teachers, and students in the area of Information Systems and Computer Science, as well as IT developers, consultants, software vendors, and senior IT executives who are looking for updates on current experiences and prospects related to information and communication technology contemporary.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 136 Documents
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E-Konsulta: Clinic Medical Record Management System with Online Follow-Up Through Google Meet: Author's Country: Pagadian City, Philippines
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 6 No. 2 (2025): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bitcs.v6i2.9136

Abstract

An operational clinic is essential in educational institutions to support the health and well-being of students and staff, directly enhancing productivity and academic achievement. However, many clinics still rely on outdated, paper-based systems, leading to inefficiencies, delays, and errors. Modernizing healthcare through digital solutions can improve efficiency, accuracy, and accessibility. This study proposed a system application to address common issues by digitizing medical records, automating workflows, and enabling online consultations. The platform enhances the accuracy and accessibility of patient data while streamlining administrative tasks, benefiting all stakeholders. Extensive testing revealed excellent performance, with a 100% pass rate in most functional areas and high scores in non-functional aspects, including reliability (97.14%), security (95.71%), and user experience (98.57%). Minor areas for improvement were noted in certain functionalities (92.90%) and accessibility (90%), underscoring the importance of continuous optimization to enhance inclusivity and usability. Future enhancements could include developing a mobile application for improved accessibility, enabling users to access records, book appointments, and participate in consultations on their devices. Advanced features such as enhanced online follow-up consultations, automated reminders, and health monitoring tools could further improve healthcare delivery. Integrating real-time analytics or telehealth capabilities may also provide broader support for patient care. These recommendations highlight the potential for the system to evolve, offering a more efficient, inclusive, and responsive approach to healthcare within educational environments
Development of a Web-Based Youth Innovation Village System Using Laravel Framework: Author's Country: Indonesia
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 6 No. 2 (2025): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bitcs.v6i2.9960

Abstract

Youth play a crucial role in social development and serve as agents of change contributing to national progress. Karang Taruna, as a youth organization, has a strategic role in enhancing social welfare, including in Mekarjati Village. Since its establishment on November 10, 2013, Karang Taruna Mekarjati has grown with 13 subunits in each neighborhood unit (RW), serving a community of 11,881 people. However, the organization's business processes, such as member registration, social activity management, leadership training, and the formation and monitoring of MSMEs or startups, are still conducted manually. This results in operational inefficiencies and increases the risk of administrative errors. This study aims to design and develop a web-based system called "Youth Innovation Village" to support the digitalization of Karang Taruna Mekarjati’s management. The system is built using the Laravel Framework, with process modeling based on Business Process Modeling Notation (BPMN), the PHP programming language, and the MySQL database. The software development approach used is Agile Scrum, allowing flexibility in adapting to the organization's needs throughout the system development life cycle. The results of this study indicate that the implementation of Youth Innovation Village significantly enhances the effectiveness and efficiency of Karang Taruna’s management. The system accelerates membership administration, optimizes social activity coordination, and provides direct benefits to the organization's members in Mekarjati Village. Through this digitalization, Karang Taruna can become more adaptive to technological advancements and expand its social impact more effectively.
Flora Folium: Plant Leaf Identification Using ConvolutionalNeural Networks (CNN): Author's Country: Philippines Loudie Suliva; Reynold Bustillo
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/cgwb2584

Abstract

Image processing is a technique to translate an image into digital form and execute some operations on it to obtain an improved image or extract some useful information from it. FloraFolium aimed to address the challenges in plant identification, especially in the Philippines, where many plant species are not well-studied or properly recorded. Many people struggle to tell which plants are edible, medicinal, or toxic because of limited access to official guides and reliable information. To solve this problem, the FloraFolium project created a mobile application that uses Convolutional Neural Networks (CNNs) to identify plant leaves and classify them into three categories: edible, medicinal, or toxic. The system was tested and evaluated based on ISO 25010 software quality standards. The results showed high ratings for functionality, usability, and efficiency, making the app reliable for everyday use. While the app performed well, some areas, like security and reliability in unusual conditions, need improvement. The study also found that the image quality greatly affects the system's accuracy. A balanced dataset of 15,000 images was divided into 80% for training and 20% for testing/validation. The model achieved a test accuracy of 99% and an overall validation accuracy of 98.8%, with the best weights saved at epoch 20 during the 30-epoch training period. The FloraFolium app is a helpful tool for outdoor enthusiasts, gardeners, farmers, and anyone interested in learning more about plants. It can also help preserve traditional knowledge about medicinal plants
Advancing Secure Communication in the Quantum Era through the Integration of Artificial Intelligence and Quantum Cryptographic Techniques: Author's Country: India Hemant N Chaudhari; Bayu Priyatna; Agustia Hananto
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/fze4t734

Abstract

Utilizing the ideas of quantum physics, quantum cryptography is quickly becoming a vital defense against the growing cybersecurity risks of the contemporary day, especially in light of developing quantum computing. This essay investigates the complex field of quantum cryptography and looks at how it can transform network security and protect private data. We examine the fundamental ideas of quantum cryptography, such as Quantum Key Distribution (QKD) protocols like BB84 and E91, which use quantum features like superposition and entanglement to provide potentially indestructible secure communication channels. We also discuss the urgent need for quantum-resistant solutions in view of the developing "quantum threat" to well-known cryptographic algorithms like RSA and AES. The potential benefits and difficulties of using artificial intelligence (AI) techniques to boost quantum cryptography systems' resilience and efficiency are also examined. The creation of effective quantum repeater networks and enhanced security proofs are among the outstanding research topics, future difficulties, and present implementations in quantum cryptography that are covered in this study. We stress how crucial quantum cryptography is to protecting sensitive communications in the quantum era for a variety of industries, including the military, government, financial industry, and healthcare. We come to the conclusion that quantum cryptography has enormous potential for protecting vital information systems from future cyberattacks that are becoming more complex, even if we acknowledge the technology's early stages of development.
Arabica Coffee Sales Forecasting Using ARIMA Neural Network (Case Study: KOTEM Bondowoso): Author's Country: Indonesia Muhammad Ariful Furqon; Saudi Efendi; Yanuar Nurdiansyah
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5a61hr55

Abstract

Kopi Tembakau (KOTEM) Bondowoso is a local coffee producer specializing in Arabica ground coffee, sourced directly from nearby farmer cooperatives. A major hurdle they face is accurately forecasting sales, a critical factor for optimizing production and inventory. To tackle this, a hybrid forecasting model blending ARIMA (for linear/seasonal trends) and Neural Networks (for non-linear patterns) was developed. The study analyzed KOTEM’s sales data from September 2019 to August 2022, preprocessed to address non-stationarity via differencing and normalization. Results revealed the hybrid model outperformed standalone ARIMA, achieving a 1.0% MAPE (vs. ARIMA’s 1.3%). It also better captured sales volatility and seasonal shifts, offering more dependable forecasts. ARIMA-NN could significantly enhance KOTEM production scheduling and stock management.
Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method: Author's Country: Indonesia
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 6 No. 2 (2025): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bitcs.v6i2.10164

Abstract

The rice farming sector plays an important role in the Indonesian economy, considering that rice is the main staple food. According to IRRI, rice farmers experience crop losses of up to 37% each year due to pests and diseases. This study aims to classify rice plant diseases using the Multi-Class Support Vector Machine (M-SVM) method based on leaf images. This study aims to provide education to farmers in recognizing and overcoming diseases in rice plant leaves. The types of rice leaf diseases classified in this study include Blast, Kresek, and Tungro. The data used in this study amounted to 1200, which were divided by varying training and testing data ratios, from 10% training and 90% testing to 90% training and 10% testing. Each variation of features and data division was evaluated by calculating the model performance parameters. The features used for classification include color (RGB) and texture (GLCM) from leaf images. The test results showed that the best accuracy obtained was 85.5% using a combination of color and texture features
Detection of Hijaiyah Letters Handwritten in Early Childhood Using Yolov8: Author's Country: Indonesia
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 6 No. 2 (2025): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bitcs.v6i2.10202

Abstract

This study investigates the effectiveness of the YOLOv8 (You Only Look Once version 8) algorithm in detecting handwritten Hijaiyah letters among early childhood learners. The introduction of technology in early childhood education is essential for enhancing literacy skills, particularly in learning the Arabic alphabet, which is crucial for reading the Quran. This research addresses the challenges faced by educators in assessing children's handwriting, which often lacks consistency and objectivity. A dataset of 3,780 images of handwritten Hijaiyah letters was collected from children at RA BAIPAS Roudlotul Jannah, including various writing styles to ensure the model's robustness. Prior to training, the images underwent preprocessing steps such as resizing, normalization, and data augmentation techniques like rotation and flipping to enhance the quality and diversity of the training data. The YOLOv8 model was trained using an 80-10-10 split for training, validation, and testing datasets. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) were used. The results showed that YOLOv8 achieved an impressive accuracy of 96.08% in detecting handwritten Hijaiyah letters, with high precision and recall rates further validating the model's reliability. This research highlights the potential of integrating advanced object detection algorithms like YOLOv8 into educational practices. By providing real-time feedback, the system can significantly enhance the learning experience for young children, facilitating their understanding and mastery of the Arabic alphabet. Future research should focus on expanding the dataset and refining the model to address handwriting variability challenges and improve accuracy.
Development of a Deep Learning-Based Text-To-Speech System for the Malang Walikan Language Using the Pre-Trained SpeechT5 and Hifi-GAN Models: Author's Country: Indonesia
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 6 No. 2 (2025): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bitcs.v6i2.10314

Abstract

The Walikan language of Malang is a form of local cultural heritage that needs to be preserved in the digital era. This study aims to develop and evaluate a deep learning-based Text-to-Speech (TTS) system capable of generating speech in the Walikan language of Malang using pre-trained SpeechT5 and HiFi-GAN models without fine-tuning. In this system, SpeechT5 is used to convert text into mel-spectrograms, while HiFi-GAN acts as a vocoder to generate audio signals from the mel-spectrograms. The dataset used consists of 1,000 sentences in the Walikan language of Malang. The system evaluation was carried out using objective metrics of Word Error Rate (WER) and Character Error Rate (CER), by comparing the results of synthetic audio transcriptions against two types of reference audio, namely the original voices of female speakers and male speakers, using the Automatic Speech Recognition (ASR) system. The female voice was recorded with controlled articulation, while the male voice used natural intonation in everyday conversation. The results show that synthetic audio has the highest error rate with a WER of 0.9786 and a CER of 0.9024. Meanwhile, female audio has a WER of 0.5471 and a CER of 0.1822, while male audio shows a WER of 0.6311 and a CER of 0.2541. These findings indicate that the TTS model without fine-tuning is not yet capable of producing synthetic voices that can be recognized accurately by the ASR system, especially for regional languages that are not included in the initial training data. Therefore, the fine-tuning process and the preparation of a more representative dataset are important so that the TTS system can support the preservation of the Walikan Malang language more effectively in the digital era.
Classification of Tulungagung Batik Images in Comparison of Convolution Neural Network and Vision Transformer Algorithms: Author's Country: Indonesia Augustiar Mahendra Mochammad; Firman Nurdiyansyah; Fitri Marisa
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/84twwe32

Abstract

Batik is a significant Indonesian cultural heritage with a vast diversity of motifs, making manual classification a challenging task. This research provides a comparative analysis of two prominent deep learning architectures, the Convolutional Neural Network (CNN), represented by VGG16, and the Vision Transformer (ViT), represented by DeiT, for the classification of Tulungagung batik images. A balanced dataset of 2,400 images, comprising two classes (Bangoan and Majanan), was utilized. The experiment was conducted using three distinct training-to-testing split ratios (80:20, 70:30, and 60:40) to evaluate model robustness. Performance was assessed using accuracy, precision, recall, F1-score, and the confusion matrix. The results indicate that the CNN (VGG16) model consistently outperformed the ViT (DeiT), achieving its peak accuracy of 96% on both the 80:20 and 60:40 split ratios, showcasing high stability. The ViT (DeiT) model was more sensitive to the data split, reaching a peak accuracy of 94% with less consistent performance. We conclude that for this specific classification task, the VGG16 architecture is more robust, stable, and effective than the DeiT architecture.
Application of XGBoost Algorithm in Sentiment Classification of MOBA Game Reviews on Google Play Store: Author's Country: Indonesia Daffa Yauzan Tusianto; Syahroni Wahyu Iriananda; Istiadi Istiadi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5x0adm73

Abstract

In the rapidly evolving digital era, business applications like GoBiz play a crucial role in supporting the operations of Micro, Small, and Medium Enterprises (MSMEs). This study aims to analyze user sentiment toward the GoBiz app based on reviews on the Google Play Store by applying two machine learning algorithms: Extreme Gradient Boosting (XGBoost) and Random Forest. Two labeling approaches were used: score-based labeling, which refers to star ratings, and lexicon-based labeling using the VADER method. Data from 10,000 reviews were collected through web scraping and processed through preprocessing, labeling, TF-IDF feature extraction, model training, and evaluation. The evaluation results showed that the XGBoost algorithm excelled in score-based labeling with the highest accuracy of 86.81%, while Random Forest was more stable than the VADER approach with an accuracy of 84.98%. Both models performed well, but their effectiveness depended on the type of labeling used. This research contributes to the development of a sentiment classification system in digital business applications, and can be utilized by GoBiz application developers to improve service quality based on user perceptions.

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