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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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Implementation of the LSTM Model for Speech-to-Text Systems in the Recognition of the Walikan Language of Malang: Author's Country: Indonesia Raynanda Raynanda; Aviv Yuniar Rahman; 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/m0pcpk09

Abstract

This study developed a Speech-to-Text (STT) system based on the Long Short-Term Memory (LSTM) model to recognize and convert speech in the Malang Walikan language into text. The Malang Walikan language has a unique linguistic structure in the form of word reversal, which poses a challenge in speech recognition. The data used consisted of 1,000 sentences collected from social media and direct recordings. The data was processed using Mel Frequency Cepstral Coefficients (MFCC) and then used to train the LSTM model.The system's performance was evaluated using the Word Error Rate (WER), Character Error Rate (CER), and Average Test Loss metrics. The best results obtained showed a WER value of 1.0 on a 699:300 data split, a CER of 0.78 on a 799:200 split, and an Average Test Loss of 11.0147 on a 299:700 split.The high Average Test Loss value indicates the model's difficulty in minimizing prediction errors, which may be caused by the model's mismatch with the data patterns or overfitting. To improve the model's performance, it is recommended to improve the quality of the training data, optimize the parameters, and apply regularization techniques.
Web-Based Marketplace Program Design AT PT. Cipta Innovasi Teknologi Jakarta: Author's Country: Indonesia Richky Faizal Amir; Andreyestha; eko pramono; Fajar Akbar
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (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/trky6359

Abstract

Business processes in today's era are dynamic, including the sale of products and services. Websites or platforms such as marketplaces are one of the effective systems in running a company's business processes. Therefore, this research aims to design a web marketplace platform to make it easier for customers to find the right solutions for IT and services. The system's features function to manage various related data and information. When a customer needs a product or service, they will register, then begin searching for the appropriate solution, and then place an order. The author used Sketch to create the interface design on the website. The author used the waterfall approach in designing this system. The author also used the PHP programming language to design the website with the support of CodeIgniter version 4 as a framework to facilitate web design, and the use of HTML and CSS in the website's front-end design. The MySQL database is used for database system processing. With this research, it can produce a platform or website that makes it easier for customers to find the right IT solutions quickly.
Integrating AI Chatbots and Blockchain Smart Contracts: Enhancing Fintech Accessibility for Low-Digital-Literacy Workers: Author's Country: Indonesia Ade Andri Hendriadi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (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: 10.36805/0tnt1y52

Abstract

Digital financial services powered by artificial intelligence (AI) and blockchain technology present transformative opportunities for workforce empowerment, yet vocational workers with limited digital literacy face significant adoption barriers. This study develops and validates an integrated framework combining AI-powered chatbots with blockchain-based smart contracts to enhance fintech accessibility for low-digital-literacy workers in Indonesia. Using sequential explanatory mixed-methods design, we analyzed data from 618 vocational workers across five Indonesian cities through structural equation modeling (SEM) and 12 focus group discussions with 86 participants. Results demonstrate that AI chatbot personalization (β=0.726, p<0.001) and smart contract automation (β=0.742, p<0.001) significantly enhance adoption, with the integrated model explaining 71.4% of adoption variance. AI chatbots reduce onboarding time by 67% through adaptive interfaces and contextual guidance, while smart contracts enable automated salary disbursement (85% efficiency gain), micro-lending decisions (72% approval accuracy), and transparent credential verification (94% accuracy). Individual digital capability (β=0.721), organizational infrastructure (β=0.586), and technical barrier mitigation (β=-0.445) emerged as critical determinants. Qualitative findings reveal three transformation pathways: automated financial processes, personalized learning journeys, and trustless verification systems. The framework provides evidence-based guidance for practitioners and policymakers implementing inclusive fintech systems that combine AI’s adaptive learning capabilities with blockchain’s transparency and automation benefits.
LSTM-Based Classification of Indonesian Regional Song Lyrics by Language: Author's Country: Indonesia Muhammad Rizky Anandita Priatama; Aviv Yuniar Rahman; Syahroni Wahyu Iriananda
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (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/jh41mx81

Abstract

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.
Implementation of a Bi-LSTM Model for Automatic Text Classification of Mathematics, Science, and Indonesian Language Questions: Author's Country: Indonesia Mochamad soffan muslim; Aviv Yuniar Rahman; Rangga Pahlevi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (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/k7tge561

Abstract

This study aims to implement the Bidirectional Long Short-Term Memory (Bi-LSTM) model for automatic text classification of Mathematics, Natural Sciences (IPA), and Indonesian language questions to support efficient question grouping in digital education systems. The dataset used consists of 2,718 questions, which are evenly distributed across three subject categories. The research stages include text preprocessing, tokenization and padding, splitting the dataset into training and testing sets, designing the Bi-LSTM model architecture, and conducting training and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Bi-LSTM model achieves an accuracy of 97% on the test data, with an average F1-score of 0.97. The confusion matrix analysis indicates that most predictions are correctly classified with a relatively low misclassification rate across categories. Based on these results, it can be concluded that the Bi-LSTM model is effective for automatic text classification of educational questions and has strong potential for further development in technology-based question grouping systems.
Quantum-Enhanced Artificial Intelligence: Bridging Quantum Physics and Machine Learning for Next-Generation Computing Paradigms: Author's Country: Afganistan Mohammad Wali Khurami; Musawer Hakimi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (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: 10.36805/5cdcfp74

Abstract

Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.

Page 14 of 14 | Total Record : 136

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