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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Jurnal Ekonomi Manajemen Sistem Informasi bit-Tech Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Darma Agung Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Brilliance: Research of Artificial Intelligence JAPI: Jurnal Akses Pengabdian Indonesia Knowbase : International Journal of Knowledge in Database Ranah Research : Journal of Multidisciplinary Research and Development International Journal of Multidisciplinary Approach Research and Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Adi Purnama
Universitas Widyatama, Bandung, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Transportation Other

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Applying NLP and Cosine Similarity in the Preliminary Selection Process of Recruitment Systems Muhammad Wildan Jaffar Rahmatullah; Adi Purnama
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.v8i2.3287

Abstract

The recruitment process plays a crucial role in determining the quality of human resources within an organization. However, many companies still rely on manual screening of Curriculum Vitae (CV), which requires considerable time and introduces a high degree of subjectivity. This study aims to develop an automated preliminary selection system by applying Natural Language Processing (NLP) and the Cosine Similarity method to measure the semantic compatibility between CVs and job descriptions. The research adopts a qualitative approach grounded in observations and interviews with recruiters, while the precision metric is used only as a supplementary measure to check system performance. A total of 92 CVs and six job descriptions were collected, and 20 CVs along with four job descriptions were selected as test data. The text processing stage applies basic normalization, including lowercasing, removal of digits and punctuation, and whitespace cleaning. The normalized text is then converted into dense vector embeddings using a pre-trained multilingual SentenceTransformer model before similarity is computed with the Cosine Similarity function. System performance was measured using precision and achieved an average score of 0.95 across four job positions, indicating consistent retrieval of relevant candidates. Despite its strong performance, the system is constrained by its reliance on text based CVs, the use of a general purpose language model, and the inclusion of precision as the only evaluation metric. These findings highlight the potential of NLP and Cosine Similarity to improve efficiency and objectivity in early stage candidate selection.
Development of an Intent-Classification Chatbot to Support Operational Services at Kadin Indonesia Rahma Aulia; Adi Purnama
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7438

Abstract

The digital transformation era demands business membership organizations such as the Indonesian Chamber of Commerce and Industry (Kadin) to provide responsive and scalable services. Operational inquiries related to the Certificate of Origin (COO), membership information (KTA), activity agendas, and administrative correspondence are still predominantly handled manually, resulting in service queues and limited operating hours. This study develops an intelligent text-based chatbot using Natural Language Processing (NLP) with an intent classification approach implemented through a Long Short-Term Memory (LSTM) model to automate initial responses to user queries. A labeled dataset consisting of more than 90 intents was constructed from Frequently Asked Questions (FAQ), Kadin service data, and data augmentation to increase text variation. The preprocessing pipeline includes normalization, tokenization, padding, and 300 dimensional FastText embeddings. The LSTM model, configured with 128 units, was trained using categorical cross-entropy with a label smoothing factor of 0.05, the Adam optimizer, a batch size of 20, and 80 epochs, and integrated into the backend for real-time inference. Evaluation on the test set achieved an accuracy of 92.08% and a Top-3 Accuracy of 96.23%. Visual analyses using the confusion matrix and accuracy–loss curves indicate strong generalization capability. These findings demonstrate that a properly configured LSTM model can effectively recognize service-related intents for Kadin.
Mapping Academic Landscapes: Topic Modeling for Institutional Repositories Nuryana, Alif; Purnama, Adi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3721

Abstract

The rapid growth of scientific output in institutional repositories has created significant challenges for the efficient retrieval of information, particularly when searches rely solely on unstructured metadata. Although topic modelling has been widely applied to large bodies of text, little attention has been given to Indonesian-language repositories and metadata-only datasets harvested through standardized protocols. This study aims to address this issue by using Latent Dirichlet Allocation (LDA) to analyze the research landscape of the Widyatama University Repository, based on titles and abstracts that were collected automatically via the OAI-PMH protocol. The proposed methodology integrates the following processes: automated metadata harvesting; Indonesian-language text preprocessing; probabilistic topic modelling; and quantitative evaluation using coherence metrics, complemented by qualitative interpretability analysis. The experimental results show that the optimal model was achieved with 12 topics, giving a Coherence Score of 0.5546 categorized as 'Good'. This demonstrates that meaningful thematic structures can be extracted even from limited textual metadata. The identified topics reflect the university's main research areas, such as Marketing Management (12.5%), Auditing (12.4%), and Human Resource Management (12.1%), as well as specific domains like Informatics (6.7%). To enhance practical usability, the model outputs were deployed in an interactive, Streamlit-based dashboard enabling dynamic exploration of topic relationships and temporal trends. This study contributes to repository analytics by demonstrating how topic modelling driven by metadata can transform institutional repositories into intelligent systems for discovering knowledge, supporting the navigation of research, landscape analysis and evidence-based decision-making for academic management.
Co-Authors Adit Oktopryadin Al Husaeni, Ahmad Badru Alif Nuryana Atep Aulia Rahman Bagus Alit Prasetyo Benny Yustim Calvin Matulessy Candimadam Esa Fauzi Fathurohman, Diman Herlistiono, Iwa Ovyawan Husni Robani Huynh, Phuoc-Hai Indriani Kautsar, Almila Aulia Kusramdani, Rizky Lerian, Adly Juliarta Luhur Bayuaji, Luhur Muhammad Wildan Jaffar Rahmatullah Nugraha, Agitama Nuryana, Alif Pangestu, Rizky Bagus Putra, Alif Firmansyah Rahma Aulia Robani, Husni Sriyani Violina Suharno, Mochammad Syahrindra Akbar Suhendar, Sidik Tumaruk, Andry Septian Syahputra Viddi Mardiansyah Widyanto, Adi Yan Puspitarani Yenie Syukriyah