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All Journal Nuansa Informatika Cloud Information Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Audience Media Jurnal Informatika Aptekmas : Jurnal Pengabdian Kepada Masyarakat International Journal Administration, Business & Organization Journal of Innovation and Sustainable Empowerment Buffer Informatika Nusantara Journal of Artificial Intelligence and Information Systems
Heru Budianto
Universitas Kuningan
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IMPLEMENTASI CHATBOT BERBASIS NATURAL LANGUAGE PROCESSING (NLP) MENGGUNAKAN MODEL BERT UNTUK MENINGKATKAN PELAYANAN KLINIK GIGI: IMPLEMENTATION OF NATURAL LANGUAGE PROCESSING (NLP) BASED CHATBOT USING BERT MODEL TO IMPROVE DENTAL CLINIC SERVICES Irsyad Ibadurrahman; Fahmi Yusuf; Heru Budianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6061

Abstract

The limited human resources in dental clinics often lead to delays in responding to patients' initial consultations, potentially reducing service quality and patient satisfaction. The advancement of Artificial Intelligence (AI) has encouraged the adoption of chatbots based on Natural Language Processing (NLP) to improve information service efficiency. This study aims to implement a chatbot using the Bidirectional Encoder Representations from Transformers (BERT) model to enhance consultation efficiency at the dental clinic of drg. Fahmi Nurdin. The system development method used is Agile, with stages including requirement analysis, user interface design, model training, and chatbot implementation. The dataset consists of 49 tags (topics) and 490 patterns (questions), formatted in JSON, and used to train the BERT model for intent classification and response generation. Evaluation results show that the chatbot can provide relevant and accurate responses, accelerating the initial consultation process. This implementation is expected to help overcome human resource limitations, improve service quality, and strengthen patient trust in the clinic's services.
Analisis Sentimen Mahasiswa Program Studi Sistem Informasi UNIKU terhadap Pelayanan Akademik dan Administrasi Menggunakan Metode Naive Bayes Classifier Dede Irawan; Dyah Puteria Wati; Heru Budianto
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.573

Abstract

Pelayanan akademik dan administrasi merupakan aspek penting dalam meningkatkan kualitas pendidikan di perguruan tinggi, karena secara langsung memengaruhi kepuasan mahasiswa. Namun, pengolahan opini mahasiswa yang berbentuk teks tidak terstruktur seringkali menjadi kendala dalam memperoleh informasi yang akurat dan objektif. Penelitian ini bertujuan untuk menganalisis sentimen mahasiswa terhadap pelayanan akademik dan administrasi menggunakan metode Naive Bayes Classifier. Data yang digunakan berupa opini mahasiswa yang dikumpulkan melalui media digital dan kuesioner. Tahapan penelitian meliputi text preprocessing yang terdiri dari cleansing, tokenizing, stopword removal, dan stemming, dilanjutkan dengan pembentukan fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF). Model kemudian dilatih menggunakan data latih dan diuji menggunakan data uji untuk mengklasifikasikan sentimen ke dalam kategori positif, negatif, dan netral. Evaluasi performa model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode Naive Bayes Classifier mampu mengklasifikasikan sentimen mahasiswa dengan baik dan memberikan gambaran yang jelas mengenai kualitas layanan. Dengan demikian, pendekatan ini dapat digunakan sebagai alat bantu dalam pengambilan keputusan untuk meningkatkan pelayanan di perguruan tinggi.
EXTENDING THE HOT-FIT MODEL WITH INFORMATION LITERACY TO EXPLAIN AI ADOPTION IN HIGH SCHOOLS Fahmi Yusuf; Yuniarti; Heru Budianto; Rio Priantama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7270

Abstract

Artificial Intelligence (AI) is increasingly transforming educational practices by enabling more adaptive and personalized learning experiences in secondary schools. Nevertheless, previous applications of the Human–Organization–Technology Fit (HOT-Fit) model have given limited attention to the roles of information literacy and trust in influencing AI adoption. To address this gap, the present study expands the HOT-Fit framework by incorporating three additional constructs: information literacy, perceived validity, and perceived trust, in order to better explain AI readiness and adoption in educational settings. A quantitative approach was employed involving 316 senior high school students from Kuningan Regency, Indonesia. Data were gathered using a structured questionnaire based on a five-point Likert scale and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS v3.0 to evaluate 29 proposed hypotheses. The findings revealed that 21 hypotheses were statistically supported. Information literacy demonstrated a strong positive effect on perceived trust (β = 0.708; p < 0.001), as well as on system use, organizational structure, environmental support, and user satisfaction. In addition, system quality significantly contributed to user satisfaction, whereas service quality affected both system use and satisfaction. Among all relationships, net benefit exerted the strongest effect on action to use (β = 0.547; p < 0.001). The R² results for several endogenous constructs were above 0.50, indicating acceptable explanatory capability of the proposed model. Practically, the findings offer implications for educators, policymakers, and system developers in designing AI-supported learning environments by emphasizing the enhancement of digital literacy, service support, and system effectiveness for sustainable AI integration in schools.
Co-Authors Acep Trian Acep Triana Adam Nurfauzan Subiyanto Ade Kurniawan Afifah, Silmi Nur Aji Permana Aji Permana Akhmad Izzudin Amin Anang Supriatna Daswa . dede Irawan Dede Irawan Dede Irawan Dyah Puteria Wati Elsa Syakira Elsa Syakira Erik Kurniadi Erlan Darmawan Fahmi Yusuf fitra imaniar Fitra Nugraha Irsyad Ibadurrahman Jejen Riana Laksono Trisnantoro Leni Nur Angraeni Lutfi Slamet Riyadi Mauldya, Sabrina Mita Hanifah Salma Muhamad Fikri Nanda Haipah Nabila Nurhasanah, Aiena Rachmat Ismaya RIO ANDRIYAT KRISDIAWAN Rio Priantama Sidik, Muhamad Akhirul Tata Sutabri Tito Sugiharto Waode Nadiah Nasrawati Yasir Maulana Yuniarti