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All Journal Information System for Educators and Professionals : Journal of Information System Jurnal Nasional Teknologi Komputer Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Indonesian Journal of Education And Computer Science Electronic Integrated Computer Algorithm Journal Jurnal Bakti Nusantara
Ramadhanu Ginting
POLITEKNIK NEGERI MEDAN
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Social Sciences Other

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Journal : electronic integrated computer algorithm journal

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Design of NLP-Based Chatbot for Academic Services at Politeknik Negeri Medan Indri Sulistianingsih; Ramadhanu Ginting; Ade Rizka; Brema Arihta Siregar
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 2 (2026): VOLUME 3, NO 2: APRIL 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i2.163

Abstract

The development of artificial intelligence technology has encouraged the use of chatbots in various sectors, including higher education. These chatbots can increase interaction, provide learning support, and facilitate the delivery of information automatically. This study aims to design a chatbot that functions as a virtual assistant in an educational environment, with an emphasis on increasing interaction between users and the system. The methods used include a systematic approach in designing interfaces, natural language processing (NLP), and testing the responsiveness of chatbots to user needs. From the literature review conducted, it is evident that chatbots make a positive contribution to learning motivation, student engagement, and the efficiency of the learning process, especially in the context of online learning. However, the current system is limited to predefined academic queries and does not yet handle complex academic advising, financial inquiries, or counseling support. The hope of this study is to provide guidance in developing educational chatbots that are effective, flexible, and able to adapt to user needs in the post-pandemic era.
A Dempster-Shafer-Based Expert System for Personalized Higher Education Major Recommendation Rahmat Widia Sembiring; Ramadhanu Ginting; Afrisawati Afrisawati; Fristi Riandari; Indri Sulistianingsih
Electronic Integrated Computer Algorithm Journal Vol. 4 No. 1 (2026): VOLUME 4, NO 1: OCTOBER 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v4i1.237

Abstract

Selecting a study program at a State University (Perguruan Tinggi Negeri/PTN) is a decision that many senior high school students approach without a systematic review of their interest, aptitude, achievement, and career goals, a gap made more pressing by the limited counseling capacity available at most schools. Earlier expert systems for major recommendation have relied on Forward Chaining or Certainty Factor, methods that combine evidence into a single rule outcome or scalar confidence without explicitly representing the uncertainty left by incomplete evidence. This study developed and implemented a web-based expert system, Peta Kampus, that applies Dempster-Shafer evidence theory to generate a personalized major recommendation from a student's answers to a structured questionnaire. The knowledge base was constructed with school counselors and an education practitioner into 275 evidence-linked questions covering 55 majors across 21 faculties, each carrying an expert-assigned belief value. The system was built on a native PHP Model-View-Controller architecture with MySQL as the data store, and its inference engine combines successive evidence using Dempster's rule of combination while tracking residual uncertainty and evidentiary conflict. The system was verified through Black Box Testing of its functional modules, with all 15 tested scenarios passing, and evaluated through an accuracy comparison against three independent evaluators (school principal and two guidance counselors, 90% average agreement across 30 cases) and a User Acceptance Test administered to 30 students, which returned an overall satisfaction score of 94.8%. The resulting system gives students and counselors a belief-and-plausibility-based recommendation that is more transparent than a single deterministic score.
Co-Authors Ade Rizka Ade Rizka Afrisawati Afrisawati Afrisawati Afrisawati Afrisawati Brema Arihta Siregar Donny Sanjaya Fristi Riandari Fristi Riandari Hikmah Adwin Adam Indri Sulistianingsih Indri Sulistianingsih Rahmat Widia Sembiring - Rama Prameswara Ritonga Rizky Maulidya Afifa Virdyra Tasril Virdyra Tasril Weno Syechu