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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA TOFEDU: The Future of Education Journal
Sinaga, Muhammad Nabil
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The Design and Implementation of the Secretariat Website of the Tanjungbalai City Regional Representative Council: An Analysis within the Framework of Field Work Practice Sinaga, Muhammad Nabil; Ramadhan, Nuzul
The Future of Education Journal Vol 4 No 2 (2025)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i2.471

Abstract

In this research, the design and implementation of the website of the Regional People's Representative Council (DPRD) of Tanjungbalai City is used as an analysis in the framework of Field Work Practice (PKL). The main objective of this research is to improve the availability of information and communication between the DPRD of Tanjungbalai City and the community through a digital platform. The research method includes the interface design phase using HTML and CSS and the application of web technologies to create a responsive and functional DPRD website. Analyzing user requirements, user interface design and integration of features that support transparency and audience. The results of this research are expected to increase the effectiveness of communication and public understanding of DPRD of Tanjungbalai City activities and strengthen DPRD's relationship with its constituents through the use of information technology.
Hyperparameter Optimization of Naive Bayes for Supervisor Recommendation in Computer Science Sinaga, Muhammad Nabil; Kurniawan R, Rakhmat
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8478

Abstract

The increasing number of students in the Department of Computer Science at UIN Sumatera Utara has made the process of selecting thesis supervisors more complex and time-consuming. This study aims to develop a system that automatically recommends the most suitable supervisor based on the similarity between thesis titles and lecturers’ areas of expertise. The proposed model applies text preprocessing techniques such as case folding, tokenization, stopword removal, and keyword extraction to transform thesis titles into meaningful features. These features are then classified using the Naive Bayes algorithm to predict the probability of each lecturer being the most relevant supervisor. The dataset consists of 794 thesis titles and 25 lecturers collected from 2019–2024. The model was evaluated using an 80:20 data split, achieving an accuracy of 87.3% with stable precision and recall scores, demonstrating reliable performance in supervisor recommendations. This enhanced Naive Bayes model can assist academic departments in ensuring a fairer and more efficient supervisor assignment process.
Co-Authors Rakhmat Kurniawan R Ramadhan, Nuzul