“COMPARATIVE STUDY OF KNN AND NAIVE BAYES ALGORITHMS WITH QUESTIONNAIRE DATA FOR STUDY PROGRAM RECOMMENDATION IN THE FACULTY OF ART AND DESIGN”
DOI:
https://doi.org/10.54314/jssr.v9i2.6267Keywords:
K-Nearest Neighbor (KNN), Naïve Bayes, Machine Learning, Classification, Study Program Recommendation.Abstract
Abstract: The purpose of this study is to analyze and compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms in providing study program recommendations in the Faculty of Arts and Design. The data were obtained from 250 respondents through a questionnaire consisting of 20 indicators related to students’ interests, abilities, creativity, technology, and career preferences. The research process included data preprocessing, data transformation, dataset splitting into training and testing data, modeling using the KNN and Naïve Bayes algorithms, and model performance evaluation using accuracy metrics. The data processing was carried out using the Python programming language on the Google Colab platform. The results showed that the KNN algorithm achieved an accuracy of 94%, while the Naïve Bayes algorithm obtained an accuracy of 92%. These findings indicate that the KNN algorithm performed better in classifying study program recommendations compared to the Naïve Bayes algorithm. It is expected that this research can serve as a foundation for developing a more effective decision support system to assist prospective students in selecting study programs that match their interests and abilities.
Keywords: K-Nearest Neighbor (KNN), Naïve Bayes, Machine Learning, Classification, Study Program Recommendation.
Abstrak: Tujuan dari penelitian ini adalah untuk menganalisis dan membandingkan kinerja algoritma K-Nearest Neighbor (KNN) dan Naïve Bayes dalam memberikan rekomendasi program studi di Fakultas Seni dan Desain. Data diperoleh dari 250 responden melalui kuesioner yang terdiri dari 20 indikator yang berkaitan dengan minat, kemampuan, kreativitas, teknologi, dan preferensi karier mahasiswa. Proses preprocessing data, transformasi data, pembagian dataset menjadi data pelatihan dan pengujian, pemodelan menggunakan algoritma KNN dan Naive Bayes, dan evaluasi kinerja model dengan akurasi adalah bagian dari penelitian. Proses pengolahan data dilakukan pada platform Google Colab menggunakan bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa algoritma KNN memiliki akurasi sebesar 94%, sedangkan algoritma Naïve Bayes memiliki akurasi sebesar 92%. Hasil ini menunjukkan bahwa algoritma KNN lebih baik dalam mengklasifikasikan rekomendasi program studi daripada algoritma Naïve Bayes. Diharapkan penelitian ini akan menjadi dasar untuk membuat sistem pendukung keputusan yang lebih baik yang membantu calon mahasiswa memilih program studi yang sesuai dengan minat dan kemampuan mereka.
Kata Kunci: KNN, Naïve Bayes, Machine Learning, Klasifikasi, Rekomendasi Program Studi.
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