IT CAREER NAVIGATION: PERFORMANCE EVALUATION OF KNN AND NAÏVE BAYES IN CAREER PATH RECOMMENDATIONS FOR COMPUTER SCIENCE STUDENTS (CASE STUDY: BATTUTA UNIVERSITY) - Surya Darma; Muhammad Irfan Sarif; Ahmad Jihad Alfayed; Andika Syahdewa; Katharina Tyas (Smart Education, 2026)
Abstract: With the rapid development of information technology, there are many career options available in the field of informatics. However, it is often difficult for students to choose a specialization that matches their interests and abilities. The purpose of this study is to develop a career path recommendation system for informatics students and to evaluate the performance of the K-Nearest Neighbor (KNN) and Naive Bayes algorithms in classification tasks. The data used in this study were collected via a questionnaire comprising 22 assessment indicators related to students’ interests, academic understanding, and preferred work styles. A total of 300 respondent data points were utilized, with 20% allocated for testing and 80% for training. The research process included preprocessing, data transformation, modeling, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes algorithm outperforms KNN, achieving an accuracy of 97%, precision of 93%, recall of 93%, and an F1-score of 93%. Therefore, Naive Bayes is considered more optimal in terms of classification performance. It is expected that the developed system can assist students in determining their career paths in a more data-driven and objective manner. Keywords: Machine Learning, KNN, Naïve Bayes, Career Recommendation, Classification Abstrak: Dengan berkembangnya teknologi informasi yang cepat, ada banyak pilihan karir di bidang informatika. Namun, sulit bagi mahasiswa untuk memilih spesialisasi yang sesuai dengan minat dan kemampuan mereka. Tujuan dari penelitian ini adalah untuk membuat sistem rekomendasi jalur karier untuk mahasiswa informatika dan juga untuk mengevaluasi bagaimana algoritma K-Nearest Neighbor (KNN) dan Naive Bayes bekerja dalam klasifikasi. Data yang digunakan diperoleh melalui kuesioner yang terdiri dari 22 indikator penilaian yang berkaitan dengan minat mahasiswa, pemahaman akademik, dan gaya kerja yang mereka sukai. Sebanyak 300 data dari responden digunakan, dengan 20% data dialokasikan untuk pengujian dan 80% untuk pelatihan. Proses penelitian termasuk tahapan preprocessing, transformasi data, pemodelan, dan evaluasi menggunakan metrik akurasi, presisi, recall, dan skor F1. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes lebih baik dibandingkan KNN dengan nilai akurasi 97%, presisi 93%, recall 93%, dan skor F1. Akibatnya, Naïve Bayes lebih optimal dalam member. Diharapkan sistem yang dibuat dapat membantu mahasiswa dalam menentukan karir mereka secara lebih berbasis data dan objektif. Kata Kunci: Machine Learning, KNN, Naïve Bayes, Rekomendasi Karier, Klasifikasi