Peningkatkan Kinerja Gaussian Naive Bayes melalui Hyperparameter Tuning pada Dataset Car Evaluation

Novi Trisna; Annisak Izzaty Jamhur; Raja Ayu Mahessya; Firna Yenila

doi:10.55382/jurnalpustakarobotsister.v4i2.2123

Authors

Novi Trisna Universitas Putra Indonesia YPTK Padang
Annisak Izzaty Jamhur Universitas Putra Indonesia YPTK Padang
Raja Ayu Mahessya Universitas Putra Indonesia YPTK Padang
Firna Yenila Universitas Putra Indonesia YPTK Padang

DOI:

https://doi.org/10.55382/jurnalpustakarobotsister.v4i2.2123

Keywords:

car evaluation, GridSearchCV, hyperparameter tuning, klasifikasi, Naive Bayes

Abstract

Evaluasi kelayakan mobil merupakan permasalahan klasifikasi yang penting karena harga beli, biaya perawatan, jumlah pintu, kapasitas penumpang, ukuran bagasi, dan tingkat keamanan berpengaruh terhadap pengambilan keputusan. Penelitian ini menerapkan dan mengoptimalkan algoritma Gaussian Naive Bayes untuk klasifikasi kelayakan mobil menggunakan dataset Car Evaluation dari UCI Machine Learning Repository. Dataset terdiri dari 1.728 data dengan enam atribut input dan empat kelas target, yaitu unacc, acc, good, dan vgood. Tahapan penelitian meliputi transformasi data kategorikal, pembagian data latih dan data uji dengan rasio 90:10, pelatihan model, hyperparameter tuning menggunakan GridSearchCV, serta evaluasi performa menggunakan accuracy, precision, recall, dan F1-score. Model Naive Bayes awal menghasilkan accuracy 69%, macro precision 39%, macro recall 51%, dan macro F1-score 37%. Setelah dilakukan hyperparameter tuning, performa model meningkat menjadi accuracy 81%, macro precision 63%, macro recall 63%, dan macro F1-score 61%. Hasil penelitian menunjukkan bahwa tuning berbasis GridSearchCV mampu meningkatkan kemampuan klasifikasi Naive Bayes, terutama dalam mengenali kelas minoritas yang sebelumnya sulit diprediksi.

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