Pendekatan Machine Learning untuk Klasifikasi Kepribadian: Studi Logistic Regression dan Naive Bayes

Laurensius Marcellano Adi Pradana; Fikri Budiman; Defri Kurniawan

doi:10.29408/edumatic.v9i3.32211

Authors

Laurensius Marcellano Adi Pradana Program Studi Teknik Informatika, Universitas Dian Nuswantoro https://orcid.org/0009-0002-6950-7443
Fikri Budiman Program Studi Teknik Informatika, Universitas Dian Nuswantoro https://orcid.org/0000-0002-8552-6778
Defri Kurniawan Program Studi Teknik Informatika, Universitas Dian Nuswantoro https://orcid.org/0009-0004-4186-1760

DOI:

https://doi.org/10.29408/edumatic.v9i3.32211

Keywords:

extrovert, introvert, logistic regression, naive bayes, personality classification

Abstract

Personality classification plays an important role in education, counseling, and recruitment. This study applies a quantitative approach in the form of computational experiments using machine learning algorithms, namely Logistic Regression and Naive Bayes. The data were obtained from a public Kaggle dataset consisting of 2,900 records, with 1,491 labeled as Extrovert and 1,409 as Introvert. The preprocessing stage involved converting categorical variables with LabelEncoder, normalizing numerical features using StandardScaler, and splitting the dataset into 80% for training and 20% for testing with 5-fold cross-validation. Model performance was evaluated using four metrics: Accuracy, Precision, Recall, and F1-Score, providing a more comprehensive assessment compared to previous studies that focused only on accuracy. Our findings show that Logistic Regression achieved an accuracy of 91%, while Naive Bayes yielded a higher accuracy of 92% and performed better in detecting the Introvert category. These results imply that simple algorithms remain relevant and effective in personality classification. The application of this research can assist counselors in identifying students’ personality types, support the development of learning strategies tailored to individual characteristics, and enhance the effectiveness of recruitment processes by aligning candidates’ personalities with organizational needs.

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