ANALISIS FAKTOR YANG MEMPENGARUHI PEMILIHAN GUBERNUR DAERAH KHUSUS JAKARTA MENGGUNAKAN ALGORITMA NAIVE BAYES DAN REGRESI LOGISTIK
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Abstract
The election of the Governor and Deputy Governor of the Special Region of Jakarta (DKJ) in 2024 involves the community in determining leaders for the 2024-2029 period. This research analyzes the factors influencing voter decisions using the Naive Bayes algorithm and Logistic Regression. Survey data was collected via a Google Form questionnaire from Jakarta residents aged 17-71. The analysis process involves several stages: problem identification, literature study, data collection, preprocessing, and dividing the data into training and test data. The Naive Bayes algorithm is used to predict classification parameters based on a candidate's education, popularity, and track record, while Logistic Regression predicts factors that influence voter decisions. Naive Bayes shows high accuracy with advantages in speed and processing large data, while Logistic Regression shows strength in binary and multinomial classification analysis. The research results show that the track record factor significantly influences voter decisions. Naive Bayes prediction accuracy reached 85.00% and Logistic Regression 80.00%. The analysis results also reveal that the candidate's popularity and education factors rationally influence voter decisions, although not as strong as the track record. In addition, using these two algorithms provides a comprehensive understanding of voter behavior in Jakarta. Based on these results, governor and deputy governor candidates should also focus on improving their track record and popularity to increase their chances of being elected.
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