Implementasi Data Mining Dalam Klasifikasi Tingkat Kesenjangan Kompetensi PNS Menggunakan Metode Naive Bayes

Civil Servants (Aparatur Sipil Negara or ASN) play crucial roles as implementers of public policy, community service providers, and national unifiers. The government's primary focus is on enhancing the quality and efficiency of public services. In the Provincial Government of Lampung, planning for the enhancement of the competencies of Civil Servants (Aparatur Sipil Negara or ASN) has become a current priority activity. This emphasis is due to the absence of reference data for determining competency development for each ASN. The Assessment Center is one method for determining the competency level of Civil Servants (ASN). However, its implementation faces several challenges such as budget constraints, time limitations, and a shortage of assessors. Based on the results of the 2023 Merit System Index assessment by the Civil Service Commission (KASN), it was recommended that mapping and evaluating employee competency gaps can be carried out through the Human Capital Development Plan (HCDP). In its implementation, a self-assessment method using a questionnaire based on the competency dictionary from the Regulation of the Minister of Administrative and Bureaucratic Reform No. 38 of 2017 is used to address the constraints of the assessment center. The questionnaire is specifically targeted at technical civil servants (PNS) in the Lampung Provincial Government. The analysis of this questionnaire data produces a classification of civil servants based on the level of competency gaps (none, low, medium, high). In this study, the classification results are tested using one of the data mining classification techniques, namely the Naïve Bayes method. The objective of this research is to evaluate the performance of the Naïve Bayes algorithm in classifying the levels of competency gaps among civil servants. Based on the research findings, it can be concluded that the classification system for competency gap levels among civil servants in the Lampung Province Government can be modeled. The testing of the model, which implemented the Naïve Bayes classification method using RapidMiner tools on the research dataset, achieved an accuracy rate of 98.02%. The conclusion is that the Naïve Bayes algorithm performs well in classifying the competency gap levels among civil servants. With the achieved accuracy level, the resulting classifications can be utilized by the Lampung Provincial Government in planning the development needs of civil servant competencies