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All Journal Teknika Jupiter PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) International Journal of Artificial Intelligence Research Jurnal CoreIT Prosiding Seminar Nasional Darmajaya Jurnal Sinergitas PkM & CSR Jurnal Teknologi Informasi MURA Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Tekno Kompak Building of Informatics, Technology and Science JPGMI (Jurnal Pendidikan Guru Madrasah Ibtidaiyah Al-Multazam) Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian kepada Masyarakat Jurnal Sains Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal Indonesia Sosial Sains Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Jurnal Ilmiah ESAI Jurnal Teknologi Informasi Mura Scientica: Jurnal Ilmiah Sains dan Teknologi Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora International Journal of Computer Technology and Science Journal of Software Engineering And Technology IJISCS (International Journal of Information System and Computer Science)
Sutedi Sutedi
IIB Darmajaya
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Journal : Building of Informatics, Technology and Science

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Implementasi Data Mining Dalam Klasifikasi Tingkat Kesenjangan Kompetensi PNS Menggunakan Metode Naive Bayes Kurniawan, Putra; Wasilah, Wasilah; Sutedi, Sutedi; Nugroho, Handoyo Widi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5641

Abstract

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
Co-Authors - Nurfiana Absor, M Ulil Adam Japal Adela, Cindy Nada Agariana, Melda Agarina , Melda Agung Nugroho Menggala Putra AGUS PURWANTO Anton Catur Atmoko Ardiansyah, Ari ARIF HIDAYAT Arman Suryadi Karim, Arman Suryadi DANU WIJAYA Diba Madya Margerefti Dona Yuliawati Edi Edi Pranyoto Fauzi Maulana, Muh Royan Fauzi, M.Royan Firdhayanti, Ayu Firmansyah Firmansyah Firmansyah Yunialfi Alfian Guntur Guntur Halimah Halimah handoyo widi nugroho Hartono Hartono Hasibuan, Said Hendra Kurniawan Hendri Purnomo Hendri Purnomo Ilhamiharja, Arbi Irianti , Artia Kaesar Azra Putra Zeva Kesuma, Martika Kurniawan, Putra Lestari, Mentari Puji Luh Putu Ratna Sundari M Said Hasibuan M Ulil Absor M. Royan Fauzi M.Ariza M.Royan Fauzi M.Royan Fauzi Maulana Maarif, Syaiful Mahfudzin, Abdul Halim Maulana, Muh Royan Fauzi Melda Agarina MS Hasibuan MUBAAROK, ABDURRAHMAN Muhamad Ariza Eka Yusendra Muhammad Ariza Eka Yusendra Nirma Nirma Nirmala Handayani Nirmala Handayani Ochi Marshella Febriani rinaldi rinaldi Rinaldo, Reyhandoni Rini Nurlistiani Risa Ayuna Sari, Erlinda Ratna Sri Karnila Sriyanto . Sriyanto Sriyanto Sriyanto Sriyanto Stefanus Rumangkit Suryadi , Arman Susanti Susanti Susanti Susanti Susanti Tria Devi Miranti Triloka, Joko Ubaidilah, Romdan Muhamad Wasilah Wasilah Yusminar Yusminar