Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier
DOI:
10.33395/sinkron.v8i1.11968Keywords:
Data Mining, Managing the Workforce, Naïve Bayes, PPPK, Python ProgrammingAbstract
The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process.
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References
Adila, N. (2022). Implementation of Web Scraping for Journal Data Collection on the SINTA Website. Sinkron, 7(4), 2478–2485. https://doi.org/10.33395/sinkron.v7i4.11576
Afdhaluzzikri, A., Mawengkang, H., & Sitompul, O. S. (2022). Perfomance analysis of Naive Bayes method with data weighting. SinkrOn, 7(3), 817–821. https://doi.org/10.33395/sinkron.v7i3.11516
Alwan, J. K., Jaafar, D. S., & Ali, I. R. (2022). Diabetes diagnosis system using modified Naive Bayes classifier. Indonesian Journal of Electrical Engineering and Computer Science, 28(3), 1766. https://doi.org/10.11591/ijeecs.v28.i3.pp1766-1774
Fitriani, F., Utami, E., & Hartanto, A. D. (2022). ANALISIS SENTIMEN MASYARAKAT TERHADAP PELAKSANAAN P3K GURU DENGAN ALGORITMA NAIVE BAYES DAN DECISION TREE. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 3(1), 23–30. https://doi.org/10.46764/TEKNIMEDIA.V3I1.53
Gupitha, R. (2018). PENERAPAN KLASIFIKASI STATUS PEGAWAI MENGGUNAKAN METODE NAÃVE BAYES DI RSU H. SYAIFUL ANWAR. Global, 5(1), 28–38. http://ejournal.unsub.ac.id/index.php/FASILKOM/article/view/418
Hamid, M. A., Aditama, D., Permata, E., Kholifah, N., Nurtanto, M., & Abdul Majid, N. W. (2022). Simulating the Covid-19 epidemic event and its prevention measures using python programming. Indonesian Journal of Electrical Engineering and Computer Science, 26(1), 278. https://doi.org/10.11591/ijeecs.v26.i1.pp278-288
Hanamunika, G., Sari, D. S., & Dai, R. M. (2021). IMPLEMENTASI KEBIJAKAN PENGADAAN PEGAWAI PEMERINTAH DENGAN PERJANJIAN KERJA (PPPK) DI LINGKUNGAN PEMERINTAH KABUPATEN BANDUNG. Responsive, 3(3), 143. https://doi.org/10.24198/responsive.v3i3.31921
Hant, M. I. P., & Hendry, H. (2022). DATA MINING TECHNIQUE USING NAÏVE BAYES ALGORITHM TO PREDICT SHOPEE CONSUMER SATISFACTION AMONG MILLENNIAL GENERATION. Jurnal Teknik Informatika (Jutif), 3(4), 829–838. https://doi.org/10.20884/1.JUTIF.2022.3.4.295
Harmaja, O. J., Purba, W., Siregar, M. P., Manurung, H. T., & Sirait, F. A. (2022). ANALISIS TINGKAT KEPUASAN PELANGGAN PADA RUDANG HOTEL BERASTAGI MENGGUNAKAN METODE CUSTOMER SATISFACTION INDEX (CSI). Jurnal Teknik Informasi Dan Komputer (Tekinkom), 5(1), 147. https://doi.org/10.37600/tekinkom.v5i1.511
Jayal, A., McRobert, A., Oatley, G., & O’Donoghue, P. (2018). Knowledge Discovery in Databases and Data Mining. In Sports Analytics (pp. 190–207). Routledge. https://doi.org/10.4324/9781315222783-10
Kurniawan, R., Rasyidin, R., Muhammad, M., & Aruni, F. (2020). Upaya Pemerintah dalam Menerapkan Peraturan Pemerintah No. 49 Tahun 2018 Tentang Pegawai Pemerintah Dengan Perjanjian Kerja (P3K) dI Kabupaten Aceh Utara. Jurnal Public Policy, 6(1), 41–44. https://doi.org/10.35308/JPP.V6I1.1899
Munandar, D., Ruchjana, B. N., & Abdullah, A. S. (2022). PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(1), 099–112. https://doi.org/10.30598/barekengvol16iss1pp099-112
Prakoso, B. S., Rosiyadi, D., Utama, H. S., & Aridarma, D. (2019). Klasifikasi Berita Menggunakan Algoritma Naive Bayes Classifer Dengan Seleksi Fitur Dan Boosting. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(2), 227–232. https://doi.org/10.29207/resti.v3i2.1042
Qomarani, L. N. (2020). ANOMALI KEHADIRAN PEGAWAI PEMERINTAH DENGAN PERJANJIAN KERJA (PPPK) DALAM CAKRAWALA KEPEGAWAIAN DI INDONESIA. Cepalo, 4(2), 95–110. https://doi.org/10.25041/cepalo.v4no2.1979
Simanjuntak, A. Y., Simatupang, I. S. E. S., & Anita, A. (2022). IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAÃVE BAYES CLASSIFIER UNTUK DATA KENAIKAN PANGKAT DINAS KETENAGAKERJAAN KOTA MEDAN. JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 5(1), 85. https://doi.org/10.54314/jssr.v5i1.804
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Copyright (c) 2023 Yosep Bustomi, Anwar Nugraha, Christina Juliane, Sri Rahayu
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