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Implementasi Metode K-Means berbasis Chi-Square pada Sistem Pendukung Keputusan untuk Identifikasi Disparitas Kebutuhan Guru

*M. Nishom orcid  -  Politeknik Harapan Bersama Tegal, Indonesia
Dega Surono Wibowo  -  Politeknik Harapan Bersama Tegal, Indonesia
Open Access Copyright (c) 2018 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract

In this research, the Chi-Square-based K-Means method was implemented in the Decision Support System (DSS) to identify the disparity in Teacher's needs compared to the real conditions of Teacher's availability in the education unit (school). This is very important, because based on data from the UNESCO Institute for Statistics shows that the ratio between teachers and students in Indonesia is the lowest in the world. This is influenced by the distribution of Teachers who do not meet the needs and exceed the number of student enrollments at all levels of education, resulting in less optimal quality of education produced in various regions in Indonesia. Thus, it is necessary to group data and label the disparity of Teacher's needs in educational units in various regions in Indonesia, especially in the Tegal City. In this case, the K-Means Clustering method was used to group data based on Teacher's availability data, and Chi-Square analysis was used to determine the disparity in Teacher's needs with the condition of Teacher's availability. Data collection methods used in research are observation methods. The results showed that the DSS application that had been produced could dynamically determine the education unit cluster based on the teacher availability disparity category in the Tegal City. In addition, labeling of the K-Means cluster based on the Chi-square test has a high degree of accuracy, which is 84.47%.

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Keywords: Clustering; K-Means; Chi-Square; Teacher disparity; Student-teacher ratio.
Funding: Politeknik Harapan Bersama

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  1. Kemendikbud, 2016. Analisis Sumber Daya Manusia Pendidikan Dasar dan Menengah 2015/2016, Pusat Data dan Statistik Pendidikan dan Kebudayaan, Jakarta
  2. Kuswantoro, E., Suprapto, K. Y., 2015. Penerapan algoritma k-means dengan optimasi jumlah cluster untuk pengelompokan angkatan kerja propinsi Jatim. JAVA Journal of Electrical and Electronics Engineering 3 (1), 58-62
  3. Larose, T.D., 2014. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience, Canada
  4. Manning, D.C., 2008. Introduction to Information Retrieval. Cambridge University Press, Cambridge
  5. Oyelade, J., Oladipupo, O., and Obagbuwa, C.I., 2010. Application of k means clustering algorithm for prediction of students academic performance. International Journal of Computer Science and Information Security (7), 292–295
  6. Prasetyo, E., 2014. Data Mining, Mengolah Data Menjadi Informasi Menggunakan Matlab. ANDI, Yogyakarta
  7. Stine, R., 2011. Statistics for Business Decision Making and Analysis with Chi-Square Tests. Addison-Wesley, Boston
  8. Sunuyeko, N., Lani, A., and Wahyuni, L., 2017. Analisis kebutuhan guru dalam pengimplementasian kurikulum 2013 di Sekolah Dasar. Portal Jurnal Elektronik Universitas Negeri Malang 2, 1-9
  9. Wu, X., Wu, B., Sun, J., Qiu, S., and Li, X., 2014. A Hybrid fuzzy k-harmonic means clustering algorithm. Applied Mathematical Modelling 39(12), 3398-3409
  10. Wu, X., Kumar, V., Quinlan, R.J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, J.G., Ng, A., Liu, B., Yu, S.P., Zhou, H., Steinbach, M., Hand, J.D., and Steinberg, D., 2008. Top 10 algorithms in data mining, Knowledge and Information Systems 14 (1), 1-37

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