PERBANDINGAN METODE BALANCED SCORECARD DAN NAÏVE BAYES DALAM PREDIKSI DAN REKOMENDASI PADA PENILAIAN KINERJA GURU (STUDI KASUS : SMK YADIKA 12 DEPOK)
Main Article Content
Abstract
Article Summary
Teacher Performance Assessment guarantees a quality learning process at all levels of education. Educational supervision will be carried out with the aim to improve the quality of teaching / teacher so that the competitiveness of students studying at the school will increase towards a better direction. Supervision assessment is a class visit technique to obtain data about the actual situation regarding the ability and skills of teachers in teaching and mastery of class. To determine the teacher's performance, one approach can be done using the Balanced Scorecard approach and Naïve Bayes classification. The determination of teacher performance is then processed using Analytic Network Process-based modeling to improve teacher evaluation criteria that are still low. With the help of Super Decision software, a decision support system was created in determining teacher performance. The results of this study are the recommendations of permanent teachers in Junior High Schools, High Schools, and Vocational Schools Yadika 12 Depok based on performance to be objective and make more efficient decisions.
Keywords
Article Keywords
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Imran. 2010. Pembinaan Guru di Indonesia. Jakarta: Pustaka Jaya.
Basrowi, S., 2008. Memahami penelitian kualitatif. Jakarta: Rineka Cipta, 12(1), pp.128-215.
Kothari, C.R., 2004. Research methodology: Methods and techniques. New Age International.
Sugiyono, P.D., 2009. Metode Penelitian Kuantitatif Kualitatif Dan R&D, Bandung: CV.
Efron, B. 1992, Missing Data, Imputation, and The Bootstrap, Division of Biostatistics Stanford University, California.
Enders, C.K., 2010. Applied Missing Data Analysis (Methodology in the Social Sciences) Guilford Press. New York.
Enders, C.K., 2010. Applied missing data analysis: Guilford press. New York.
Junaedi, H., Budianto, H., Maryati, I. and Melani, Y., 2011. Data transformation pada data mining. Prosiding Konferensi Nasional Inovasi dalam Desain dan Teknologi-IDeaTech, 7(3), pp.93-99.
Ridwan, M., Suyono, H. and Sarosa, M., 2013. Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier. Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 7(1), pp.59-64. DOI: https://doi.org/10.21776/jeeccis.v7i1.204.
Falahah, & Nur, D. D. 2015. Pengembangan Aplikasi Sentiment Analysis Menggunakan Metode Naive Bayes. Seminar Nasional Sistem Informasi Indonesia, 336-340.
Hamzah, A., 1979. Klasifikasi Teks Dengan Naïve Bayes Classifier (NBC) Untuk Pengelompokan Teks Berita dan Abstract Akademis. In Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) Periode III ISSN (p. 911X).
Kurniawan, B., Fauzi, M.A. and Widodo, A.W., 2017. Klasifikasi Berita Twitter Menggunakan Metode Improved Naïve Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, p.964X.
Moeheriono. 2012. Indikator kinerja utama.Jakarta : PT. Raja Grafindo Persada.
Rivai, V. 2004. Manajemen Sumber Daya Manusia untuk Perusahaan Dari Teori Ke Praktik. Jakarta: PT Raja Grafindo Persada.
Wirawan. 2009. Evaluasi Kinerja Sumber Daya Manusia. Jakarta: Salemba Empat.
Azwar, Saifuddin. 2010. Metode Penelitian. Yogyakarta: Pustaka Pelajar.
Belavagi, M. C., & Muniyal, B. 2016. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia - Procedia Computer Science, 89, 117–123. https://doi.org/10.1016/j.procs.2016.06.016.
Han, J., Kamber, M. and Pei, J., 2012. Data mining: concepts and techniques, Waltham, MA. Morgan Kaufman Publishers, 10, pp.978-1.
Han, J., Pei, J. and Tong, H., 2022. Data mining: concepts and techniques. Morgan kaufmann.