Penetapan Instruktur Diklat Menggunakan Metode Clustering K-Means dan Topsis Pada PT PLN (Persero) Udiklat Jakarta

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Nurul Dyah Budiana
Riki Ruli A. Siregar
Meilia Nur Indah Susanti

Abstract

Instructor is the main aspect that exists in the implementation of the training. The increasing number of instructors and the need for training is also increasing every year there is no system that can help the process of determining quickly and precisely. In need of a method that can classify the instructor data in accordance with the title of training materials and can be assigned instructor each of the training materials and do not ignore aspects of assessment of the instructor. In this study data mining techniques are used to help recommend instructors for each subject matter of the training based on the cluster data group approach. So it can be used in determining the instructor's assignment per training materials in the future. K-Means clustering method is used to group data into clusters by looking at the centroid  value that has been determined. And the Topsis method is used to assign one instructor's name through the rankings of preference values. In this research CRISP-DM method is used as software engineering method system work done in sequence or linearly. In the testing process has been generated if the manual data and data processing if the application system is the same. This application to facilitate the Supervisor and Learning Development staff in setting instructors per training materials.

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References

Siregar, R. R. A., Sinaga, F. A., & Arianto, R. (2017). Aplikasi Penentuan Dosen Penguji Skripsi Menggunakan Metode TF-IDF dan Vector Space Model. Computatio: Journal of Computer Science and Information Systems, 1(2), 171-186.
[7] T. Nisa, R. Siregar, And W. Suliyanti, “Estimasi Daya Beban Listrik Pada Gardu Induk Cengkareng Dengan Menggunakan Metode Time Series Model Dekomposisi”, Teknologia, Vol. 1, No. 2, Apr. 2019.
[8] Sangadji, I., & Arvio, Y. (2018, March). Dynamic Segmentation Of Behavior Patterns Based On Quantity Value Movement Using Fuzzy Subtractive Clustering Method. In Journal of Physics: Conference Series (Vol. 974, No. 1, p. 012009). IOP Publishing.
[9] Siregar, R., Siregar, Z., & Arianto, R. (2019). Klasifikasi Sentiment Analysis Pada Komentar Peserta Diklat Menggunakan Metode K-Nearest Neighbor. KILAT, 8(1). https://doi.org/10.33322/kilat.v8i1.421
[10] A. Prianty, R. Siregar, and R. Arianto, “Penanganan Gangguan Listrik Rumah Tangga Menggunakan Algoritma Greedy Untuk Penentuan Jarak Optimal”, Teknologia, vol. 2, no. 1, Aug. 2019.
[11] Siregar Rr, Putri Dr. Metode Support Vector Machine Pada Klasifikasi Audit Energi: Studi Kasus Gedung STT-PLN Jakarta. Jurnal Informatika Dan Komputasi. 2017 Mar 2;8(2):98-104.