Optimasi Jumlah Cluster pada Algoritme K-Means untuk Evaluasi Kinerja Dosen
DOI:
https://doi.org/10.32493/informatika.v6i1.9522Keywords:
Elbow Method, K-Means, Lecturer performance evaluation, OptimizationAbstract
Lecturers are one of the main actors in universities. Lecturer performance can affect the quality of a college. Because the quality of higher education is strongly influenced by the lecturers, the performance of the lecturers needs to be assessed. Lecturer performance can be assessed by evaluating the lecturer's performance in teaching. Lecturer performance can be evaluated by classifying student assessments of lecturers. Lecturers are assessed based on how the lecturer mastered the material, the lecturer's discipline in teaching, and the presentation of the material. The process of grouping lecturer scores can be done using the K-Means algorithm. K-Means is a popular clustering algorithm that performs well. K-Means requires a parameter that is K or the number of clusters. The importance of the number of clusters so that there is a need for optimization in determining the number of K. In this study, optimization was carried out using the Elbow method so as to produce the ideal number of groups of 4 groups. The results of the clustering evaluation calculated using the SSE were 54.4% which showed that the results were not optimal. The results of the cluster evaluation are not optimal due to the lack of data for the K-Means application.
References
Agarwal, V. (2016). Research on Data Preprocessing and Categorization Technique for Smartphone Review Analysis. November. International Journal of Computer Applications. 131(4):30-36. doi : https://doi.org/10.5120/ijca2015907309
Andri & Marlindawati. (2015). Pengelompokan Minat Belajar Mahasiswa Menggunakan Teknik. Jurnal Ilmiah MATRIK. 3(1):67–76
Muningsih, E., & Kiswati, S. (2018). Sistem Aplikasi Berbasis Optimasi Metode Elbow Untuk. JOUTICA. 3(1):1-8.
Nishom, M. (2019). Perbandingan Akurasi Euclidean Distance, Minkowski Distance, Dan Manhattan Distance Pada Algoritma K-Means Clustering Berbasis Chi-Square. Jurnal Informatika: Jurnal Pengembangan IT. 4(1):20–24. doi : https://doi.Org/10.30591/Jpit.V4i1.1253
Purwayoga, V. (2020). Implementasi Algoritme K-Means Untuk Evaluasi Kinerja Dosen Pada Rancangan Sistem Informasi Manajemen ( SIM ) Kampus. INTI TALAFA : Jurnal Teknik Informatika.12(1):1-6. doi:Https://Doi.Org/10.32534/Int.V12i01.1265
Purwayoga, V, & Sitanggang, I. S. (2020). Clustering Potential Area Of Fusarium Oxysporum As A Disease Of Garlic. International Conference on Environment and Forest Conservation. 528:012040
Putu, N., Merliana, E. (2015). Analisa Penentuan Jumlah Cluster Terbaik Pada Metode K-Means. 978–979.
Rahman, A. T., Wiranto, & Rini, A. (2017). Coal Trade Data Clustering Using K-Means (Case Study Pt. Global Bangkit Utama). ITSMART: Jurnal Teknologi Dan Informasi, 6(1):24–31. doi:https://Doi.Org/10.20961/ITS.V6I1.11296
Rahmansyah, A., Dewi, O., Andini, P., Hastuti, T., Ningrum, P., & Suryana, M. E. (2018). Membandingkan Pengaruh Feature Selection Terhadap Algoritma Naïve Bayes Dan Support Vector Machine. 1–7.
Retnowati, T. H. (2019). Model Evaluasi Kinerja Dosen : Pengembangan Instrumen untuk Mengevaluasi Kinerja. Jurnal Penelitian dan Evaluasi Pendidikan. 21(2):206-214.
Safira, W. (2016). Pengelompokan Minat Baca Mahasiswa Menggunakan Metode K-Means. ILKOM Jurnal Ilmiah. 8(2):89–94.
Seniwati, E. (2011). Perhitungan Penilaian Mahasiswa Terhadap Mengajar Dosen. Jurnal DASI.12(2):1–5.
Singh, A., Yadav, A., & Rana, A. (2013). K-Means With Three Different Distance Metrics. International Journal Of Computer Applications. 67(10):13–17. doi:Https://Doi.Org/10.5120/11430-6785
Widaningrum, I. D. A. (2012). Sistem Informasi Evaluasi Kinerja Dosen Aspe Kompetensi dan Tridarma di Lingkungan Universitas Muammadiyah Ponorogo. 6(1), 49–60.
Widystuti, W., & Darmawan, J. B. B. (2018). Pengaruh Jumlah Data Set Terhadap Akurasi Pengenalan Dalam Deep Convolutional Network. 8–13.
Downloads
Published
Issue
Section
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-NonCommercial 4.0 International (CC BY-NC 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 (See The Effect of Open Access).
Jurnal Informatika Universitas Pamulang have CC-BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Informatika Universitas Pamulang recognize that free access is better than priced access, libre access is better than free access, and libre under CC-BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
Jurnal Informatika Universitas Pamulang is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
YOU ARE FREE TO:
- Share : copy and redistribute the material in any medium or format
- Adapt : remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms
