Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids
|
Keywords:
Promotion Technique, New Students, Clusterization, K-Medoids, Rapidminer
AbstractPromotion technique is one of the methods used by AMIK and STIKOM Tunas Bangsa Pematangiantar in finding new students. By developing promotional techniques that are more effective and efficient, it can produce the information needed to get new student candidates. The data that is processed in this research is the data of new student candidates who come from the AMIK New Student Admissions (PMB) and STIKOM Tunas Bangsa Pematangsiantar. Based on promotional data in 2020, there are several promotions carried out, including through print media, banners, collaboration with SMA / SMK schools in Pematangsiantar city, through alumni, and the web. Furthermore, the data is processed using Rapidminer software. The processing stages are grouped using the K-Medoids algorithm on the data of prospective students, including name, place and date of birth, address, religion, cellphone number, school origin, choice of study program and source of information. Followed by the process of improving the data so that more accurate data is obtained to be processed. The results of the testing of this method are knowing which regions are applying to AMIK and STIKOM Tunas Bangsa Pematngsiantar which are grouped into 2 clusters, namely the highest and the lowest. The K-Medoids algorithm that is used makes a big contribution by providing new information that can be used as a reference for AMIK and STIKOM Tunas Bangsa Pematngsiantar in terms of Promoting New Student Admissions in the coming year. Downloads
Download data is not yet available.
ReferencesHarahap, P. N., & Sulindawaty, S. (2019). Implementasi Data Mining dalam Memprediksi Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus PT. Arma Anugerah Abadi Cabang Sei Rampah). MATICS, 11(2), 46-50. DOI: http://doi.org/10.18860/mat.v11i2.7821 . Wira, B., Budianto, A. E., & Wiguna, A. S. (2019). Implementasi Metode K-Medoids Clustering untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang. RAINSTEK : Jurnal Terapan Sains & Teknologi, 1(3), 53 - 68. Asmiatun, S., Wakhidah, N., & Putri, A. N. (2020). Penerapan Metode K-Medoids untuk Pengelompokkan Kondisi Jalan di Kota Semarang. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 6(2), 171–180. DOI: https://doi.org/10.35957/jatisi.v6i2.193 . Marlina, D., Lina, N., Fernando, A., & Ramadhan, A. (2018). Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi, 4(2), 64. DOI: http://doi.org/10.24014/coreit.v4i2.4498 . Anggreini, N. L. (2019). Teknik Clustering dengan Algoritma K-Medoids Untuk Menangani Strategi Promosi di Politeknik Tedc Bandung. Jurnal Teknologi Informasi dan Pendidikan, 12(2), 1-7. DOI: https://doi.org/10.24036/tip.v12i2.215 . Nahdliyah, M. A., Widiharih, T., & Prahutama, A. (2019). Metode K-Medoids Clustering dengan Validasi Silhouette Index dan C-Index (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018). Jurnal Gaussian, 8(2), 161–170. DOI: http://doi.org/10.14710/j.gauss.v8i2.26640 . Hardiyanti, F., Tambunan, H. S., & Saragih, I. S. (2019). Penerapan Metode K-Medoids Clustering Pada Penanganan Kasus Diare di Indonesia. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1). DOI: http://doi.org/10.30865/komik.v3i1.1666 . Nakagawa, K., Imamura, M., & Yoshida, K. (2018). Stock Price Prediction using K-Medoids Clustering with Indexing Dynamic Time Warping. IEEJ Transactions on Electronics, Information and Systems, 138(8), 986–991. DOI: http://doi.org/10.1541/ieejeiss.138.986 . Kerstens, E. (2020). Non-Exhaustive, Overlapping K-Medoids for Document Clustering. Proceedings of the 53rd Hawaii International Conference on System Sciences. DOI: http://doi.org/10.24251/hicss.2020.097 . Syukra, I., Hidayat, A., & Fauzi, M. Z. (2019). Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations. Indonesian Journal of Artificial Intelligence and Data Mining, 2(2), 107. DOI: http://doi.org/10.24014/ijaidm.v2i2.8326 . Budiaji, W., & Leisch, F. (2019). Simple K-Medoids Partitioning Algorithm for Mixed Variable Data. Algorithms, 12(9), 177. DOI: http://doi.org/10.3390/a12090177 . Samudi, S., Widodo, S., & Brawijaya, H. (2020). The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic. SinkrOn, 5(1), 116. DOI: http://doi.org/10.33395/sinkron.v5i1.10649 . AS, W., Aidid, M. K., & Nusrang, M. (2019). Pengelompokan Kabupaten/Kota Provinsi Sulawesi Selatan dan Barat Berdasarkan Angka Partisipasi Pendidikan SMA/SMK/MA Menggunakan K-Medoid dan CLARA. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 1(3), 48. DOI: http://doi.org/10.35580/variansiunm12899 . Purba, L., Saifullah, S., & Dewi, R. (2019). Pengelompokan Kasus Penyakit AIDS Berdasarkan Provinsi dengan Data Mining K-Medoids Clustering. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1). DOI: http://doi.org/10.30865/komik.v3i1.1679 . Ningsih, S. R., Damanik, I. S., Windarto, A. P., Tambunan, H. S., Jalaluddin, J., & Wanto, A. (2019). Analisis K-Medoids dalam Pengelompokkan Penduduk Buta Huruf Menurut Provinsi. Prosiding Seminar Nasional Riset Information Science (SENARIS), 1, 721. DOI: http://doi.org/10.30645/senaris.v1i0.78 |
Published
2021-03-20
Issue
Section
Articles
How to Cite
Darma, S., & Nurcahyo, G. W. (2021). Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids. Jurnal Informatika Ekonomi Bisnis, 3(3), 89-94. https://doi.org/10.37034/infeb.v3i3.87
Copyright (c) 2021 Jurnal Informatika Ekonomi Bisnis  This work is licensed under a Creative Commons Attribution 4.0 International License. |
11.jpg)
