KLASIFIKASI TINGKAT KEPUASAN MAHASISWA TERHADAP FASILITAS PADA FTIK UNIVERSITAS DHARMAWANGSA MEDAN DENGAN ALGORITMA NAIVE BAYES

Medi Hermanto Tinambunan, Alfiansyah Hasibuan, Sri Wahyuni, Arif Sobirin Wibowo

Abstract


ABSTRACT

Facilities are a support for the implementation of a process in a business in this case the Dharmawangsa University campus, by increasing the level of student satisfaction with the facilities available, student comfort in learning will be achieved. This study used questionnaire data on 70 respondents who were students of Dharmawangsa University. Previously, the questionnaire consisted of 42 questions. After being tested for validity and reliability, 20 questions were obtained. Then from the results of the questionnaire data, a classification of student satisfaction levels will be carried out using one of the algorithms in data mining, namely Naive Bayes. The results obtained by using the rapid miner application with 50 training data and 19 data testing data, the results obtained are a classification accuracy of 73.68% with a recall value of 83.33% and a precision of 83.33%, then there are 9 attributes that have a value dissatisfaction is higher than the satisfaction score given by respondents, this can be a concern of the leadership to improve these facilities so as to increase the level of satisfaction with the facilities provided by Dharmawangsa University.

Keywords: Student Satisfaction, Data Mining, Naive Bayes


Keywords


Student Satisfaction; Data Mining; Naive Bayes

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References


Cindy Mutia Annur, “Jumlah Perguruan Tinggi di Indonesia Capai 3.107 Unit pada 2022, Mayoritas dari Swasta,†Mar. 01, 2023. https://databoks.katadata.co.id/datapublish/2023/03/01/jumlah-perguruan-tinggi-di-indonesia-capai-3107-unit-pada-2022-mayoritas-dari-swasta#:~:text=Jumlah%20itu%20turun%200%2C25,tinggi%20di%20Indonesia%20pada%202022. (accessed May 22, 2023).

Universitas Dharmawangsa, “Visi, Misi Tujuan dan Sasaran,†May 23, 2022. https://dharmawangsa.ac.id/visi-misi-tujuan-dan-sasaran/ (accessed May 23, 2023).

M. Yusa, A. F. Sofwan Alqap, and N. Hidayati, “ANALISIS TINGKAT KEPUASAN MAHASISWA TERHADAP PELAYANAN AKADEMIK DI FAKULTAS TEKNIK UNIVERSITAS BENGKULU,†vol. 18, p. 103, 2021, doi: 10.26487/jbmi.v18i2.14104.

A. Natuzzuhriyyah, N. Nafisah, and R. Mayasari, “Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naïve Bayes,†2021.

J. P. Wilayah, D. Kota, D. Taluke, R. S. M. Lakat, and A. Sembel, “ANALISIS PREFERENSI MASYARAKAT DALAM PENGELOLAAN EKOSISTEM MANGROVE DI PESISIR PANTAI KECAMATAN LOLODA KABUPATEN HALMAHERA BARAT,†Jurnal Spasial, vol. 6, no. 2, 2019.

S. Mulyati, “Penerapan Data Mining dengan Metode Clustering untuk Pengelompokan Data Pengiriman Burung,†Prosiding Seminar Ilmiah Nasional Teknologi Komputer (SENATKOM), vol. 1, no. Senatkom, pp. 30–35, 2015.

S. N. S. S. Sumathi, “Data Mining Tasks , Techniques , and Objectives :,†Studies in Computational Intelligence (SCI), vol. 216, pp. 195–216, 2006.

H. D. Honesqi, “Klasifikasi Data Mining Untuk Menentukan Tingkat Persetujuan Kartu Kredit,†Jurnal Teknoif, vol. 5, no. 2, pp. 57–62, 2017, doi: 10.21063/jtif.2017.v5.2.57-62.

E. D. Sikumbang, “Penerapan Data Mining Penjualan Sepatu Menggunakan Metode Algoritma Apriori,†Jurnal Teknik Komputer AMIK BSI (JTK), vol. Vol 4, No., no. September, pp. 1–4, 2018.

R. Kamal, Iman Mustofa Hendro P, Tachbir Ilyas, “Prediksi Penjualan Buku Menggunakan Data Mining Di Pt. Niaga Swadaya,†Seminar Nasional Teknologi Informasi & Multimedia, no. February, pp. 49–54, 2017, [Online]. Available: http://ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/1712

J. Liu et al., “Data Mining and Information Retrieval in the 21st century: A bibliographic review,†Comput Sci Rev, vol. 34, 2019, doi: 10.1016/j.cosrev.2019.100193.

D. Fitriati et al., “Data Mining Dengan Teknik Clustering Dalam Pengklasifikasian Data Mahasiswa Studi Kasus Prediksi Lama Studi Mahasiswa Universitas Bina Nusantara,†Universitas Stuttgart, vol. 2, no. 1, pp. 79–93, 2017, doi: 10.1152/physrev.00015.2003.

S. Butsianto and N. T. Mayangwulan, “Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering,†Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 3, no. 3, pp. 187–201, 2020, doi: 10.32672/jnkti.v3i3.2428.

R. Rachman, R. N. Handayani, and I. Artikel, “Klasifikasi Algoritma Naive Bayes Dalam Memprediksi Tingkat Kelancaran Pembayaran Sewa Teras UMKM,†JURNAL INFORMATIKA, vol. 8, no. 2, 2021, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji

M. Siddik, R. Noratama Putri, and Y. Desnelita, “CLASSIFICATION OF STUDENT SATISFACTION ON HIGHER EDUCATION SERVICES USING NAÃVE BAYES ALGORITHM,†Journal of Information Technology and Computer Science (INTECOMS), vol. 3, no. 2, 2020.

A. H. Nasrullah, “IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI PRODUK LARIS,†vol. 7, no. 2, 2021, [Online]. Available: http://ejournal.fikom-unasman.ac.id




DOI: https://doi.org/10.46576/bn.v6i1.3356

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