IMPLEMENTASI METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM UNTUK MENENTUKAN TINGKAT KEPUASAN TERHADAP PELAYANAN AKADEMIK UNIVERSITAS PRIMA INDONESIA

  • Winda Nia Purba Universitas Prima Indonesia
  • Cristine Mendyeta Universitas Prima Indonesia
  • Reno Simanullang Universitas Prima Indonesia
  • Olwen Kienan Universitas Prima Indonesia
  • Pravin Raj Universitas Prima Indonesia

Abstract

This study highlights the importance of understanding student satisfaction with academic services at Universitas Prima Indonesia. Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method, the research identifies factors such as teaching quality, access to academic resources, and class schedules that influence student satisfaction. Data was collected through questionnaires and observations, then processed with ANFIS to clean, train, and test the data. The findings indicate that the variables of tangibles, responsiveness, reliability, empathy, and assurance significantly affect student satisfaction. The ANFIS model used demonstrates that student satisfaction can be predicted with relatively high accuracy, with an average root mean square error of 0.083297. This study provides guidance for improving the quality of academic services at Universitas Prima Indonesia, emphasizing the importance of professional and high-quality services to meet student expectations.

References

[1] P. D. H. A. Irianto, Pendidikan Sebagai Investasi Dalam Pembangunan Suatu Bangsa. Kencana, 2017.
[2] Suffiyah Arrafiatus, “Pengaruh Kualitas Layanan Akademik dan Birokrasi Terhadap Kepuasan Mahasiswa,” J. Ilm. Aset, vol. Vol. 13, no. No. 2, hal. hlm. 84, 2011.
[3] E. Manik dan I. Sidharta, “The impact of academic service quality on student satisfaction,” Int. Conf. Accounting, Manag. Econ. Soc. Sci., no. 80878, hal. 1–6, 2017.
[4] A. Sriyanto, “Analisis Faktor-Faktor Yang Memengaruhi Kepuasan Layanan Akademik Mahasiswa Prodi Diploma I Kepabeanan Dan Cukai,” J. Perspekt. Bea Dan Cukai, vol. 1, no. 1, hal. 26–38, 2017, doi: 10.31092/jpbc.v1i1.124.
[5] T. Widiastuti, K. Karsa, dan C. Juliane, “Evaluasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Klasifikasi Algoritma C4.5,” Technomedia J., vol. 7, no. 3, hal. 364–380, 2022, doi: 10.33050/tmj.v7i3.1932.
[6] M. Yusa, A. S. F. Alqap, Helmizar, dan N. Hidayati, “Analisis Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Di Fakultas Teknik Universitas Bengkulu,” JBMI (Jurnal Bisnis, Manajemen, dan Inform., vol. 18, no. 2, hal. 103–118, 2021, doi: 10.26487/jbmi.v18i2.14104.
[7] N. Ibrahim Saif, “The Effect of Service Quality on Student Satisfaction: A Field Study for Health Services Administration Students,” Int. J. Humanit. Soc. Sci., vol. 4, no. 8, hal. 172–181, 2014.
[8] N. Nurjannah, “Evaluasi Kepuasan Mahasiswa Terhadap Pelayanan Akademik Fakultas Tarbiyah Dan Ilmu Keguruan Iai Muhammadiyah Sinjai,” J. Eval. Pendidik., vol. 11, no. 2, hal. 51–57, 2020, doi: 10.21009/10.21009/jep.0122.
[9] W. Purba et al., “Penerapan Data Mining Untuk Pengelolaan Data Rekam Medis Menggunakan Metode K-means Clustering Pada Rumah Sakit Royal Prima Medan,” J. TEKINKOM, vol. 6, no. 1, hal. 158–168, 2023, doi: 10.34012/jusikom.v2i2.429.
[10] P. H. Simbolon, “Implementasi Data Mining Pada Sistem Persediaan Barang Menggunakan Algoritma Apriori (Studi Kasus: Srikandi Cash Credit Elektronic dan Furniture),” Jurikom), vol. 6, no. 4, hal. 401–406, 2019.
[11] A. J. Wahidin dan D. I. Sensuse, “Perbandingan Algoritma K-Means, X-Means Dan K-Medoids Untuk Klasterisasi Awak Kabin Lion Air,” J. ICT Inf. Commun. Technol., vol. 20, no. 2, hal. 298–302, 2021, doi: 10.36054/jict-ikmi.v20i2.387.
[12] A. Srirahayu dan L. S. Pribadie, “Review Paper Data Mining Klasifikasi Data Mining,” J. Ilm. Inform. Glob., vol. 14, no. 1, 2023, doi: 10.36982/jiig.v14i1.2981.
[13] S. L. M. Sitio, “Penerapan Fuzzy Inference System Sugeno untuk Menentukan Jumlah Pembelian Obat (Studi Kasus: Garuda Sentra Medika),” J. Inform. Univ. Pamulang, vol. 3, no. 2, hal. 104, 2018.
[14] D. Dedi, P. Prayogo, S. D. Hapid, dan ..., “Sistem Pendukung Keputusan Penilaian Pegawai Dengan Menggunakan Logika Fuzzy,” J. Sisfotek …, vol. 5, no. 1, 2015.
[15] R. Rumfot, Y. A. Lesnussa, dan D. L. Rahakbauw, “Perbandingan Metode Fuzzy Mamdani, Sugeno Dan Tsukamoto Untuk Menentukan Jumlah Produksi Batu Pecah,” MATHunesa J. Ilm. Mat., vol. 12, no. 1, hal. 157–168, 2024, doi: 10.26740/mathunesa.v12n1.p157-168.
[16] K. Kiswanto, B. W. Benny, Y. Yuri, M. Mar, S. Sarwindah, dan S. Supardi, “Penerapan Logika ANFIS Sistem Penilaian Kinerja Dosen Pada Tri Dharma dan Perilaku Kerja,” J. Sist. Inf. Bisnis, vol. 12, no. 1, hal. 57–65, 2022, doi: 10.21456/vol12iss1pp57-65.
[17] P. Harianto, “Penerapan Metode Fuzzy Mamdani Untuk Penentuan Peminatan Program Studi Teknik Informatika di STMIK Pontianak,” Semin. Nasonal Corisindo, hal. 134–138, 2023.
[18] S. S. Winarto dan T. Sutojo, “Menentukan Harga Mobil Bekas Dengan Menggunakan Metode Fuzzy Mamdani Dan Metode Jaringan Syaraf Tiruan,” Techno.COM, vol. 11, no. 3, hal. 134–141, 2012.
[19] A. Rosen et al., “No Covariance Structure Analysis Of Health-Related Indicators For Elderly People Living At Home, Focusing On Subjective Sense Of Health,” Teach Educ, vol. 12, n.
[20] L. Wiranda dan M. Sadikin, “Penerapan Long Short Term Memory pada Data Time Series untuk Memprediksi Penjualan Produk PT. Metiska Farma,” J. Nas. Pendidik. Tek. Inform., vol. 8, no. 3, hal. 184–196, 2019.
[21] R. A. Anggraini, G. Widagdo, A. S. Budi, dan M. Qomaruddin, “Penerapan Data Mining Classification untuk Data Blogger Menggunakan Metode Naïve Bayes,” J. Sist. dan Teknol. Inf., vol. 7, no. 1, hal. 47, 2019, doi: 10.26418/justin.v7i1.30211.
[22] F. T. Prasojo, “Identifikasi Sistem Pada Modul-PV Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS),” 2017.
[23] F. N. W. Sari, “Adaptive Neuro Fuzzy Inference System (ANFIS) terhadap Klasifikasi Kendaraan Roda Dua Mahasiswa Teknik Perkeretaapian Angkatan 2019 di Politeknik Negeri Madiun,” J. Tek. Elektro Uniba (JTE UNIBA), vol. 7, no. 1, hal. 247–254, 2022, doi: 10.36277/jteuniba.v7i1.161.
[24] S. Arslankaya, “Comparison of performances of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) for estimating employee labor loss,” J. Eng. Res., vol. 11, no. 4, hal. 469–477, 2023, doi: 10.1016/j.jer.2023.100107.
Published
2024-06-30
How to Cite
PURBA, Winda Nia et al. IMPLEMENTASI METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM UNTUK MENENTUKAN TINGKAT KEPUASAN TERHADAP PELAYANAN AKADEMIK UNIVERSITAS PRIMA INDONESIA. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 300-309, june 2024. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1212>. Date accessed: 21 july 2024. doi: https://doi.org/10.37600/tekinkom.v7i1.1212.
Section
Articles