Penerapan Metode Rough Set Menganalisa Tingkat Kepuasan Mitra Cleaning Service


Authors

  • Dewi Berutu Universitas Budi Darma, Medan, Indonesia

DOI:

https://doi.org/10.64366/adajisr.v2i1.56

Keywords:

Data Mining; Rough Set; Customer satisfaction

Abstract

PT. ISS Medan is a company operating in the service sector. The main activity of PT.iss Medan is to provide leading integrated facility services to more than 62,000 staff, 3,000 clients and nine branch offices in Medan. The company always tries to maintain a good reputation in the eyes of its customers. Good quality service will attract those who use the company's services. However The company does not have measuring instruments to determine the level of customer satisfaction with the services provided. Rough Set is a data mining method that is related to category analysis and data classification and aims to synthesize a conceptual approach from the data tables obtained. Rough sets find hidden relationships from data sets and reduct attributes from a series of attribute classifications, and the reduct will produce a general rule. The results of the customer satisfaction level rule illustrate that confidence is the main dimension in determining the level of customer satisfaction which is then supported by integrity. , pride (pride), and passion (desire).

Downloads

Download data is not yet available.

References

A. S. L. T. T. H. Hafizah, “Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), no. Vol 1, No 6 (2022): EDISI NOVEMBER 2022, pp. 888–897, 2022, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jsi/article/view/5732/1938

Y. L. Nainel, E. Buulolo, and I. Lubis, “Penerapan Data Mining Untuk Estimasi Penjualan Obat Berdasarkan Pengaruh Brand Image Dengan Algoritma Expectation Maximization (Studi Kasus: PT. Pyridam Farma Tbk),” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 2, p. 214, 2020, doi: 10.30865/jurikom.v7i2.2097.

M. Azhari, Z. Situmorang, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 640, 2021, doi: 10.30865/mib.v5i2.2937.

S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

H. Maulidiya and A. Jananto, “Asosiasi Data Mining Menggunakan Algoritma Apriori dan FP-Growth sebagai Dasar Pertimbangan Penentuan Paket Sembako,” Proceeding SENDIU 2020, vol. 6, pp. 36–42, 2020.

S. Samaray, “Implementasi Algoritma Rough Set dengan Software Rosetta untuk Prediksi Hasil Belajar,” J. Eksplora Inform., vol. 11, no. 1, pp. 57–66, 2021.

J. Jeprianto and R. Z. A. Aziz, “Implementasi algoritma rough set dan naive bayes untuk mendapatkan rule dalam menyeleksi pemohon bantuan fasilitas rumah ibadah (studi kasus: pemerintah kabupaten Pringsewu),” JTKSI (Jurnal Teknol. Komput. dan Sist. Informasi), vol. 3, no. 2, pp. 74–83, 2020.

R. H. Sukarna and Y. Ansori, “Implementasi Data Mining Menggunakan Metode Naive Bayes Dengan Feature Selection Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. Ilm. Sains dan Teknol., vol. 6, no. 1, pp. 50–61, 2022, doi: 10.47080/saintek.v6i1.1467.

F. O. Lusiana, I. Fatma, and A. P. Windarto, “Estimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Simalungun,” J. Informatics Manag. Inf. Technol., vol. 1, no. 2, pp. 79–84, 2021, [Online]. Available: https://hostjournals.com/

Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI

G. Gunadi and D. I. Sensuse, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth )?:,” Telematika, vol. 4, no. 1, pp. 118–132, 2012.

A. Z. Siregar, “Implementasi Metode Regresi Linier Berganda Dalam Estimasi Tingkat Pendaftaran Mahasiswa Baru,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 2, no. 3, pp. 133–137, 2021, [Online]. Available: https://tunasbangsa.ac.id/pkm/index.php/kesatria/article/view/73

S. S. S, A. T. Purba, V. Marudut, M. Siregar, T. Komputer, and P. B. Indonesia, “SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN PEMBERIAN PINJAMAN,” vol. 3, pp. 25–30, 2020, doi: 10.37600/tekinkom.v3i1.131.

M. M. Effendi, “Menentukan Prediksi Kelulusan Siswa Dengan Membandingkan Algoritma C4. 5 Dan Naive Bayes Studi Kasus SMKN. 1 Cikarang Selatan,” J. SIGMA, vol. 11, no. 3, pp. 143–148, 2020.

S. U. Putri, E. Irawan, and F. Rizky, “Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4. 5,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 2, no. 1, pp. 39–46, 2021.

K. Erwansyah, B. Andika, and R. Gunawan, “Implementasi Data Mining Menggunakan Asosiasi Dengan Algoritma Apriori Untuk Mendapatkan Pola Rekomendasi Belanja Produk Pada Toko Avis Mobile,” J. Teknol. Sist. Inf. dan Sist. Komput. TGD, vol. 4, no. 1, pp. 148–161, 2021.

A. Rivandi, E. Bu’ulolo, and N. Silalahi, “Penerapan Metode Regresi Linier Berganda Dalam Estimasi Biaya Pencetakan Spanduk (Studi Kasus: PT. Hansindo Setiapratama),” Pelita Inform. Inf. dan Inform., vol. 7, no. 3, pp. 263–268, 2019.

P. Purwadi, P. S. Ramadhan, and N. Safitri, “Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 18, no. 1, pp. 55–61, 2019.

S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4, 5, Naïve Bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019.

F. Harahap, “Perbandingan Algoritma K Means dan K Medoids Untuk Clustering Kelas Siswa Tunagrahita,” TIN Terap. Inform. Nusant., vol. 2, no. 4, pp. 191–197, 2021.

M. A. Rofiq, A. Qoiriah, S. Kom, and M. Kom, “Pengelompokan Kategori Buku Berdasarkan Judul Menggunakan Algoritma Agglomerative Hierarchical Clustering Dan K-Medoids,” J. Informatics Comput. Sci., vol. 2, no. 03, pp. 220–227, 2021.

B. Harli Trimulya Suandi As and L. Zahrotun, “PENERAPAN DATA MINING DALAM MENGELOMPOKKAN DATA RIWAYAT AKADEMIK SEBELUM KULIAH DAN DATA KELULUSAN MAHASISWA MENGGUNAKAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING (Implementation Of Data Mining In Grouping Academic History Data Before Students And Stud,” J. Teknol. Informasi, Komput. dan Apl., vol. 3, no. 1, pp. 62–71, 2021, [Online]. Available: http://jtika.if.unram.ac.id/index.php/JTIKA/

A. Damuri, U. Riyanto, H. Rusdianto, and M. Aminudin, “Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 6, pp. 219–225, 2021.

I. A. Nikmatun and I. Waspada, “Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 10, no. 2, pp. 421–432, 2019.

H. Hozairi, A. Anwari, and S. Alim, “Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes,” Netw. Eng. Res. Oper., vol. 6, no. 2, pp. 133–144, 2021.

H. Maulidiya and A. Jananto, “Asosiasi Data Mining Menggunakan Algoritma Apriori Dan Fpgrowth Sebagai Dasar Pertimbangan Penentuan Paket Sembako,” 2020.

A. S. Kurniawansyah, “Penerapan Algoritma Rough Set Dalam Memprediksi Hasil Ujian Kompetensi Kebidanan,” J. Sci. Appl. Informatics, vol. 4, no. 2, pp. 135–140, 2021.

E. F. Amrullah, A. Nilogiri, and I. Saifudin, “Klasifikasi Atlet Karate Menggunakan Algoritma Rough Set Pada Dojo Shinkyokushin Roxy Jember,” J. Smart Teknol., vol. 3, no. 6, pp. 592–601, 2022.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Metode Rough Set Menganalisa Tingkat Kepuasan Mitra Cleaning Service

Dimensions Badge

ARTICLE HISTORY

Published: 2024-10-30

Abstract View: 159 times
PDF Download: 40 times

How to Cite

Dewi Berutu. (2024). Penerapan Metode Rough Set Menganalisa Tingkat Kepuasan Mitra Cleaning Service. ADA Journal of Information System Research, 2(1), 23-34. https://doi.org/10.64366/adajisr.v2i1.56

Issue

Vol. 2 No. 1 (2024): October 2024

Section

Articles

Copyright (c) 2024 Dewi Berutu

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. 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 acknowledgment of its initial publication in this journal.
  3. 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 (Refer to The Effect of Open Access).