Classification of Customer Satisfaction with the K-Nearest Neighbor Algorithm in Relation to Employee Performance at PT. Airkon Pratama

Authors

  • Ahmad Suprianto Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Untung Surapati Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Yuma Akbar Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Aditya Zakaria Hidayat Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI:

https://doi.org/10.35870/ijsecs.v4i3.2948

Keywords:

PT. Airkon Pratama, RapidMiner Application, K-Nearest Neighbor Algorithm

Abstract

PT. Airkon Pratama is the technical consultancy company in the field of maintenance, repair, and operate system. Among its projects are a four-building, multi-story tax office complex. PT. Airkon Pratama experience obstacles to know how its customer satisfaction with their services that is was measured by a questionnaireobtained from work order form. The purpose of this study is to determine how well K-Nearest Neighbor data classification accurately classifies customer satisfaction based on employee performance by PT. Airkon Pratama. The data used in this study is from PT. Airkon Pratama with the data processing using RapidMiner with the K-Nearest Neighbor method which produces an accuracy of 96.53%. Among them four performance indicators were rated as "good", and two as "adequate". Of the 196 that were correctly predicted to be "good," three were "adequate." Most of the 04 respondents gave a positive response indicating their satisfaction with the management of tax office facilities provided by PT. Airkon Pratama in January 2024.

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Author Biographies

  • Ahmad Suprianto, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

    Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

  • Untung Surapati, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

    Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

  • Yuma Akbar, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

    Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

  • Aditya Zakaria Hidayat, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

    Digital business Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

References

Saragih, D. A. R., Safii, M., & Suhendro, D. (2021). Penerapan Data Mining Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Sistem Informasi di Program Studi Sistem Informasi. Journal of Information System Research (JOSH), 2(2), 173-177.

Hendriyanto, M. D., & Sari, B. N. (2022). Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Judul Berita Hoax. Jurnal Ilmiah Informatika, 10(02), 80-84. https://doi.org/10.33884/jif.v10i02.5477

Halim, T. N., Martin, R., Hutasoit, R. K. B., & Aisyah, S. (2023). Klasifikasi Kepuasan Pelanggan Terhadap Platform E-Commerce dengan Metode K–Nearest Neighbor (K-NN). Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 8(2), 512-523. http://dx.doi.org/10.30645/jurasik.v8i2.636

Ariani, F., & Taufik, A. (2020). Perbandingan Metode Klasifikasi Data Mining untuk Prediksi Tingkat Kepuasan Pelanggan Telkomsel Prabayar. SATIN-Sains dan Teknologi Informasi, 6(2), 46-55. https://doi.org/10.33372/stn.v6i2.666

Diansyah, S. (2022). Klasifikasi tingkat kepuasan pengguna dengan menggunakan metode K-Nearest Neighbour (KNN). Jurnal Sistim Informasi dan Teknologi, 4, 7–12. https://doi.org/10.37034/jsisfotek.v4i1.114

Supriyadi, D., Safitri, S. T., Amriza, R. N. S., & Kristiyanto, D. Y. (2022). Klasifikasi loyalitas pengguna sistem e-learning menggunakan Net Promoter Score dan machine learning. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 8(1), 38. https://doi.org/10.26418/jp.v8i1.49300

Sundari, S., Windarto, A. P., Purba, Y. P., & Artikel, G. (2022). Penerapan data mining klasifikasi terhadap kepuasan pelanggan toko Bombay Textile. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(4), 2828–9099. https://doi.org/10.55123/jomlai.v1i4.1672

Widiana, S. A., Firdaus, I., Tenda, E., & Ketaren, E. (2023). Sistem Informasi Prediksi Penjualan Produk Thrift Di Toko Manado Menggunakan Algoritma K-Nearest Neighbor (K-Nn). Jurnal TIMES, 12(2), 52-57. https://doi.org/10.51351/jtm.12.2.2023708

Martiani, E., Setyanto, A., & Nasiri, A. (2024). Studi literatur mengenai prediksi kepuasan GrabFood menggunakan machine learning. Dielektrika, 11(1), 22–31. https://doi.org/10.29303/dielektrika.v11i1.367

Pradana, M. G., Nurcahyo, A. C., & Saputro, P. H. (2021). Penerapan metode K-Means klustering untuk menentukan kepuasan pelanggan. Creative Information Technology Journal, 7(1), 42. https://doi.org/10.24076/citec.2020v7i1.185

Febriyani, A., Prayoga, G. K., & Nurdiawan, O. (2021). Index kepuasan pelanggan Informa dengan menggunakan algoritma C.45. JURIKOM (Jurnal Riset Komputer), 8(6), 330. https://doi.org/10.30865/jurikom.v8i6.3686

Nugraha, F. T., & Hendry, H. (2023). Implementasi machine learning sebagai analisis kepuasan pelanggan terhadap penggunaan aplikasi Kai Access. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 8(4), 1248–1255. https://doi.org/10.29100/jipi.v8i4.4185

Abdy, S., Gultom, E. R. B., Ramadhany, S., & Afifudin, A. (2022). Prediksi penjualan sparepart mobil terlaris menggunakan metode K-Nearest Neighbor. JURIKOM (Jurnal Riset Komputer), 9(6), 2003. https://doi.org/10.30865/jurikom.v9i6.5189

Suwandi, A., & Fauzi, R. (2023). Analisis kepuasan konsumen terhadap kualitas produk menggunakan algoritma Naive Bayes. Computer and Science Industrial Engineering (COMASIE), 9(7), 30. https://doi.org/10.33884/comasiejournal.v9i7.7921

Salsabilah, B. N., Kadek, I., & Nuryana, D. (2023). Komparasi algoritma Naive Bayes dan K Nearest Neighbor dalam kepuasan pengguna fitur Tiktok Shop. Journal of Emerging Information System and Business Intelligence (JEISBI), 4(3), 31–39. Retrieved from https://ejournal.unesa.ac.id/index.php/JEISBI/article/view/54211

Riadi, I., & et al. (2024). Prediksi kelulusan tepat waktu berdasarkan riwayat akademik menggunakan metode K-Nearest Neighbor. Jurnal Teknik Informatika, 11(2), 249–256. https://doi.org/10.25126/jtiik.2024117330

Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883. https://doi.org/10.4249/scholarpedia.1883

Yuliarina, A. N., & Hendry, H. (2022). Comparison of prediction analysis of Gofood service users using the KNN & Naive Bayes algorithm with Rapidminer software. Jurnal Teknik Informatika (Jutif), 3(4), 847–856. https://doi.org/10.20884/1.jutif.2022.3.4.294.

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Published

2024-12-01

Issue

Vol. 4 No. 3 (2024): DECEMBER 2024

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Copyright (c) 2024 Ahmad Suprianto, Untung Surapati, Yuma Akbar, Aditya Zakaria Hidayat

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How to Cite

Suprianto, A., Surapati, U., Akbar, Y., & Hidayat, A. Z. (2024). Classification of Customer Satisfaction with the K-Nearest Neighbor Algorithm in Relation to Employee Performance at PT. Airkon Pratama. International Journal Software Engineering and Computer Science (IJSECS), 4(3), 882-895. https://doi.org/10.35870/ijsecs.v4i3.2948

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