ANALISIS CLUSTER DENGAN MENGGUNAKAN K-MEANS UNTUK PENGELOMPOKKAN ONLINE CUSTOMER REVIEWS (OCR) PADA ONLINE MARKETPLACE

Authors

  • Rena Nainggolan Universitas Methodist Indonesia
  • Fenina A.T Tobing Universitas Multimedia Nusantara

Keywords:

Clustering, K-Means Clustering, OCR, Marketplace

Abstract

Technological advances at this time are very influential on people's shopping culture, plus during the current pandemic, it has resulted in an increasing number of people shopping for daily necessities online. There are many conveniences offered in online shopping that make people switch to using these facilities. Besides the advantages of online shopping, there are also some disadvantages of online shopping, including the rise of online sales fraud such as goods not being shipped, damaged goods, items not as ordered, and much more. For this reason, in conducting online transactions, trust is needed between the seller and the buyer, and one of the factors that greatly affect the prospective buyer is to know the history of the seller, namely by looking at the reviews given by the buyer on the seller's homepage which is called Online Customers Reviews (OCR). OCR is considered to be very influential on customer buying interest. One of the indicators that are considered very important in influencing consumer buying interest and trust is OCR. This study aims to analyze OCR clustering in one of the marketplaces in Indonesia using the K-Means Clustering Method. K-Means is a clustering algorithm that is quite effective because it has the ability to group large amounts of data and with high speed, the K-Means algorithm partitions data into clusters so that they have the similarity of being in one cluster.

 

References

Asosiasi Penyelenggara Jasa Internet Indonesia, “Laporan Survei Internet APJII 2019 – 2020,” Asos. Penyelenggara Jasa Internet Indones., vol. 2020, pp. 1–146, 2020, [Online]. Available: https://apjii.or.id/survei.

A. N. Ardianti and M. A. Widiartanto, “Pengaruh Online Customer Review dan Online Customer Rating terhadap Keputusan Pembelian melalui Marketplace Shopee.,” J. Ilmu Adm. Bisnis, pp. 1–11, 2019.

S. T. Adeliasari, Vina Ivana, “ELECTRONIC WORD-OF-MOUTH (e-WOM) DAN PENGARUHNYA TERHADAP KEPUTUSAN PEMBELIAN DI RESTORAN DAN KAFE DI SURABAYA Adeliasari,” no. 2010, pp. 218–230, 2017.

A. A. Laksmi and F. Oktafani, “PENGARUH ELECTRONIC WORD OF MOUTH (eWOM) TERHADAP MINAT BELI FOLLOWERS INSTAGRAM PADA WARUNK UPNORMAL,” J. Comput. Bisnis, vol. 10, no. 2, pp. 78–88, 2016, [Online]. Available: www.dailysocial.id.

A. Farki, I. Baihaqi, and M. Wibawa, “Pengaruh online customer review rating terhadap kepercayaan place di indonesia,” vol. 5, no. 2, 2016.

Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., vol. 2, no. 2, pp. 213–219, 2017, doi: 10.22202/ei.2016.v2i2.1465.

R. A. Asroni, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,” Ilm. Semesta Tek., vol. 18, no. 1, pp. 76–82, 2015.

L. M. Pratiwi, Diana, and E. P. Agustin, “Penerapan K-Means Clustering Untuk Memprediksi Minat Nasabah Pada Pt . Asuransi Jiwa Bersama 1912 Bumiputera Prabumulih,” Univ. Bina Darma, pp. 1–16, 2016.

N. R. Ayu and D. L. Chaerowati, “Hubungan Online Customer Review pada Media Sosial Instagram,” pp. 410–417.

et al., “Evaluasi Cluster Social Media Data In Tourism Domain Menggunakan K-Means Clustering,” METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., vol. 4, no. 1, pp. 89–93, 2020, doi: 10.46880/jmika.v4i1.148.

S. Natania, “Analysis Of The Effect Of Online Customer Review On Millennial’s Purchase Decision In Bandung (Case Study Of Gadget Products At Tokopedia),” no. 227, pp. 1–17, 2018.

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Published

23-07-2021

How to Cite

Nainggolan, R., & Tobing, F. A. . (2021). ANALISIS CLUSTER DENGAN MENGGUNAKAN K-MEANS UNTUK PENGELOMPOKKAN ONLINE CUSTOMER REVIEWS (OCR) PADA ONLINE MARKETPLACE . METHODIKA: Jurnal Teknik Informatika Dan Sistem Informasi, 6(1), 1–5. Retrieved from https://ejurnal.methodist.ac.id/index.php/methodika/article/view/246

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