The Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering

Nainggolan, Rena; Purba, Eviyanti Novita

doi:10.32734/jocai.v4.i2-2855

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Sahputra et al [13] analyzed the factors that influence public transport passengers to switch to Go-Jek online transportation using the K-Means Clustering method. The application of case-based reasoning and k-means clustering in e-commerce product recommendations is also supported by various other studies such as Aldayel and Benhidour [14], Xiao et al [15], Kumar et al [16], Bandyopadhyay et al [17], Xian et al [18], Andra [19], Nainggolan and Purba [20], and Mulyawan et al [21].…”

Section: Related Workmentioning

confidence: 64%

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

Legito,

Wattimena,

Yulianto Umar Rofi'i

et al. 2023

ijsecs

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This research proposes and implements an e-commerce product recommendation system that combines Case-Based Reasoning (CBR) and K-Means Clustering algorithms. The main aim of this research is to provide more personalized and relevant product recommendations to e-commerce users. The CBR approach leverages users' transaction history to provide customized recommendations, whereas K-Means Clustering groups users with similar preferences increase the relevance of recommendations. This study assesses the effectiveness of the system by conducting a comprehensive evaluation by comparing system recommendations with actual user preferences. The results of this study reveal that the combined approach of CBR and K-Means Clustering can improve the performance of e-commerce product recommendations, ensure the accuracy of recommendations, and produce a more satisfying shopping experience for users. Although there are limitations in terms of the dataset used and the choice of algorithm parameters, this research makes an important contribution in developing a more adaptive and personalized recommendation system for e-commerce platforms.

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Section: Related Workmentioning

confidence: 64%

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

Legito,

Wattimena,

Yulianto Umar Rofi'i

et al. 2023

ijsecs

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“…If a term is on this list found, it is removed from the original text. When matching queries to documents, stop words have no value [19]. Consequently, removing the stop words did not impact the page's semantics.…”

Section: ) Filtering Stopwordsmentioning

confidence: 99%

Cluster Analysis of Japanese Whiskey Product Review Using K-Means Clustering

Witarsyah,

Akbar,

Praditha

et al. 2024

JOIV : Int. J. Inform. Visualization

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Since 2008, the Japanese whiskey business has grown steadily. Overall, the whiskey market (at factory price) is expected to reach $2.95 billion in 2019, accounting for 8.6 percent of the entire alcoholic beverage industry. The rise in popularity of Japanese whiskey is associated with the country's growing international reputation. Founded 1985 as an independent bottler, Master of Malt was the first company to service clients who ordered single malt whiskey through the mail-order system. Master of Malt's omnichannel approach encompasses all channels available to the company. Known as their 'omnichannel,' this refers to the organization's capability to provide speed and precision from any place at any time. As their brand has grown over the years, they have used various marketing strategies, including a website redesign and rebuild that involved the creation of all relevant content and designing and constructing landing pages for their website. Following a clustering technique, we discovered that the data is being divided into four distinct groups and that these clusters may serve as a recommender system based on the occurrence of terms in each of the categories. Our summarizing component combined phrases related to the exact subtopics and provided users with a concise summary and sentimental information about the group of phrases.

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“…K-Means merupakan algoritma pengelompokan secara iteratif yang melakukan partisig untuk mengklasifikasikan atau mengelompokkan sejumlah besar objek [12]. Adapun metode yang digunakannya adalah clustering dengan Algoritma K-Means [13]. Salah satu metode yang termasuk dalam Data Mining adalah clustering.…”

Section: Pendahuluanunclassified

Klasterisasi Menggunakan Metode Algoritma K-Means dalam Meningkatkan Penjualan Tupperware

Mulyadi

2022

INFEB

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Persaingan dalam dunia bisnis sangatlah ketat,pelaku dunia bisnis memiliki tantangan untuk mengatur strategi penjualan.Toko Asrafi Raya merupakan suatu toko yang bergerak di bidang penjualan tuppeware yang berada di daerah Kabupaten Pasaman Barat. Banyaknya data produk tuppeware dan stok barang yang harus dianalisis, maka pemilik toko harus bekerja keras dalam menentukan barang yang akan dibeli berikutnya dilihat dari stok yang ada.Sehingga kesulitan yang dialamipemilik Toko Asrafi Raya adalah kurangnya stok produk yang laku karena penjualan tinggi, dan menumpuknya produk yang tidak laku karena penjualannya rendah. Penelitian ini bertujuan agar memudahkan Toko Asrafi Raya dalam meningkatkan penjualan tuppeware dengan mengelompokkan produk yang sangat laris, laris dan tidak laris. Data yang digunakan dalam penelitian ini adalah data laporan penjualan terhadap produk tuppeware pada bulan februari sampai juni 2021 yang ada di Toko Asrafi Raya, dengan menggunakan metode algoritma K-Means clustering. Hasil dari penelitian ini mendapatkan 3 cluster yaitu cluster 1(C1)Sangat Laris,Cluster 2 (C2) Laris,Cluster 3 (C3) Tidak Laris. Hasil Penelitian ini digunakan untuk membantu pemilik toko Asrafi Raya dalam menentukan strategi penjualan pada Toko Asrafi Jaya.

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The Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering

Cited by 7 publications

References 2 publications

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

Cluster Analysis of Japanese Whiskey Product Review Using K-Means Clustering

Klasterisasi Menggunakan Metode Algoritma K-Means dalam Meningkatkan Penjualan Tupperware

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