Penerapan Clustering Algorithm Untuk Mendukung Promosi Server Pulsa Reload

Nurdiyansyah, Firman; Arifin, Samsul; Marisa, ‪Fitri

doi:10.37438/jimp.v3i2.174

Cited by 4 publications

(4 citation statements)

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“…Penelitian terdahulu dengan metode clustering serta algoritma K-Means yang pertama dengan tema sebaran pelanggan serta karakteristiknya menghasilkan 3 cluster yaitu cluster C1 3 kelompok pelanggan dengan transaksi banyak dan bertempat tinggal di Malang Kota, C2 3 kelompok pelanggan dengan transaksi sedang yang bertempat tinggal di Malang Kota dan Kabupaten, dan C3 2 kelompok pelanggan dengan transaksi sedikit serta bertempat tinggal diluar Malang [16]. Penelitian kedua dengan tema mengelompokkan harga beras di Indonesia menghasilkan 3 cluster yaitu cluster harga tinggi sebanyak 11 kota, cluster harga sedang sebanyak 11 kota dan cluster harga rendah sebanyak 11 kota [17].…”

Section: Pendahuluanunclassified

Penerapan Algoritma K-Means Pada Penjualan Saldo Transportasi Online Studi Kasus Konter XYZ

Mutaqin

2023

Jurnal Algoritma

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Konter XYZ merupakan salah satu konter yang berada di Tasikmalaya dengan salah satu produk yang dijualnya adalah saldo transportasi online seperti Maxim, Gojek, dan Grab. Dengan semakin bertambahnya jumlah pengemudi online maka semakin banyak transaksi yang berlangsung sehingga data yang dibuat juga semakin banyak. Akan tetapi data tersebut tidak diolah sehingga semakin menumpuk dan tidak memberikan pengetahuan baru bagi pemilik konter. Tujuan penelitian ini adalah membuat klasterisasi penjualan saldo transportasi online agar menjadi masukan khususnya strategi promosi konter XYZ. Metode yang digunakan adalah clustering dengan algoritma K-Means agar menghasilkan cluster berdasarkan dengan nominal harga pembelian. Dataset yang digunakan dalam penelitian ini berjumlah 4.867 data tetapi setelah di cleansing menjadi 2.642 data. Dataset diperoleh dari penjualan konter XYZ pada bulan Oktober tahun 2022. Penelitian ini menghasilkan jumlah cluster sebanyak 2 buah dengan cluster C1 yaitu nominal rendah sebanyak 2.499 data dengan persentase 94,59%, sedangkan jumlah cluster C2 nominal tinggi sebanyak 143 data dengan persentase 5,41. Hasil dari penelitian ini menunjukkan bahwa algoritma k-means dapat digunakan untuk clustering penjualan saldo transportasi online di konter XYZ, serta penjualan saldo transportasi online cukup baik karena jumlah data cluster nominal rendah lebih banyak daripada cluster nominal tinggi.

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Section: Pendahuluanunclassified

Penerapan Algoritma K-Means Pada Penjualan Saldo Transportasi Online Studi Kasus Konter XYZ

Mutaqin

2023

Jurnal Algoritma

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“…With a deeper understanding of the needs of each customer segment, skincare companies can design more effective loyalty programs, adjust pricing strategies, and improve operational efficiency. (Nurdiyansyah et al, 2018). In this context, customer data mapping is the key to creating accurate and relevant personas.…”

Section: Introductionmentioning

confidence: 99%

Persona-Based Segmentation: Customer Data Mapping as the Foundation of Corporate Marketing Strategy (Case Study on Scarlett Whitening)

Afwan,

Pradina,

Kurniawati

et al. 2024

jemsi

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This research aims to analyze customer segmentation in energy resource companies using the K-Means Clustering method. In the era of increasingly fierce business competition, a deep understanding of customer preferences and behavior is crucial for companies to design effective marketing strategies. Customer data, including purchase frequency, customer service field, company type and geographic location, were collected and analyzed using the K-Means Clustering algorithm. The analysis results showed the formation of homogeneous customer groups, allowing the company to identify the distinctive characteristics of each group. Five main groups were formed from the processed customer data using SPSS. The first group consists of scarlett's facial wash lovers who are dominated by women with an average age of 31 years, earning an average income of Rp15 million per month who live outside Jakarta and work as private employees. This group recognizes the majority of scarlett products from Tiktok. The second group consists of scarlett serum lovers who are dominated by women with an average age of 32 years, earning an average income of IDR 23 million per month who live in South Jakarta and work as private employees. This group also recognizes the majority of scarlett products from Tiktok. The third group consists of scarlett's shower scrub lovers who are dominated by single women with an average age of 31 years, earning an average income of IDR 10 million per month who live in South Jakarta and work other than private employees. This group recognizes scarlett products mostly from Instagram. The fourth group consists of scarlett's body lotion lovers who are dominated by women with an average age of 32 years, earning an average income of IDR 19 million per month who live outside Jakarta and work as private employees. This group recognizes the majority of scarlett products from Instagram. The fifth group consists of scarlett's body scrub lovers who are dominated by women with an average age of 28 years, with an average income of IDR 6.5 million per month who live in South Jakarta and work as entrepreneurs. This group recognizes the majority of scarlett products from Tiktok. The implementation of marketing strategies based on the results of this analysis shows that Scarlett needs to use cluster 2 as its target segment in order to continue to increase its revenue. Because by choosing this cluster, Scarlett will find it easier to design its marketing strategy, namely by increasing social media strategies by involving creative content and collaboration on the Tiktok platform, offering promotions and special offers for customers who buy Serum. Scarlett could present a special bundle offer for the product or provide points that can be redeemed for further discounts or free products after reaching a certain amount and since cluster 2 is predominantly in the South Jakarta area, focus the marketing campaign in that area. Scarlett's value proposition lies in the use of brand ambassadors, digital marketing, product quality and brand trust. Thus, this study concludes that customer segmentation analysis using K-Means Clustering brings strategic benefits to scarlett sellers. A deeper understanding of customers enables sellers to improve the relevance of marketing strategies, achieve better customer satisfaction, and remain responsive to changes in the business environment.

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“…Adanya pengumpulan data bisa memberikan solusi dan manfaat bagi pemilik usaha (Wulandari et al, 2022) seperti penentuan strategi promosi. Padahal promosi memiliki peran yang penting bagi pengusaha (Nurdiyansyah et al, 2018).…”

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Mengidentifikasi Strategi Promosi pada Jasa Penjualan Saldo Digital menggunakan Pendekatan Clustering

Nugraha,

Mutaqin,

Fathah

et al. 2023

Edumatic

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The rapid development of information technology has impacted various fields, one of which is cellular telephony. The increasing number of people who have cell phones impacts the need for digital products, especially digital balances, which are increasing. This study aims to identify digital balance sales so that they become input, especially the ZAR counter-promotion strategy. This study uses the clustering method and the K-Means Algorithm to obtain several clusters of transaction types based on the nominal sales price. The silhouette is used to find the number of clusters. The dataset used in this study is the sale of digital ZAR counterbalances from November 2021 to October 2022. This research resulted in a total of 2 clusters. First cluster with low price nominal sales was 49,213 data, while second with high nominal price sales was 3,076. The study results show that the k-means algorithm can be used to cluster sales of ZAR counter digital balances, and ZAR counter digital balances are quite good. Still, there is data in clusters with high nominal prices. ZAR counter owners can create promotional strategies by providing discounts below 142,000 so that customers who buy at a high nominal price move to a lower nominal price.

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Penerapan Clustering Algorithm Untuk Mendukung Promosi Server Pulsa Reload

Cited by 4 publications

References 1 publication

Penerapan Algoritma K-Means Pada Penjualan Saldo Transportasi Online Studi Kasus Konter XYZ

Penerapan Algoritma K-Means Pada Penjualan Saldo Transportasi Online Studi Kasus Konter XYZ

Persona-Based Segmentation: Customer Data Mapping as the Foundation of Corporate Marketing Strategy (Case Study on Scarlett Whitening)

Mengidentifikasi Strategi Promosi pada Jasa Penjualan Saldo Digital menggunakan Pendekatan Clustering

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