Analisis Pemasaran Bisnis dengan Data Science : Segmentasi Kepribadian Pelanggan berdasarkan Algoritma K-Means Clustering

Harahap, Mawaddah; Lubis, Yusniar; Situmorang, Zakarias

doi:10.47709/dsi.v1i2.1348

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Another machine learning algorithm can use the cluster results from K-means as targets in the classification process [10]. Studies such as the segmentation of customer personality [11], the classification of malaria parasite patterns [12], and the grouping of employee performance levels [13] prove this statement.…”

Section: Introductionmentioning

confidence: 93%

Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Irfan¹,

Gunawan²,

Wanayumini³

2023

ICoSTEC

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Many measures have been taken to protect endangered species by using "camera trap" technology which is widespread in the field of technology-based nature protection field research. In this study, a machine learning-based approach is presented to identify endangered wildlife images with a data set containing 5000 images taken from Kaggle and some other sources. The Gradient Descent optimization method is often used for Artificial Neural Network (ANN) training. This method plays a role in finding the weight values that give the best output value. Three optimization methods have been implemented, namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam on the Artificial Neural Network system for animal data classification. In some of the studies reviewed there are differences in the results of SGD and ADAM, which on the one hand SGD is superior, and on the one hand ADAM is superior with the appropriate learning rate. The results of this study show that the CNN method with the Adam optimization function produces the highest accuracy compared to the SGD and RMSprop optimization methods. The model trained using Adam's optimization function achieved an accuracy of 89.81% on the test, showing the feasibility of the approach.

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Section: Introductionmentioning

confidence: 93%

Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Irfan¹,

Gunawan²,

Wanayumini³

2023

ICoSTEC

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“…Memiliki eksekusi yang berbeda di pasar karena pandangan yang berbeda tentang barang tersebut dalam kepribadian pelanggan. Kepribadian pelanggan untuk membangun penegasan (Harahap et al, 2022). Membangun penegasan harus melalui saluran merek.…”

Section: Pendahuluanunclassified

Keputusan Pembelian Pocari Sweat Isotonik Dipengaruhi Kesadaran Merek Serta Harga

Anggapratama

2023

Innovative

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Melalui pergeseran cara individu bertambah banyak aktivitas memerlukan akal sehat dalam beragam cara. Berkembangnya perhatian masyarakat terhadap kesehatan, kemudian mempengaruhi perilaku pelanggan dalam berkonsumsi. Penelitian ini bertujuan untuk melihat keputusan pembelian Pocari sweat isotonik dipengaruhi oleh kesadaran merek dan harga. Metode penelitian yakni Regresi Linier Berganda. Penelitian ini diharapkan dapat menunjukkan keputusan pembelian Pocari Sweat isotonik dipengaruhi oleh kesadaran merek serta harga. Mahasiswa-Mahasiswi Universitas Bojonegoro yang meminum Pocari Sweat sebagai sampel. Non-probability sampling digunakan buat penelitian. Kuesioner yang memuat 12 pernyataan yang disebarkan kepada 100 responden untuk mengumpulkan informasi. Regresi linier berganda untuk metode penelitian. Hasil penelitian tersebut menunjukkan variabel kesadaran merek serta harga secara keseluruhan memengaruhi keputusan pembelian dimana F hitung 11,571 > F tabel 3,09. Hasilnya juga menunjukkan bahwa kesadaran merek mempengaruhi keputusan pembelian dimana t-hitung 4,505 > ttabel 1,984. Sementara itu, variabel harga yang tidak mempengaruhi keputusan pembelian t-hitung 1,649 lebih kecil dari t-tabel 1,984. Mengingat koefisien determinasi dari uji definisi (adjusted R2) menunjukkan bahwa 17,6% variasi dalam faktor keputusan pembelian bisa dipahami oleh kesadaran merek serta melihat faktor harga. Sebagai perbandingan, kelebihan 82,4% dipahami berbagai element diluar penelitian ini.

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“…Customer segmentation is a strategic method of categorizing customers into groups based on shared characteristics or behaviors (Harahap et al, 2021). This approach enables organizations to effectively engage with each group, optimizing their outreach efforts (Wicaksono, 2019;MR et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

Akande,

Asani,

Dautare

2024

ceddi j. inf. syst. technol.

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Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order to cater to their unique requirements. Through the process of categorizing clients according to common attributes, organizations can acquire valuable knowledge regarding their requirements, inclinations, and purchasing behaviors. This comprehension empowers firms to develop focused marketing strategies and provide customized experiences that foster customer loyalty and enhance revenue generation. The prevalent categories of criteria in consumer segmentation encompass demographic, psychographic, geographic, and behavioral factors, whilst the prevailing methodologies for constructing customer segments involve rule-based and cluster-based techniques. Rule-based segmentation involves the utilization of pre-established rules to allocate clients into distinct segments. Conversely, cluster-based segmentation employs statistical techniques to identify inherent clusters or groups within the customer population. This research investigates the application of the K-Means clustering technique for the purpose of segmenting customer behavioral data into several categories, namely Platinum, Gold, Silver, Bronze, or Bad. The clustering approach employed demonstrated a notable degree of accuracy and precision. Through the implementation of an appropriate strategy for customer segmentation, organizations have the potential to strengthen their product offers, concentrate their marketing communications, and augment client loyalty.

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Analisis Pemasaran Bisnis dengan Data Science : Segmentasi Kepribadian Pelanggan berdasarkan Algoritma K-Means Clustering

Cited by 3 publications

References 14 publications

Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Keputusan Pembelian Pocari Sweat Isotonik Dipengaruhi Kesadaran Merek Serta Harga

Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

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