The Implementation of Data Mining Techniques for Sales Analysis using Daily Sales Data

Isa, Naimah Mat; Branch, Kuala Terengganu Campus Terengganu

doi:10.30534/ijatcse/2019/1681.52019

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…Every firm must be aware of its competitors, including factors such as client demand, purchasing patterns, and sales performance. In their study, Isa et al (2019) introduced a sales analysis approach that utilizes prediction and association techniques on sales data. They implemented their suggested methodology on the sales data of Chan Furniture and Electrical Appliances Company.…”

Section: Review Related Workmentioning

confidence: 99%

Intelligent Sales Forecasting Technique Application

Agomoh,

Ukabuiro

2023

ejtas

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The primary objective of this study was to design and implement a machine learning-based sales forecasting system to enhance the production capacity and sales trajectory. The deployed model features a web-based interface that allows users to input parameters to generate predictions. The application of an intelligent forecasting technique, namely a machine learning model, significantly contributed to determining the optimal manufacturing output for a specific product in this study. The data analysis was conducted utilizing statistical software known as Tableau. The machine learning algorithm employed for constructing the model was the multiple linear regression model, which is particularly well-suited for trend analysis. The supplied dataset was utilized to train and test a supervised machine learning model, which was subsequently deployed on a local web server. Furthermore, a database system was effectively implemented to facilitate data storage, retrieval, and manipulation. The model was evaluated using two commonly employed metrics, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE), within the Jupyter notebook environment. The resulting evaluation scores were 2.364858669808942 for RMSE and 1.7610409547966064 for MAE. These metrics were deemed effective in accurately predicting outcomes and efficiently presenting results.

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Section: Review Related Workmentioning

confidence: 99%

Intelligent Sales Forecasting Technique Application

Agomoh,

Ukabuiro

2023

ejtas

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“…Queen in 1967 is the most popular and relevant clustering model [21]. Predictive classification models have been used to study customer purchasing behavior in past researches [13], [22], [23] in which classification models like K-Means and Decision Tree were commonly employed. This study explores these two models for segmenting and classifying customers.…”

Section: A Data Mining and Machine Learningmentioning

confidence: 99%

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Abdul-Rahman¹,

Arifin²,

Hanafiah³

et al. 2021

IJACSA

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Customer segmentation and profiling has become an important marketing strategy in most businesses as a preparation for better customer services as well as enhancing customer relationship management. This study presents the segmentation and classification technique for insurance industry via data mining approaches: K-Modes Clustering and Decision Tree Classifier. Data from an insurance company were gathered. Decision Tree Algorithm was applied for customer profile classification comparing two methods which are Entropy and Gini. K-Modes Clustering segmentized the customers into three prominent groups which are "Potential High-Value Customers", "Low Value Customers" and "Disinterested Customers". Decision Tree with Gini model with 10-fold cross validation was found as the best fit model with average accuracy of 81.30%. This segmentation would help marketing team of insurance company to strategize their marketing plans based on different group of customers by formulating different approaches to maximize customer values. Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies.

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“…N. Isa, N. S. M. Yusof, and M. A. Ramlan (2019) mempredikasi penjualan barang perabotan dari sebuah perusahaan perabotan sebagai studi kasus. Penelitian terdahulu ini memprediksi penjualan dengan metode asosiasi dari transaksi penjualan perusahaan [7]. Berbeda dengan penelitian pada artikel ini memprediksi penjualan mebel dengan menggunakan metode KNN.…”

Section: Pendahuluanunclassified

Pengembangan Sistem Aplikasi Cerdas Memprediksi Penjualan Mebel Berbasis website

Dasriani

Anggrawan

2021

matrik

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Industri mebel di Indonesia merupakan usaha yang memiliki laju perkembangan sangat pesat. Konsumen memilih menggunakan mebel hasil dari industri karena mebel produksi industri memiliki inovasi disain yang indah. Mebel berperan juga sebagai sumber pemasukan keuangan negara di Indonesia terutama penjualan kepada konsumen negara lain. Karenan terjadi kompetisi yang begitu ketat antar industri mebel, keadaan ini berimplikasi produsen mebel berusaha secara ketat dalam menciptakan produksi yang bermutu dan memenuhi selera konsumen. Permasalahannya adalah produsen masih mengelola data atau mendapatkan informasi hasil produksi dalam system manual. Karenanya tujuan dari penelitian ini adalah membangun sistem aplikasi komputer cerdas website untuk memprediksi penjualan mebel. Perancangan dan pembuatan system ini menggunakan metodologi Waterfall. Metode yang digunakan untuk mengklasifikasi data penjualan barang mebel dengan metode K-Nearest Neightbor (KNN). KNN merupakan metode untuk mengektraksi data mining yang bisa berguna dalam prediksi penjualan. Hasil yang dicapai yaitu dihasilkannya system prediksi penjualan barang dan juga laporan prediksi penjualan barang dalam bentuk lembar kerja (Spreadsheet) sehingga membantu pimpinan perushaan dalam usahanya. Kesimpulan dari penelitian yang dilakukan menggunakan metode KNN cocok digunakan untuk mengklasifikasi data penjualan barang mebel. Hal ini tersebut dibuktikan dengan tingkat akurasi yang mencapai 90% pada proses pengujian menggunakan confussion matrik.

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The Implementation of Data Mining Techniques for Sales Analysis using Daily Sales Data

Cited by 6 publications

References 1 publication

Intelligent Sales Forecasting Technique Application

Intelligent Sales Forecasting Technique Application

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Pengembangan Sistem Aplikasi Cerdas Memprediksi Penjualan Mebel Berbasis website

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