PENERAPAN DATA MINING DENGAN METODE CLASSIFICATION, FORECASTING DENGAN MODEL GAUSSIAN NAÏVE BAYES PADA TOKO BANGUNAN (STUDI KASUS: TB SINAR JAYA)

ARIANSYAH, ILHAM (2023) PENERAPAN DATA MINING DENGAN METODE CLASSIFICATION, FORECASTING DENGAN MODEL GAUSSIAN NAÏVE BAYES PADA TOKO BANGUNAN (STUDI KASUS: TB SINAR JAYA). S1 thesis, Universitas Mercu Buana Bekasi.

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Abstract

Dalam pembangunan, bisnis bahan bangunan adalah sebuah kebutuhan, salah satu bisnis yang saat ini bertumbuh dengan pesat yaitu bisnis properti, sehingga kebutuhan masyarakat dalam tempat tinggal menjadi peluang usaha yang dicari oleh masyarakat. Transformasi 4.0 menghadirkan toko bahan bangunan melalui toko online salah satunya adalah Tokopedia. TB. Sinar Jaya mempunyai dataset stok barang dan penjualan dengan jumlah baris data kurang lebih 15000 baris data yang diperbarui setiap satu bulan sekali. Dengan jumlah data yang besar membutuhkan metode data mining dan machine learning dalam pengelolaan data. Perkembangan pesat TB. Sinar jaya selama 5 tahun tidak lepas dari masalah, seperti persaingan dengan toko online yang menawarkan harga lebih murah dibanding toko offline dan kurang kuatnya strategi marketing. Dalam masalah ini pihak TB Sinar Jaya menginginkan bantuan dalam membuat keputusan strategi marketing dengan pemanfaatan teknologi sistem informasi dan meminimalisir masalah yang ada. Berdasarkan masalah tersebut diperlukan implementasi data mining dan machine learning algoritma gaussian naïve bayes untuk mengetahui harga rata - rata yang tersedia di Tokopedia untuk meningkatkan penjualan dan melakukan classification, forecasting dan TSA (Time Series Analysis) pada TB Sinar Jaya. Berdasarkan hasil penelitian, algoritma gaussian naive bayes memiliki hasil akurasi yang baik dengan tingkat akurasi 0.71 dan mendapatkan insight yaitu untuk potential pembeli bisa melakukan wishlist efficiency, dan untuk segmen yang menghasilkan profit dibawah 50% dari total profit peneliti merekomendasikan untuk melakukan program champaign sesuai customer profile agar memperbaiki profitabilitas yang dihasilkan. Kata kunci: Classification, Data Mining, Forecasting, Gaussian Naïve Bayes, Machine Learning. In construction industry, the building materials business is a necessity, one of the businesses that is currently growing rapidly is the property business. The public’s need for a place to live becomes a business opportunity sought by the public. The Industry 4.0 transformation brought building materials stores to the online market, one of which is Tokopedia. TB. Sinar Jaya has a dataset of inventory and sales with a total of approximately 15,000 data rows which are updated each month. With large amounts of data, data mining and machine learning methods are needed in data management. 5 years rapid development of TB. Sinar Jaya has not been without problems, such as competition with online stores that offer lower prices than offline stores and a lack of strong marketing strategy. In this case, TB. Sinar Jaya wants help in making marketing strategy decisions by utilizing information system technology and minimizing existing problems. Based on these problems, it is necessary to implement data mining and machine learning gaussian algorithms naïve bayes to find out the average prices available on Tokopedia to increase sales and carry out classification, forecasting and TSA (Time Series Analysis) at TB Sinar Jaya. Based on the results of research, the gaussian naive Bayes algorithm has good accuracy results with an accuracy level of 0.71 and gains insight, that is, for potential buyers they can do Wishlist efficiency, and for segments that generate profits below 50% of the total profit the researcher recommends carrying out a campaign program according to customer profile in order to improve the resulting profitability. In construction industry, the building materials business is a necessity, one of the businesses that is currently growing rapidly is the property business. The public’s need for a place to live becomes a business opportunity sought by the public. The Industry 4.0 transformation brought building materials stores to the online market, one of which is Tokopedia. TB. Sinar Jaya has a dataset of inventory and sales with a total of approximately 15,000 data rows which are updated each month. With large amounts of data, data mining and machine learning methods are needed in data management. 5 years rapid development of TB. Sinar Jaya has not been without problems, such as competition with online stores that offer lower prices than offline stores and a lack of strong marketing strategy. In this case, TB. Sinar Jaya wants help in making marketing strategy decisions by utilizing information system technology and minimizing existing problems. Based on these problems, it is necessary to implement data mining and machine learning gaussian algorithms naïve bayes to find out the average prices available on Tokopedia to increase sales and carry out classification, forecasting and TSA (Time Series Analysis) at TB Sinar Jaya. Based on the results of research, the gaussian naive Bayes algorithm has good accuracy results with an accuracy level of 0.71 and gains insight, that is, for potential buyers they can do Wishlist efficiency, and for segments that generate profits below 50% of the total profit the researcher recommends carrying out a campaign program according to customer profile in order to improve the resulting profitability.

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