Saat ini proses penjualan dan pemasaran yang di lakukan oleh Yana Sport masih sudah menggunakan aplikasi E-commerce, maka dari itu dengan pemanfaata teknologi informasi berupa E-commerce dapat memberikan peningkatan penjualan produk bagi Yanaspot. Akan tetapi persaingan dalam dunia bisnis pasti ada karena yang menjual barang masih ada toko lain yang membuka usaha yang sama. Kondisi tersebut menyebabkan pemilik toko ini dituntut untuk menemukan strategi yang dapat meningkatkan penjualan dan pemasaran toko olahraga. Data penelitian ini bersumber dari data transaksi penjualan toko yang sport yang beralamat di Jl. Fatahillah No.225, Watubelah, Kec. Weru, Kabupaten Cirebon, Jawa Barat 45154 Model algoritma k-means bahwa operator yang digunakan yaitu retrive dipergunakan untuk memanggil data set pada penelitian ini. Kemudian clustring kmeans untuk memodelkan data set yang telah ada, serta clusterdistance performance digunakan untuk menguji hasil pengelompokan, penelitian ini menetapkan 2 kelompok yaitu laris terjual dan tidak laris terjual. Hasil Data Performance menejelaskan bahwa Cluster 0 dengan nilai 110 dikategorikan sebagai Tidak Laris Terjual sedangkan 21389 dikategorikan sebagai laris terjual. Hasil rekomendasai penelitian ini mendapatkan informasi atau pola dari penerapan algoritma k-means dengan data penjualan terdapat sebanyak 99 item barang yang laris terjual dan terdapat 23 item barang yang tidak terjual sehingga pemilik dapat melakukan strategi penjualan dan pembelian ulang berdasarkan barang yang laris terjual.

The phenomenon of the beginning of the year, what some football fans have been waiting for, is the publication of the latest jersey from their favorite team. When the new jersey was launched, football fans flocked to buy the jersey, but there were several shops available for the new jersey. This was experienced by the Eighteen Sport shop, in fulfilling the wishes of fans, there were obstacles to re-stock the jerseys that were most in demand. So many items that have not been sold. The focus of this research lies in managing jersey sales data in June, July and August, as well as high interest in the demand for club jerseys. The high demand for jerseys is influenced by the achievements of the club itself. This study uses the FP Growth algorithm with the aim of getting a recommendation pattern from the wishes of football fans. Based on the results of the support management, it was found that consumers by buying 1 jersey item will buy back 1 different jersey item as many as 15 patterns. Consumers by buying 2 jersey items will repurchase 1 different jersey item as many as 46 patterns. Consumers by buying 3 jersey items will repurchase 1 different jersey item as many as 37 patterns. Consumers by buying 4 jersey items will repurchase 1 different jersey item for 10 patterns. So that the pol data becomes the owner's recommendation to make a repeat purchase.

In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.

The Indonesian government in obtaining Real Time data on MSMEs who are entitled to assistance, accuracy in distributing MSME assistance, and accelerating Indonesia's economic growth through MSMEs, especially the Cirebon Regency area. There are several ways so that cash transfer assistance for micro-scale SMEs from the government is right on target, in this study the authors will use data mining techniques with the k-nearest neighbors method in classifying receiving assistance from SMEs. The data used in this study uses secondary data with attributes of Regency, District, Business Name, Product Name, Business License, Assets and Turnover. The application of the KNN algorithm uses the retrieval operator, cross validation, and in developing the model using the KNN algorithm operator, apply model and performance. The results of the accuracy are 98.46 % with details, namely the Prediction Results are Eligible and it turns out to be true as many as 339 Data. The Prediction Result is Eligible and it turns out to be true Not Eligible as much as 2 Data. Prediction results are not eligible and it turns out to be true as much as 4 data. Prediction results are not eligible and it turns out to be true, 42 data are not eligible. Recommendations for the pattern of knowledge obtained using the K-NN algorithm. Researchers provide recommendations that are feasible to be given assistance for MSMEs as many as 339 MSME participant data spread across the Cirebon district and included in the affected category. Then there are several MSME participants who cannot receive MSME assistance according to the application of the KNN algorithm, which is 42 data, and there are 2 data from participants who are proposed to receive MSME assistance. The hope of the research for participants who receive assistance from the government can survive in conditions like this covid 19

UD. Anugerah Sukses Mandiri merupakan perusahaan yang bergerak dibidang distribusi food dan non food. Transaksi barang yang berjalan terus meningkat, sehingga perusahaan mengalami permasalahan dalam menentukan jumlah persediaan barang, dikarenakan jumlah permintaan barang yang dibutuhkan selalu berubah setiap waktu. Persediaan barang merupakan suatu aktivitas lancar yang meliputi barang-barang milik perusahaan dengan maksud dijual kembali pada suatu periode usaha normal. Data mining merupakan proses yang menggunakan teknik statistik, matematika, kecerdasan buatan dan machine learning untuk mengekstraksi dan mengidentifikasi informasi yang terkait dari berbagai warehouse. Tujuan penelitian ini dengan memanfaatkan data mining yaitu untuk melakukan pengelompokan barang dan meningkatkan akurasi klasterisasi data persediaan barang dengan menggunakan metode K-Means Clustering. Dengan metode K-Means ini dapat mempartisi data ke dalam kelompok sehingga data berkarakteristik sama akan dimasukan ke dalam satu kelompok yang sama dan data yang berkarakteristik berbeda dikelompokan kedalam kelompok yang lain, karena metode ini menggunakan centroid (rata-rata) sebagai model dari cluster. Hasil penelitian yang didapat berupa pengelompokan data menjadi 2 kluster yaitu data dengan kluster terendah/sedikit dan kluster tertinggi/terbanyak. Sehingga mendapatkan kesimpulan bahwa clustering persediaan barang dengan menggunakan metode K-Means ini cukup baik dari sisi nilai average within distance dan kompleksitas waktu. Keyword : Data Mining, K-Means Clustering, Persediaan barang