RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method that can be applied to market segmentation. RFM analysis is used to analyze customer’s behavior which consists of how recently the customers have purchased (recency), how often customer’s purchases (frequency), and how much money customers spend (monetary). In this study, RFM analysis has been used for product segmentation is to be arrayed in terms of recent sales (R), frequent sales (F), and the total money spent (M) using the data mining method. This study has proposed a new procedure for RFM analysis (in product segmentation) using the k-Means method and eight indexes of validity to determine the optimal number of clusters namely Elbow Method, Silhouette Index, Calinski-Harabasz Index, Davies-Bouldin Index, Ratkowski Index, Hubert Index, Ball-Hall Index, and Krzanowski-Lai Index, which can improve the objectivity and similarity of data in product segmentation so that it can improve the accuracy of the stock management process. The evaluation results showed that the optimal number of clusters for the k-Means method applied in the RFM analysis consists of three clusters (segmentation) with a variance value of 0.19113.

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Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.

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The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting) using Linear Discriminant Analysis (LDA) and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing). The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.Keywords: Computer vision; Euclidean distance; Linear discriminant analysis; 10-Fold Cross Validation. AbstrakKemampuan manusia dalam mengenali berbagai macam objek, seberapa pun rumitnya objek tersebut, merupakan kemampuan istimewa yang dimiliki manusia. Manusia normal manapun tidak akan mengalami kesulitan dalam mengenali objek tulisan tangan antara seorang penulis dengan penulis lainnya. Permasalahannya apabila komputer melakukan pengenalan tulisan tangan yang memiliki ukuran, ketebalan, bentuk, dan kemiringan yang berbeda (ciri khas tersendiri dalam menulis dengan tulisan tangan). Dengan pesatnya perkembangan teknologi digital maka kemampuan manusia untuk mengenali objek tulisan tangan telah diterapkan dalam suatu program yang dikenal dengan nama Computer Vision. Penelitian ini bertujuan membuat sistem identifikasi berbagai jenis huruf kapital tulisan tangan menggunakan metode Linear Discriminant Analysis (LDA) dan Euclidean Distance. LDA digunakan untuk mendapatkan karakteristik ciri dari citra dan memberikan jarak antara kelas menjadi lebih besar, sedangkan jarak antara data training dalam satu kelas menjadi lebih kecil, sehingga waktu pengenalan citra digital huruf kapital tulisan tangan dengan menggunakan Euclidean Distance menjadi lebih cepat waktu komputasi (dengan mencari jarak terdekat antara data training dengan data testing). Hasil pengujian data sampel menunjukan bahwa resolusi citra sebesar 50x50 pi...