Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Indonesian Journal of Electrical Engineering and Computer Science

A Sales Prediction Model Adopted the Recency-Frequency-Monetary Concept Rendra Gustriansyah; Dana Indra Sensuse; Arief Ramadhan
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i3.pp711-720

Abstract

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.
Clustering optimization in RFM analysis Based on k-Means Rendra Gustriansyah; Nazori Suhandi; Fery Antony
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp470-477

Abstract

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.
Tree-based models and hyperparameter optimization for assessing employee performance Gustriansyah, Rendra; Puspasari, Shinta; Sanmorino, Ahmad; Suhandi, Nazori; Sartika, Dewi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp569-577

Abstract

The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.