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All Journal IJISCS (International Journal Of Information System and Computer Science) IJISCS (International Journal of Information System and Computer Science)
Rustam Rustam
Informations System, Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia, Lampung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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IMPLEMENTATION OF LINEAR REGRESSION METHOD FOR PREDICTING CIMORY MILK SALES Hana Atthifa Ryantika; Merry Parida; Rustam Rustam; Herman Afandi; Sani Hanika Lubis
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 1 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i1.1333

Abstract

PT. Rasa Prima Sejati Wall's is a company engaged in the field of ice cream food and Cimory drinks. This company has various types of ice products and Cimory drinks to offer. Every year the company can create new products, especially in various flavors, not only that, the company guarantees the quality of the products it produces. The problem faced by the company is that the sale of available goods does not match consumer demand. The company also has not used predictors or plans for the sale of goods so that there is a buildup of goods which results in losses in the company. In this case the research will make predictions by looking at past sales data. The data taken is only sales data for Cimory products for the last three years from 2019-2021. Research using the Liner Regression method is one of the methods in the function of predicting sales. Linear regression is a statistical method used to construct a model or relationship between one or more independent variables X and response variable Y. The software used to support data processing is Rapidminer. The purpose of this study is to use data mining to identify the most popular, least popular, and desirable products or items at Pt. Rasa Prima so that companies can use this information as a guideline for managing their inventory and attending to customer disappointment properly available using a simple linear regression method, the prediction results for 2022 and RapidMinier are 770000. This prediction can help companies make decisions and reduce inventory shortages.
IMPLEMENTATION OF LINEAR REGRESSION METHOD FOR PREDICTING CIMORY MILK SALES Hana Atthifa Ryantika; Merry Parida; Rustam Rustam; Herman Afandi; Sani Hanika Lubis
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 1 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i1.1333

Abstract

PT. Rasa Prima Sejati Wall's is a company engaged in the field of ice cream food and Cimory drinks. This company has various types of ice products and Cimory drinks to offer. Every year the company can create new products, especially in various flavors, not only that, the company guarantees the quality of the products it produces. The problem faced by the company is that the sale of available goods does not match consumer demand. The company also has not used predictors or plans for the sale of goods so that there is a buildup of goods which results in losses in the company. In this case the research will make predictions by looking at past sales data. The data taken is only sales data for Cimory products for the last three years from 2019-2021. Research using the Liner Regression method is one of the methods in the function of predicting sales. Linear regression is a statistical method used to construct a model or relationship between one or more independent variables X and response variable Y. The software used to support data processing is Rapidminer. The purpose of this study is to use data mining to identify the most popular, least popular, and desirable products or items at Pt. Rasa Prima so that companies can use this information as a guideline for managing their inventory and attending to customer disappointment properly available using a simple linear regression method, the prediction results for 2022 and RapidMinier are 770000. This prediction can help companies make decisions and reduce inventory shortages.
DATA MINING TO PREDICT INVENTORY AT DAPUR PINTAR STORE USING MULTIPLE LINEAR REGRESSION METHODS Yuli Syafitri; Putri Mayang Sari; Rustam Rustam; Supriyanto Supriyanto
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1511

Abstract

Dapur pintar store is a shop that sells household furniture. However, there are several obstacles faced by Dapur Pintar Store, such as the shop still having difficulty predicting future inventory, and the absence of the application of data mining in predicting inventory. This is interesting to solve in order to make it easier for the store to determine inventory predictions at Dapur Pintar Store. Utilizing data mining, especially using linear regression, can provide valuable insight for predicting sales of household products at Smart Kitchen Stores. Identify and collect relevant data for sales analysis. This includes historical sales data, customer demographic data, promotions or discounts applied, and other factors that may influence household product sales. Perform data preprocessing to clean incomplete or inaccurate data. In addition, perform data transformations such as normalization to ensure consistency and accuracy in analysis. Apply a linear regression model to understand the linear relationship between the independent variable (for example, time, promotion, or demographics) and the dependent variable (household product sales). This model can be used to make predictions based on historical patterns. Use historical data to train a linear regression model. The training process involves adjusting the model parameters to fit the training data, so that it can provide accurate predictions. Validate the model using data that was not used in training to ensure that the model can provide good predictions on new data. This helps avoid overfitting and ensures generalization of the model. In making inventory predictions for goods, we will use the Multiple Linear Regression method. Software that will be used to support data processing is RapidMiner. 
Co-Authors Hana Atthifa Ryantika Herman Afandi Merry Parida Putri Mayang Sari Sani Hanika Lubis Supriyanto Supriyanto Yuli Syafitri