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All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering
Rasyidah, -
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Comparative Analysis of Imputation Methods for Enhancing Predictive Accuracy in Data Models Zamri, Nurul Aqilah; Jaya, M. Izham; Irawati, Indrarini Dyah; Rassem, Taha H.; Rasyidah, -; Kasim, Shahreen
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1666

Abstract

The presence of missing values within datasets can introduce a detrimental bias, significantly impeding the predictive algorithm's ability to discern patterns and accurately execute prediction. This paper aims to elucidate the intricacies of data imputation methods, providing a more profound understanding of prevalent imputation methods, including list-wise deletion (IGN), mean imputation (AVG), K-Nearest Neighbors (KNN), MissForest (MF), and Predictive Mean Matching (PMM). The dataset employed in this study consists of financial data about S&P 500 companies in the Compustat North America database. The training and validation dataset encompasses 1973 instances, consisting of data during the fourth quarter of 2009, the first quarter of 2010, and the third quarter of 2014. Within this set, 457 missing values were identified and imputed. The test dataset comprises 197 randomly selected instances from the fourth quarter of 2014, equivalent to ten percent of the total instances in the training dataset. The evaluation findings prominently position the dataset derived from MF imputation as the leading performer among all the imputed datasets. The insights derived from this study are intended to assist practitioners in making informed choices when selecting the most suitable data imputation method, particularly in the context of predictive modeling tasks.
Implementation of The Moving Average Method for Forecasting Inventory in CV. Tre Jaya Perkasa Huriati, Putri; Erianda, Aldo; Alanda, Alde; Meidelfi, Dwiny; Rasyidah, -; Defni, -; Suryani, Ade Irma
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 2 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.89 KB) | DOI: 10.62527/ijasce.4.2.77

Abstract

The supply chain is an organization's place to distribute production goods and services to customers. This chain is a network of various organizations that are interrelated and have the aim of carrying out the procurement or supply of goods. Inventory is storing goods in the form of raw materials, semi-finished goods or finished goods that will be used in the production or distribution process. CV. Tre Jaya Perkasa is a company engaged in the distribution of goods such as snacks, drinks and daily necessities. CV. Tre Jaya Perkasa is located in Solok, West Sumatra, Indonesia. From January 2020 to June 2021, CV. Tre Jaya Perkasa has made more than 10 thousand transactions. Based on the sales data, each period (month) of sales transactions can increase and decrease, and the company must plan product sales in the coming period. To maximize profits and minimize losses, a strategy is needed to maintain the availability of goods that are often purchased by customers. From historical transaction data, the company can predict how much stock should be provided for transactions in the coming period. The method used is the moving average method, to measure the error rate of forecasting, MAD, MSE and MAPE are used. Based on the research that has been done, then carried out on the product Trick Potato Biscuit BBQ 24 BOX X 10X18 forecasting comparison between using 3 periods and 5 periods, using 5 product data that are most often purchased by buyers, it was found that the average value of MAD, MSE and MAPE closer to 0 is to use 3-period forecasting.
Co-Authors Ade Irma Suryani Alde Alanda, Alde Aldo Erianda, Aldo Defni, - Dwiny Meidelfi Huriati, Putri Indrarini Dyah Irawati Jaya, M. Izham Rassem, Taha H. Shahreen Kasim, Shahreen Zamri, Nurul Aqilah