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Journal : Arcitech: Journal of Computer Science and Artificial Intelligence

Sales and Stock Purchase Prediction System Using Trend Moment Method and FIS Tsukamoto Firmansyah, Riko; Puspitorini, Sukma; Pariyadi, Pariyadi; Syah, Tamrin
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 1 No. 1 (2021): June 2021
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.661 KB) | DOI: 10.29240/arcitech.v1i1.3057

Abstract

The purpose of this research is to build decisions support model to predict sales and stock purchase using Trend Moment method and Tsukamoto Fuzzy Inference System. Trend moment is a simple statistical-based forecasting method widely used to forecast sales in a company using historical data. Tsukamoto is a fuzzy inference system that uses monotonic reasoning to determine output. The object of this research is sales and purchase data for Ice Cream X Depo Jambi products from August 2019 to April 2020. The study aims to build a decision support model web-based to predict sales and purchases of ice cream X stock at Jambi depots. Fuzzy Tsukamoto in this study will be used to predict product stock purchases after predicting future sales using trend moments. The system input is in product data form, data of ice cream sales history, and data of ice cream stock purchase. Sales history data will be use to calculate slope and constanta that will predict future sales trends. Stocks  purchase history data along with sales trend prediction value will be use to calculate the membership degree of fuzzy variables, perform the aggregation process on fuzzy rules, and then carry out the defuzzification process to produce output prediction values for future ice cream stock purchases. from the prediction model implemented in the decision support system, sales prediction data has an accuracy of 71% while stock purchase predictions have an accuracy of 85%.
Predicting Early Childhood Readiness to Enter Elementary School Using the Naive Bayes Classification Puspitorini, Sukma; Kahar, Novhirtamely; Kartika, Ikah
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11635

Abstract

This study aims to examine the readiness and maturity of early childhood in entering elementary school using the Naïve Bayes method. This analysis involves variables such as gender, age, aspects of physical-motor, cognitive, social-emotional development, and literacy skills which include reading, writing, arithmetic, and children's level of independence. The readiness category is classified into two classes, namely "ready" and "not ready". This prediction model is designed to provide a comprehensive understanding of the factors that affect the classification results, so that the evaluation process can be carried out in a transparent, objective, and data-driven manner. This research is expected to be a reference for other educational institutions in implementing a similar model to evaluate student readiness systematically. By adjusting variables and data according to local needs, this model has the potential to support more accurate and standardized decision-making, as well as improve the quality of early childhood preparation in entering formal education. The results show that the Naïve Bayes method is able to achieve an accuracy level of 93.33%, confirming its effectiveness in identifying early childhood readiness optimally.