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Journal : Jurnal Mantik

FP-Growth Implementation in Frequent Itemset Mining for Consumer Shopping Pattern Analysis Application I Made Dwi Putra Asana; I Komang Arya Ganda Wiguna; Ketut Jaya Atmaja; I Putu Anjas Sanjaya
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.1075.pp2063-2070

Abstract

Most retail companies have implemented computer-based information systems for recording sales transaction data. In the implementation of information systems, the data collected in the database is processed limited to making reports such as sales reports and inventory reports. Database generated from computer-based information systems can be further processed to obtain more valuable information. One strategy for using sales transaction data is to analyze consumer spending patterns. Consumer spending patterns can be in the form of associations of items that are often purchased simultaneously. The association between goods can be determined using the frequent itemset search technique. The Fp-growth algorithm is an algorithm that can be used to determine frequent itemsets in a data set. This article describes the results of implementing the FP-Growth algorithm in the consumer shopping pattern analysis application. The resulting shopping pattern is in the form of goods that are often purchased simultaneously by consumers. From the results of the application of the fp-growth algorithm, it was found that the minimum value of support had an effect, namely the smaller the input value of support, the more pairs of items were obtained. The application of the FP-Growth algorithm in determining frequent itemsets in association data mining can find customer spending habits in buying goods simultaneously.
Sales Forecasting System Using Single Exponential Smoothing Ketut Jaya Atmaja; Ida Bagus Gede Anandita
Jurnal Mantik Vol. 4 No. 4 (2021): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2021.1207.pp2552-2557

Abstract

In a trading business, meeting customer demand is very important to do. Fulfilling customer demand can be done with good stock inventory management. Accuracy in carrying out stock management is important to maintain the level of satisfaction of consumers because of the needs being met. In addition, accuracy in carrying out stock management can affect the financial cash flow of a trading business. Over-stocking, over time it will become dead-stock because the goods being sold become obsolete, changes in market tastes, not to mention merchandise that has an expiration date. Meanwhile, too little stock can cause lost of sales because the level of demand from consumers is greater than the amount of existing stock. Forecasting systems can help maximize stock inventory management in meeting customer demand needs. Forecasting is an activity in predicting and predicting something that will happen in the future. Forecasting is done through calculation analysis techniques based on past data references. This data can be in the form of qualitative data and quantitative data. The exponential smoothing method is a forecasting method based on qualitative data from a time series of previous sales trends to predict the future. This method is best used to analyze fluctuating sales trends. To determine the accuracy of forecasting, the results of the forecasting are then analyzed using the MSE and MAPE methods.
Sales Forecasting Applications For Retail Companies Using Double Exponential Smoothing And Golden Section Methods I Made Dwi Putra Asana; I Made Deni Kurniadi; Sugihya Artha Dwipayani; Ketut Jaya Atmaja
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2584

Abstract

The availability of goods becomes one of the keys to the success of supermarket business in fulfilling consumer needs. The Forecasting methods can help to predict the sales in the future and it can help to find sales statistics daily, monthly or yearly. The application of exponential smoothing requires the process of determining the smoothing value by performing several tests. The determination of smoothing is a challenge in the forecasting process because it takes several tests of the optimal smoothing value to reduce forecasting errors. This study proposed the application of the golden section in optimizing the determination of the smoothing value. Golden section is an optimization method that provides extreme values ??of a non-linear function by reducing the range of values ??that contain extreme values. The results of the Forecasting method were based on the training data in which the trend and the result of the forecasting approached to the training data that used for forecasting. According to the results of the forecasting which conducted based on the training data was MAPE 26.460474 % and MAPE results from comparison of testing data obtained was 21.89696%.