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Journal : Jurnal Matematika

Data Mining Pada Faktor-Faktor Potensi Daerah di Kabupaten Sidoarjo Provinsi Jawa Timur Trianingsih Eni Lestari; Hendro Permadi; Sri Susilowati
Jurnal Matematika Vol 10 No 2 (2020)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2020.v10.i02.p124

Abstract

Abstract: Sidoarjo is one of the districts located in East Java that has developed rapidly. The remarkable progress can be achieved due to several potentials had by its people, for instance, industries, trades, small and medium businesses. Therefore, this research aims to find out the information regarding dominating factors had by the Sidoarjo using data mining. The result shows that Keboansikep, Sawotratap, Tebel, Keboananom, Gedangan, Keboguyang, Ketajen, Sidomulyo, Terik, Ponokawan, Sedengan Mijen, and Barengkrajan villages are the most potential villages in Sidoarjo. Based on the classification method, it is found that the villages of Keboansikep, Sawotratap, Tebel, Keboananom, Gedangan, and Ketajen (Gedangan District) have local potential in the form of agricultural factors such as rice and secondary crops. All residences in Keboguyang Village (Jabon District) already have an IMB. Meanwhile, the villages of Sidomulyo, Terik, Ponokawan, Sedengan Mijen, and Barengkrajan (Krian District) have high early childhood education factors such as kindergarten students, kindergarten teachers, and kindergarten schools Keywords: Data Mining, Local Potential, Biplot analysis
Implementasi Model Fungsi Transfer dan Neural Network untuk Meramalkan Harga Penutupan Saham (Close Price) Nila Rahmawati; Trianingsih Eni Lestari
Jurnal Matematika Vol 9 No 1 (2019)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2019.v09.i01.p107

Abstract

The multivariate forecasting model is a model of forecasting that takes into the causal relationship between a prediction factor with one or more independent variables. This study uses multivariate forecasting model that are transfer function and neural network model. The transfer function and neural network model are used for forecasting of closing stock price data by considering the opening stock price data as the independent variable in the forecasting model. The data used in this study is the monthly closing stock price and opening stock price data of PT. Bank Central Asia, Tbk. The best model for forecasting of closing stock price is a transfer function model that has MSE, MAPE, and MAE values ??smaller than the neural network model. Keywords: transfer function, neural network, opening stock price, closing stock price