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All Journal Jurnal Riset Mahasiswa Matematika
Hairur Rahman
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Mathematics

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Syarat Cukup Ketaksamaan Holder di Ruang Lebesgue dengan Variabel Eksponen Mohamad Abdul Ba'is; Hairur Rahman; Erna Herawati
Jurnal Riset Mahasiswa Matematika Vol 2, No 1 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.588 KB) | DOI: 10.18860/jrmm.v2i1.14619

Abstract

Hӧlder inequality is a basic inequality in functional analysis. The inequality used for proofing other inequalities. In this research, the development of the application of the Hӧlder inequality in the Lebesgue spaces with variable exponent and Morrey spaces with variable exponent. The integral Hӧlder inequality is used because the Lebesgue spaces with variable exponent and Morrey spaces with variable exponent is a function space.This research shows the sufficient condition of Hӧlder inequality in Lebesgue spaces with variable exponent and the Morrey spaces with variable exponent according to the norm of the function and its characteristics.
Implementasi Backpropagation Neural Network pada Prediksi Jumlah Penjualan Toyota Avanza di Indonesia Nur Fatin Mufinnun; Hairur Rahman; Mohammad Nafie Jauhari
Jurnal Riset Mahasiswa Matematika Vol 1, No 6 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.057 KB) | DOI: 10.18860/jrmm.v1i6.14594

Abstract

Prediction is a branch of science that is used to predict events that may occur in the future based on past events. One of the developed prediction methods, Backpropagation Neural Network, a method that has a good level of effectiveness. This study aims to determine the model and the accuracy of the model in predicting the total sales of the Toyota Avanza and to find out the results of sales predictions for the next 12 months by analyzing the number of sales in January 2010 to October 2021. The prediction model for the number of Toyota Avanza sales using the Backpropagation Neural Network is 12-13-1, where there are 12 variables in the input layer, 13 variables in the hidden layer and 1 variable in the output layer with a learning rate value of 0.5 and momentum 0. The predictions for the number of Toyota Avanza sales for 12 months are at an average upper limit of 6215 and an average lower limit of 3415 with a MAPE value of 9,39135%, so that the model can be said to be very good. 
Implementasi Data Mining Menggunakan Algoritma C4.5 pada Klasifikasi Penjualan Hijab Faridatul Husna; Hairur Rahman; Juhari Juhari
Jurnal Riset Mahasiswa Matematika Vol 2, No 2 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.311 KB) | DOI: 10.18860/jrmm.v2i2.14891

Abstract

Indonesia is known as a country with a majority Muslim population, this makes the need for clothing in Indonesia must also pay attention to the criteria for Muslim clothing, one of which is the hijab. Business developments in the fashion world, especially hijab, have become a trend setter at this time so that the large amount of data in the fashion business world creates conditions where there are businesspeople who have a lot of data but lack of information from that data. To deal with these conditions, it is necessary to classify the data. A classification is a process to find the same properties in a data set to be classified into different classes.  One of the classification methods is the Decision tree using the C4.5 Algorithm.  This research aims to determine the model and the accuracy of the C4.5 algorithm in classifying hijab sales from several hijab brands.  The Decision tree model is obtained using the C4.5 algorithm with the first root being the price attribute, where the first root is the attribute that most affected the sale of the hijab.  The result of calculating the accuracy value is 87% so that the Decision tree model and the classification process using the C4.5 Algorithm are classified as good. This research is expected to help businesspeople in the fashion sector, especially hijab, to find out the factors that influence consumer interest in a hijab product.
Co-Authors Erna Herawati Faridatul Husna Juhari Juhari Mohamad Abdul Ba'is Mohammad Nafie Jauhari Nur Fatin Mufinnun