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All Journal Jurnal Informatika Ekonomi Bisnis Indonesian Research Journal on Education
Nurul Aulya
Universitas Bung Hatta, Indonesia
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Prediksi Kunjungan Wisata Kota Payakumbuh Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Nurul Aulya
Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 2022)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v4i4.157

Abstract

Tourism is a whole related elements which consist of tourists, tourist destinations, travel, industry and so on which are tourism activities and abundant natural wealth. The tourism sector is a very important service-based sector. Tourism is the fastest growing, vibrant and strong economic sector development, it also contributes to Gross Domestic Product (GDP), job creation, social and economic development. Artificial Neural Networks are computer programs that can imitate thought processes and knowledge to solve a specific problem. One of which is applied by the Artificial Neural Network to predict tourist visits. By using the Backpropagation method, it will be known the prediction of the number of tourist visits. The Backpropagation method is very useful for Artificial Neural Networks predicting the number of tourist visits the following year. The data processed in this study were 12 data sourced from the tourism section of the Payakumbuh City Youth and Sports Tourism Office. Furthermore, the data is processed using Matlab software. The stages of backpropagation are initialization, activation, training and iteration. The calculation of the network pattern used and the accuracy level of the expected error is continued. The result of testing this method is that it can predict tourist visits. So the level of accuracy is 95%. The prediction process has been carried out to predict tourist visits to the city of Payakumbuh. With the level of accuracy obtained is met, it can be used to help the Payakumbuh City Tourism Office increase the number of tourist visits in the future and further improve tourism management.
Penerapan Metode Data Mining Clustering terhadap Penjualan Produk Detergen pada Ritel Mart Menggunakan Algoritma K-Means Nurul Aulya; Fitri Safnita
Indonesian Research Journal on Education Vol. 6 No. 3 (2026): Irje 2026
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/irje.v6i2.4171

Abstract

Data mining dapat membantu pengguna mendapatkan informasi yang berguna serta meningkatkan pengetahuan bagi penggunanya. Informasi yang dihasilkan kemudian diolah untuk menghasilkan yang biasanya berbentuk sebuah pola. kemudian pola tersebut diubah menjadi sebuah pengetahuan. salah satu metode data mining adalah Clustering yaitu pengelompokkan data berdasarkan karakteristik data tertentu kedalam karakteristik objek yang sama untuk menghasilkan pengelompokkan objek yang mirip satu sama lain dalam kelompok-kelompok. K-means merupakan salah satu algoritma Clustering yang bertujuan untuk membagi data menjadi beberapa kelompok. Pada penelitian ini, Algorritma K-means akan digunakan untuk pengelompokkan pembelian detergen pada sebuah retail mart yang terletak dikota Payakumbuh. Pengelompokkan ini akan menghasilkan detergen mana yang paling laku atau yang paling banyak dibeli dan juga detergen mana yang kurang diminati pembeli sehingga pemilik toko dapat mencari alternatif lain agar produk detergen yang kurang diminati menjadi laku dan banyak diminati oleh konsumen.
Co-Authors Fitri Safnita