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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
M. Fauzan
STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
Computer Science & IT Control & Systems Engineering

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Implementasi Data Mining Clustering Tingkat Kepuasan Konsumen Terhadap Pelayanan Go-Jek Sinta Maria Sinaga; Jaya Tata Hardinata; M. Fauzan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 2 (2021): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i2.66

Abstract

Increasingly high demands for mobility in today's society, activities are also increasingly crowded, especially parents, employees, and even students so it is increasingly difficult to find free time to meet the needs of daily life. So that people need something that can answer and be a solution to the complaint without having to drain time and energy with results that do not disappoint. Gojek is a solution to the complaints of people who do not have much free time and want to relax while waiting for their needs to be met, Gojek is an online application that can be downloaded via a smartphone, has more than six services provided therein but the author only takes some of the services to be standard the level of community satisfaction with Gojek services. The purpose of this study was to determine the level of community satisfaction with Gojek services. One method contained in Data Mining used in this study is the Clustering method. To find out the level of community satisfaction done with interviews / questionnaires 120 people in the city of Pematangsiantar. The benefits are to make it easier for Gojek companies to know how the quality of services provided to the community is based on the level of community satisfaction and improve the quality of services provided to the community.
Sistem Pendukung Keputusan Menentukan Benih Padi Terbaik Menggunakan Metode TOPSIS Rahel Nita Trides Siahaan; Irfan Sudahri Damanik; M. Fauzan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 1 (2021): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i1.53

Abstract

Farmers are engaged in agriculture in a way to manage land to grow and maintain plants, farmers play an important role in Indonesia. The majority of the population is the majority of farmers and is very dependent on rice. But there are some communities that are very difficult to determine which rice seeds are good and quality to be replanted. The best rice seeds are factors that influence the business productivity of farmers. Most of the farmers have not fully understood the various types of rice seeds and are still looking for solutions to choose quality rice seeds, of course. To use these problems a Decision Support System is needed which is expected to solve these problems. The author chooses the TOPSIS method which will provide information while helping farmers in making decisions about the rice seeds they will use. By applying the TOPSIS Method can produce the right decision to choose the best rice seeds.
Penerapan Algoritma Backpropagation Dalam Memprediksi Jumlah Pengguna Kereta Api Di Pulau Sumatera Vivi Auladina; Jaya Tata Hardinata; M. Fauzan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 1 (2021): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i1.58

Abstract

The purpose of this study is to analyze and test whether the number of train passengers in Indonesia can be predicted by using artificial intelligence techniques. In this study, the artificial intelligence technique used is the Artificial Neural Network Technique (ANN) with the Backpropagation method. Artificial neural network is a method that has been widely used to solve forecasting cases. The main difficulties in implementing neural network methods in forecasting are finding the right architectural combination, determining the appropriate learning rate parameter values and selecting the optimal training algorithm. The research data is secondary data sourced from the bps.go.id website from 2006 - 2019. The data in this study were computerized using the matlab application. From the 5 architectural models used, the best model based on computerized results with the Matlab application is 3-3-1 with an output value of 0.0215923 MSE. The accuracy of the truth obtained is 92%.
Co-Authors Irfan Sudahri Damanik Jaya Tata Hardinata Rahel Nita Trides Siahaan Sinta Maria Sinaga Vivi Auladina