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All Journal Jurnal Statistika dan Aplikasinya Jurnal Informatika Ekonomi Bisnis
Aulya, Nurul
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences Other

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Prediksi Kunjungan Wisata Kota Payakumbuh Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Aulya, Nurul
Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 4 (December 2022)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (582.455 KB) | 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.
APPLICATION OF MARKOV CHAIN IN MONTHLY RAINFALL PREDICTION IN AMBON CITY Rumeon, Sahril G.; Aulya, Nurul; Telussa, Silvia W.; Patty, Christi A.; Sopaliu, Fera F.; Rumalean, Fadila; Rumangun, Chelsy T.; Tuankotta, Winda; Yudistira
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09204

Abstract

Asia has a tropical climate with two main seasons influenced by monsoons, namely the rainy season and the dry season. However, in recent years, seasonal patterns have shifted due to climate change, making it difficult to predict weather, including rainfall. Ambon City, as one of the regions with high and varied rainfall in eastern Indonesia, is highly dependent on weather conditions, especially since most of its inhabitants work as fishermen and farmers. Therefore, rainfall prediction is important to support appropriate decision-making in the marine, agriculture, and hydrometeorological disaster risk mitigation sectors. This study aims to model and predict the status of monthly rainfall in Ambon City in 2025 using the Markov chain method, a first-order probability-based approach that describes transitions between circumstances based on historical data, where the chances of subsequent events depend only on current circumstances. The data used is in the form of monthly rainfall from 2015 to 2024 obtained from the Pattimura–Ambon Meteorological Station. The data were classified into four categories of precipitation: light, medium, high, and very high, which were further used to compile a one-step probability transition matrix. The results showed that the steady-state distribution of rainfall in Ambon City tended to be in the moderate category (47.90%), followed by very high (26.5%), light (20.17%), and high (5.88%). The rainfall prediction for 2025 shows a transition pattern that is close to a steady state, where month after month there is a stable trend. With this information, fishermen can be wiser in determining safe times to go to sea, and the government can design climate change adaptation and mitigation policies more effectively.
Co-Authors Patty, Christi A. Rumalean, Fadila Rumangun, Chelsy T. Rumeon, Sahril G. Sopaliu, Fera F. Telussa, Silvia W. Tuankotta, Winda Yudistira