Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Computer Science and Technology (JCS-TECH)
Arya Prayuda
Universitas Mercu Buana Yogyakarta
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan Website E-Business dengan Metode Analisis SWOT dan PEST di Kedai S'Ajian Ndeso Scholastica Lewoema; Arya Prayuda; Meizika Ayu Riski
Journal of Computer Science and Technology Vol 3 No 1 (2023): Mei 2023
Publisher : LPPM Universitas Widya Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54840/jcstech.v3i1.95

Abstract

Currently, many business people take advantage of technological developments as a medium in the process of buying and selling, advertising, and disseminating information that can support progress in the business being run. Business design using technology (e-business) must pay attention to several important factors so that business systems can be realized in accordance with company goals. SWOT analysis is the most important analysis that must be considered in every business because this analysis will provide several points based on internal factors, namely strengths and weaknesses and external factors, namely opportunities and threats both from within and outside the company. Following up on the SWOT analysis, it would be better for companies/business actors to be able to analyze PEST which will explain how important political, economic, social, and technological factors are for a company. To be able to realize the solution from the previous analysis, we took the initiative to build a website using WordPress as the website builder. WordPress comes with open source and free properties that can be utilized by business people, especially MSMEs which are the object of our research. The application of e-business in the form of a WordPress website is expected to be able to keep up with the times and can provide benefits for related MSMEs. Keywords: E-Business, MSMEs, SWOT, PEST
PREDIKSI JUMLAH KEDATANGAN WISATAWAN MANCANEGARA DI INDONESIA BERDASARKAN PINTU MASUK KEDATANGAN UDARA: PREDICTION OF THE NUMBER OF ARRIVALS OF FOREIGN TOURISTS IN INDONESIA BASED ON AIR ARRIVAL ENTRANCES Arya Prayuda; Irfan Pratama
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4787

Abstract

Indonesia has diversity and natural wealth that attracts tourism in Indonesia. Tourism is one of the industries that provides the highest foreign exchange for the country because it has a positive impact. However, the existence of COVID-19 has resulted in a decrease in the number of visits due to restrictions on foreign tourists. From January to November 2021, there was a drastic decrease of 61.82% in the number of foreign tourist visits compared to the same period in 2020. In addition to COVID-19, as well as support in building facilities that support the increase in the number of foreign tourists. From these conditions, predictions are needed that are used as a basis for planning and helping decision making. The purpose of this study is to develop a more accurate prediction model in similar studies using the same data in predicting foreign tourist arrivals in Indonesia through air entrances using the XGBoost, Random Forest, and Catboost methods by focusing on the accuracy evaluation results metrics RMSE, MAE, and MAPE and making predictions for the next 12 months. The dataset used is taken from the Central Statistics Agency (BPS), namely data on foreign tourist arrivals based on the arrival entrance in the period January 2017 to November 2021. The data used are time series and non-stationary. From the research results, it can be seen based on the accuracy evaluation results that the XGBoost model of this study gets better accuracy evaluation results than the other two models by getting the results of the RMSE accuracy evaluation value of 671935.2, MAE 648139.1, and MAPE 20985.35. The XGBoost model is better with a smaller accuracy error value than the Random Forest model, Catboost, and similar research using the ARIMA method with an RMSE value of 779670.7, MAE 749030.4, and MAPE 23196.45.
Co-Authors Irfan Pratama Meizika Ayu Riski Scholastica Lewoema