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All Journal Journal of Computer Science and Informatics Engineering (J-Cosine) Prosiding Seminar Nasional Official Statistics
Prayoga, Suhendra Widi
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Comparison of ARIMA and Machine Learning Methods for Predicting Urban Land Surface Temperature in Jakarta Suhendi, Brigitta Aurelia Putri; Pratiwi, Pratiwi; Prayoga, Suhendra Widi; Kartiasih, Fitri
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 2 (2024): Desember 2024
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i2.579

Abstract

Climate change is a global challenge that requires serious attention from various parties, including the government. The existence of surface temperature and various other parameters is certainly closely related to climate change. In this context, this study was conducted to identify the best model in predicting urban land surface temperature in the Jakarta area, as one of the steps to understand and deal with the impacts of climate change. The research data used comes from MERRA-2, NASA, which provides datasets for various climate analyses. A comparison of ARIMA, SVR, LSTM, and ANN methods was conducted to evaluate the performance of each model in forecasting land surface temperature. The results show that the Long-Short Term Memory (LSTM) model provides the best performance with MAPE and values of 0.8381 and 0.8628. This model has an advantage over other models because it can remember various information that has been stored for a long period of time and can delete irrelevant information. This shows that LSTM is effective in capturing the pattern and variability of the Earth's surface temperature in the Jakarta area. Based on these findings, the government is expected to take concrete steps to address the impacts of climate change, especially issues related to increasing urban land temperature in Jakarta, such as reducing the use of private vehicles and switching to public transportation, expanding green open space, and relocating residents to reduce density.
Optimasi Prediksi Jumlah Wisatawan Nusantara ke Provinsi Bali Melalui Big Data Analytics dengan Integrasi Google Trends dan Tingkat Penghunian Kamar Hotel Prayoga, Suhendra Widi; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2114

Abstract

The tourism sector plays an important role in the Indonesian economy. Bali, as a major tourist destination, attracts a large number of domestic tourists, which has a significant impact on the local economy. However, providing accurate and real time data remains a challenge. This data limitation makes it difficult to effectively monitor tourism conditions. Therefore, this research optimises the prediction of the number of domestic tourists to Bali using hotel room occupancy rate and Google Trends index. Real-time hotel availability and search interest play an important role in this prediction. The application of big data analytics allows the analysis of large amounts of data quickly and accurately. The results show that the best model is Support Vector Regression with Mean Absolute Percentage Error, Root Mean Square Error, and Mean Absolute Error of 14.8366, 94.5575, and 77.1152, respectively. This prediction is expected to help stakeholders monitor the condition of Bali tourism.
Co-Authors Arie Wahyu Wijayanto Fitri Kartiasih Pratiwi Pratiwi Suhendi, Brigitta Aurelia Putri