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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Alfian, Haris
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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TEKNIK MACHINE LEARNING UNTUK ANALISA KLASIFIKASI KUALITAS UDARA: A REVIEW Alfian, Haris; Wahyuni, Sri; Revalino, Aqil; Mirano, M. Fitter; Rahmayana, Elsa; Mukhtar, Harun
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7617

Abstract

Air quality has a significant impact on human health and the environment, making its monitoring and classification extremely important. This review explores the application of machine learning techniques in analyzing and classifying air quality. Various methods such as decision trees, support vector machines, neural networks, and ensemble learning are evaluated to assess their effectiveness in processing complex and multidimensional air sensor data. This study also discusses challenges in data collection and preprocessing, selection of relevant features, and interpretation of classification results. Furthermore, this review identifies recent trends and future research opportunities in the use of machine learning to improve the accuracy and efficiency of air quality monitoring systems. The analysis results show that machine learning techniques have great potential to enhance our understanding of air quality dynamics and support better decision-making in environmental management
Perbandingan model SARIMA dan Prophet dalam memprediksi jumlah kunjungan wisatawan mancanegara ke Indonesia berdasarkan data deret waktu bulanan Alfaridzi, M Ilmi; Gunawan, Rahmad; Alfian, Haris; Mirano, Muhammad Fitter; Nazifah, Hayatun; Wahyuni, Sri; Illahi, Kevanda Sondani
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9963

Abstract

Forecasting international tourist arrivals is a critical aspect of tourism planning and policy-making. This study compares two time series forecasting methods, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet in modeling and predicting the monthly number of international tourists visiting Indonesia, based on data from January 2018 to May 2025. The methodology includes data preprocessing, stationarity testing using the Augmented Dickey-Fuller test, and selecting optimal SARIMA parameters based on the lowest AIC. Model performance was evaluated using MAE and RMSE on the testing data from January to May 2025. The results indicate that SARIMA outperforms Prophet, with a lower average MAE of 1336.41 and RMSE of 1616.67, compared to Prophet’s MAE of 5591.33 and RMSE of 5739.71. Based on this evaluation, SARIMA was selected as the best model and used to project international tourist visits for the period June to December 2025. These findings highlight SARIMA’s superior ability to capture seasonal patterns in tourism data, making it a reliable tool for short-term tourism forecasting in Indonesia.
Co-Authors Alfaridzi, M Ilmi Gunawan, Rahmad Harun Mukhtar Illahi, Kevanda Sondani Mirano, M. Fitter Mirano, Muhammad Fitter Nazifah, Hayatun Rahmayana, Elsa Revalino, Aqil Sri Wahyuni