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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Prosiding Seminar Nasional Official Statistics
Muzakki, Naufal Fadli
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Forecasting the Air Quality Index by Utilizing Several Meteorological Factors Using the ARIMAX Method (Case Study: Central Jakarta City) Muzakki, Naufal Fadli; Putri, Azmi Zulfani; Maruli, Surya; Kartiasih, Fitri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 8 No 3 (2024): JULY-SEPTEMBER 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v8i3.2012

Abstract

Today's society really pays attention to air quality because the impact of exposure to pollutants in the air is starting to be felt. PM 2.5 pollutants are very dangerous because their small size can penetrate the alveoli of human lungs. The value calculation of the Air Quality Index (AQI) is important to prepare mitigation and defensive measures to reduce the negative impact of air quality and as a basis for future policymaking. Several method comparisons have been carried out by researchers to predict AQI. However, researchers have not studied much regarding the use of meteorological factors in the form of average air temperature (°C), average air humidity (percent), and average wind speed (m/s) in forecasting AQI values, even though meteorological factors have a significant link, according to previous researchers. This research forecasts AQI using the ARIMAX method, which includes meteorological factors as exogenous variables, using daily AQI PM 2.5 data in Central Jakarta. The best modeling of the data is ARIMA (1,1,1) without X and ARIMAX (1,1,1). Based on the calculation of AIC, BIC, RMSE, and MAPE values, ARIMAX (1,1,1) modeling produces better forecasting, so it can be concluded that forecasting involving meteorological factors can make forecasting more precise. Predicting AQI using ARIMAX with upcoming meteorological factors is beneficial, as precise prediction results can assist in policy-making to prevent the adverse impacts of air quality on public health. In future research, other meteorological factors could be studied and combined with other modeling besides ARIMA.
Peramalan Tinggi Muka Air Menggunakan Long-Short Term Memory dengan Mekanisme Multi-Head Attention Atmaja, Anugerah Surya; Muzakki, Naufal Fadli; Oktavian, Zulfaa Dwi
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.v2024i1.2125

Abstract

This study aims to predict the Ciliwung River water level in DKI Jakarta using an Long-Short Term Memory (LSTM) model with a multi-head attention mechanism. Increasing flood frequency due to climate change necessitates an effective early warning system. Utilizing historical water level data and related meteorological variables, the LSTM model with multi-head attention demonstrated superior performance, with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE) of 31.74, 3.3%, and 3.3%, respectively. Predictions for the next 72 hours indicate safe water levels between 450 cm and 500 cm, suggesting no flooding. In conclusion, the LSTM model with multi-head attention enhances water level forecasting accuracy and serves as a useful flood risk mitigation tool in Jakarta. This research significantly contributes to the development of flood early warning systems and the application of machine learning in disaster mitigation.
Analisis Kualitas Modal Manusia Tingkat Provinsi di Indonesia Menggunakan K-Means Clustering dan Regresi Logistik Biner Aurellia, Nur Aisya; Sari, Riska Meyliana; Muzakki, Naufal Fadli
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2577

Abstract

The Human Capital Index (HCI) is one of the indicators used in human development evaluations, with the aim of improving the welfare and advancement of human resources in various sectors of life. Limitations in provincial-level HCI data, as well as limitations in the data of HCI components, hinder the HCI calculation process. Therefore, an alternative approach was applied to assess human capital quality by examining components such as life expectancy, average years of schooling, and stunting prevalence using K-Means cluster analysis. The results indicate that provinces in Indonesia form two clusters: the low HCI group and the high HCI group. This study aims to examine the influence of several variables on HCI categories using binary logistic regression analysis. The results show that per capita GDP, internet penetration rates, and rice productivity have a significant positive impact on human capital quality in Indonesia.
Co-Authors Atmaja, Anugerah Surya Aurellia, Nur Aisya Fitri Kartiasih Maruli, Surya Oktavian, Zulfaa Dwi Putri, Azmi Zulfani Sari, Riska Meyliana