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All Journal ComTech: Computer, Mathematics and Engineering Applications Parameter: Journal of Statistics
Fitriana Nur Afifa
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

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CATEGORICAL ANALYSIS OF STUDENT ENTHUSIASM RELATED TO THE 2024 INDONESIAN ELECTION WITH CHI-SQUARE INDEPENDENCE TEST Fitriana Nur Afifa; Ilham Darussalam; Evi WIjayawati; M. Syahrie Khamdani
Parameter: Journal of Statistics Vol. 5 No. 1 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i1.17551

Abstract

General elections in Indonesia are crucial for democracy, enabling citizens to directly choose their leaders. But the abstention or the "whites" in Indonesian elections is growing due to perceived political bias in the state apparatus operating under bureaucratic democracy principles. KPU RI has designated the National Permanent Voter List for the 2024 election, with 52% being young voters consisting of students. Ranging in age from 17 to 22 years old, the students will make a major contribution to the number of votes and the number of abstinences. In this study, a chi-square independence test was conducted to analyze the factors that are thought to influence student enthusiasm regarding the 2024 Indonesian elections. The results of this study indicate that social media intensity has a significant effect on student enthusiasm regarding the 2024 Indonesian elections, while gender and regional origin have no significant effect.
Temperature Forecast at Djuanda International Airport using ARIMA, ANN, and Hybrid ARIMA-ANN Elly Pusporani; Fitriana Nur Afifa; Fidela Sahda Ilona Ramadhina
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.13219

Abstract

This research evaluates the performance of Artificial Neural Network (ANN) models in forecasting temperature at Djuanda Airport, comparing them with the traditional Autoregressive Integrated Moving Average (ARIMA) model and a hybrid ARIMA–ANN approach. Although statistical models such as ARIMA are widely applied, their capacity to capture nonlinear dynamics in tropical climate conditions is limited, particularly when the data exhibit irregular fluctuations that linear models cannot adequately represent. Forecasting temperatures in tropical airport settings, which is crucial for flight planning, operational safety, and the reliability of aviation operations, remains relatively underexplored. This gap underscores the importance of alternative modeling techniques that can effectively address nonlinear relationships. Using one year of observed data, the models are evaluated with three accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The ANN model achieves the lowest error values (MAE 0.7630, MAPE 2.7067%, RMSE 1.0074) compared to both ARIMA and hybrid approaches. The metrics and the testing graph collectively indicate that ANN has a stronger ability to capture nonlinear temperature dynamics in tropical contexts. Nonetheless, the findings must be interpreted with caution due to the limited dataset and single case study. These limitations highlight the need for extended data and alternative architectures to improve forecasting accuracy and strengthen support for safer aviation operations.
Co-Authors Elly Pusporani Evi WIjayawati Fidela Sahda Ilona Ramadhina Ilham Darussalam M. Syahrie Khamdani