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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Multica Science and Technology STATISTIKA
Meirinda Fauziyah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Energy Library & Information Science Mathematics Physics

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Peramalan Tren Pencarian Kata Kunci “Sarung Wadimor” Di Indonesia Pada Data Google Trends Menggunakan Time Series Regression with Calender Variation dan Arima Box-Jenkins Andrea Tri Dani; Meirinda Fauziyah; Hardina Sandariria
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 3 (2023): MAY, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i3.24551

Abstract

The impact of this 4.0 era is that data is growing and can be collected very easily and then reprocessed to obtain information. One of the search engines for various data and information that is often used is Google, causing a high search intensity and will further impact on increasing the amount of data generated by search engines. Google Trends is one of the official websites from Google that reflects or takes pictures of events in society based on search keywords. The search keyword that will be studied in this article is “Sarung Wadimor”. Therefore, the purpose of this research is to forecast the search trend for the keyword "Sarung Wadimor" which is interesting because the resulting time series data pattern shows a recurring pattern due to the effect of calendar variations which are thought to be related to the month of Ramadan. Forecasting modeling uses Autoregressive Integrated Moving Average (ARIMA) and Time Series Regression (TSR). The goodness of the model used in this article is the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE). Based on the results of the analysis, using three goodness-of-fit measures shows that the TSR model with the Calendar Variation of Ramadan + Month Periods has smaller MSE, RMSE, and SMAPE values than the other models with goodness-of-fit values of 88.602, 9.413, and 26.950, respectively. Forecasting results for the next 6 periods show that the search trend for the keyword "Sarung Wadimor" tends to decrease, this is because the month of Ramadan is still quite far in 2023.
Comparison of Generalized Poisson Regression and Negative Binomial Regression Models Based on Akaike Information Criterion Values Sinta Qorri Aina; Darnah; Meirinda Fauziyah; Wiwit Pura Nurmayanti
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5402

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Abstract. Poisson regression models discrete data and assumes equidispersion, where the variance equals the mean. It is frequently observed that discrete data exhibits a variance exceeding its mean, a phenomenon known as over-dispersion. Over-dispersion may be addressed through various methodologies, such as Generalized Poisson Regression (GPR) and Negative Binomial Regression (NBR). Model selection is predicated on the smallest Akaike Information Criterion (AIC) value. This study aimed to identify the best model in the comparison of models between GPR and NBR based on the smallest AIC value so that it can be known what factors influence the number of cases of pulmonary tuberculosis (TB) in Indonesia in 2022. The results of the study showed that the NBR model was the best model, with an AIC value of 688.49. Factors that influence cases of pulmonary TB in Indonesia in 2022 are the percentage of households that have access to proper sanitation, nursing staff, and the percentage of education levels completed are high school or equivalent.
COMPARISON OF MEAN CENTERING REGRESSION AND SPLINE TRUNCATED NONPARAMETRIC REGRESSION ON FACTORS AFFECTING THE NUMBER OF CRIMES IN INDONESIA Felicia Joy Rotua Tamba; Liana Oklas Ranly; Andrea Tri Rian Dani; Meirinda Fauziyah; Narita Yuri Adrianingsih; Mislan Mislan
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/fpp74f96

Abstract

Crime remains one of the major challenges facing Indonesia, with the national crime rate showing an upward trend in 2022. This increase is driven by various social, economic, and demographic factors. To investigate these influences, this study applies the nonparametric truncated spline regression method to identify the determinants of crime rates across provinces in Indonesia. The response variable is the number of recorded crimes, while the predictor variables include the percentage of people living in poverty, mean years of schooling, average monthly per capita expenditure on food and non-food items, number of beneficiary households, budget for food social assistance, liberty aspects from the Indonesia Democracy Index, and the percentage of people with mental disorders. The analysis reveals that the linear truncated spline regression model with three knot points provides the best fit, achieving a coefficient of determination (R²) of 87.31%. These findings highlight the model’s capability to capture complex, nonlinear relationships between socio-economic indicators, democratic freedoms, mental health, and crime incidence in Indonesia.
Co-Authors Andrea Tri Dani Andrea Tri Rian Dani Darnah Felicia Joy Rotua Tamba Hardina Sandariria Liana Oklas Ranly Mislan NARITA YURI ADRIANINGSIH Sinta Qorri Aina Wiwit Pura Nurmayanti