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The Estimation of Excess Mortality during the COVID-19 Pandemic in Jakarta, Indonesia Madona Yunita Wijaya
Jurnal Kesehatan Masyarakat Nasional Vol 17, No 1 (2022): Volume 17, Issue 1, February 2022
Publisher : Faculty of Public Health Universitas Indonesia

Indonesia is among the countries affected by the coronavirus disease 2019 (COVID-19) pandemic, and DKI Jakarta Province recorded the highest number of deaths. This study aimed to analyze the excess mortality across five administrative cities in Jakarta stratified by gender to assess the pandemic impact on mortality. The monthly mortality data from January 2018 to December 2020 was obtained through government sources. This data helped to measure excess mortality by estimating the baseline mortality had the COVID-19 pandemic not occurred. The analysis used a linear mixed model because of its ease and flexibility in forecasting subject-specific mortality. The results showed 13,507 or 35% excess deaths in Jakarta [95% CI: 11,636 to 15,236] between June and December 2020. The excess numbers were found relatively higher among men than women. Furthermore, Jakarta has underreported the COVID-19 deaths at least seven times higher than the reported number of confirmed deaths.

Relative Importance Analysis for Psychological Research Madona Yunita Wijaya
JP3I (Jurnal Pengukuran Psikologi dan Pendidikan Indonesia) Vol 10, No 1 (2021): JP3I
Publisher : Fakultas Psikologi UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jp3i.v10i1.20552

Multiple linear regression analysis is widely used among psychological researchers to answer their research question related to causality relationship. Exploring the relative importance of independent variables in explaining the total variation in dependent variable is one of the primary interests upon finding a good fit model from the data. This paper considers two popular methods to obtain relative importance, namely Shapley value regression and relative weight analysis. Both are able to break down the R2 of the full model into individual contribution proportion of each independent variable while accounting for the correlations between independent variables and thus offer easily interpretable effect size measures for regressions. Kaggle’s empirical data from the World Happiness 2019 will illustrate the theoretical concept of methods above.

Estimation Parameter d in Autoregressive Fractionally Integrated Moving Average Model in Predicting Wind Speed Devi Ila Octaviyani; Madona Yunita Wijaya; Nina Fitriyati
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 2 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

AbstractWind speed is one of the most important weather factors in the landing and takeoff process of airplane because it can affect the airplane's lift. Therefore, we need a model to predict the wind speed in an area. In this research, the wind speed forecast using the ARIMA model is discussed which has differencing parameters in the form of fractions. This model is called the ARFIMA model. In estimating differencing parameters two methods are considered, namely parametric and semiparametric methods. Exact Maximum Likelihood (EML) is used under parametric method. Meanwhile, four methods semiparametric estmation are used, i.e Geweke and Porter-Hudak (GPH), Smooth GPH (Sperio), Local Whittle and Rescale Range (R/S). The result shows the best estimation method is GPH with the selected model is ARFIMA (2,0.334,0).Keywords: ARFIMA, Parametric Method, Semiparametric Method. AbstrakKecepatan angin merupakan salah satu faktor cuaca yang penting dalam proses pendaratan dan tinggal landas pesawat karena dapat mempengaruhi daya angkat pesawat. Oleh karena itu, diperlukan suatu model untuk memprakirakan kecepatan angin di suatu wilayah. Artikel ini membahas prakiraan kecepatan angin dengan menggunakan model ARIMA yang memiliki parameter differencing berupa bilangan pecahan. Model ini disebut model ARFIMA. Pada estimasi parameter differencing terdapat dua metode yang digunakan pada penelitian ini, yaitu metode parametrik dan metode semiparametrik. Metode parametrik yang digunakan adalah Exact Maximum Likelihood (EML) dan empat metode semiparametrik yang digunakan adalah Geweke and Porter-Hudak (GPH), Smooth GPH (Sperio), Local Whittle dan Rescale Range (R/S). Hasil analisis menunjukkan pada kasus ini metode estimasi terbaik adalah GPH dengan model terpilih adalah ARFIMA(2,0.334,0).Kata kunci: ARFIMA, Metode Parametrik, Metode Semiparametrik.

World Gold Price Forecast using APARCH, EGARCH and TGARCH Model Yanne Irene; Madona Yunita Wijaya; Aisyah Muhayani
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 2 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

AbstractInvestment is a process of investing money for profit or material result. One investment commodity is gold. Gold is a precious metal in which the value tends to fluctuate over time. This indicates that there is a non-constant variance called heteroscedasticity. The appropriate time-series model to solve this heteroscedasticity problem is ARCH/GARCH. However, this model can't be applied for the financial cases that have an asymmetric effect (the downward and increase tendency in the level of volatility when returns rise and vice versa). Therefore, in this research, we forecast the world gold prices using APARCH, EGARCH, and TGARCH methods. We use the monthly world gold price data from June 1993 until May 2018. The result shows that the best-fitted model to forecasting the world gold prices is EGARCH (1.1). This model has the smallest error than the other models with a Mean Absolute Percentage Error (MAPE) value of 4.66%.Keywords: return; volatilities; heteroscedasticity; asymmetric effect; APARCH; EGARCH; TGARCH. AbstrakInvestasi adalah proses menginvestasikan uang untuk keuntungan atau hasil material. Salah satu komoditas investasi adalah emas. Emas adalah logam mulia yang nilainya cenderung berfluktuasi dari waktu ke waktu. Ini menunjukkan bahwa ada varian non-konstan yang disebut heteroskedastisitas. Metode deret waktu yang tepat untuk menyelesaikan masalah ini adalah ARCH/GARCH. Namun model ini tidak dapat digunakan untuk kasus keuangan yang memiliki efek asimetris (kecenderungan menurun dan meningkatnya volatilitas ketika nilai return naik dan sebaliknya). Oleh karena itu, dalam penelitian ini, kami memprediksi harga emas dunia menggunakan metode APARCH, EGARCH, dan TGARCH dengan data harga emas dunia bulanan pada bulan Juni 1993 - Mei 2018. Hasilnya menunjukkan bahwa, di antara ketiga metode itu, model terbaik untuk memprediksi harga emas dunia adalah EGARCH (1.1) dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 4,66%.Kata kunci: return; volatilitas; heteroskedastisitas; efek asimetris; APARCH; EGARCH; TGARCH.

Prediction of The Number of Ship Passengers in The Port of Makassar using ARIMAX Method in The Presence of Calender Variation Laili Nahlul Farih; Irma Fauziah; Madona Yunita Wijaya
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

AbstractIndonesia is an archipelago with the largest Muslim population in the world. Every year, Indonesian people have a tradition of meeting relatives in other areas or take a vacation on Eid al-Fitr. People use different modes of transport to travel such as air, water, and land transport. Port plays a role in supporting water transportation because it is a knot of inter-regional relations. The celebration of Eid al-Fitr moves forward by about 11 days every year. The purpose of this thesis is to make an estimate of the total departure of ship passengers in the main port of Makassar using the ARIMAX method with the effects of calendar variations. The ARIMAX method is a method that can be used when there are exogenous variables, where in this case the exogenous variable is in the form of variable dummy wich is Eid holidays. These forecasting results show that the ARIMAX method has a relatively small accuracy with the MAPE value of .Keywords: water transportation; calendar variations effects; Eid Al-Fitr. AbstrakIndonesia merupakan negara kepulauan dengan mayoritas muslim terbesar didunia. Setiap tahun masyarakat Indonesia memiliki tradisi bertemu sanak saudara di daerah lain ataupun berlibur pada hari raya Idul Fitri. Jalur transportasi yang digunakan yaitu melalui darat, udara dan laut. Pelabuhan memiliki peran yang sangat penting dalam mendukung transportasi laut karena menjadi titik simpul hubungan antar daerah. Perayaan hari raya Idul Fitri dalam setiap tahun mengalami pergeseran 11 hari. Tujuan penulisan skripsi ini adalah untuk membuat prakiraan total keberangkatan penumpang kapal di Pelabuhan Utama Makassar menggunakan metode ARIMAX dengan efek variasi kalender. Metode ARIMAX merupakan metode yang dapat digunakan ketika data tersebut menggunakan variable eksogen, dimana dalam kasus ini variable eksogennya berupa variable dummy libur hari raya idul fitri. Hasil peramalan ini menunjukan bahwa metode ARIMAX memiliki tingkat akurasi yang lebih baik dibandingkan ARIMA musiman dengan nilai MAPE sebesar 14,08%.Kata Kunci: transportasi air; efek variasi kalender, Hari Raya Idul Fitri.

Non-linear Mixed Models in a Dose Response Modelling Madona Yunita Wijaya
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

AbstractStudy designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained. However, this still be considered a limiting factor and non-linear models is another option offering its flexibility. Non-linear model involves complexity of non-linear dependence on parameters than that in the generalized linear class. It has been utilized in many situations such as modeling of growth curves and dose-response modeling. The latter modeling will be the main interest in this study to construct a dose-response relationship, as a function of time to IBD (inflammatory bowel disease) dataset. The dataset comes from a clinical trial with 291 subjects measured during a 7 week period. Both linear and non-linear models are considered. A dose time response model with generalized diffusion function is utilized for the non-linear models. The fit of non-linear models are found to be more flexible than linear models hence able to capture more variability present in the data.Keywords: IBD; longitudinal; linear mixed model; non-linear mixed model. AbstrakDesain studi dimana hasil diukur berulang kali dari waktu ke waktu menghasilkan data longitudinal. Sebagian besar metodologi yang digunakan untuk menganalisis data longitudinal adalah model linear dan model linear umum (generalized linear model) dimana sejumlah linearitas tertentu dipertahankan. Asumsi linearitas ini masih dipandang memiliki keterbatasan dan model non-linear adalah pilihan metode lainnya yang menawarkan fleksibilitas. Model non-linear telah digunakan di berbagai macam situasi seperti model kurva pertumbuhan , model farmakokinetika, dan farmakodinamika, dan model respon-dosis. Model respon-dosis akan menjadi fokus dalam penelitian ini untuk membangun hubungan dosis-respon sebagai fungsi waktu dari data IBD dengan menggunakan model linear dan non-linear. Hasil penelitian menunjukan bahwa model non-linear lebih fleksibel daripada model linear sehingga mampu menangkap lebih banyak variabilitas yang ada di dalam data.Keywords: IBD; longitudinal; model linear; model non-linear.

A Monte Carlo Simulation Study to Assess Estimation Methods in CFA on Ordinal Data Nina Fitriyati; Madona Yunita Wijaya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.14434

Likert-type scale data are ordinal data and are commonly used to measure latent constructs in the educational, social, and behavioral sciences. The ordinal observed variables are often treated as continuous variables in factor analysis, which may cause misleading statistical inferences. Two robust estimators, i.e., unweighted least square (ULS) and diagonally weighted least square (DWLS) have been developed to deal with ordinal data in confirmatory factor analysis (CFA). Using synthetic data generated in a Monte Carlo experiment, we study the behavior of these methods (DWLS and ULS) and compare their performance with normal theory-based ML and GLS (generalized least square) under different levels of experimental conditions. The simulation results indicate that both DWLS and ULS yield consistently accurate parameter estimates across all conditions considered in this study. The Likert data can be treated as a continuous variable under ML or GLS when using at least five Likert scale points to produce trivial bias. However, these methods generally fail to provide a satisfactory fit. Empirical studies in the field of psychological measurement data are reported to present how theoretical and statistical instances have to be taken into consideration when ordinal data are used in the CFA model.Keywords: confirmatory factor analysis, diagonally weighted least square, generalized least square, Likert data, maximum likelihood.

Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS Nina Fitriyati; Mahmudi Mahmudi; Madona Yunita Wijaya; Maysun Maysun
Desimal: Jurnal Matematika Vol 5, No 3 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v5i3.14093

This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.

The Effect of Ensemble Averaging Method on Rainfall Forecasting in Jakarta Using ARIMA and ARIMAX Mahmudi Mahmudi; Afnenda Rachmalia Hidayat; Madona Yunita Wijaya
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 9 No. 2 (2024): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v9i2.608

This research discusses rainfall modeling using ARIMA and ARIMAX models in Jakarta. This is important because rainfall forecasting in Jakarta has a significant impact on flooding and infrastructure. The focus of this research is on significant ARIMA and ARIMAX models, which are then subtotaled using ensemble averaging. Humidity and temperature variables are of particular interest in ARIMAX modeling due to their high correlation with rainfall. This quantitative research uses secondary data analysis from Tanjung Priok and Kemayoran Stations through the BMKG website, from July 2018 to June 2023. The results obtained at Tanjung Priok Station there are five significant ARIMA models and three significant ARIMAX models. While at Kemayoran Station there are 6 significant ARIMA models and two significant ARIMAX models. After using the ensemble averaging method on both ARIMA and ARIMAX models, the resulting SMAPE value is not better than the best ARIMA or ARIMAX models at both stations. Of all the models performed, the best model in forecasting with the smallest SMAPE is ARIMAX (0,0,1) at Tanjung Priok Station which is 37.83% and at Kemayoran Station which is 27.59%. This research provides new insights and significant contributions in understanding and developing rainfall forecasting in Jakarta using the ensemble averaging method.

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