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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jambura Journal of Biomathematics (JJBM) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS The Indonesian Journal of Computer Science Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)

Wijaya, Madona Yunita

Department Of Mathematics, Faculty Of Science And Technology, Universitas Islam Negeri Syarif Hidayatullah Jakarta, South Tangerang, Indonesia

Author-ID : 849673

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Articles

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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.

Trajectory of life expectancy and its relation with socio-economic indicators among developing countries in Southeast Asian Madona Yunita Wijaya; Yanne Irene; Iqbal Rachadi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.307

Life expectancy is a one of key global health indicators and plays an important role in health policy measures. The status of a country indirectly influences the life expectancy of a nation. Developing countries have slower economic progress compared to developed countries, which in turn affects the well-being of the population. Therefore, this study aims to analyze the trend of life expectancy among developing countries in Southeast Asian and assess the influence of socio-economic indicators in life expectancy. Linear mixed effects model is used to model the association between socioeconomic factors and life expectancy. The results indicate that GDP growth rate, GDP per capita, and unemployment rate have significant impact on life expectancy and the impacts depend on gender. Life expectancy among females is generally higher than males. Prediction of life expectancy in males in year 2025 is found the lowest in Myanmar with average of 64.2 years (95%CI: 60.8-77.1) and the highest in Thailand with average of 76.2 years (95%CI: 60.7-76.9). Meanwhile, prediction of life expectancy in females is found the lowest in Timor Leste with average of 71.1 years (95%CI: 67.8-83.9) and the highest in Thailand with average of 84.3 years (95%CI: 68.7-84.9).

PERBANDINGAN METODE BACKWARD DAN FORWARD PADA SELEKSI MIXED-EFFECTS MODEL: ANALISIS FRAGILE STATE INDEX ASIA TENGGARA Mega Maulina; Madona Yunita Wijaya; Nina Fitriyati
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.495

In the era of globalization, concerns about the stability and vulnerability of nations have become a primary focus in the field of international relations research. One of the indicators measuring a country’s vulnerability is the Fragile State Index. This research discusses the analysis of Fragile State Index in Southeast Asian countries using panel data observed from 2010 to 2021, focusing on economic impacts. The factors involved include Gross Domestic Product, General Government Net Lending/Borrowing, Inflation, and Life Expectancy. A mixed-effects model is employed to examine the influence of economic factors on the Fragile State Index. The selection of the best model is carried out by comparing Backward Elimination and Forward Selection procedures. The research findings indicate that the best model for interpreting the influence of economic factors on the Fragile State Index is the one using the Backward Elimination method. Significant variables in this model include Gross Domestic Product, General Government Net Lending/Borrowing, Life Expectancy, and the interaction between General Government Net Lending/Borrowing and Inflation

PENERAPAN CROSS-VALIDATION PADA MODEL EFEK CAMPURAN: DAMPAK FAKTOR EKONOMI DAN KESEHATAN TERHADAP INDEKS KERENTANAN NEGARA-NEGARA DI ASIA TENGGARA Jasmin Nur Hanifa; Madona Yunita Wijaya; Suma’inna Suma’inna
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.565

This research aims to investigate the impact of economic growth on the stability of countries in Southeast Asia during the period 2010–2021. The research method involves the use of data from the International Monetary Fund (IMF), the World Health Organization (WHO), the World Bank, and the Fragile State Index (FSI). A mixed-effects model was used to analyze the relationship between these variables, and K-fold cross-validation was employed to determine the optimal subset model within the mixed-effects model framework. The research results indicate a model with quadratic nonlinear trend, produces lower root mean squared error (RMSE) and mean absolute error (MAE) values compared to other models, indicating a higher level of accuracy in predicting data. The conclusion of this research is that the mixed-effects model with a quadratic non-linear assumption, particularly the third model, exhibits superior predictive performance with an RMSE of 0.628 and MAE of 0.536. However, it should be highlighted that just a few variables, particularly life expectancy, GDP, and the square of GDP, contribute considerably to the variation in the mean FSI (Fragile States Index The findings provide insights into the model's ability to capture the complexity of relationships among predictor variables and mean FSI, as well as identify the variables influencing a country's vulnerability

PREDIKSI HARGA PENUTUPAN SAHAM BANK CENTRAL ASIA: IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY DAN PERBANDINGANNYA DENGAN SUPPORT VECTOR REGRESSION Rizky Azriel Fahrezi; Madona Yunita Wijaya; Nina Fitriyati
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.582

Stock is an instrument of the financial market that is very popular among other instruments because it has an attractive yield. The research discusses the prediction of Bank Central Asia shares, named BBCA, using the Long Short-Term Memory (LSTM) method. The LSTM model is a very popular deep learning algorithm that is suitable for predicting time-related data, historical data, and sequential data. We configure the LSTM model with the following hyperparameters: number of neurons of 60, batch_size of 64, timesteps of 32, epoch of 12, and dense layer of one unit while the configuration for SVM support vector machine model with Gaussian Radial Basic Function kernel and hyperparameter γ = 0.0001 and c = 1000. BBCA prediction results are quite good when compared to the SVM model with a MAPE of 1.07%.

Co-Authors Achmad Reza Fahcruroji Aisyah Muhayani Alamsyah, Mochammad Rafli Alkadrie, Syarifah Syila Andrew Fiade Aprilia, Ananda Bisyri, M. Alvi Devi Ila Octaviyani Hasanah, Sri Indriati Hidayat, Afnenda Rachmalia Inna, Suma Iqbal Rachadi Irene, Yanne Irma Fauziah Jasmin Nur Hanifa Khasanah, Dwi Windari Nur Kurnia, Hafsah Laili Nahlul Farih MAHMUDI Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Mega Maulina Muhammad Manaqib, Muhammad Nina Fitriyati Nina Fitriyati Nina Fitriyati Nur Inayah NURLIANA NURLIANA Ramadita, Mutiara Rizky Azriel Fahrezi Sari, Flori Ratna Suma’inna Suma’inna Yahya, Amelia Nur Yanne Irene Yanne Irene, Yanne Zulkifli, Dhea Urfina

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