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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
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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Pemodelan kasus tingkat kemiskinan di Indonesia periode 2015-2021 dengan model regresi panel terboboti geografis Kurnia, Hafsah; Fauziah, Irma; Wijaya, Madona Yunita
Majalah Ilmiah Matematika dan Statistika Vol. 24 No. 2 (2024): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v24i2.39392

Abstract

Poverty is a major concern of the Indonesian government and the government's efforts to reduce poverty are a national development priority. Therefore, it is interesting to identify the factors that influence poverty in Indonesia. Considering a spatial perspective, Geographically Weighted Panel Regression (GWPR) method is applied to the panel data set of 34 Indonesia provinces over the period 2016-2021. The best fitted model is found when using the adaptive kernel weighting function with poverty rate, length of schooling, provincial minimum wave, human development index, literacy rate, and unemployment rate as the predictor variables. The result suggests that provinces in Indonesia can be divided into seven groups based on significant predictors on poverty rate. The fixed effect GWPR model is the final model selected for the data which can explain about 75.64% of the variability in poverty rate in Indonesia. Keywords: Fixed effect model, Geographically Weighted Panel Regression, Adaptive kernel. MSC2020: 62P25.
A Monte Carlo Simulation Study to Assess Estimation Methods in CFA on Ordinal Data Fitriyati, Nina; Wijaya, Madona Yunita
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

Abstract

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.
Analysis of the linear navier stokes korteweg model with neumann boundary conditions in three dimensional half-space Khasanah, Dwi Windari Nur; Inna, Suma; Wijaya, Madona Yunita; Hasanah, Sri Indriati
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 7 No. 1 (2024): Jurnal Absis (In Press)
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v7i1.2589

Abstract

This study discusses the solution of the Navier-Stokes Korteweg model, which describes two-phase fluid flow with capillary effects, with Neumann boundary conditions in the half-space. The main objective is to detail the resolution process of the resolvent equation system in the half-space related to the Navier-Stokes Korteweg model with Neumann boundary conditions. The resolution is carried out in several steps. First, the resolvent equation system is reduced using even and odd extensions. Then, a partial Fourier transform is applied, resulting in a simpler ordinary differential equation. The findings of this research indicate the existence of a solution operator for the resolvent equation of the Navier-Stokes Korteweg model with Neumann boundary conditions in the half-space. This solution applies for two cases involving the coefficients, depending on certain conditions related to the fluid properties.
Variasi spasial dan temporal nilai-b pada gempa bumi di wilayah Sulawesi Tengah, Gorontalo, dan sekitarnya menggunakan metode robust fitting Fitriyati, Nina; Wijaya, Madona Yunita; Bisyri, M. Alvi
Majalah Ilmiah Matematika dan Statistika Vol 22 No 2 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i2.33817

Abstract

This study discusses variation in seismic and tectonic modeled by a Gutenberg-Richter relationship for earthquakes in the Central Sulawesi, Gorontalo, and surrounding areas using the Robust Fitting Method (RFM) with the weight function of Tukey’s bisquare. The declustering process on earthquake data is carried out using the Reasenberg equation. The values for both parameters are analyzed spatially and temporally. In the spatial analysis, the research area is divided into 43 grids. In the temporal analysis, the research area is divided into zone A and zone B. The data grouping is done using a sliding time window method, i.e., grouping 50 earthquake catalogs with 5 overlapping events. The results according to spatial analysis show that the b-values range from 0.38 – 1.19. Areas with low b-values (0.38 – 0.7) occur around the Palu-Koro Fault, i.e., Palu city, Malacca strait, and to Toli-Toli, and also in the northern region of Gorontalo, i.e., the subduction plate of the Sulawesi Sea. Meanwhile, high b-values (0.71 – 1.19) are in the Tomini Bay area which is an area with frequent occurrence of earthquakes but has the small potential to generate large-scale earthquakes. The results of the temporal b-value estimation in zones A and B range between values of 0.38 - 1.25. The b-values appear to decrease before the occurrence of major earthquakes in 1996 and 2018 in zone A. The b-values decreased before the occurrence of major earthquakes in 1990, 1991, 2000, and 2008 in zone B. However, the b-values cannot be used as a precursor before the big earthquake in 1997. Keywords: Tukey’s bisquare, Reasenberg equation, Gutenberg-Richter relationship, sliding time window, Robust Fitting Method. MSC2020: 86A15
The Effect of Ensemble Averaging Method on Rainfall Forecasting in Jakarta Using ARIMA and ARIMAX Mahmudi, Mahmudi; Hidayat, Afnenda Rachmalia; Wijaya, Madona Yunita
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

Abstract

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. Cuanbet88 merupakan salah satu portal link slot gacor 777 resmi untuk generasi muda, cukup dengan situs slot777 pasti gampang menang 2025. 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.
Analysis of the linear navier stokes korteweg model with neumann boundary conditions in three dimensional half-space Khasanah, Dwi Windari Nur; Inna, Suma; Wijaya, Madona Yunita; Hasanah, Sri Indriati
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 7 No. 1 (2024): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v7i1.2589

Abstract

This study discusses the solution of the Navier-Stokes Korteweg model, which describes two-phase fluid flow with capillary effects, with Neumann boundary conditions in the half-space. The main objective is to detail the resolution process of the resolvent equation system in the half-space related to the Navier-Stokes Korteweg model with Neumann boundary conditions. The resolution is carried out in several steps. First, the resolvent equation system is reduced using even and odd extensions. Then, a partial Fourier transform is applied, resulting in a simpler ordinary differential equation. The findings of this research indicate the existence of a solution operator for the resolvent equation of the Navier-Stokes Korteweg model with Neumann boundary conditions in the half-space. This solution applies for two cases involving the coefficients, depending on certain conditions related to the fluid properties.
PERBANDINGAN NILAI AKURASI PERAMALAN EMISI KARBON DIOKSIDA DENGAN MODEL SARIMA DAN HYBRID SARIMA-QR Nurliana, Nurliana; Wijaya, Madona Yunita; Fauziah, Irma
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.887

Abstract

Carbon dioxide (CO₂) emissions are one of the primary causes of climate change, which has a significant impact on the environment and human health. As the second-largest emitter of carbon dioxide after China, the United States requires an effective forecasting system to monitor and control these emissions. This study aims to develop a time series model with independent variables using a combined SARIMA-QR method and compare its accuracy with the SARIMA model. The independent variables used include total industrial energy consumption, total electricity consumption, and the forecast values from the SARIMA model. The comparison of model accuracy is based on the MAPE values from the testing data between the SARIMA model and the SARIMA-QR model at the 0.50 quantile. The analysis results show that the SARIMA model achieves a MAPE value of 5.78%, while the SARIMA-QR model at the 0.50 quantile has a lower MAPE value compared to the SARIMA model. The improvement in accuracy in the SARIMA-QR model is due to the integration of independent variables, which provide additional relevant information, such as total industrial and electricity consumption, as well as the forecast values from the SARIMA model. This demonstrates that the use of independent variables can improve the accuracy of CO₂ emission predictions. The comparison of the accuracy of these two models is expected to serve as an important reference for the United States government in formulating more effective policies to manage carbon dioxide emissions more optimally.
IMPLEMENTASI ALGORITMA CNN MOBILENET UNTUK KLASIFIKASI GAMBAR SAMPAH DI BANK SAMPAH Achmad Reza Fahcruroji; Madona Yunita Wijaya; Irma Fauziah
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 1 (2024): Prosisko Vol. 11 No. 1 Maret 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i1.8101

Abstract

Waste is a global problem that must be resolved so that the environment is well maintained, especially in developing countries such as Indonesia. A good understanding of waste will have an impact on optimal waste management. Waste banks are one way out in waste management so that what was originally useless becomes marketable. In its implementation, the waste bank must have data collection and transparency of customer waste data in real time. Technology is one of the main solutions to produce digital products that make it easier for people to access information accurately. By using the Convolutional Neural Network (CNN) algorithm, an image of image data can be predicted with good accuracy. Along with the development of technology today, various kinds of architectures are present, one of which is Mobilenet. This architecture has the ability to run machine learning models on mobile and IoT devices. Furthermore, the resulting model is quite good with an accuracy rate of 96% on Metal waste, 92% on Paper and Organic waste, 80% on Cardboard waste, 76% on Glass waste, and 72% on Plastic waste. The disadvantages that exist in this model when predicting with almost similar shapes and images that have many objects in them, the error will be greater so there is a possibility of error in predicting the results of the garbage image.
Prediksi Curah Hujan di DKI Jakarta Menggunakan Model Hybrid (DWT-SVR-Prophet) Ramadita, Mutiara; Mahmudi; Wijaya, Madona Yunita
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4357

Abstract

Rainfall has a significant influence in the planning and management of various industries such as forestry, agriculture, and water resources. This research aims to develop a Hybrid model that combines Discrete Wavelet Transform (DWT), Support Vector Regression (SVR) and Prophet models to predict rainfall in the DKI Jakarta area more accurately. Using DWT, rainfall data is divided into high and low frequency components, then the prediction results from each model are combined. At Kemayoran Station, the Hybrid model (High Frequency SVR and Low Frequency Prophet) provides the best performance with SMAPE: 36.44%. At Tanjung Priok Station, the non-Hybrid model gave the best results, with Prophet without DWT achieving SMAPE: 29,82%. This study provides a clear understanding of how effective rainfall prediction models are in DKI Jakarta, which helps water resources planning and management.
Evaluasi Performa Metode Exponential Smoothing pada Data Runtun Waktu Hierarkis Alkadrie, Syarifah Syila; Wijaya, Madona Yunita; Fitriyati, Nina
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4783

Abstract

Penelitian ini bertujuan untuk menyalakan metode Simple Exponential Smoothing (SES), Double Exponential Smoothing (Metode Holt), dan Triple Exponential Smoothing (Holt-Winters) dalam memperkirakan jumlah wisatawan di Australia dari tahun 1998 sampai dengan tahun 2016. Data yang digunakan memiliki struktur hierarki dengan empat tingkat: Australia, negara bagian, kawasan, dan tujuan kunjungan. Pendekatan bottom-up diterapkan untuk menghasilkan ramalan pada tingkat hierarki teratas dengan menggabungkan ramalan dari tingkat terendah. Evaluasi dilakukan dengan menggunakan metrik Symmetric Mean Absolute Percentage Error (SMAPE) pada setiap tingkat hierarki dan cakrawala peramalan. Hasil penelitian menunjukkan bahwa Metode Holt berkinerja terbaik pada tingkat Australia (SMAPE 3,26%–9,28%) dan tingkat negara bagian (6,96%–12,29%). Sementara itu, Holt-Winters mencapai kinerja terbaik pada tingkat wilayah (16,57%–21,43%) dan tingkat tujuan kunjungan (43,98%–47,63%). Penelitian ini menyoroti efektivitas Exponential Smoothing dalam menangkap pola dan tren musiman dalam hierarki data dan pentingnya pendekatan bottom-up dalam menghasilkan prakiraan yang konsisten di semua tingkat hierarkis.
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