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All Journal HAYATI Journal of Biosciences Jurnal Pengolahan Hasil Perikanan Indonesia FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL KIMIA SAINS DAN APLIKASI Jurnal Manajemen Teknologi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmu Komputer dan Agri-Informatika The Journal of Pure and Applied Chemistry Research JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Knowledge Engineering and Data Science Jurnal Matematika Sains dan Teknologi Syntax Literate: Jurnal Ilmiah Indonesia Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications Martabat: Jurnal Perempuan dan Anak MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Ilmiah Ecosystem Jambura Journal of Mathematics Jurnal Samudra Ekonomi dan Bisnis Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Inferensi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Statistika dan Aplikasinya Xplore: Journal of Statistics Molekul: Jurnal Ilmiah Kimia Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education Limits: Journal of Mathematics and Its Applications
Utami Dyah Syafitri
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, IPB University, Jl. Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia
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Effectiveness of GPCA in Reducing Data Dimensions and its Application to Human Development Dimension Indicators Data Zubedi, Fahrezal; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Syafitri, Utami Dyah
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.21506

Abstract

Analysis of human development growth at the regency/city level is challenging because the data is high-dimensional, indicators are correlated, and the regencies/cities are correlated. In this study, we propose a Generalized Principal Component Analysis to analyze human development growth by reducing the dimensions of regency/city and indicator. Thus, human development growth at the regency/city level is analyzed using the GPCA results in Biplot to describe each regency/city and its indicators. This study aims to evaluate GPCA in reducing the dimensionality of data whose observations are correlated, and indicators are correlated through simulation and empirical study; to analyze the growth of human development at the regency/city level based on the results of GPCA-Biplot. This research shows that GPCA works well in reducing data dimensions from correlated observations and correlated variables. Based on the results of the GPCA-Biplot visualization, the growth of human development in the Nduga regency from 2019 to 2022 showed significant fluctuations. Although some indicators show progress, especially in 2021, significant challenges remain. In the same way, the growth of human development in each regency/city can be analyzed. Thus, government policy focuses on real problems in the field.
Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning Aziza, Vivin Nur; Syafitri, Utami Dyah; Fitrianto, Anwar
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4052

Abstract

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
A-Optimal Pada Mixture Amount Design Dengan Modifikasi Rancangan Petak Terbagi Menggunakan Algoritma Point-Exchange Sari, Mutia Dwi Permata; Syafitri, Utami Dyah; Djuraidah, Anik
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 2 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i2.4072

Abstract

Abstract:A Mixture Amount Experiment (MAE) is a design that consists of a mixture variable and the total amount variable. In practice, the composition of the mixture is run on each total amount of mixture, which consequently cannot be completely randomized, so that a split-plot design approach is needed. This research aims to develop an algorithm to find a A-Optimal design for a mixture amount experiment with a modified split-plot design. A-Optimal design is seeking a design in which minimizing the covariance of the model parameter. The study case of this research involved three ingredients and two total amounts of mixtures with different constraints. In this research, the whole plot factor is the total amount of mixtures, while the subplot factor is the composition of the mixture. The A-Optimal design was generated based on the Second-Order Scheefe model. To Construct the A-optimal design, we used the point exchange algorithm. The result from this algorithm produced an optimum composition in each total amount of mixture. Abstrak:Rancangan Jumlah Campuran (MAE) terdiri dari komponen campuran dan jumlah total. Dalam prakteknya, komposisi campuran dijalankan pada setiap jumlah total campuran, akibatnya tidak dapat diacak sempurna, sehingga diperlukan pendekatan rancangan petak terbagi. Penelitian ini bertujuan untuk mengembangkan suatu algoritma untuk menemukan rancangan dengan kriteria A-Optimal untuk percobaan jumlah campuran dengan menggunakan modifikasi rancangan petak terbagi. Rancangan A-Optimal mencari rancangan yang meminimalkan kovarian parameter model. Studi kasus penelitian ini terdiri dari tiga bahan dan dua jumlah total campuran yang berbeda. Dalam penelitian ini, factor petak utama adalah jumlah total campuran, sedangkan faktor anak petak adalah komposisi campuran. rancangan A-Optimal dihasilkan berdasarkan model Second-Order Scheefe. Untuk Membangun rancangan A-optimal, menggunakan pendekatan algoritma point-exchange. Hasil dari algoritma ini menghasilkan komposisi optimum pada setiap jumlah total campuran.
A-OPTIMAL DESIGN IN NON-LINEAR MODELS TO INCREASE SILICON DIOXIDE PURITY LEVELS Weisha, Ghea; Erfiani, Erfiani; Irzaman, Irzaman; Syafitri, Utami Dyah
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.36-44

Abstract

Silica is the most mineral found on earth and is widely used in industry. Silica used in industry is usually silicon dioxide with a purity ≥ 95% and its often sold at a higher cost. To obtain the silica at a lower cost, silica extraction from biomass such as rice husk can be conducted. The purity of silica extracted from biomass tends to be lower than that of mineral silica. Silica with low purity can be increased by adjusting the temperature and the rate of temperature rise. This research aims to obtain the best design to determine the purity of silicon dioxide. The design of this study was generated based on the A-optimality criterion using the DETMAX algorithm. The A-optimality criterion is minimizing the trace of the variance-covariance of the parameter estimation. The best design points obtained using A-optimal design consist of three temperature groups: the minimum temperature of 800°C, the middle temperature of 850°C, and the maximum temperature of 900°C, with varying rates of temperature rise. Points were repeated at the temperature of 850°C, with rates of temperature rise of 1.67°C/min and 3.34°C/min. 
Perbandingan Metode Regresi Multilevel dan Beta Generalized Linear Mixed Models pada Data Longitudinal Capaian IPK Mahasiswa Meilania, Gusti Tasya; Syafitri, Utami Dyah; Sumertajaya, I Made
Limits: Journal of Mathematics and Its Applications Vol 21, No 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v21i3.21788

Abstract

Penelitian ini membandingkan kinerja model Beta Generalized Linear Mixed Model (Beta GLMM) dengan Regresi Multilevel pada data Indeks Prestasi Kumulatif (IPK) mahasiswa. Data IPK yang digunakan dalam penelitian ini terlihat miring ke sisi kiri atau memiliki ekor kiri yang lebih panjang yang mencerminkan kecenderungan mahasiswa memperoleh nilai yang lebih besar daripada rata-rata IPK keseluruhan. Hal ini mengindikasikan bahwa data tidak berdistribusi normal, melainkan diduga berdistribusi Beta. Tujuan dari penelitian ini adalah melakukan perbandingan terhadap metode regresi multilevel dan Beta Generalized Linear Mixed Model (GLMM) untuk melihat faktor-faktor yang memengaruhi IPK mahasiswa setiap semester. Data yang digunakan adalah data longitudinal dimana setiap mahasiswa diamati IPK per semester serta beberapa peubah penjelas lainnya. Pendekatan Beta GLMM digunakan karena Beta GLMM menggabungkan antara pendekatan Linear Mixed Model (LMM) dengan Generalized Linear Model (GLM)Berdasarkan analisis yang dilakukan, diperoleh hasil bahwa metode Beta GLMM memiliki nilai Akaike Information Criterion (AIC) yang lebih rendah dibandingkan metode regresi multilevel. Adapun faktor-faktor yang mempengaruhi capaian IPK mahasiswa berdasarkan analisis Beta GLMM diantaranya semester mahasiswa, SKS mahasiswa setiap semester, status perkawinan, jalur masuk kuliah, sumber biaya pendidikan (beasiswa), interaksi semester dengan status perkawinan, interaksi antara semester dengan jalur masuk kuliah, dan interaksi antara semester dengan beasiswa. Selain itu, diketahui bahwa proporsi keragaman IPK yang dapat dijelaskan oleh perbedaan antar mahasiswa adalah sebesar 0.837. Hal ini menunjukkan bahwa 83.7% dari total variasi IPK dapat dijelaskan oleh perbedaan antar mahasiswa (Level 2), sedangkan sisanya 16.3% dijelaskan oleh variasi pada setiap mahasiswa disetiap semester (Level 1).
Authentication of Java Turmeric (Curcuma xanthorrhiza) from Turmeric (Curcuma longa) Using a Combination of UV-VIS-IR Spectrum and Chemometrics Izzati, Mumpuni Nur; Syafitri, Utami Dyah; Mohamad Rafi
Jurnal Jamu Indonesia Vol. 10 No. 1 (2025): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v10i1.313

Abstract

Java turmeric (Curcuma xanthorriza) and turmeric (Curcuma longa) show similar colors, so they have the potential to be adulterated with each other, especially if they are presented in powder. This research aims to develop an analytical method for authenticating both types of samples with adulterant concentrations of 0.01% w/w and 0.005% w/w for the infrared range and 0.5 μg/g and 1 μg/g for the UV-Vis range. The pure sample was extracted for 40 minutes with 1:10 ethanol using ultrasonication. The extract was then concentrated using a rotary evaporator and freeze dryer. Adulterant samples were prepared by mixing both types of extracts. The absorption of the solution was measured at a wavelength of 200–800 nm and a wave number of 4000–400 cm-1. Multivariate analysis using partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) was performed on the spectra. PLS-DA has not been able to authenticate adulterated samples. However, SIMCA analysis can detect differences between pure curcuma and adulterated samples in the infrared range until a concentration of 0.005% w/w, while it can only authenticate correctly in the UV-Vis range until a 1 μg/g concentration.
The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide Aliu, Muftih Alwi; Syafitri, Utami Dyah; Fitrianto, Anwar; Irzaman, Irzaman
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26253

Abstract

One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.
PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES Andrew Donda Munthe; I Made Sumertajaya; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.169

Abstract

Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.
KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT Isti Rochayati; Utami Dyah Syafitri; I Made Sumertajaya; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.171

Abstract

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.
ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015 Intan Lukiswati; Anik Djuraidah; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.331

Abstract

The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.
Co-Authors Aam Alamudi Abdul Rohman Abdul Rohman Agus Mohamad Soleh Agustin Faradila Aidi, Muhammad Nur Aji Hamim Wigena Akbar Rizki Alfi Hudatul Karomah ALIU, MUFTIH ALWI Anang Kurnia Andreas Nicholas Gandaputra Simbolon Andrew Donda Munthe Anggrahini, Ervina Dwi Anggraini Sukmawati Anik Djuraidah Anissa Permatasari Antonio Kautsar ASEP SAEFUDDIN Auliya Ilmiawati Aziza, Vivin Nur Azkiya, Azka Al Baehera, Seta Bagus Sartono Bambang - Riyanto Bambang Prajogo Eko Wardoyo Bambang Riyanto Bartho Sihombing Bayu Pranata, Bayu Budi Susetyo Christin Halim Cici Suhaeni Dea Amelia, Dea Dwi Agustin Nuriani Sirodj Dwi Putri Kurniasari Eka Dewi Pertiwi Eka Winarni Sapitri Eminita, Viarti Endina Fatihah Yasmin Erfiani Erfiani Erfiani, Erfiani Erlinda Widya Widjanarko Ernawati, Fitrah Eti Rohaeti Evita Choiriyah Fadhila Hijryani FAHREZAL ZUBEDI Farit M Afendi Fatimah, Zahra Nurul Fitrianto, Anwar Gusti Tasya Meilania Hari Wijayanto I Made Sumertajaya Idqan Fahmi Immatul Ulya Indahwati Indonesian Journal of Statistics and Its Applications IJSA Intan Lukiswati Irmanida Batubara Irzaman, Irzaman Isti Rochayati Izzati, Mumpuni Nur Joko Santoso Jumansyah, L. M. Risman Dwi Khairil Anwar Notodiputro Kusman Sadik Laradea Marifni Lidiasari, Melisa Lismayani Usman M. Iqbal M. Rafi Meilania, Gusti Tasya Mohamad Rafi Mohamad Rafi Mohamad Rafi Mohammad Masjkur Muhamad Insanu Muhammad Bachri Amran Muhammad Nur Aidi Muhammad Nursid Mulianto Raharjo Muslim, Muhammad Irfai Muthahari, Wadudi Nanik Siti Aminah Nariswari Karina Dewi Ni Kadek Manik Dewantari Noer Endah Islami Nofrida Elly Zendrato Novia Yustika Tri Lestari. YR Nur Aidi, Muhammad Nurhajawarsi Nurhajawarsi Nursifa Mawadah R, Arifuddin Rifki Husnul Khuluk Ririn Fara Afriani Riswan Riswan Sanusi, Ratna Nur Mustika Sari, Mutia Dwi Permata Septaningsih, Dewi Anggraini Setyowati, Silfiana Lis Sifa Awalul Fikriah Siwi Haryu Pramesti Soleh, Agus M Soni Yadi Mulyadi Sony Hartono Wijaya Sri Sulastri Sri Wahyuningsih Syam, Ummul Auliyah Syifa Muflihah Tania Amalia Darsono Thasya Putri Topan . Ruspayandi Triyani Oktaria Vega, Iliana Patricia Vivin Nur Aziza Weisha, Ghea Wini - Trilaksani Wulan Tri Wahyuni Yenni Angraini Yuan Millafanti Yuni Suci Kurniawati Yuniar Istiqomah