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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) HAYATI Journal of Biosciences Jurnal Pengolahan Hasil Perikanan Indonesia FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography 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) Cetta: Jurnal Ilmu Pendidikan 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 Enthusiastic : International Journal of Applied Statistics and Data Science Xplore: Journal of Statistics Molekul: Jurnal Ilmiah Kimia Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Utami Dyah Syafitri
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, IPB University, Jl. Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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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. 
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.
PENGEMBANGAN MODEL PERAMALAN SPACE TIME: Studi Kasus: Data Produksi Padi di Sulawesi Selatan Evita Choiriyah; Utami Dyah Syafitri; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 4 (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.v4i4.584

Abstract

Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.
PENGGEROMBOLAN SUBSEKTOR INDUSTRI BERDASARKAN PERKEMBANGAN INDEKS PRODUKSI MENGGUNAKAN PREDICTION-BASED CLUSTERING Agustin Faradila; Utami Dyah Syafitri; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 3 (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.v4i3.585

Abstract

Statistics Indonesia (BPS) noted that there has been a decrease in the contribution of the industrial sector to the national GDP even though it had provided a significant multiplier effect on national economic growth. Therefore, it is necessary to cluster the industrial subsector based on its growth patterns so that the optimization of development results can be achieved. Prediction-based clustering is part of time series clustering (TSclust) which aims to form clusters based on prediction characteristics so that it can be used to choose a cluster that will become a mainstay industry in the future. This study focused on applying prediction-based clustering in the large and medium industrial sub-sector for a prediction period of 1 month, 1 quarter, and 1 semester. The data used in this study was the production index data from January 2010 to December 2018. The results showed that the best cluster for 1 month consisted of 5 groups, for 1 quarter consisted of 4 groups and for 1 semester consisted of 2 groups. Thus, it was concluded that the food industry; leather industry, leather goods, and footwear; and the pharmaceutical industry, chemical drug products, and traditional medicine could be chosen to be the mainstay industry in the future.
K-prototypes Algorithm for Clustering Schools Based on The Student Admission Data in IPB University Sri Sulastri; Lismayani Usman; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
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.v5i2p228-242

Abstract

The new student admissions was regularly held every year by all grades of education, including in IPB University. Since 2013, IPB University has a track record of every school that has succeeded in sending their graduates, even until they successfully completed their education at IPB University. It was recorded that there were 5,345 schools that included in the data. It was necessary to making every school in the data into the clusters, so IPB could see which schools were classified as good or not good in terms of sending their graduates to continue their education at IPB based on the characteristics of the clusters. This study using the k-prototypes algorithm because it can be used on the data that consisting of categorical and numerical data (mixed type data). The k-prototypes algorithm could maintain the efficiency of the k-means algorithm in handling large data sizes, but eliminated the limitations of k-means. The results showed that the optimal number of clusters in this study were four clusters. The fourth cluster (421 school members) was the best cluster related to the student admission at IPB University. On the other hand, the third cluster (391 school members) was the worst cluster in this study.
Analyzing multilevel model of educational data: Teachers’ ability effect on students’ mathematical learning motivation Eminita, Viarti; Saefuddin, Asep; Sadik, Kusman; Syafitri, Utami Dyah
Journal on Mathematics Education Vol. 15 No. 2 (2024): Journal on Mathematics Education
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jme.v15i2.pp431-450

Abstract

Motivation to learn mathematics decreased due to the inability of teachers to implement innovative learning models and techniques. Therefore, this study aimed to investigate the effects of teachers' ability on students' motivation to learn mathematics by using quantitative methods and survey approaches. There were 32 mathematics teachers and 542 students in the 24 schools within the Depok region, selected as respondents through a stratified random sampling method. The research instruments of two questionnaires of teachers’ competence and students’ learning motivation were distributed to the respondents. Data analysis was conducted to test the random effect of teachers’ ability on students’ motivation to learn mathematics by using the effect of teachers’ random intercepts and competence as models 1 and 2, respectively. These two models were analyzed using the n-level Structural Equation Model (nSEM), and the result showed that model 2 was the best one to investigate the random effect of teachers’ ability and students’ learning motivation. The data analysis showed that the variance among teachers’ ability (0,0027) was less than learning motivation among students (0.0597). These findings indicated that the motivation levels of students taught by the same teacher varied significantly, whereas the effects of the teachers were relatively homogeneous. In other words, teachers’ ability was somewhat the same in increasing students’ learning motivation. Based on these findings, this research work suggests teachers keep improving their teaching techniques. Hence, students will be well motivated to learn so that the learning objectives will be well achieved.
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 Amany, Nurfatimah Anang Kurnia Andrew Donda Munthe Anggrahini, Ervina Dwi Anggraini Sukmawati Anik Djuraidah Anissa Permatasari Antonio Kautsar Anugrah, Cahya Ireno Ardiansyah, M. Ficky Haris Aryasa, Komang Budi 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 Fachry Abda El Rahman Fadhila Hijryani FAHREZAL ZUBEDI Farit M Afendi Fatimah, Zahra Nurul Fitrianto, Anwar Gandaputra Simbolon, Andreas Nicholas Hari Wijayanto I Made Sumertajaya Idqan Fahmi Immatul Ulya Indahwati Indonesian Journal of Statistics and Its Applications IJSA Indradewa, Rhian 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 Putri, Thasya R, Arifuddin Rifki Husnul Khuluk Ririn Fara Afriani Riswan Riswan Sanusi, Ratna Nur Mustika Sari, Mutia Dwi Permata Septaningsih, Dewi Anggraini Setiabudi, Nur Andi Setyowati, Silfiana Lis Sifa Awalul Fikriah Simbolon, Andreas Nicholas Gandaputra Siwi Haryu Pramesti Soleh, Agus M Soni Yadi Mulyadi Sony Hartono Wijaya Sri Sulastri Syam, Ummul Auliyah Syifa Muflihah Tania Amalia Darsono 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