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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Science and Technology Indonesia NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Matematika: MANTIK Jurnal Elementer (Elektro dan Mesin Terapan) Desimal: Jurnal Matematika Syntax Literate: Jurnal Ilmiah Indonesia JWP (Jurnal Wacana Politik) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications Buletin Ilmiah Nagari Membangun Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Journal of Science and Technology Makara Journal of Health Research Lattice Journal : Journal of Mathematics Education and Applied STATISTIKA Limits: Journal of Mathematics and Its Applications
Yanuar, Ferra
Universitas Andalas
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Modeling of Human Development Index Using Bayesian Spatial Autoregressive Approach Yanuar, Ferra; Wulandari, Sintya; Asdi, Yudiantri; Zetra, Aidinil; Haripamyu
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.1.72-79

Abstract

Spatial regression analysis is a technique employed to examine the relationship between independent and dependent variables in datasets that exhibit regional neighborhood influences or spatial effects. When a spatial effect exists for the independent variable, the Spatial Autoregressive (SAR) regression can be utilized. The Maximum Likelihood Estimation (MLE) is a commonly used parameter estimator for SAR. However, due to the limitations of MLE, the Bayesian method provides an alternative approach for parameter estimation. This study compares the results of SAR estimations using both MLE and Bayesian methods to determine the most accurate estimation model. Both methods were implemented in this research to model the factors affecting the Human Development Index (HDI) in East Java Province for the year 2022. The findings indicate that the Bayesian SAR offers a superior proposed model compared to the MLE SAR. The factors influencing the HDI in East Java Province in 2022 include poverty, per capita expenditure, and the presence of an upper middle-class manufacturing industry.
PEMODELAN PERTAMBAHAN TINGGI BADAN BALITA STUNTING MENGGUNAKAN METODE REGRESI TOBIT KUANTIL BAYESIAN BOOTSTRAP Khatimah, Havifah Husnatul; Yanuar, Ferra; Devianto, Dodi
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.v6i1.927

Abstract

The purpose of this study was to analyze the model of height increase in stunted toddlers in West Sumatra Province and the factors that influence it using the Bayesian tobit quantile regression method. Then the parameters of the resulting model will be tested for accuracy using the Bootstrap method. The data used in this study are secondary data in the form of data on the height of stunted toddlers in West Sumatra Province obtained from the West Sumatra Health Office in August 2021 and February 2022 for 1755 toddlers. After analyzing the data on the height increase in stunted toddlers using the Bayesian tobit quantile regression method, it was found that the model at quantile 0.50 was the best model because it produced smaller MAD and RMSE values ​​than other quantiles. Furthermore, the parameter estimation of the Bayesian tobit quantile regression model has produced an acceptable estimated value because it is within the Bootstrap confidence interval. The significant factors influencing the height increase in stunted toddlers in West Sumatra Province are exclusive breastfeeding and immunization.
SMALL AREA ESTIMATION DENGAN METODE PENDEKATAN NONPARAMETRIK KERNEL TERHADAP INDEKS PEMBANGUNAN PEMUDA DI INDONESIA Syauqi, Irfan; Yanuar, Ferra; Devianto, Dodi
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.v6i1.946

Abstract

Youth Development Index (YDI) is a measuring tool for youth development, YDI can describe the progress of youth development at the regional and national levels. This study aims to analyze the YDI model in Indonesia and the factors that influence it using the Small Area Estimation (SAE) method with the Kernel nonparametric approach. This Kernel nonparametric approach is not bound by classical assumptions. The Kernel function approach is based on the approach of using the availability of common variables between censuses and surveys so that it is in accordance with the SAE method which estimates the regression function based on survey information..The data used in this study are secondary data in the form of YDI data in Indonesia obtained from the National Socio-Economic Survey of the Central Statistics Agency (CSA) of Indonesia in 2022 for 34 provinces. In this study, the evaluation of the results of the SAE Kernel method model estimation by calculating the coefficient of determination of 94.56% and the accuracy of the model estimation by finding the Mean Absolute Percentage Error (MAPE) value. Significant factors influencing YDI in Indonesia are the level of youth participation in formal and non-formal education and training.
UPAYA MEMBANGUN KARAKTER SISWA MELALUI INTEGRASI KONSEP HIMPUNAN DAN AL-QUR’AN DALAM PEMBELAJARAN MATEMATIKA Izzati Rahmi HG; Admi Nazra; Budi Rudianto; Mahdhivan Syafwan; Ferra Yanuar; Hazmira Yozza; Narwen Narwen; Monika Rianti Helmi; Maiyastri Maiyastri
BULETIN ILMIAH NAGARI MEMBANGUN Vol. 6 No. 4 (2023)
Publisher : LPPM (Institute for Research and Community Services) Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/bina.v7i4.538

Abstract

The Quran is the source of all knowledge, including mathematics. On the other hand, mathematics is closely related to everyday life and the development of other fields of knowledge. Mathematics is one of the disciplines closely connected to the verses of the Quran. Mathematics education is expected to improve to meet the advancements in time and technology continually. It is also anticipated that mathematics education can build the character of each student through religious values. This activity aims to introduce the concept of sets integrated with the content of verses found in the Quran. The activity was conducted as an online Zoom meeting and YouTube streaming seminar. Participants included mathematics lecturers, teachers, and students from Islamic junior and senior high schools from ten provinces in Indonesia. The event was titled "The Quran and Set Theory" and received high appreciation from the seminar participants. This was evident from the enthusiastic participation and numerous questions raised during the Q&A session. This activity has motivated teachers and lecturers to integrate the mathematical concepts learned with the Quranic verses. Teachers who participated in this activity are expected to act as agents in popularizing the method of integrated mathematics education with the content of Quranic verses, especially set theory.
COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA Hasibuan, Lilis Harianti; Yanuar, Ferra; Devianto, Dodi; Maiyastri, Maiyastri; Rudiyanto, Rudiyanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1587-1596

Abstract

The poverty line is the threshold income level below which a person or household is considered to be living in poverty. The poverty line is a representation of the minimum rupiah amount needed to meet the minimum basic food needs equivalent to 2100 kilocalories per capita per day and basic non-food needs. According to data from the Central Bureau of Statistics (BPS), although the poverty rate in West Sumatra has decreased in recent years, the issue of poverty is still very relevant to be discussed and addressed. The issue of the poverty line is important to discuss because it is directly related to the welfare of people and the development of a country. For modeling the poverty line and its influencing factors, appropriate statistical methods are needed. This research is about the comparison of two methods, namely the Bayesian quantile regression method and Bayesian LASSO quantile regression. The two methods are compared with the aim of seeing which method produces the smallest error. Bayesian quantile regression is one method that can model data assuming heteroscedasticity violations. This study compares the ordinary Bayesian quantile regression method with penalized LASSO. These two methods are applied in modeling the poverty line in West Sumatra. The purpose of this study is to see the best method for modeling data. The data used amounted to 133 data points from BPS in the years 2017 and 2023. Model parameters were estimated using MCMC with a Gibbs sampling approach. The results show that the Bayesian LASSO method is superior to the method without LASSO. This is evidenced that the superior method produces the smallest MSE value, 0.208, at quantile 0.5. Model poverty line in West Sumatra is significantly influenced by per capita spending ), Gross Regional Domestic Product ), Human Development Index ), Open Unemployment Rate , and minimum wages .
Modeling Classification Of Stunting Toddler Height Using Bayesian Binary Quantile Regression With Penalized Lasso Hasibuan, Lilis Harianti; Yanuar, Ferra; Devianto, Dodi; Maiyastri, Maiyastri
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 2 (2025): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v10i2.928

Abstract

Stunting is a child who has a height that is shorter than the age standard. One of the main indicators of stunting is a height that is lower than the standard for toddlers. Stunting in Indonesia is of great concern due to the high prevalence of stunting. Stunting children are at risk of impaired cognitive development, which will result in the development of human resources. This study aims to develop a classification model to detect stunted toddlers based on height using the Bayesian binary quantile regression method with LASSO (Least Absolute Shrinkage and Selection Operator). This method was chosen because of its ability to handle multicollinearity and variable selection problems automatically, as well as provide better estimates on non-normally distributed data. The data used in this study includes five independent variables such as age, weight at birth, gender, how to measure height and nutritional status. The results showed that independent variables that significantly affect the height of stunting toddlers can be a concern to reduce the problem of stunting in Indonesia. The results of model show that variable age, weight at birth, and nutritional status have a significant influence to classification of stunting toddler height. Indicator of model goodness is seen from the quantile that has the smallest MSE value. The model that has the smallest MSE is in quantile 0.25 with an MSE value of 0.1622.
Performance Quantile Regression and Bayesian Quantile Regression in Dealing with Non-normal Errors (Case Study on Simulated Data) Lilis Harianti Hasibuan; Ferra Yanuar; Harahap, Vika Pradinda; Qalbi, Latifatul
Numerical: Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 2 (2024)
Publisher : Universitas Ma'arif Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v8i2.4922

Abstract

This research discusses the performance of quantile regression and Bayesian quantile regression methods. Quantile regression uses parameter estimation by maximizing the value of the likelihood function, while Bayesian quantile regression uses parameter estimation with the Bayesian concept. The Bayesian concept in question looks for solutions from the posterior distribution with Gibbs Sampling. The purpose of the study is to compare the two methods. The data used is simulated data with a total of 100 generated data. The results obtained by the Bayesian quantile regression method are superior to the indicator used MSE with the result of 1.7445. The smallest MSE value is obtained in the model that is in quantile of 0.5
Comparison of quantile regression and censored quantile regression methods in the case of chicken consumption Sarmada, Sarmada; Yanuar, Ferra; Devianto, Dodi
Desimal: Jurnal Matematika Vol. 6 No. 2 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i2.18949

Abstract

The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met. This research aims to compare the quantile regression method and the censored quantile regression method on data on chicken consumption cases in West Sumatra. The smallest RMSE (Root Mean Square Error) is an indicator of the goodness of the model. This research proves that the censored quantile regression method tends to produce smaller RMSE values than the quantile regression method. So it is concluded that the censored quantile regression method is the appropriate method for estimating parameters with censored data.
Penggunaan Metode Support Vector Machine (SVM) dalam Mengidentifikasi Tingkat Keparahan Pada Kecelakaan Lalu Lintas Rahmi, Fatihatur; Ferra Yanuar; Yudiantri Asdi
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.1690

Abstract

ABSTRAK Kendaraan sudah menjadi kebutuhan pokok yang digunakan semua orang untuk berpindah dari satu tempat ke tempat lain dengan cepat. Namun, bertambahnya jumlah kendaraan juga menimbulkan dampak negatif, salah satunya adalah kecelakaan. Berdasarkan data yang diperoleh dari website Badan Pusat Statistik (BPS) Sumatera Barat pada tahun 2018-2021, kasus kecelakaan terbanyak terjadi di Kota Padang yaitu sebesar 22,79% dari semua kasus kecelakaan yang terjadi di provinsi Sumatera Barat. Tingkat keparahan yang dialami korban pasca kecelakaan dikelompokkan kedalam 3 kategori yaitu korban yang mengalami luka ringan, luka berat dan meninggal dunia. Metode klasifikasi dapat digunakan untuk mengklasifikasi tingkat keparahan kecelakaan berdasarkan faktor-faktor yang mempengaruhi terjadinya kecelakaan. Salah satu metode yang dapat digunakan adalah metode Support Vector Machine (SVM). SVM adalah suatu learning machine yang digunakan untuk mengklasifikasi data secara statistika dalam ruang fitur berdimensi tinggi dan solusi yang dihasilkan dari klasifikasi menggunakan SVM bersifat sama untuk setiap percobaan yang dilakukan. Pada penelitian ini akan digunakan klasifikasi dengan SVM multiclass dengan metode one againts one (satu lawan satu) dengan dua fungsi kernel yang selanjutnya akan dilakukan perbandingan kualitas model berdasarkan akurasi, nilai APER dan F1-score. Data yang digunakan pada penelitian ini adalah data kecelakaan yang dialami pengendara sepeda motor di kota Padang pada bulan Januari-Maret 2022. Hasil penelitian menunjukkan bahwa penggunaan kernel RBF lebih baik dibanding kernel linear dengan tingkat akurasi sebesar 94,62% dengan nilai APER sebesar 5,38% dan diperoleh F1-score untuk kategori luka ringan sebesar 97,07%, luka berat sebesar 59,90% dan meninggal dunia sebesar 80%. ABSTRACT Transportation has become a basic necessity that everyone uses to move from one place to another quickly. However, the increasing number of transportation also has negative impacts, one of them was a traffic accident. According to BPS, the highest number of accidents occurred in Padang city, which was around 22.79% of the total cases that occurred in West Sumatra. The classification method can be used to classify the severity of accidents based on the factors that influence the occurrence of accidents. One method that can be used is the Support Vector Machine (SVM). SVM is a learning machine that is used to classify data statistically in a high-dimensional feature space and the solution resulting from classification using SVM is the same for every experiment carried out. In this research, multiclass SVM classification will be used with the one-against-one method with two kernel functions, then the model quality will be calculated based on accuracy, APER value and F1 score. The data used in this research is traffic accidents by motorcyclists in Padang City in January-March 2022. The results of the research show that the RBF kernel is better than the linear kernel with an accuracy level is 94.62%, an APER value is 5.38% and a F1-score for the minor injuries category is 97.07%, while serious injuries and deaths are 59.90% and 80%.
ANALISIS SURVIVAL UNTUK PARAMETER SKALA DARI DISTRIBUSI WEIBULL MENGGUNAKAN MLE DAN METODE BAYESIAN Yanuar, Ferra; Wulandari, Sisca; Rahmi HG, Izzati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.091 KB) | DOI: 10.30598/barekengvol15iss1pp147-156

Abstract

Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.
Co-Authors Abdi Mulya Admi Nazra AMALIA DWI PUTRI Amalia Dwi Putri ANGGUN CITRA DELIMA ANNISA RAHMADIAH Arfarani Rosalindari Arrival Rince Putri Asdi, Yudiantri Astari Rahmadita ATIKAH RAHMAH PUTRI Azmi Arsa Bahri, Susila Baqi, Ahmad Iqbal Boby Canigia Budi Rudianto Catrin Muharisa Cichi Chelchillya Candra Cichi Chelchillya Candra Cici Saputri Cintya Mukti Cintya Mukti Des Welyyanti Deva, Athifa Salsabila Devianto, Dodi Dila Mulya Dina Monica DINIE ANEFI HAJARA Efendi Efendi Elfa Rafulta Ermanely Ermanely Fadilla Nisa Uttaqi Fajriyah, Rahmatika Farhah Anggana Febriyuni, Rahmi Firdawati, Firdawati FITARI RESMALANI Fitri Aulia FITRI SABRINA Gusmanely Z Harahap, Vika Pradinda Haripamyu Haripamyu Hasibuan, Lilis Harianti Hazmira Yozza Helmi, Monika Rianti Ihsan Kamal Ikhlas Pratama Sandi Indah Pratiwi Izzati Rahmi HG Izzati Rahmi HG Jenizon Jenizon Kamarni, Neng Kartini Aboo Talib @Khalid Khatimah, Havifah Husnatul Lilis Harianti Hasibuan Livia Amanda M. Pio Hidayatullah M. Rizki Oktavian Maiyastri Maiyastri, Maiyastri Majbur, Ridha Fauza Mardha Tillah Mawanda Almuhayar MEILINA DINIARI Melisa Febriyana Mesi Oktafia Meutia Fikhri MIFTAHUL JANNAH HB Mira Serma Teti Mita Oktaviani Muhammad Iqbal Muhammad Qolbi Shobri Muharisa, Catrin Mutiara Fara Nabilla Nadia Cindi Eka Putri Nadiah Ramadhani NADYA PUTRI ALISYA Nadya Putri Alisya Narwen Narwen Nayla Desviona Nova Noliza Bakar Noverina Alfiany Nurmaylina Zaja Nurwijayanti Qalbi, Latifatul Radhiatul Husna RAHMI HG, IZZATI Rahmi, Fatihatur Ramadhani, Eza Syafri Religea Reza Putri Rescha, Ratna Vrima Resti Mustika Sari Resti Nanda Yani Riau, Ninda Permata Ridhatul Ilahi Riri Lestari Riri Lestari Rudiyanto Rudiyanto, Rudiyanto SAIDAH . Sani, Ridha Fadhila Saputri, Ovi Delviyanti Sari, Putri Trisna Sarmada Sarmada Sarmada, Sarmada Selfinia, Selfinia SHINTA MUTIA KARNEVA Shinta Wulandari SHINTA YULIANA Silvia . SILVIA YUNANDA Sisi Andriani Siti Juriah SITI LATHIFAH IRMA SUMINDANG YUZAN Surya Puspita Sari, Surya Puspita Susi Marisa Syafwan, Mahdhivan Syauqi, Irfan Tari Adriana Musana Tasya Abrari Tasya Abrari Uswatul Hasanah VIKI ANDRIANI Widya Wijayanti WINDA LIDYA Winda Oktari WULANDARI, FRILIANDA Wulandari, Sintya wulandari, sisca Yanita Yanita Yosika Putri Yulmiati Yulmiati Yurinanda, Sherli Zahratul Aini Zetra, Aidinil Zetra, Aidinil Zulakmal, Zulakmal Zulhazizah .