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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Antropologi: Isu-Isu Sosial budaya Jurnal Sains Matematika dan Statistika Science and Technology Indonesia AGRISAINTIFIKA Jurnal Ilmu-ilmu Pertanian JURNAL GALUNG TROPIKA Jurnal Matematika: MANTIK Jurnal Elementer (Elektro dan Mesin Terapan) Desimal: Jurnal Matematika Syntax Literate: Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Supply Chain Management JURNAL AGRIBISAINS Eksakta : Berkala Ilmiah Bidang MIPA M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Journal of Applied Engineering and Technological Science (JAETS) JAS (Jurnal Agri Sains) Jurnal Baabu Al-ilmi : Ekonomi dan Perbankan Syariah Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Jurnal Kajian Matematika dan Aplikasinya Daengku: Journal of Humanities and Social Sciences Innovation Journal of Science and Technology Lattice Journal : Journal of Mathematics Education and Applied Jurnal Ilmiah Membangun Desa dan Pertanian STATISTIKA Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika IIJSE Prosiding Seminar Nasional Unimus Jurnal Research Ilmu Pertanian BAABU AL-ILMI: Ekonomi dan Perbankan Syariah Limits: Journal of Mathematics and Its Applications
Devianto, Dodi
Department Of Mathematics And Data Science, Andalas University
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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.
TIME SERIES MODEL WITH LONG SHORT-TERM MEMORY EFFECT FOR GREENHOUSE GAS ESTIMATION IN INDONESIA Saputra, Ridho; Nisa, Alvi Khairin; Ramadhani, Nia; Almuhayar, Mawanda; Devianto, Dodi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp949-960

Abstract

Climate change is one of the major challenges in the world today, characterized by changes in meteorological values, such as rainfall and temperature, caused by the concentration of greenhouse gases in the atmosphere, such as CO2, N2O, and CH4. These accumulated greenhouse gases form a layer that prevents heat radiation from escaping, causing the greenhouse effect and global warming. Addressing the effects of greenhouse gas emissions requires appropriate strategies, one of which is to predict future greenhouse gas emissions for planning appropriate actions. Time series models such as the Autoregressive Integrated Moving Average (ARIMA) model are often used but have drawbacks due to their assumption of linear relationships. On the other hand, the Long Short-Term Memory (LSTM) model, introduced by Hochreiter and Schmidhuber in 1997, can learn complex and nonlinear relationships in data. This study uses LSTM to estimate greenhouse gas emissions in Indonesia based on emitting sectors, hoping to anticipate negative impacts and reduce greenhouse gas emissions. The results show that the LSTM model has good performance with an error below 20%, and it is predicted that greenhouse gas emissions will continue to increase.
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 .
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.
Bantuan Dana Hibah Anggaran Pendapatan dan Belanja Daerah (APBD) dan Pengaruhnya Terhadap Kinerja Manajemen BAZNAS di Provinsi Sumatera Barat Elvira, Rini; Yaswirman, Yaswirman; Effendi, Nursyirwan; Devianto, Dodi
Jurnal BAABU AL-ILMI: Ekonomi dan Perbankan Syariah Vol 8, No 2 (2023): Islamic economics and banking research
Publisher : Universitas Islam Negeri Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/ba.v8i2.5015

Abstract

This study aims to determine the causal-comparative relationship between the variable of APBD grant funding assistance and BAZNAS management performance. This study involved all BAZNAS in West Sumatra Province selected by total sampling with data sourced from PuskasBAZNAS publication. Simple linear regression analysis tested the comparative causal relationship between the two variables. The research show that the variable of APBDfunding does not affect to the management performance of BAZNAS in West Sumatra Province, but it is influenced by other factors, such as leadership, employees and organisational culture, availability of financial resources, governance, policies and strategies, technology utilisation, muzakki trust in BAZNAS, transparency and accountability, and local cultural values. The implication of this research is as a basis for future local government policy considerations in allocating APBD grant funding assistance, and for BAZNAS efforts to improve the efficiency and effectiveness of BAZNAS in zakat management.
THE BAYESIAN SEM APPROACH ON RELIGIOUS TOURISM AND SME'S ENTREPRENEURIAL OPPORTUNITY INTERRELATION IN RURAL AREA Wulandari, Frilianda; Devianto, Dodi; Yanuar, Ferra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (7605.664 KB) | DOI: 10.30598/barekengvol16iss3pp815-828

Abstract

Economics, social and culture are interrelated fields in developing a country. The social and cultural conditions that grow in an area affect how the economy develops in that area and its surrounding. This study analyzed a causal relationship from 60 nascent entrepreneurs at rural area of religious tourism with Bayesian SEM to handle a small amount of data. Based on the results of the analysis, it was found that entrepreneurial motivation and cultural motivation had a significant effect on rural religious tourism. The latent variable of rural religious tourism and entrepreneurial motivation have a significant effect on SME's entrepreneurial opportunity. The entrepreneurial motivation variable has a correlation with the cultural motivation variable.This characteristics has established the Minangkabau heritage of rural area described on its strong religious tourism aspect into SME's entrepreneurial challenge of nascent entrepreneurs.
TIME SERIES MODELING OF NATURAL GAS FUTURE PRICE WITH FUZZY TIME SERIES CHEN, LEE AND TSAUR Devianto, Dodi; Zuardin, Aulia; Maiyastri, Maiyastri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.953 KB) | DOI: 10.30598/barekengvol16iss4pp1185-1196

Abstract

Investment is the process of investing money or capital for profit or material results. The investor carefully calculates the investment object to minimize losses and maximize profits. One of the essential investment objects is the futures price of natural gas considered a commodity that plays a vital role in the Indonesian economy. The movement of natural gas futures prices can be modeled using a time series model. The data in the time series model is believed to have particular pattern to model the data in the future. The natural gas futures price is modeled into a time series method by using fuzzy time series (FTS) approach of the FTS Chen, Lee and Tsaur. Model accuracy is calculated using the criteria of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The three FTS methods have good performance of accuracy for this time series data, where FTS Tsaur as fuzzy times series approach with average based method shows the best results with the smallest error rate to the data of natural gas future price.
Generalized Linear Models in Determining Factors Affecting the Number of Community Visits to Health Service with Bayesian Inference Approach Selfinia, Selfinia; Devianto, Dodi; Yanuar, Ferra
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.15186

Abstract

Public health plays an important role in achieving the Sustainable Development Goals (SDGs) set by the United Nations. The SDGs are a series of global targets and commitments aimed at addressing various challenges facing the world today, such as poverty, hunger, gender inequality, climate change, and others. Public health, as one of the important aspects of the SDGs, is closely linked to several sustainable development goals. Efforts made to achieve the SDGs in the health sector are to improve health services. The objective of this study was to identify factors that influence the number of community visits to health services. The data used is a small sample size as one hundred community respondents in Padang City, West Sumatra Province. In this study, the number of respondents' visits to health service was the measured variable, while the predictor variables consisted of five variables, namely the status of the implementation of clean and healthy living behavior, health history, distance to health services, type of insurance owned, and consumption patterns. The generalized linear models is used to identify predictor variables that have significance using the Bayesian inference approach. It was found that there are two predictor variables that are significant in influencing the number of community visits to health services, namely the consumption patterns of respondents and the health history of respondents. These two variables have a very dominant effect on the number of visits to health service facilities in Padang City. This result indicates the community has to pay attention to their consumption patterns and living behavior to prevent periodic disease outbreaks and take care of their health history factors.
Model Volatilitas Return Index Saham Syariah Indonesia Melalui Pendekatan Bayesian Markov Switching GARCH Afnanda, Afridho; Maiyastri, Maiyastri; Devianto, Dodi
Lattice Journal : Journal of Mathematics Education and Applied Vol. 4 No. 1 (2024): Juni 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/lattice.v4i1.8381

Abstract

Volatility is an important aspect of financial analysis that plays a crucial role in risk management and investment decision making. Modeling the volatility of financial asset prices is challenging due to its dynamic and complex nature. One approach used to address this problem is the GARCH model. In volatility problems, there is a tendency for structural changes in more complex data so that the GARCH model cannot be used, to overcome this, the Markov Switching GARCH (MS-GARCH) model is used to overcome the problem of changing the data structure. Furthermore, the Bayesian model is also used in combination with the MS-GARCH model to overcome the small sample size. This research uses Indonesia Sharia Stock Index (ISSI) return data from January 1, 2023 to December 31, 2023. From the comparison of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values to see the best model for forecasting ISSI data, the best model in forecasting ISSI data is the Bayesian MS-GARCH model with the smallest AIC value of -252.544 and BIC value of -237.0894, compared to the MS-GARCH model the AIC value is smaller than the Bayesian MS-GARCH model of -251.1048 and its BIC is -235.6502.   Volatilitas merupakah salah satu aspek penting dalam analisis keuangan yang memainkan peran krusial dalam manajemen resiko dan pengambilan keputusan investasi. Pemodelan volatilitas harga aset keuangan menjadi suatu tantangan karena sifatnya yang dinamis dan kompleks. Salah satu pendekatan yang digunakan untuk mengatasi masalah ini adalah model GARCH. Pada masalah volatilitas kecenderungan terjadinya perubahan struktur pada data yang lebih kompleks sehingga tidak bisa digunakan model GARCH, untuk mengatasi hal ini digunakan model Markov Switching GARCH (MS-GARCH) untuk mengatasi masalah perubahan struktur data. Selanjutnya digunakan juga model Bayesian yang dikombinasikan dengan model MS-GARCH untuk mengatasi jumlah sampel yang kecil. Penelitian ini menggunakan data return Index Saham Syariah Indonesia (ISSI) dari tanggal 1 Januari 2023 hingga 31 Desember 2023. Dari hasil perbandingan nilai Akaike Information Criterion (AIC) dan Bayesian Information Criterion (BIC) melihat model terbaik untuk meramalkan data ISSI, diperoleh model terbaik dalam meramalkan data ISSI adalah model Bayesian MS-GARCH dengan nilai AIC yang terkecil yaitu sebesar -252,544 dan nilai BIC yaitu -237.0894, dibandingkan pada model MS-GARCH nilai AICnya lebih kecil dibandingkan model Bayesian MS-GARCH sebesar -251,1048 dan BIC nya sebesar -235.6502.
Co-Authors Abdi Mulya Acnesya, Vivin Admi Nazra Afnanda, Afridho Afrimayani Afrimayani Ainul Mardhiyah, Ainul Almuhayar, Mawanda AMALIA DWI PUTRI Amalia Dwi Putri ANNISA RAHMADIAH Arda Yunianta Arfarani Rosalindari ARNEZDA PUTRI Arrival Rince Putri Asdi, Yudiantri Astari Rahmadita Aulia Safitri Bahri, Susila Baqi, Ahmad Iqbal Boby Canigia Bukti Ginting Cesa Febri Desti Cichi Chelchillya Candra Cichi Chelchillya Candra Cindyana Aldrifisia Cintya Mukti Citra Ariadini Chairunnisa Claudia Putri Zoelanda Darvi Mailisa Putri Defriman Djafri Delvia Alhusna Des Welyyanti Desi Susanti Dina Monica DIRAMADHONA MUTIASALISA Efendi Efendi Eka Rahmi Kahar Elfa Rafulta Elfindri, Elfindri Elisa Sri Hastuti Elsa Wahyuni Elvi Yati Ermanely Ermanely Fadila Aulia Fadila Rasyid Fadilla Nisa Uttaqi Fajriyah, Rahmatika Faldo Aditya Farhah Anggana Fazrol Rozi Fery Murtiningrum Fery Murtiningrum Fery Murtiningrum, Fery Finti Warni FITARI RESMALANI FITRI SABRINA Fitria Sarah Ginting, Yanti Mayasari Gusmanely Z Hafiz Rahman HANDIKA WAHYU VIKRANTHA Hasibuan, Lilis Harianti Hazmira Yozza Herliani Evinda Husnul Fikri Ihsan Kamal Ikhlas Pratama Sandi Irfan Suliansyah Istiqamah . Iswahyuli . Izzati Rahmi HG Jatu Visitasari Jayanti Herli Kamarni, Neng Khatimah, Havifah Husnatul Kiki Ramadani Lana Fauziah Lathifah Yulyanisa Lily Zuhrat Lita Wulandari Aeli Livia Amanda LOLANDA SYAMDENA M. Pio Hidayatullah M. Rizki Oktavian Maisan Nusa Putri Maiyastri Maiyastri, Maiyastri Majbur, Ridha Fauza maMaiyastri Maiyastri Mardha Tillah Maulini Septya Mawanda Almuhayar Mayastri Mayastri Melinda Noer Melisa Febriyana MUHAMMAD HAFANDRY Muhammad Iqbal Muhammad Qolbi Shobri Muhammad Ridho Muharisa, Catrin Mutia Yollanda Nadia Husna Nadya Risna Putri Narwen Narwen NASTHASYA, NOVALISA Nisa, Alvi Khairin Nova Noliza Bakar NOVALISA NASTHASYA Noverina Alfiany Nursyirwan Effendi, Nursyirwan NURUL AISHAH Nurwijayanti Olivia Prima Dini Partini Partini Partini Partini, Partini Primawati Primawati Puteri Bulqis Azhari Putri Bulqis Azhari Putri Permathasari Putri Permathasari Putri Putri Putri Riza Chaniago Radhiatul Husna Rahma Diana Safitri Rahmawati Ramadhan RAHMI HG, IZZATI Ramadhani, Eza Syafri Ramadhani, Nia Rasyid, Fadila Religea Reza Putri Remon Lapisa Riau, Ninda Permata Ridhatul Ilahi Ridho Pascal Willmar Ridho Saputra, Ridho Rini Elvira Riri Lestari Risma Yulia Rosi Ramayanti Rudiyanto Rudiyanto, Rudiyanto SAIDAH . Sani, Ridha Fadhila Saputri, Ovi Delviyanti SARAH SARAH Sarmada, Sarmada Selfinia, Selfinia SHINTA YULIANA Siska Dwi Kumala Siti Maha Rani Sri Meiyenti Sri Wahyuni Sri Wahyuni Suci Sari Wahyuni SUMINDANG YUZAN Surya Puspita Sari Surya Puspita Sari, Surya Puspita Syauqi, Irfan Tasya Abrari Tessy Oktavia Mukhti Tiara Shofi Edriani Tomi Desra Yuliandi ULLYA IZZATY UMMU BUTSAINATUL EL KHAIR Uqwatul Alma Wisza Uswatul Hasanah Vira Agusta Wikasanti Dwi Rahayu William Huda Willmar, Ridho Pascal WULANDARI, FRILIANDA Yanuar, Ferra Yaswirman, Yaswirman Yosika Putri Yurinanda, Sherli Zetra, Aidinil Zuardin, Aulia Zul Ahmad Ersyad Zulakmal, Zulakmal