Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

7.188

P-Index

This Author published in this journals

All Journal FORUM STATISTIKA DAN KOMPUTASI Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma Journal of Mathematical and Fundamental Sciences JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Socioeconomics and Development Zero : Jurnal Sains, Matematika, dan Terapan Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Jurnal Jamu Indonesia Jurnal Penelitian Kebijakan Pendidikan Indonesian Journal of Educational Assesment Jurnal Pendidikan dan Kebudayaan Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Bulletin of Computer Science Research Jurnal Penelitian Kebijakan Pendidikan Aceh International Journal of Science and Technology Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Jamu Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications Indonesian Journal on Learning and Advanced Education (IJOLAE)

Budi Susetyo

Departemen Statistika, IPB University

Author-ID : 2371379

Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

Published : 62 Documents Claim Missing Document

Claim Missing Document

Articles

3 4 5 6 7

Rotation Double Random Forest Algorithm to Predict The Food Insecurity Status of Households Rais; Agus Mohamad Soleh; Budi Susetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5540

The ensemble tree method has been proven to handle classification problems well. The strength of the ensemble tree technique lies in the diversity and independence between each tree. Increasing the diversity of mutually independent decision trees improves the performance of the model. Various studies propose the development of ensemble tree-based models by forming algorithms that create decision trees that are formed independently of each other and have various inputs. These include random forest (RF), rotation forest (RoF), double random forest (DRF), and the latest is rotation double random forest (RoDRF). RoDRF rotates or transforms data with the intent of producing better diversity among the learning base. RoDRF applies the concept of variable rotation to trees based on the DRF algorithm. Random rotations or transformations on different feature subspaces produce different projections, leading to better generalization or prediction performance. This research aims to compare the performance of RoDRF with the RF, RoF, and DRF models on unbalanced data in cases of food insecurity. Class imbalance will be handled with two methods, namely EasyEnsemble and SMOTE-NC. The research results show that the DRF's model with EasyEnsemble techniques produces a model with the best performance among several algorithms tested. Although the resulting precision is 0.62274 and the AUC value is 0.68501, the model can predict each class equally. All algorithms with EasyEnsemble treatment have average AUC values significantly different from each other based on statistical test results. This research also used SHAP to explain variables that significantly contribute to the household's food insecurity status model.

MULTIVARIATE MULTILEVEL MODELLING TO ASSESS FACTORS AFFECTING THE QUALITY OF VOCATIONAL HIGH SCHOOLS IN SOUTH SULAWESI PROVINCE Pannu, Abdullah; Wijayanto, Hari; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study analyzes the quality of Vocational High Schools (VHS), which have a hierarchical data structure and have more than one response variable. Data gathered for this study is from the Basic Education Data (DAPODIK) in the form of raw data variables of several variables that characterize the quality of VHS and other independent variables in South Sulawesi for four years (2018 to 2021) from the Ministry of Finance Republic of Indonesia (KEMENKEU), and Statistics Indonesia (BPS). The explanatory variable at the regency level consists of four years (2018 to 2021), a multi-year and high-dimensional data structure. Therefore, Principal Component Analysis (PCA) is used to overcome this. The modelling is done by using multivariate multilevel modelling (MVMM) on one-level and two-level structures. This study aims to model the average National Examination and Accreditation scores of Vocational High School in South Sulawesi using MVMM modelling that considers the regency/city level and identifies the factors that influence the average National Examination and Accreditation scores. The results showed that the two-level multivariate model with a random intercept as a hierarchical component was better than the one-level multilevel model based on a minor Deviance Information Criterion (DIC) value. Simultaneously, at the 5% level of significance, variables that contribute significantly to the quality of Vocational High Schools in South Sulawesi Province are produced. The variables that have a significant effect on the quality of Vocational High Schools at the school level are the ratio of the number of students/pupils per study group, the percentage of certified teachers to the number of teachers, the ratio of the number of students/pupils per number of toilets, the ratio of laboratory availability, and the ratio of the availability of supporting rooms. Meanwhile, at the regency level, it was found that the percentage of poverty and Gross Regional Domestic Product (GRDP) had a significant effect on the quality of Vocational High Schools.

SIMULATION OF THE SARIMA MODEL WITH THREE-WAY ANOVA AND ITS APPLICATION IN FORECASTING LARGE CHILLIES PRICES IN FIVE PROVINCES ON JAVA ISLAND Sanusi, Ratna Nur Mustika; Susetyo, Budi; Syafitri, Utami Dyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Commodities that become potential in the Horticulture Sub-sector are large chilies, so supply and prices must be controlled. One of the efforts that can be made is to predict the price of large chili in the future. However, forecasting is sometimes constrained by several things, such as small sample sizes and outliers. The effect of several factors on the parameter estimation bias can be determined by experimental design by simulating the data obtained from the generation results with several scenarios. The results of the analysis show that all factors have a significant effect on the magnitude of the parameter bias, so that all factors can affect forecasting results. When applying forecasting methods to actual data, paying attention to these three factors is necessary. The application of actual data using the SARIMA method gives good results. It can be seen from the RMSE and MAPE values , which tend to be small. Based on the forecast results for the following 12 periods, it is estimated that the price of big chili in 2022 in five provinces will still fluctuate. The high price of chili in five provinces is predicted to reach its highest in the first three months of 2022. The highest price is predicted to occur in DIY Province in February, which is Rp. 74.230.00/kg. However, from the middle to the end of the year, prices will tend to fall and stabilize. The price will be the lowest in Middle Java Province in December, which is Rp. 20,689.00/Kg.

A STUDY OF SMALL AREA ESTIMATION TO MEASURE MULTIDIMENSIONAL POVERTY WITH MIXED MODEL POISSON, ZIP, AND ZINB Adwendi, Satria June; Saefuddin, Asep; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The research began with calculating the value of multidimensional poverty at the district level in West Java Province from SUSENAS 2021. The calculation of multidimensional poverty was based on individuals in each district or city household. The dimensional weights are weighed the same, and the indicators in the dimensions are also weighed the same. Furthermore, the simulation study used the Poisson, ZIP, and ZINB mixed models to examine the model's performance on data with cases of excess zero values and overdispersion. The simulation was by generating data without overdispersion and with overdispersion. Overdispersion data was generated with parameters of ω (0.1, 0.3, 0.5, and 0.7), and the model was evaluated from the AIC value. The best method in the simulation study was used to estimate multidimensional poverty in sub-districts in West Java Province using PODES 2021. Simulation studies on data without overdispersion showed no difference in the model's goodness. Overdispersion data shows Mixed Model ZIP and ZINB are better than Mixed Model Poisson. The percentage of the multidimensional poverty population at the sub-district level in West Java Province is quite diverse, from 0.04% to 75.54%.

COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA Ratnasari, Andika Putri; Susetyo, Budi; Notodiputro, Khairil Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0757-0766

The performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data is under-fitting. Another machine learning method, Long Short-Term Memory Networks (LSTMs) have capability to analyze nonlinear data. Since the study compare both methods has not been existed in literature, it is interesting to compare the performance of both methods using Indonesian data, especially economic indicator data which have been found to be under-fitting, non-underfitting, and nonlinear data. The indicators used in this study are Export, Import, Official Reserves Asset, and Exchange Rate data. The results showed that overall, the LSTMs method outperforms DRF method in analyzing the data.

MULTILEVEL REGRESSIONS FOR MODELING MEAN SCORES OF NATIONAL EXAMINATIONS Nurfadilah, Khalilah; Aidi, Muhammad Nur; Notodiputro, Khairil A.; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0323-0332

National Exam known as UN score is the final evaluation to determine the achievement of national graduate competency standards in the school. The determinants of the achievement of the standards can’t be separated from the role of schools and local governments in which this regard is known as nested. In the field of statistics, this phenomenon can be described with a multilevel model, where level-1 is the school while level-2 is the district where the school is located. Several multilevel models are used to describe the phenomenon, the result shows that the two-level regression model without interaction is selected as the best model and the variables which affect the UN average scores significantly at level-1 are school status , the ratio between laboratories and students , while the variable at level-2 is expenditure per capita of district/city . From this study, that educational institutions' steps in achieving a graduation standard can be right on the target.

Integrating VAR and CNN Models for Accurate Forecasting of Money Supply in Indonesia Warsono; Sulandra, Ardelia Maharani; Kurniasari, Dian; Usman, Mustofa; Susetyo, Budi
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252230

Economic forecasting serves as a fundamental element in supporting decision-making processes across multiple sectors. One of the main areas of interest in this field is the estimation of the money supply within an economy. The Vector Autoregressive (VAR) model is a commonly applied method for forecasting; however, it often encounters limitations when processing data with nonlinear patterns. Convolutional Neural Networks (CNNs) offer an alternative approach, particularly effective in identifying nonlinear structures that are not adequately captured by VAR models. A hybrid VAR-CNN model is therefore proposed, combining the respective strengths of both techniques to improve the accuracy of predictions. This research applies to the hybrid VAR-CNN model to forecast economic variables for the period from July 2022 to June 2023. The model consists of two main components: the first utilizes forecasted values generated by the VAR model, while the second processes the residuals from the VAR output using a CNN. With 80% of the data allocated for training and 20% for testing, the hybrid VAR-CNN model demonstrates improved performance over alternative forecasting methods. Evaluation based on Mean Absolute Percentage Error (MAPE), supremum (D) values, and p-values confirms the effectiveness of this hybrid approach.

Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java Khikmah, Khusnia Nurul; Sartono, Bagus; Susetyo, Budi; Dito, Gerry Alfa
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1012

This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. The dataset used in this research is based on the Socio-Economic Survey by Statistics Indonesia in 2020. The novelty of this research is comparing the performance of the four methods used, which all are the tree ensemble approaches. In addition, due to the imbalance class problem, the authors also applied three imbalance handling techniques in this study. The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. This approach has a balanced accuracy value of 65.795%. The best classification method results show that the food insecurity experience scale in West Java can be identified by considering the factors of floor area (house size), the number of depositors, type of floor, health insurance ownership status, and internet access capabilities.

Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis Susetyo, Budi; Fitrianto, Anwar
Jurnal Pendidikan Progresif Vol 12, No 1 (2022): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis. Hierarchical elements or higher levels often influence school accreditation and the national exam because education units are nested in the characteristics of the province. Objectives: This study aims to evaluate the relationship between accreditation and the national exam at the level of Junior high school/Madrasa in Java which are nested in province. Methods: The analysis employs multilevel GSCA analysis (MGSCA). Findings: UNBK has good convergent validity and it can explain each of the subjects tested in each province up to more than 90%. Concerning the estimates of path coefficients, the study found eight patterns of relationship between SNP and UNBK that have a significant effect in the six provinces. Conclusion: The relationship between content and competency standard for UNBK shows that there are significant differences in all provinces in Java island. This shows that provincial characteristics affect school quality. The model can explain the total variability of all variables is 72.44%. Keywords: multilevel generalized structured component analysis, national education standards, national examination.DOI: http://dx.doi.org/10.23960/jpp.v12.i1.202223

Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) Susetyo, Budi; Fitrianto, Anwar
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): 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.v9i1.24824

Missing data may occur in various types of research. Regression and multiple imputation by chained equations (MICE) are two methods that can be used to estimate missing data in panel data types. This study aims to compare the accuracy of the missing panel data estimation using the regression and the MICE methods. The data used in this study are 161 random samples of senior high schools and vocational schools in DKI province for the year 2016-2020. Based on the results of the Chow test, Hausman test, and Lagrange Multiplier test on panel data regression, it shows that the appropriate model for the student-teacher ratio (X5) is random, the percentage of teachers who have an educator certificate (X6) is a fixed model with the specific effect of individual school and time, while the percentage of teachers who hold a bachelor degree (X7) is a fixed model with the specific effect of individual. Based on this model, the estimation of missing data is then carried out. The accuracy of the missing data estimation was carried out by comparing the MAPE, MAE, and RMSE values. The results show that the MICE method is quite good for estimating missing data at X5, quite feasible for estimating X6, and very good for estimating missing data at X7. In general, MICE is more accurate than panel data regression

Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Agus Mohamad Soleh Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani Anang Kurnia Andi Illa Erviani Nensi Andina Fahriya Anis Sulistiyowati Anisa, Rahma ASEP SAEFUDDIN Aulia Dwi Oktavia Aulia Rizki Firdawanti Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur Dela Gustiara Dewi Jasmina Dewi Jasmina, Dewi Dhea Dewanti Dian Kurniasari Dito, Gerry Alfa Dyah R. Panuju Endah Febrianti Erfiani Erfiani Fadjrian Imran Fahriya, Andina Faisal Arkan Farit Mochamad Afendi Fitrianto, Anwar Gerry Alfa Dito H Karwono Hafidz Muksin Hamid, Assyifa Lala Pratiwi Hari Wijayanto Herlina Herlina Hermawati, Neni Hiola, Yani Prihantini I Gusti Ngurah Sentana Putra I Made Sumertajaya Inayatul Izzati Diana Yusuf Indahwati Indahwati Indahwati Indahwati, NFN Intan Juliana Panjaitan Iswan Achlan Setiawan Izzati Rahmi HG Jap Ee Jia Jia, Jap Ee Karwono, H Kesuma Millati Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kristuisno Martsuyanto Kapiluka Kriswan, Suliana Kusman Sadik Kusni Rohani Rumahorbo La Ode Abdul Rahman La Ode Abdul Rahman La Ode Abdul Rahman M Nur Aidi M Nur Aidi, M Nur Mahmud A. Raimadoya Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman Nurfadilah, Khalilah Nurfajrin, Tria Ermina Nurul Qomariasih Pannu, Abdullah Pika Silvianti Pika Silvianti Putri, Mega Ramatika Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Rais Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki Alifah Putri Rizki, Akbar Robert, Zahira Rahvenia Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Shalshabilla Shafa Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Syella Zignora Limba Sylvia P. Soetantyo Syukri, Nabila Tina Aris Perhati Tiya Wulandari Ulfa Afilia Shofa Utami Dyah Syafitri Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Warsono Wulan Andriyani Pangestu Yani Prihantini Hiola Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji Zulhijrah

Title

Found 62 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 62 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 62 Documents
Search