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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Gizi dan Pangan FORUM STATISTIKA DAN KOMPUTASI SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Forum Geografi Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Eksakta : Berkala Ilmiah Bidang MIPA STI Policy and Management Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Xplore: Journal of Statistics Scientific Journal of Informatics Media Penelitian dan Pengembangan Kesehatan Indonesian Journal of Statistics and Its Applications
Muhammad Nur Aidi
Department Of Statistics, Faculty Of Mathematics And Sciences, IPB University, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Other

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Analysis of Geographically and Temporally Weighted Regression (GTWR) GRDP of the Construction Sector in Java Island Sugi Haryanto; Muhammad Nur Aidi; Anik Djuraidah
Forum Geografi Vol 33, No 1 (2019): July 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v33i1.7332

Abstract

The construction sector is one of the sectors that have strategic value in the national economy. Economic activity in an area is measured using the Gross Regional Domestic Product (GRDP). The development of economic activities in the construction sector can be seen from the GRDP of the construction sector. The Geographically and Temporally Weighted Regression (GTWR) model is a development of the Geographically Weighted Regression (GWR) model taking into account the diversity of locations and times. This study used secondary data, namely the data of GRDP the construction sector as a response variable and four explanatory variables, namely the number of population, local revenue, area, and the number of construction establishments. The purpose of this study is to determine the factors that influence each regency/municipality and each year observing the GRDP of the construction sector in Java with the GTWR model. GTWR model is more effective to describe the value of GRDP the construction sector of regencies/municipalities in Java Island in 2010-2016. This is indicated by the decrease in values of Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and the Mean Absolute Percentage Error (MAPE).
Prediksi Fenomena Ekonomi Indonesia Berdasarkan Berita Online Menggunakan Random Forest Khairani, Fitri; Kurnia, Anang; Aidi, Muhammad Nur; Pramana, Setia
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11401

Abstract

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category
Modified Mixed Effects Random Forest in Small Area Estimation Using PCA and Rotation Forest with Correlated Auxiliary Variables Ananda, Rizki; Notodiputro, Khairil Anwar; Aidi, Muhammad Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10633

Abstract

Purpose: The per capita expenditure data in Jambi Province, Indonesia have been plagued with severe multicollinearity problems. To address the issue, this study developed an effective small area estimation (SAE) method, which is essential for formulating comprehensive regional development policies in Jambi Province. By modifying the mixed effects random forest (MERF) method, we introduced PCA-MERF (which applies principal component analysis prior to MERF) and MERoF (which replaces the standard random forest with rotation forest) to handle multicollinearity more effectively. Data from the National Socioeconomic Survey (Susenas) in March 2021 and Village Potential (PODES) in 2021 were utilized. The methods were evaluated using metrics such as root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of variation (CV), and their ability to capture random area effects. The random effect block (REB) bootstrap approach was employed to obtain MSE estimates for evaluating area-level estimate quality. Result: The results showed that MERoF outperformed both MERF and PCA-MERF, particularly in unit-level (village) estimation. Additionally, MERoF demonstrated superior capability in capturing variation between subdistricts compared to MERF and PCA-MERF. PCA-MERF performed better than MERF and MERoF at the area level (subdistrict). All three methods showed acceptable performance with RRMSE and CV values ranging between 8% and 10%, indicating precise and reliable predictions for per capita expenditure in small areas. These modifications to MERF prove effective and advantageous for small-area estimation in datasets with significant multicollinearity. Novelty: This research introduces a novel semi-parametric, tree-based SAE approach, enhancing the precision of per capita expenditure estimates and supporting more informative regional policy decisions, thus filling a gap in current SAE methodologies.
Identification of Atherosclerosis Based on The Differences in Cholesterol and Creatinine in Indonesia with Multivariate Analysis of Variance Maulana Achiar, Anshari Luthfi; Aidi, Muhammad Nur; Kurnia, Anang; Widoretno, Widoretno
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 24 No. 03 (2023): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol23-iss03/417

Abstract

Atherosclerosis is a chronic inflammatory disease indicated by plaque build-up in the arteries due to increased total cholesterol, low-density lipoproteins (LDL), triglycerides, and decreased high-density lipoproteins (HDL). It is also associated with disruption of renal function high creatinine blood level. This study aims to identify atherosclerosis based on differences in total cholesterol, HDL, LDL, triglycerides, and creatinine levels in 35.509 residents from 33 provinces and rural-urban areas in Indonesia. This study uses two-factor MANOVA where the province and rural-urban are the factors, followed by ANOVA and Tukey's test. Results show differences between total cholesterol, HDL, LDL, triglyceride, and creatinine levels of the residents among provinces and rural-urban areas. The Residents from Bangka Belitung and North Sulawesi provinces have the highest risk of atherosclerosis, and Jambi province has the most balanced condition. Urban residents tend to be at risk for atherosclerosis due to high levels of LDL, while rural residents are at risk by low HDL or high creatinine levels
THE INFLUENCE OF AWARENESS, TRIAL, PREFERENCE, DEVOTION, AND FANATICISM ON THE REPURCHASE INTENTION OF INDOMIE PRODUCTS Qital, Dari Aulia; Munandar, Jono M.; Aidi, Muhammad Nur
Jurnal Aplikasi Manajemen Vol. 21 No. 3 (2023)
Publisher : Universitas Brawijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jam.2023.021.03.12

Abstract

The purpose of this study is to analyze the level and influence of awareness, trial, preference, devotion and fanaticism of Indomie customers in Jabodetabek. The research was carried out using an online questionnaire given to respondents who were consumers of Indomie products in Jabodetabek. Based on the descriptive analysis of Indomie consumers in Jabodetabek, the level of awareness is very good, the level of trial is good, the level of preference is good, the level of devotion and fanaticism tends to be quite good. As for purchase intention, purchase decision and repurchase intention, they are included in the fairly good criteria. Judging from the results of the hypothesis testing, it is known that The results indicate that awareness significantly influences both purchase intention and purchase decision. Similarly, trial positively impacts purchase intention and purchase decision. Furthermore, preference has a significant effect on purchase intentions but not on purchase decisions. The findings also reveal that preference does significantly affect repurchase intention. Devotion significantly influences the repurchase decision. But contrary to expectations, devotion does not affect repurchase intentions. Similarly, fanaticism has no significant effect on purchase decisions. Lastly, the study confirms that fanaticism influences repurchase intention, purchase intention influences purchase decision, and purchase decision positively affects repurchase intention. That highlights the importance of the initial purchase decision in shaping future repeat purchases.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
KAJIAN SIMULASI OVERDISPERSI PADA REGRESI POISSON DAN BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA BALITA GIZI BURUK Puput Cahya Ambarwati; Indahwati Indahwati; Muhammad Nur Aidi
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.v4i3.684

Abstract

One type of geographically weighted regression (GWR) that can be used to explain the relationship between the response variables in the form of count data and explanatory variables is the geographically weighted Poisson regression (GWPR). In the GWPR, there is an assumption that should be fulfilled called equidispersion, a condition where the variance equals the mean. If that condition is ignored, overdispersion will occur. Overdispersion is a condition when the variance is greater than the mean. The use of GWPR analysis in an overdispersion situation will produce a smaller standard error than it should be (underestimate). This may produce a significant test result leading to the rejection of the null hypothesis. One of the classic approaches commonly used to handle overdispersion in GWR is geographically weighted negative binomial regression (GWNBR). GWNBR is derived from a mixture of Poisson and Gamma distributions which is similar to the negative binomial distribution. Simulation data and real data were used in this study. The results showed that the application of GWPR on overdispersion data could increase the number of rejections of H0 or the number of p-values. The application of GWNBR on the East Java malnutrition toddler data in 2017 showed that the GWNBR model is better than GWPR based on the comparison of AIC, Pseudo R2, and RMSE.
KAJIAN VARIANCE MEAN RATIO PADA SIMULASI SEBARAN DATA BINOMIAL NEGATIF Choirun Nisa; Muhammad Nur Aidi; 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.v4i4.689

Abstract

The negative binomial distribution is one of the data collection counts that focuses on success and failure events. This study conducted a study of the distribution of negative binomial data to determine the characterization of the distribution based on the value of Variance Mean Ratio (VMR). Simulation data are generated based on negative binomial distribution with a combination of p and n parameters. The results of the VMR study on negative binomial distribution simulation data show that the VMR value will be smaller when the p-value is large and the VMR value is more stable as the sample size increases. Simulation data of negative binomial distribution when p≥0.5 begins to change data distribution to the distribution of Poisson and binomial. The calculation VMR value can be used as a reference for detecting patterns of data count distribution.
ROBUST STOCHASTIC PRODUCTION FRONTIER TO ESTIMATE TECHNICAL EFFICIENCY OF RICE FARMING IN SULAWESI SELATAN Pranata, Ismail; Djuraidah, Anik; Aidi, Muhammad Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.162 KB) | DOI: 10.30598/barekengvol17iss1pp0391-0400

Abstract

The stochastic production frontier (SPF) is the stochastic frontier analysis (SFA) method used to estimate the production frontier by accounting for the existence of inefficiency. The standard SPF assumes that the noise component follows a Normal distribution and the inefficiency component follows a half-Normal distribution. The presence of outliers in the data will affect the inaccuracy in estimating the parameters and leads to an exaggerated spread of efficiency predictions. This study uses two alternative models, the first with SPF Normal-Gamma and the second with SPF Student's t-half Normal, then the results are compared with standard SPF. This study uses data from statistics Indonesia on the cost structure of paddy cultivation household survey in 2014. This study aims to examine the effect of changes in distribution assumptions on the standard SPF model in estimating parameter value and the technical efficiency score in the presence of outliers. The parameter coefficient estimates similar results that apply to three SPF models. Only the standard error value in the alternative SPF model tends to be smaller than the standard SPF model. The Normal-Gamma model performs better in assessing residual with smaller root mean square error (RMSE) than the others, but the results of the estimated technical efficiency still contain outliers. The Student's t-half Normal model estimates technical efficiency no longer contains outliers, the range is shorter than the other models, and the results of estimating technical efficiency are not monotonous in the distribution of residual tails. The SPF Student's t-half Normal model is more robust in presence outliers than SPF Normal-half Normal and SPF Normal-Gamma.
OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION Tiara, Yesan; Aidi, Muhammad Nur; Erfiani, Erfiani; Rachmawati, Rika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.136 KB) | DOI: 10.30598/barekengvol17iss1pp0417-0426

Abstract

Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value. A negative binomial regression can overcome an unfulfilled equidispersion assumption where the mean is greater than the standard deviation value (overdispersion). This method is more flexible because it does not require that the variance be equal to the mean. The case studies used in this research are cases of anemia in women of childbearing age (WCA) in 33 provinces of Indonesia. This study aims to apply the Poisson regression method and negative binomial in the case data of anemia in WCA to prove the model's goodness and find the factors that influence anemia in WCA. This data was obtained from biomedical sample data for Riset Kesehatan Dasar (Riskesdas) and data obtained from the website of the Badan Pusat Statistik (BPS) in 2013. By applying these two methods, the result is that negative binomial regression is the best model in modeling WCA cases with anemia in Indonesia because it has the smallest AIC value of 221.72; however, the difference is not too far from the AIC in the Poisson regression model, which is 221.83. It can also be supported that Poisson regression is unsuitable for the analysis because of the case of overdispersion. With a significance level of 10%, the number of WCA affected by malaria per 100 population influences cases of WCA anemia. At the same time, other independent variables have no effect.
Co-Authors . Erfiani Agus Mohamad Soleh Ahmad Nur Rohman Aji Hamim Wigena Akbar Rizki Anang Kurnia Andi Setiawan Anik Djuraidah Anis Dyah Rahmawati ASEP SAEFUDDIN Bagus Sartono Bayu Pranata, Bayu Budi Susetyo Budi Susetyo Budi Utomo Choirun Nisa Cynthia Wulandari Dwi Agustin Nuriani Sirodj Efriwati Efriwati Efriwati Efriwati Erfiani Erfiani Faqih Udin dan Jono M. Munandar Meivita Amelia Fildzah Hanum Syazwina Fitrah Ernawati Fitrah Ernawati Hapsari, Nimas Ayu Haq, Irvanal Hari Wijayanto Haryanto, Sugi Hikmah, Zetil I Made Sumertajaya Indahwati Irene Muflikh Nadhiroh Itasia Dina Sulvianti Khairani, Fitri Khairil Anwar Notodiputro La Ode Abdul Rahman La Ode Abdul Rahman Lailan Syaufina LILIK BUDIPRASETYO Lulu Mahdiyah Sandjadirja Mahfuz Hudori Maulana Achiar, Anshari Luthfi Muhammad Nuruddin Prathama mutiah, siti Nadya Amelia Dewi Suryana Nuramaliyah, Nuramaliyah Nurfadilah, Khalilah Pramana, Setia Pranata, Ismail Puput Cahya Ambarwati Putu Gita Karlina Jayanti Qital, Dari Aulia Rahma Anisa Retno Ariyanti Pratiwi Rifaldi, Destriana Aulia Rika Rachmawati Rizki Ananda Sandhi Imam Maulana Santy Suryanty Sugi Haryanto Sukisman Tjitrosemito Sulaeman, Sulthan Naufal Syella Sumampouw Tiara, Yesan Ulfa Afilia Shofa Utami Dyah Syafitri Widoretno, Widoretno Zulhijrah, Zulhijrah Zunita Sari