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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography 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

Author-ID : 222772

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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Other

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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

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

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

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

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.

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

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

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.

SELECTION OF THE BEST SEM MODEL TO IDENTIFY FACTORS AFFECTING MARKETING PERFORMANCE IN THE ICT INDUSTRY Hikmah, Zetil; Wijayanto, Hari; Aidi, Muhammad Nur
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/barekengvol17iss2pp1149-1162

The digital revolution in society and the advances in marketing practices create tremendous challenges for companies and even more so for Information and Communication Technology (ICT) service providers. They are faced with increasingly complex and rapidly changing market competition, knowing these problems can use SEM to form a research model and find out the relationship between latent variables and their indicators. The purpose of this study is to identify the best structural equation model that can describe Marketing Performance in the ICT Industry in Indonesia. The data used in this study is primary data obtained from the results of distributing offline and online questionnaires to 300 management levels working in the ICT Industry. The methods compared in this study are Covariance Based Structural Equation Modeling and Partial Least Square Structural Equation Modeling. The results showed that the best model to determine the factors that influence Marketing Performance in the ICT Industry in Indonesia is PLS-SEM with the goodness-of-fit model R2 for the latent variable Marketing Performance is 0.436. This shows that the accuracy of the variables CEM, DBI and DOE together in predicting MP variables is relatively weak. Based on the PLS-SEM model, it is found that Digital Operational Excellence is a mediator that can increase the influence of Customer Experience Management on Marketing Performance. Meanwhile, Digital Business Innovation has no significant effect in increasing the influence of Customer Experience Management on Marketing Performance. The novelty of this research is the development of the best SEM models (CB-SEM and PLS-SEM) in the field of Information and Communication Technology in Indonesia.

A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA Haq, Irvanal; Aidi, Muhammad Nur; Kurnia, Anang; Efriwati, Efriwati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1749-1760

An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.

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.

APPLICATION OF THE COKRIGING METHOD TO ESTIMATE IRON DEFICIENCY PREVALENCE BASED ON FERRITIN AND C-REACTIVE PROTEIN Mutiah, Siti; Aidi, Muhammad Nur; Saefuddin, Asep; Ernawati, Fitrah
Media Penelitian dan Pengembangan Kesehatan Vol. 35 No. 3 (2025): MEDIA PENELITIAN DAN PENGEMBANGAN KESEHATAN
Publisher : Poltekkes Kemenkes Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34011/jmp2k.v35i3.3167

Analisis data spasial memiliki peranan penting dalam bidang kesehatan, khususnya ketika distribusi masalah kesehatan tidak merata di seluruh wilayah. Salah satunya adalah metode Cokriging, yang diterapkan untuk memprediksi prevalensi di daerah yang belum teramati, sekaligus mengatasi tantangan ketidaklengkapan data spasial akibat keterbatasan biaya, sumber daya, atau akses ke lokasi tertentu. Penelitian ini bertujuan untuk mengestimasi prevalensi kekurangan zat besi di Indonesia menggunakan metode Cokriging. Sebagai analisis lanjut dari data Riset Kesehatan Dasar (Riskesdas) 2018, penelitian ini menggunakan data dari 15.045 individu yang memiliki informasi kadar ferritin dan C-Reactive Protein (CRP), yang tersebar di 154 kabupaten/kota di empat pulau: Sumatera, Jawa, Kalimantan, dan Sulawesi. Ferritin digunakan sebagai variabel utama, sementara CRP sebagai variabel sekunder. Evaluasi model dilakukan dengan Leave-One-Out-Cross-Validation (LOOCV), dan akurasi model diukur menggunakan Mean Error (ME) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan prevalensi kekurangan zat besi bervariasi signifikan antar wilayah. Kabupaten Batang dan Minahasa Selatan teridentifikasi dalam kategori "tidak ada masalah kesehatan". Selain itu 274 kabupaten/kota di Indonesia berada pada kategori prevalensi ringan, seperti Kabupaten Berau, Gunung Mas, dan Bangkayang, sementara 132 kabupaten/kota tercatat dengan prevalensi sedang seperti Kabupaten Sidenreng Rappang, Tapanuli Tengah, dan Sukoharjo. Kabupaten Pare-pare terdeteksi pada prevalensi tinggi (≥40%), tingginya prevalensi di wilayah ini perlu dicermati lebih lanjut karena kemungkinan disebabkan oleh jumlah sampel yang sangat sedikit. Temuan ini menunjukkan bahwa sebagian besar kabupaten/kota di Indonesia tergolong dalam kategori prevalensi ringan hingga sedang. Gambaran ini dapat menjadi dasar penting dalam merancang kebijakan kesehatan terkait penanggulangan kekurangan zat besi di Indonesia.

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 Pranata, Bayu Budi Susetyo Budi Utomo Choirun Nisa Cynthia Wulandari Dari Aulia Qital Dwi Agustin Nuriani Sirodj Dwi Agustin Nuriani Sirodj Efriwati Efriwati Erfiani Erfiani Faqih Udin dan Jono M. Munandar Meivita Amelia Fildzah Hanum Syazwina 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 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 Rahma Anisa Retno Ariyanti Pratiwi Rifaldi, Destriana Aulia Rika Rachmawati Rizki Ananda Sandhi Imam Maulana Sugi Haryanto Sukisman Tjitrosemito Sulaeman, Sulthan Naufal Syella Sumampouw Tiara, Yesan Ulfa Afilia Shofa Utami Dyah Syafitri Widoretno, Widoretno Zulhijrah, Zulhijrah Zunita Sari

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