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All Journal FORUM STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Desimal: Jurnal Matematika Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications

Aam Alamudi

Department Of Statistics, Bogor Agricultural University (IPB)

Author-ID : 3379300

Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Social Sciences Other

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Analisis Unggahan Media Sosial pada Instagram Rachel Vennya Menggunakan Metode Importance Performance Else Virdiani; Aam Alamudi; Yenni Angraini
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Instagram is one of the social media applications that can publish photos or videos for its users. Rachel Vennya is a well-known Instagram user who has more than five million followers. This research was conducted to see the expected posts by Rachel Vennya's followers on Instagram. Through the importance-performance analysis (IPA) it will be known the types of posts that are interesting and need to be increased in publication. This study's two IPA approaches, namely expected performance analysis (EPA) and importance-performance matrix analysis (IPMA). The results of each analysis are then mapped into a Cartesian diagram so that it is known that several posts increase follower loyalty and posts that need to be increased or decreased. After comparing the two Cartesian diagrams, it is known that there is no difference in the placement of variables between the two analyzes. Posts that deserve to be maintained include Motivation, Cooking, Family, and posts considered excessive in the publication are Business and Endorsements. Furthermore, customer satisfaction index (CSI) analysis was carried out to see follower satisfaction. The CSI value obtained is 72.69, which indicates the follower satisfaction index belongs to the satisfied criteria.

Kajian Metode Pohon Model Logistik (Logistic Model Tree) dengan Penanganan Ketakseimbangan Data Akmala Firdausi; Aam Alamudi; Kusman Sadik
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Logistic model tree is a nonparametric modelling method that combines decision tree with linear logistic regression. Logistic model tree handles multicollinearity well, but is not immune to problems that arise due to data imbalance. This study was carried to compare the performance of undersampling, SMOTE, and ROSE in handling imbalanced data when used in tandem with logistic model tree. The data used in the simulation was obtained by generating random numbers following the Bernoulli distribution as the response variable and the Bivariate Normal distribution as the explanatory variables, based on five different imbalance levels. Comparisons done on the AUC value showed that logistic model trees built with methods to handle imbalanced data performed better than logistic model trees built without applying any such method on every level of tested data imbalance in classifying objects. Among those, logistic model trees built with ROSE performed better than logistic model trees built with other methods. On datasets with low level of imbalance, the performance of logistic model trees built with ROSE and undersampling do not significantly differ.

Penggerombolan Data Panel Emiten Sektor Pertambangan selama Pandemi Covid-19 Nadhif Nursyahban; Aam Alamudi; Farit Mochamad Afendi
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

The Covid-19 pandemic has made people start looking for new income, one of whichis stock investment. Mining Stock recorded the highest sectoral index increase in 2020.The high increase in the mining sector index doesnâ€™t indicate all of the stocks have agood performance. Clustering data of mining stock can help to see which stock has thebest performance. Variables used in clustering are technical factors with details: return,trading volume, transaction frequency, bid volume, and foreign buy. Data in this researchis longitudinal data from March 2020 until January 2022 and the clustering techniqueused is k-means. Clustering on outliers data and non-outliers data is done separately.Definition of outliers is exploratively with biplot analysis. Clustering on outliers dataresults obtained are five clusters and clustering on non-outliers data results obtained aretwo clusters. Best cluster is cluster who obtained ANTM because has highest value inreturn, transaction frequency, and foreign buy.

Klasifikasi Sekolah dalam Penerimaan Mahasiswa Baru Vokasi IPB Jalur USMI Menggunakan Metode CART Erlinda Widya Widjanarko; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

The selection of new student admissions for the IPB vocational school consists of several routes, one of which is the USMI route. To improve its performance, it is necessary to evaluate the USMI new student admission system. Previously, research with the same objective had been carried out using the clustering method. The study resulted in three clusters in which schools were differentiated based on commitment and quality. This study aim to create a classification model of the clusters obtained using the CART method. Classification and Regression Tree (CART) is a nonparametric classification technique that produces a single decision tree. The CART method can involve mixed-type data. The classification model generated from the 2019 data yields an accuracy of 98.52%. However, the results of the 2019 model evaluation with the 2020 data are still not good enough to predict with an accuracy of 57.22%, so the 2020 data is re-clustered and produces three clusters. Furthermore, the classification model was remade with 2020 data, resulting in an accuracy of 97.47%. However, the results of the 2020 model evaluation with the 2021 data are still not good enough to predict with an accuracy of 44.34%, so the clustering in the previous year cannot be used for predictions of the following year's data. The grouping of schools for USMI applicants needs to be done by grouping schools every year.

Identifikasi Peubah yang Berpengaruh terhadap Ketidaklulusan Mahasiswa Program Sarjana BUD IPB dengan Regresi Logistik Biner Mahdiyah Riaesnianda; Aam Alamudi; Agus Soleh; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

One of the entrances available at the Bogor Agricultural University (IPB) is the Regional Representatives Scholarship (BUD). Not all BUD IPB students were able to complete their studies because they dropped out (DO) or resigned. One of the efforts that IPB can do to reduce the dropout rate for BUD IPB students is to find out the variables that affect the failure of BUD IPB students. The variables that influence the failure of BUD IPB students are analyzed by binary logistic regression. There is an imbalance of data classes in the response variables so that the method that can be used to overcome this is the Synthetic Minority Over-Sampling Technique (SMOTE). The classification model with SMOTE resulted in a higher average sensitivity than the model without SMOTE from 10,66% to 61,91%. This confirms that the model with SMOTE is better at predicting the minority class (BUD IPB students who do not pass). The variables that affect the failure of BUD IPB students are gender, school status of origin, study program groups, the presence or absence of Pre-University Programs (PPU), type of sponsor, average report cards, and GPA in the Joint Preparation Stage (TPB) or General Competency Education Program (PPKU).

PENDUGAAN PARAMETER FUNGSI COBB-DOUGLAS GALAT ADITIF DENGAN ALGORITME GENETIKA Iqbal Hanif; Agus M Soleh; Aam Alamudi
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
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.v1i1.54

Cobb-Douglas function with additive errors is a function which can be used to analyse the relationship between production output and production factors. The method commonly used to estimate the parameter of that function is Nonlinear Least Square (NLS) and a common algorithm for this method is Gauss Newton iteration (NLS-GN). However, NLS-GN method has less-optimum results when analysing multicolinearity data. A possibly better method for this analysis is Genetic Algorithm (NLS-GA). The purpose of this study is to analyse the use of Genetic Algorithm to estimate parameters of Cobb-Douglas function with additive errors. The results show that NLS-GA method could not produce a better parameter estimator than NLS-GN method does but it produced a better parameter estimator in analysing multicolinearity data. NLS-GA method is capable of producing a better model with predictive ability than NLS-GN method does with real data. Keywords: cobb-douglas function, genetic algorithm, nonlinear least square

CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH Rahma Anisa; Bagus Sartono; Pika Silvianti; Aam Alamudi; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (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.v3i1.198

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study. The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.

Missing Value Estimation Using Fuzzy C-Means in Classification of Chronic Kidney Disease: Pendugaan Missing Values Menggunakan Fuzzy C - Means Pada Pengklasifikasian Penyakit Ginjal Kronik Eria, Raisa Nida; Alamudi, Aam; Sulvianti, Itasia Dina; Silvianti, Pika; Rahardiantoro, Septian
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p21-32

Based on World Health Organization (WHO) the cases of death due to Chronic Kidney Disease (CKD) ranked the 10th worldwide in 2020. CKD need to be done prevent early. History data to identify individuals predisposed to CKD in this research. In this research data contains missing values, therefore using Fuzzy C - Means (FCM) method to address it. The percentage of error in clustering CKD using FCM method is 20,25% and balanced accuracy of 84,80%. The result from classification using Classification and Regression Trees (CART) shows that accuracy value of 97,50%; sensitivity of 100,00%; and specificity of 92,86%. Individual suffer from CKD if having (1) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; serum creatinine less than 1,3; albumin 1 or 2 or 3 or 4 or 5; and sugar 0 or 2 or 3 or 4 or 5, (2) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; and serum creatinine more than or equal 1,3, (3) hemoglobin more than or equal 13 and spesific gravity 1,005 or 1,010 or 1,015, (4) hemoglobin less than 13 and red blood cell count less than 5,5.

Statistical integration in excise supervision: Multinomial logistic regression for detecting risk factors of tobacco factory excise violations Riansyah, Boy; Nisa Nur Aisyah; Anwar Fitrianto; Aam Alamudi
Desimal: Jurnal Matematika Vol. 8 No. 2 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/xxvpmv08

Excise tax serves as a fiscal instrument used by the government to control the consumption of goods with negative externalities for society and the environment, such as tobacco and alcoholic beverages. In Indonesia, tobacco excise remains the largest contributor to national excise revenue, amounting to IDR 216.9 trillion in 2024, or approximately 95.8% of the total. Monitoring violations in the tobacco excise sector is crucial to safeguarding state revenue and ensuring regulatory effectiveness. In the context of classifying violations with more than two categories, multinomial logistic regression is an appropriate statistical method for analyzing the influence of independent variables on the probability of each violation type. This study aims to classify types of excise violations based on internal characteristics of tobacco manufacturers using multinomial logistic regression. The data were obtained from enforcement documentation in 2023 by the Directorate General of Customs and Excise, with four categories of violations serving as the response variable. The issue of class imbalance was addressed by comparing oversampling and weighting techniques. Evaluation results indicate that oversampling produced superior model performance. Partially, variables such as business entity type, asset ownership status, and company age significantly influenced the likelihood of specific violations. Companies with non-permanent asset ownership and complex organizational structures tend to have a higher risk of non-compliance. These findings underscore the importance of implementing risk-based supervision that considers operational profiles as key indicators of potential violations.

Performance Evaluation of Multinomial Logistic Regression, Random Forest, and XGBoost Methods in Data Classification Mega Maulina; Hiola, Yani Prihantini; Indahwati; Aam Alamudi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8459

The development of data volume and complexity in the digital era increases the need for effective classification methods to support decision-making. Decision-making in classification tasks often requires methods that are well-suited to the data, along with the ability to produce accurate and reliable predictions. As scientific knowledge continues to advance, a wide range of classification methods have been developed. This study aims to analyze the performance of three commonly used classification methods Multinomial Logistic Regression, Random Forest, and XGBoost, in handling diverse data characteristics. Ten varied public datasets were used in this research, with differences in the number of classes, features, instances, balanced and imbalanced data conditions. Evaluation was conducted based on accuracy, F1-score, precision, and recall. The analysis results show that Random Forest consistently delivers the best performance particularly on imbalanced data. XGBoost demonstrates superiority on more complex datasets, while Multinomial Logistic Regression proves more effective on relatively small datasets. This research provides valuable insights into selecting appropriate classification methods based on data characteristics and highlights the effectiveness of ensemble-based approaches in handling diverse data. Based on the findings, it is recommended that the selection of classification algorithms be tailored to the characteristics of the dataset. Random Forest is preferable for handling imbalanced data, while XGBoost is ideal for complex datasets requiring robust hyperparameter tuning. Multinomial Logistic Regression remains a viable option for simpler datasets with fewer observations and features. Future research could explore hybrid models that combine these approaches to further optimize classification performance across various domains.

Co-Authors . Aunuddin Agus M Soleh Agus M Soleh Agus Soleh Akmala Firdausi Anik Djuraidah Aulia Dwi Oktavia Ayoe Indria Winuri Azira Irawan Bagus Sartono Budi Susetyo Dhika Firmansyah Dian Kusumaningrum Else Virdiani Eria, Raisa Nida Erlinda Widya Widjanarko Farit Mochamad Afendi Fitrianto, Anwar Hanifatun Nufusia Hiola, Yani Prihantini Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iqbal Hanif Itasia Dina Sulvianti Kusman Sadik Mahdiyah Riaesnianda Manaf, Silmi Annisa Rizki Maya Deanti Mega Maulina Nadhif Nursyahban Ni Kadek Manik Dewantari Nisa Nur Aisyah Pika Silvianti Putri, Oktaviani Aisyah Rahardiantoro, Septian Rahma Anisa Rahma Anisa Resty Fanny Riansyah, Boy Tania Amalia Darsono Utami Dyah Syafitri Yasmin Erika Faridhan Yenni Angraini Zahid, Muhammad Farhan

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