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All Journal Xplore: Journal of Statistics
Aam Alamudi
Department of Statistics, IPB University, Indonesia
Decision Sciences, Operations Research & Management Engineering Mathematics

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Metode Alternatif dalam Pencarian Peringkat E-Commerce di Indonesia Berdasarkan Rating Pelanggan Azira Irawan; Aam Alamudi; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.744 KB) | DOI: 10.29244/xplore.v10i1.280

Abstract

The existence of the internet raises an online trading system using applications. The rise of online trading systems has triggered the emergence of various e-commerce in Indonesia that provide various kinds of customer needs. This also causes problems for customers, namely the difficulty in choosing quality e-commerce. The effort to overcome this problem is to rank e-commerce in Indonesia based on customer ratings. The method commonly used for ranking is the analytical hierarchy process (AHP) method, but in practice there are several variables that are not found in e-commerce so the AHP method cannot be used. The alternative method chosen is the ant colony optimization (ACO) method. The feasibility test of the ACO method in searching rankings for e-commerce data needs to be done because not all variables are in e-commerce. Simulations for ranking search are carried out using 2 generated data scenario with analytical hierarchy process (AHP) and ant colony optimization (ACO) method. The simulation results show that the ACO method is feasible to be used for ranking with blank data, then the application of the ACO method for e-commerce data in Indonesia. The best taboo results are based on the highest opportunity value and the highest correlation coefficient, namely in the second taboo, with three major ratings, namely JD, SP, and TP
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.787 KB) | DOI: 10.29244/xplore.v12i1.948

Abstract

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 doesn’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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (249.84 KB) | DOI: 10.29244/xplore.v11i3.1019

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.727 KB) | DOI: 10.29244/xplore.v12i1.1055

Abstract

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).
Co-Authors Agus Soleh Azira Irawan Erlinda Widya Widjanarko Farit Mochamad Afendi Mahdiyah Riaesnianda Nadhif Nursyahban Rahardiantoro, Septian Utami Dyah Syafitri