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Contact Name
Dr. Muhammad Ahsan
Contact Email
muh.ahsan@its.ac.id
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+6281331551312
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inferensi.statistika@its.ac.id
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Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Keputih Sukolilo Surabaya Indonesia 60111
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INDONESIA
Inferensi
ISSN : 0216308X     EISSN : 27213862     DOI : http://dx.doi.org/10.12962/j27213862
The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims; and any approach in data science. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where the original methodology is involved and original contributions to the foundations of statistical science. It also sometimes publishes review and expository articles on specific topics, which are expected to bring valuable information for researchers interested in the fields selected. The journal contributes to broadening the coverage of statistics and data analysis in publishing articles based on innovative ideas. The journal is also unique in combining traditional statistical science and relatively new data science. All articles are refereed by experts.
Articles 11 Documents
Search results for , issue "Vol 8, No 3 (2025)" : 11 Documents clear
CART and Random Forest Analysis on Graduation Status of Halu Oleo University Students Rahman, Gusti Arviana; Notodiputro, Khairil Anwar; Sartono, Bagus; Surimi, La
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23336

Abstract

Classification and Regression Tree (CART) is a popular classification method and it is used in various fields. The method is capable to be applied on various data conditions. An alternative method of CART is random forest. These two methods of classification were studied in this paper using graduation data of Halu Oleo University. This data was interesting due to the imbalance problem existed in the data. We compared several scenarios, namely the CART and Random Forest methods, Random Forest with oversampling, and Random Forest with undersampling. There were three explanatory variables considered in the model including Study Program, GPA, and TOEFL score. The results showed that the best method to classify the student’s graduation status at Halu Oleo University is Random Forest without handling imbalanced data, as it provided the highest sensitivity. This suggests that Random Forest, even without specific adjustments for data imbalance, can effectively capture the patterns in the data and provide accurate classifications, making it a robust choice for this dataset.

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