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All Journal BIMASTER CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Kebijakan Perikanan Indonesia Sarwahita : Jurnal Pengabdian Kepada Masyarakat Variance : Journal of Statistics and Its Applications Insan Cita Jurnal Pengabdian Kepada Masyarakat JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Debataraja, Naomi Nessyana
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Variance : Journal of Statistics and Its Applications

 1 
APPLICATION OF THE QUEST AND CHAID METHODS IN CLASSIFYING STUDENT GRADUATION Banu, Syarifah Syahr; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Satyahadewi, Neva
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page155-164

Abstract

Graduation is the final result of the learning process during the course. Student graduation time is affected by many factors. Whether or not the time of student graduation is appropriate is an important thing that must be considered. Graduating well and on time is one measure of success in the learning process. This research aims to build a student graduation classification model by applying the QUEST (Quick, Unbiased, and Efficient, Statistical Tree) and CHAID (Chi-squared Automatic Interaction Detection) methods, examining the factors that affect student graduation, and comparing the classification results of the two methods. Both methods produce output in the form of tree diagrams, making it easier to interpret. Based on the classification tree formed from the two methods, four final nodes of the classification tree were generated, and three categories were grouped. Factors that affect student graduation include age and IPK. The classification results show that the percentage of classification accuracy for student graduation with QUEST and CHAID methods is 76.1%.
APPLICATION OF C4.5 ALGORITHM WITH FEATURE SELECTION IN CLASSIFICATION OF DISCHARGE STATUS OF HEAD INJURY PATIENTS ., Putri; Sulistianingsih, Evy; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page165-174

Abstract

Head trauma is a medical emergency that can cause brain damage and disability, leading to death. The discharge status of injured patients is classified into two: alive and dead. The purpose of this study is to apply the C4.5 algorithm without feature selection and by using Chi-Square and Mutual Information feature selection to show independent variables that significantly influence the discharge status of head injury patients. This research data is secondary data of patients who suffered head injuries at Dr. Abdul Aziz Hospital, Singkawang City, in 2019-2021. The independent variables used were age, gender, length of hospitalization, etiology of head injury, Suprasellar Cistern, and Glasscow Coma Scale, with the dependent variable being discharge status. Based on the study results, the Chi-Square feature selection results identified two variables that had a significant effect. In contrast, for the Mutual Information feature selection results, five variables had a significant impact on the dependent variable. The C4.5 Algorithm classification model without feature selection produces an accuracy of 88.57%, the C4.5 Algorithm classification model with Chi-Square feature selection produces an accuracy of 88.57%, and the C4.5 Algorithm classification model with Mutual Information feature selection produces an accuracy value of 91.42% with the highest accuracy obtained from the results of the C4.5 Algorithm model formation with Mutual Information feature selection.
Co-Authors ., Putri Andani, Wirda Anugrahnu, Joannes Fregis Philosovio Aprizkiyandari, Siti Banu, Syarifah Syahr Bertiani, Marsela Dian Cahyani, Cristy Framedia Dadan Kusnandar Desdianti, Maycandra Etika, Ezra Evy Sulistianingsih Hamdari, Ela Hendra Perdana HUDA, NUR’AINUL MIFTAHUL Imro'ah, Nurfitri IMRO’AH, NURFITRI Irvandi, Firzakalpa Syafiq Marlisa, Herina Martha, Shantika Neva Satyahadewi Nuruljannah, Auliya Perangin Angin, Christi Alemsa Pratama, Pandu Pasha Pratama, Yogi Prianti, Sabrina Eka Priyanto, Dwi Ari Rizki, Setyo Wira Rossie Wiedya Nusantara Salsabila, Yumna Hanum Simanjuntak, Martina Sumarni, Sania Lina Tamtama, Ray Utari, Shindy Yoke Lestyowati Yudhi