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Contact Name
Dr. Atina Ahdika, M.Si
Contact Email
146110101@uii.ac.id
Phone
+62 817-2384-386
Journal Mail Official
esds@uii.ac.id
Editorial Address
Universitas Islam Indonesia. Jl. Kaliurang KM 14,5, Sleman Yogyakarta
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Emerging Statistics and Data Science Journal
ISSN : 29884004     EISSN : 29864178     DOI : https://doi.org/10.20885.25
Core Subject : Science, Education,
ESDS publishes scientific articles on statistics and its applications, and also in terms of big data and data science. The article can be a research result, a case study, or a literature review, with coverage: - Statistical Methodology – Articles dealing with new and innovative data analysis techniques and methodologies include, but are not limited to: bootstrapping, classification techniques, design of experiments, parametric and nonparametric methods, functional data, fuzzy statistical analysis, nonlinear models, partial least squares, structural equation models, Bayesian analysis, survey sample analysis, and statistics computation. - Applied Statistics in Business, Industry and Social Studies – Articles dealing with econometrics, demography, spatial analysis, time series analysis, longitudinal analysis, spatio-temporal analysis, quality control, and other subjects related to Applied Statistics in Business, Industry and Social Studies. - Data Science – Articles dealing with big data, data exploration, data mining, data science, data visualisation, and machine learning. - Another field which is related to statistics and the applications
Articles 131 Documents
Analisis Perbandingan Metode XGBoost dan Extra Trees untuk Klasifikasi Penyakit Jantung pada Dataset UCI Heart Disease: Analisis Perbandingan Metode XGBoost dan Extra Trees untuk Klasifikasi Penyakit Jantung pada Dataset UCI Heart Disease Rifki, Erik Saputra; Azaria, Siti Rania; Praktiko, Angelina; Pratama, Alfan Rizaldy
Emerging Statistics and Data Science Journal Vol. 4 No. 3 (2026): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol4.iss.3.art02

Abstract

Penelitian ini membandingkan XGBoost dan Extra Trees untuk klasifikasi penyakit jantung pada 303 data UCI Cleveland tanpa SMOTE. Data dibagi 80:20 secara stratifikasi, dengan imputasi median dan standardisasi berdasarkan data latih. XGBoost memperoleh akurasi 86,89% dan AUC 0,9437, sedangkan Extra Trees memperoleh akurasi 81,97% dan AUC 0,9535. Interval kepercayaan bootstrap untuk selisih kinerja mencakup nol, sementara uji McNemar menunjukkan perbedaan klasifikasi tidak signifikan (p = 0,4531). Berdasarkan skor komposit eksploratif, XGBoost dipilih sebagai model utama. Analisis permutation importance mengidentifikasi thal, ca, dan cp sebagai fitur prediktif penting. Pengujian pada dataset independen diperlukan untuk menilai kemampuan generalisasi model.