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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pendidikan Sains Jurnal Simetris JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding of the Electrical Engineering Computer Science and Informatics Ekonomi Bisnis Edcomtech Jurnal Kajian Teknologi Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Matrix : Jurnal Manajemen Teknologi dan Informatika Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Mnemonic Sains, Aplikasi, Komputasi dan Teknologi Informasi E-Link: Jurnal Teknik Elektro dan Informatika Belantika Pendidikan Journal of Applied Data Sciences Bulletin of Social Informatics Theory and Application Jurnal Informatika Polinema (JIP) Journal of Information Technology and Cyber Security
Utomo Pujianto
Universitas Negeri Malang
Aerospace Engineering Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences

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Journal : Knowledge Engineering and Data Science

 1 
Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement Utomo Pujianto; Asa Luki Setiawan; Harits Ar Rosyid; Ali M. Mohammad Salah
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.467 KB) | DOI: 10.17977/um018v2i22019p58-71

Abstract

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.
The Effect of Resampling on Classifier Performance: an Empirical Study Utomo Pujianto; Muhammad Iqbal Akbar; Niendhitta Tamia Lassela; Deni Sutaji
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p87-100

Abstract

An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier used in the classification process is Naïve Bayes Classifier, Decision Tree, and Neural Network. The classification results in accuracy, precision, recall, and f-measure values are tested using paired t-tests to determine the significance of the classifier's performance from datasets that were not resampled and those that had applied the resampling. The paired t-test is also used to find a combination between the classifier and the resampling that gives significant results. This study obtained two results. The first result is that resampling on imbalanced class datasets can substantially affect the classifier's performance more than the classifier's performance from datasets that are not applied the resampling technique. The second result is that combining the Neural Network Algorithm without the resampling provides significance based on the accuracy value. Combining the Neural Network Algorithm with the SMOTE technique provides significant performance based on the amount of precision, recall, and f-measure.
Co-Authors Achmad Murdiono Aderyan Reynaldi Fahrezza Dewandra Agung Bella Putra Utama Aguwin Ardi Pranata Aji Prasetya Wibawa Ali M. Mohammad Salah Andrew Nafalski Angeline, Grace Anik Nur Handayani Anis Nikmatul Kasanah Annas Gading Pertiwi Annisa Putri Ayudhitama Aris Maulana Arya Yudhi Wijaya Asa Luki Setiawan Asmoro, Achmad Shoddiq Bayu Ayudhitama, Annisa Putri Bahalwan, Lugas Anegah Baihaqi, Dimas Imam Bintang Romadhon Binti Afifah Daniel Oranova Siahaan Deni Sutaji Dewinta Nilan Sari Didik Dwi Prasetya Didin Rosyadi Dwi Jaelani, Mardian Eliyani Eliyani Elsa Dwi Rochmah Rachmanto Fauzan Cahya Arifin Filby, Brilliant Gradiyanto Radityo Kusumo Hakkun Elmunsyah Hammad, Jehad A. H. Harits Ar Rosyid Harits Ar Roysid Haviluddin Haviluddin Hery Widijanto I Ketut Suada Icca Astrina Izdihar, Zahra Nabila Joumil Aidil Saifuddin Kusumo, Gradiyanto Radityo Lazuardi Noorca Rachmadi M. Zainal Arifin Martha, Jefry Aulia Meiga Ayu Ariyanti Muhamad Jauharul Fuady Muhammad Aqshal Muhammad Iqbal Akbar Muis Muhtadi Muladi Murad, Safwan Marwin Abdul Nafalski, Andrew Nanda Riski Septania Niendhitta Tamia Lassela Nur Hidayat, Wahyu Nurroby Wahyu Saputra Prananda Anugrah Putri yuni Ristanti Raffi Taufik Gushardana Santoso, Priyo Aji Saputra, Irzan Tri Setiadi Cahyono Putro Swasono Raharjo, Swasono Triyanna Widiyaningtyas Utama, Agung Bella Putra Wahyu Sakti Gunawan Irianto Yudhistira, Moch Rajendra