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All Journal Jurnal Transportasi Scientific Journal of Informatics Journal of Natural Sciences and Mathematics Research PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JURNAL IT: Media Infromasi STMIK Handayani Makassar Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Indonesian Journal of Statistics and Its Applications Warta Penelitian Perhubungan
Armin Lawi
Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Transportation Other

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Application of Adaptive Synthetic Nominal and Extreme Gradient Boosting Methods in Determining Factors Affecting Obesity: A Case Study of Indonesian Basic Health Research Survey 2013: Aplikasi Metode Adaptive Synthetic Nominal dan Extreme Gradient Boosting dalam Menentukan Faktor yang Memengaruhi Obesitas: Studi Kasus Riset Kesehatan Dasar Indonesia 2013 Rombe, Yoris; Thamrin, Sri Astuti; Lawi, Armin
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p309-317

Abstract

Obesity is the accumulation of excessive body fat and can be harmful to health. According to recent studies, several factors that contribute to the increasing prevalence of obesity in Indonesia include poor diet, lack of consumption of vegetables and fruits, high consumption of fast food, area of residence, and lack of physical activity. In addition, psychological factors, high consumption of alcohol and cigarettes, cultural differences, and stress factors also trigger obesity. The rapid development of the medical field cannot be separated from the availability of data that is increasingly easy to access and increasing knowledge in the medical field. This makes machine learning increasingly needed for pattern recognition from very large medical data, including obesity data. In this study, the factors that influence obesity status in Indonesia will be determined. In order to achieve this, Extreme Gradient Boosting (XGBoost) was used. This method is one of the classification methods that has better scalability and more efficient over its previous methods. Besides that, to overcome the imbalanced data, Adaptive Synthetic Nominal Algorithm (ADASYN-N) is used in order to balance the data and improve its prediction accuracy. Both the ADASYN-N and XGBoost methods will be applied to obesity data from the Indonesian Basic Health Research Survey in 2013. This study shows that female is more at risk in determining obesity status in Indonesia based on the highest gain value (37%). In addition, age 35-54 years, strenuous activity, and eating vegetables for 6 days are also risk factors of obesity.
Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data Rifqah Fahira, Nurul; Lawi, Armin; Aqsha, Masjidil
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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Abstract

Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection.
Exploration of Obesity Status of Indonesia Basic Health Research 2013 With Synthetic Minority Over-Sampling Techniques: Eksplorasi Status Obesitas Riset Kesehatan Dasar 2013 Indonesia dengan Teknik Synthetic Minority Over-Sampling Thamrin, Sri Astuti; Sidik, Dian; Kuswanto, Hedi; Lawi, Armin; Ansariadi, Ansariadi
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p75-91

Abstract

The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.
Co-Authors ', Wardi Amal, Muhammad Ikhlasul Andani Achmad Andi Ilham Latunra, Andi Ilham Anggitamaya, Anggitamaya Ansariadi Ansariadi Aqsha, Masjidil Dian Sidik Eliyah Acantha Manapa Sampetoding Ery Murniyasih Faisal, Faradias Izza Azzahra Fajri S, Ahmad Fatmawati Sabur Gunawan Gunawan Kuswanto, Hedi M. Yamin Jinca Miftahulkhairah, Miftahulkhairah Raden Mohamad Herdian Bhakti Rifqah Fahira, Nurul Rombe, Yoris S, Mulyadi Salsabila, Unik Hanifah Sir, Muhammad Mochsen Sri Astuti Thamrin Wahyuni Ekasasmita Yamin Jinca