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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Keteknikan dan Sains (JUTEKS) Jurnal Matematika UNAND Jurnal Varian JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Sri Astuti Thamrin
Departemen Statistika, Universitas Hasanuddin, Indonesia
Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL Siswanto, Siswanto; Thamrin, Sri Astuti
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.681

Abstract

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.
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 Adhel, Fadilah Amirul Afifah Afifah Ainun Mawaddah Abdal Ainun Utari Budistiharah Al Anshory, Fadhil Amil, Sri Indriani Andreza Andreza Andreza, Andreza Anisa Anisa Anna Islamiyati Anna Islamiyati Anna Islamiyati Ansariadi Ansariadi Ansariadi Ansariadi Arifin, Samsul Armin Lawi Armin Lawi Dewi Rahma Ente Dian Ayu Permata Sari Rusdy Dian Sidik Dian Sidik Edy Saputra Rusdi Ente, Dewi Rahma Ernasari, Ernasari Hedi Kuswanto Herdiani, Erna Tri I Gusti Agung Komang Diafari Djuni Hartawan Inayah, Ika Irma Andriani Iwan Kurniawan Jaya, A. Kresna Jusmawati Massalesse Kasbawati Kasbawati Khaerati, Rusydah Kuswanto, Hedi Muralia Hustim Naimah Aris Naimah Aris Najhah Aris Nur Erawaty Nur Erawaty Nuravia Nuravia Nurdin Nurdin Okta Nofri PRANANDA, ADHIYAKSA Ramadhan, Muh. Nur Bahri Rezki, Nurul Rombe, Yoris Rosdiana Rosdiana S Siswanto Sahriman, Sitti Samsu Arif Siddik, A. Muh. Amil Sirajang, Nasrah Siswanto Siswanto Sitti Sahriman Sumaya, Andi Dewi Utami, Mamik Wahda, Wahda