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All Journal Media Kesehatan Masyarakat Indonesia Indonesian Journal of Statistics and Its Applications
Dian Sidik
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Computer Science & IT Mathematics Public Health Other

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FAKTOR RISIKO LINGKUNGAN TERHADAP KEJADIAN TUBERKULOSIS PARU DI WILAYAH KERJA PUSKESMAS SOMBA OPU Nisgunawan Sidiq; Wahiduddin Wahiduddin; Dian Sidik
Media Kesehatan Masyarakat Indonesia Vol. 9 No. 1: MARET 2013
Publisher : Faculty of Public Health, Hasanuddin University, Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (211.643 KB) | DOI: 10.30597/mkmi.v9i1.436

Abstract

Lingkungan merupakan salah satu faktor penyebab kejadian TB paru. Puskesmas Somba Opu tercatat dari tahun 2009 ditemukan BTA positif sebanyak 84 orang. Mengalami peningkatan pada 2011 tercatat dari sebanyak 116 orang. Penelitian ini bertujuan mengetahui besar risiko kondisi lingkungan terhadap kejadian TB paru di Kabupaten Gowa. Desain penelitian adalah observasional dengan pendekatan case control study. Populasinya adalah penderita TB paru yang tercatat di kartu registrasi pasien di Puskesmas Somba Opu tahun 2011. Sampel penelitian ini adalah penderita TB paru dan tetangga pasien TB paru yang pernah berkunjung ke Puskesmas Somba Opu. Penarikan sampel menggunakan exhaustive sampling dan kelompok kontrol menggunakan purposive sampling dengan besar sampel 130. Perbandingan kasus dengan kontrol 1 : 1. Analisa data yang dilakukan adalah univariat dan bivariat dengan uji odds ratio (OR). Hasil penelitian ini menunjukkan bahwa dari empat variabel yang diteliti rumah yang padat bukan merupakan faktor risiko (OR=0,775), kelembaban rumah yang tidak memenuhi syarat merupakan faktor risiko (OR=2,974), pencahayaan rumah yang tidak memenuhi syarat bukan merupakan faktor risiko (OR=1,070), ventilasi rumah yang kurang bukan merupakan faktor risiko (OR=1,220).
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 Ansariadi Ansariadi Armin Lawi Kuswanto, Hedi Nisgunawan Sidiq Sri Astuti Thamrin Wahiduddin Wahiduddin