Modeling Household Food Security of Tuberculosis Patients With The Methods of Logistic Regression And MARS, A Case Study in Coastal Region of Surabaya

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1 2018, TextRoad Publication ISSN Journal of Basic and Applied Scientific Research Modeling Household Food Security of Tuberculosis Patients With The Methods of Logistic Regression And MARS, A Case Study in Coastal Region of Surabaya Mutiah Salamah*, Destri S., Sri Pingit W., Brodjol Sutijo S.U. Business Statistics Department of Vocational Faculty Institut Teknologi Sepuluh Nopember Jl. Arief Rahman Hakim, Keputih Sukolilo Surabaya, Indonesia ABSTRACT Received: August 26, 2017 Accepted: November 21, 2017 The case of tuberculosis ( TB ) is an epidemic problem in an area. If someone live in the same area or region as a TB patient, he may have a high risky infected this disease. The difference in the number of TB cases between regions caused by the differences in the characteristics of the region, one of them is a level of food security of the household determine ability of family members in counteracting disease. In the level of household food insecurity, the family members easily infected the disease, therefore in this research want to find out the model of household food security of TB patients. The aims of this research is to classified the best model of the Household Food Security of TB patients in coastal Surabaya, between Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS) models, through the value of accuration measure of Apparent Error Rate (APER) from both. The results of research conducted in coastal Surabaya indicates that the percentation the number of household food insecurity is quite serious there are more than 50% household food insecurity is about 64 %. And also indicates that kind of TB with Acid Fast Basillus (AFB)+ still high about 58 % so this area still has a high enough risk of TB. A Model that obtained by binary logistic regression shows that the household food security of TB patients were affected by variables: The Wife Education, Household density and The size of air vent at home, with percent of classifications accuracy APER about 71,8%. While a model that obtained by MARS were affected by variables: The Husband Education, The Wife Education, The number of households members, Household density, The Toilet Ownership Status, the Size of Air Vent at the House and the Expenditure of household, with the percent classifications of accuracy APER about 85%. Looking at the percent number of classification accuracy APER, then MARS model more suggested to used in the case of food security households of TB patients in the Coastal Region of Surabaya. KEYWORDS: APER, Binary Logistic Regression, Classification, Coastal, Food security, MARS 1. INTRODUCTION Food security is a fundamental problem that must be addressed immediately. If a person in a food insecurity condition, he will experience malnutrition that will affect to the ability of his body in warding off infectious diseases such as tuberculosis (TB). TB is an infectious disease caused by infection with the bacteria Mycrobacterium tuberculosis. Tuberculosis can spread through droplets of people who have been infected with TB bacilli. Tuberculosis becomes one of the diseases that its control becomes a global commitment in the Millennium Development Goals[1] TB patients in East Java Province in 2015 recorded up to February 2016 reached 38,912 people. The high number of cases of tuberculosis also affects the high death rate in East Java which reached 119 cases during 2014 to March 2015 (Provinsi Jawa Timur, 2016) and Surabaya is the first place in East Java province as the biggest contributor of tuberculosis cases up to 4,754 cases, then Jember, Sidoarjo and Malang districts [2] The research done before stated that in the coastal Surabaya there are 54% of the household in food insecurity condition [3], therefore based on the explanation above, in this research will be sought model about the factors that affect household food security of TB patients in coastal Surabaya. With Binary Logistic Regression and MARS and then to find out which is the best one in the estimate of food security condition by the accuration in clasifying between observation and prediction value using Apparent Error Rate (APER) [4] *Correspondence Author: Mutiah Salamah., Business Statistics Department of Vocational Faculty Institut Teknologi Sepuluh Nopember Jl. AriefRahman Hakim, Keputih Sukolilo, Surabaya, Indonesia. Mutiahsalamah@yahoo.com 1

2 Salamah et al, MATERIALS AND METHODS 2.1. Source of data The data on this research is the primary data obtained through surveys. The sample taken from eleven subdistrics in coastal Surabaya about 1338 households using the Simple Random Sampling (SRS) method [5]. With bound of error 0,57% and proportion of household with food secure status 0,28, the sample size are 142 household. The alocation of sample size each subdistrics are in Table 1. Table 1. The Subdistrics Sample Size No Subdistrics Population size Sample size 1 Asemrowo Benowo Pabean Cantikan Semampir Krembangan Bulak Kenjeran Rungkut Gunung Anyar Sukolilo Mulyorejo 67 7 Total Research Variables Variables in this research are consist of respon and predictor variable. Response variable Y here is household food security status of TB patients in coastal Surabaya with the category Secure ( y = 1) and Insecure (y = 0). While the predictor variables about 22 variables given in Table 2. X 1: Age of Householder X 2: Age of Wife X 3:Background Householder Education X 4: Background Wife Education X 5: Occupation of Householder X 6: Wife Occupation Status X 7: Number of Household member X 8: Number of school children Table 2. Variables Used X 9: Number of Toddler X 10: Income per month X 11: Expenditure per month X 12: House ownership status X 13: Household Density X 14: Type of roof X 15: Type of wall X 16: Type of floor X 17: Ventilation width X 18: Toilet ownership status X 19: Source of water consumed X 20: Landfills X 21: Sewerage system X 22: Electrical source Methods of analysis used here are Binary Logistic Regression [6] and Multivariate Adaptive Regression Splines/ MARS [7], and APER [4] to obtain the percentage value of the accuracy in classification between observation and prediction values = 1 - APER 3. RESULTS AND DISCUSSION 3.1. Household Characteristics of TB Patient The description of TB patient in each subdistricts in coastal region Surabaya shown in Figure 1, Mulyorejo is subdistrict with highest number of TB Patient about 22% and the lowest subdistricts are Asemrowo and Benowo each about 2%. Figure 1. The Percentage Number of TB Patients Each Subdistricts in Coastal Surabaya 2

3 An overview of household food security conditions in each sub-district in coastal Surabaya is given in Figure 2. Figure 2. The Percentage Number of Household Food Security of Patients TB Each Subdistricts in Coastal Surabaya Benowo is a subdistrict with highest number of household food secure about 80%, while there are none of household in subdistrict Bulak and Rungkut have food secure. 3.2 Model of Binary Logistic Regression The Model obtain from Binary Logistic Regression methode is: ĝ( x) = 2, , 703X 4 ( 1) + 2, 296X 4( 2) , X 4( 3) , X 4( 4) + 0, 833X13( 1) + 0, 761X17( 1) With variables or factors that affect the household food security are : X4 (1): Education of Wife ( University) X4 (2): Education of Wife ( Senior High School) X4 (3): Education of Wife ( Junior High School) X4 (4): Education of Wife ( Elementary School) X13(1) : Household Density ( Qualify ( 8 m 2 / person)) X17(1) : Ventilation width ( > 10% of floor area) Table 3. The Accuracy of Classify The Prediction and Observation Household Food Security Status from Model Binary Logistic Regression Observation Prediction Food Security Status Insecure Secure Food Security Status Insecure Secure APER = = , than the percentation value of the accuracy in classification between observation and prediction values = (1- APER) x 100% = 71,81% 3.3 Model of Multivariate Adaptive Splines Regression (MARS) Model obtain from MARS methode is : Y = * BF * BF E-06 * BF19 The forming variable the above function is the Basis Function (BF): BF1 = (Number of Household member = 1); BF3 = (Household Density = 1) * BF1; BF6 = (Education of Wife = 1 or Education of Wife = 2 or Education of Wife = 3 or Education of Wife = 4)* BF1; BF7 = (Toilet Ownership = 1) * BF6; BF9 = (Education of Householder = 1 or Education of Householder = 2 or Education of Householder = 3) * BF3; 3

4 Salamah et al, 2018 BF12 = (Ventilated Width = 2) * BF7; BF13 = (Education of Householder = 1 or Education of Householder = 3) * BF12; BF19 = max(0, Household Expenditure per month ) * BF13; Based on the Basis Function above, then the factors or variables that affect the MARS model on household food security are 1. Education of Householder (X 3), 2. Education of Wife (X 4), 3. Number of Household member (X 7), 4. Household Expenditure per month (X 11), 5. Household Density (X 13), 6. Ventilated Width (X 17) and 7. Toilet Ownership (X 18). Table 4. The Accuracy of classify the Prediction and Observation Food Security Status from MARS Model Observation Prediction Food Security Status Insecure Secure Food Security Status Insecure Secure APER = = 01831, than the percentation value of the accuracy in classification between observation and prediction = (1- APER)x 100% = 81,69% Mulyorejo sub-district has the highest percentage number of tuberculosis patients about 22%, and also the number of household food secure is quite high (63%). Benowo sub-district is the best sub-district because it has the smallest number of TB patients (2%) with the highest number of household food secure is about 80%. The model with logistic regression provide factors that affect the household of TB patients are wife education, household density and presence of ventilation with the value of accuracy in classification between observation and prediction 71,81%, whereas in MARS model the influencing factors other than the above three variables are also influenced by Householder Education, Number of Household member, Household Expenditure per month and Toilet Ownership [8] with the value of accuracy in classification 81,69%. 4. CONCLUSION Based on the level of accuracy in classification between observation and prediction then the MARS model is more recommended for modelling Household food security of TB patients in the coastal area of Surabaya. ACKNOWLEDGEMENT The authors are very grateful to the Direktorat Riset Dan Pengabdian Masyarakat, Direktorat Jenderal Penguat Riset dan Pengembangan Kementerian Riset, Teknologi dan Pendidikan Tinggi Republik Indonesia for funding this research. 5. REFERENCES [1]. RI, K. K Strategi Nasional Pengendalian Tuberkulosis diindonesia Jakarta: DITJEN Pengendalian Penyakit dan Penyehatan Lingkungan KEMENKES RI. [2]. Provinsi Jawa Timur, D. K Profil Kesehatan Provinsi Jawa Timur Tahun Surabaya: Dinas Provinsi Jawa Timur. [3]. Wulandari, S. P., Susilaningrum, D., & Latra, I. N Analisis Ketahanan Pangan Rumah Tangga Terhadap Kasus Penderita Penyakit Tuberculosis Dengan pendekatan Geographically Weighted Poisson Regression (Studi Kasus Pantai Pesisir Surabaya). Surabaya: LPPM-ITS. [4]. Johnson, R. A dan Wichern, D. W Applied Multivariate Statistical Analysis (sixth edition). United States of America: Person Education Inc. [5]. Scheaffer, R. L., Mendenhal III, W., Ott, R. L., & Gerow, K Elementary Survey Sampling 7th ed. Boston: Brooks/Cole. [6]. Hosmer, D., & Lemeshow, S Applied Logitic Regression. New York: John Wiley & Sons. 4

5 [7]. Friedman, J. H Multivariate Adaptive Regression Splines. The Annals of Statistics, Vol 19 No. 1, [8]. Mangkoedihardjo, S Seleksi teknologi pemulihan untuk ekosistem laut tercemar minyak. Institut Teknologi Sepuluh Nopember, Surabaya