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All Journal Jurnal Vektor Penyakit Jurnal Berkala Epidemiologi Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) The Indonesian Journal of Public Health Health Notions Jurnal Wiyata : Penelitian Sains dan Kesehatan

Arief Wibowo

Biostatistics And Demography Department, Master’s Degree Of Public Health Department, Faculty Of Public Health, Universitas Airlangga, Indonesia

Author-ID : 2535482

Dentistry Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 19 Documents Claim Missing Document

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Articles

Title

Bagging Approach for Increasing Classification Accuracy of CART on Family Participation Prediction in Implementation of Elderly Family Development Program Wisoedhanie Widi Anugrahanti; Arief Wibowo; Soenarnatalina Meilanani
Health Notions Vol 1, No 2 (2017): April-June
Publisher : Humanistic Network for Science and Technology (HNST)

Classification and Regression Tree (CART) was a method of Machine Learning where data exploration was done by decision tree technique. CART was a classification technique with binary recursive reconciliation algorithms where the sorting was performed on a group of data collected in a space called a node / node into two child nodes (Lewis, 2000). The aim of this study was to predict family participation in Elderly Family Development program based on family behavior in providing physical, mental, social care for the elderly. Family involvement accuracy using Bagging CART method was calculated based on 1-APER value, sensitivity, specificity, and G-Means. Based on CART method, classification accuracy was obtained 97,41% with Apparent Error Rate value 2,59%. The most important determinant of family behavior as a sorter was society participation (100,00000), medical examination (98,95988), providing nutritious food (68.60476), establishing communication (67,19877) and worship (57,36587). To improved the stability and accuracy of CART prediction, used CART Bootstrap Aggregating (Bagging) with 100% accuracy result. Bagging CART classifies a total of 590 families (84.77%) were appropriately classified into implement elderly Family Development program class. Keywords: Bagging Classification and Regression Tree, Classification Accuracy, Family Participation

Social Support and Substance Abuse Relapse Adelia Perwita Sari Perwita Sari; Chatarina Umbul Wahyuni; Arief Wibowo
Health Notions Vol 2, No 1 (2018): January
Publisher : Humanistic Network for Science and Technology (HNST)

Substance abuse is the health problem that affects physical, mental and social health. Rehabilitation program is one of the strategies to reduce the number of addictive substance users but the relapse is common happen to the users that taking rehabilitation. The aim of this study was to assess social support as risk factors for substance abuse relapse. This case-control study was conducted to 39 people in each control and case group. The samples were obtained with simple random sampling. The cases were the person who relapses after completed rehabilitation program, while the controls were the person who still being abstinence after completed rehabilitation program. Data were collected with the questionnaire and analyzed with Chi-square test. The result showed that social support was related to substance abuse relapse (p=0.000). The lack of social support was related to the higher risk of substance abuse relapse (OR=6.92, 95%CI=2.51 – 19.22). The appraisal support was the dominance risk factor (OR=10.88, 95%CI=3.48 - 33.98) of substance abuse relapse compared to informational, instrumental, and emotional support. The involvement of the source of social support in rehabilitation program is important to help the users stay abstinence after released from the rehabilitation center. Keywords: Substance abuse, Social support, Relapse risks

Comparison of MICE and Regression Imputation for Handling Missing Data Berliana Devianti Putri; Hari Basuki Notobroto; Arief Wibowo
Health Notions Vol 2, No 2 (2018): February
Publisher : Humanistic Network for Science and Technology (HNST)

Data collection activities have a higher risk of missing data. Missing data may produce biased estimates and standard errors increased, so imputation method is needed. The purpose of this study was to investigate which imputation method is the most appropriate to use for handling missing data. The strategies evaluated include complete case analysis, Multivariate Imputation by Chained Equation (MICE), and Regression Imputation. This study was non-reactive study and used raw data RPJMN 2015 Survey from BKKBN East Java Province. There were three incomplete data sets were generated from a complete raw dataset with 5%, 10%, and 15% missing data. Incomplete data sets were made missing completely at random. Based on Friedman Test, both of imputation methods produced estimates which was no different with complete raw data set. Based on Mean Square Error analysis, MICE provided MSE values less and more stable than Regression Imputation in all scenarios. Conclusion: Multivariate Imputation by Chained Equation (MICE) was the most recommended method to use for handling missing data less than 15%. Keywords: Missing data, MICE, Regression imputation

The Affecting Factors to Grade of Breast Cancer in Dr. Soetomo Hospital of Surabaya Ulfa Aulia; Arief Wibowo; Hari Basuki Notobroto
Health Notions Vol 2, No 6 (2018): June
Publisher : Humanistic Network for Science and Technology (HNST)

Prognosis of cancer depends on variables, other factors, the stage of cancer, the biological warfare and general conditions when the cancer is diagnosed. Social status, economic status, and demographic issues choose in determining the stage of cancer when the patient first comes to the hospital. The purpose of this study to examine the role, nutritional status, and family history with breast cancer patients in Dr. Soetomo hospital. The study conducted in this study was a non-reactive or non-intrusive method. The sample in the analysis using simple random sampling with sample size of 95 patients. Does not contain the effect of variables associated with grade of breast cancer with p-value 0.795. While for variable of nutritional status and family history with cancer to breast cancer level with p-value 0.033 and 0.005. The p-value in the fitting information table was 0.003 model which contains not only the intercept that was not displayed. The value of Nagelkerke 0.157 or 15.7% means that variable cost, nutritional status and family history with cancer can be used only by 15.7%. From the existing variables was 2 significant variables, namely nutritional status with p-value 0.033 and family history with cancer with p-value 0.005. While time did not significantly influence breast cancer rate with p-value 0.795. Keywords: Grade of breast cancer, Age, Nutritional status, Family history

Co-Authors Adelia Perwita Sari Perwita Sari Berliana Devianti Putri Chatarina Umbul Wahyuni Desy Yuanita Nugroho Diky Novariyanto Dwi Wahyu Ningtyas Dwi Yanti Fidya Panorama Damayanti Fita Mega Kusuma Hari Basuki N Hari Basuki Notobroto Irwanto, Irwanto Iswari Hariastuti Krisnita Dwi Jayanti Lina Septi Danasari Mahmudah Mahmudah Mardiana Mardiana Monika Noshirma Mugawati Aisya Muhammad Bintang Pamungkas Muhammad Kazwaini Pratiwi Purbaningrum Ruben Wadu Willa Salihati Hanifa Siti Fera Irawati Soenarnatalina Meilanani Ulfa Aulia Wilianarti, Pipit Festi Wisoedhanie Widi Anugrahanti

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