IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI GIZI BURUK DAN GIZI KURANG PADA BALITA MENGGUNAKAN ANALISIS REGRESI SPASIAL

Masalah gizi merupakan masalah kesehatan masyarakat yang dapat terjadi pada semua kelompok umur, tetapi yang perlu lebih diperhatikan adalah masalah gizi pada kelompok balita atau kelompok umur 0-5 tahun. Masalah gizi pada balita dipengaruhi oleh banyak faktor, termasuk faktor lokasi dimana balita berada. Lokasi sering kali mempunyai ketergantungan spasial. Faktor ini mengindikasikan bahwa nilai dari pengamatan dari suatu wilayah dipengaruhi oleh nilai dari pengamatan di wilayah lainnya. Faktor kesehatan balita, kesehatan ibu, kesehatan lingkungan, dan wilayah diduga berpengaruh terhadap prevalensi gizi buruk dan gizi kurang pada balita. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi gizi buruk dan gizi kurang pada balita di Pulau Sumatera serta model regresi spasialnya. Data yang digunakan bersumber dari Riskesdas dan IPKM tahun 2013 yang terdiri dari 125 kabupaten/kota di Pulau Sumatera. Analisis regresi spasial digunakan untuk menentukan faktor-faktor yang mempengaruhi masalah gizi pada balita. Hasil penelitian menunjukkan adanya ketergantungan spasial pada prevalensi gizi buruk dan gizi kurang pada balita sehingga model yang digunakan adalah Spatial Autoregressive Model (SAR). Secara spasial, faktor-faktor yang signifikan berpengaruh terhadap prevalensi gizi buruk dan gizi kurang pada balita di Pulau Sumatera yaitu kekurangan energi kronis pada wanita usia subur, proporsi pengguna KB, rumah tangga berperilaku hidup sehat dan bersih (PHBS), dan cakupan akses air bersih

PEMBERDAYAAN MASYARAKAT PESISIR PEMANFAATAN MANGROVE Sonneratia alba SEBAGAI SELAI BUAH PEDADA DI DESA LHOK BUBON, ACEH BARAT

Mangrove forest play important role in coastal ecosystem. In other hand, mangrove have economical benefit as source of functional food. The aimed of this empowerment programme is to give knowledge and skill to coastal community of Lhok Bubon Aceh regarding Pedada fruit jam (Sonneratia alba). The method of empowerment includes did socialization, prepare raw material, and did training to coastal community of Lhok Bubon West Aceh. Based on the result shown that the coastal communities are consist of the women of Lhok Bubon coastal have spirit for learning and enthusiasm to follow the empowerment community programme. The expected in future, the coastal communities can be applied the technique of pedada fruit jam of S. alba in order to give them value added

Penaksiran Parameter dan Pengujian Hipotesis pada Model Geographically Weighted Multivariate Poisson Inverse Gaussian Regression

Tujuan dari penelitian ini adalah mengembangkan model regresi mixed Poisson untuk menangani kasus overdispersi pada data cacahan, yaitu model Multivariate Poisson Inverse Gaussian Regression (MPIGR) yang melibatkan variabel eksposur dan lebih dari dua variabel respon. Selanjutnya, model MPIGR dikembangkan menjadi model Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) dengan memasukkan efek lokasi pada model. Kajian teori dilakukan untuk mendapatkan penaksir parameter model MPIGR dan GWMPIGR menggunakan metode Maximum Likelihood Estimation (MLE) dengan iterasi Newton-Raphson. Selanjutnya, statistik uji untuk pengujian hipotesis parameter model MPIGR dan GWMPIGR ditentukan menggunakan metode Maximum Likelihood Ratio Test (MLRT). Studi simulasi dilakukan untuk mengevaluasi turunan, program, dan kebaikan penaksiran parameter model MPIGR. Hasil studi simulasi menunjukkan bahwa turunan dan program penaksiran parameter model MPIGR sudah benar dan baik dalam menangani overdispersi yang tinggi. Selanjutnya, model MPIGR dan GWMPIGR digunakan untuk menentukan faktor yang mempengaruhi kematian bayi, anak balita, dan ibu di Jawa pada tahun 2019. Hasil penelitian menunjukkan bahwa pada model MPIGR, semua prediktor yaitu persentase posyandu aktif, persentase peserta aktif KB, persentase penduduk yang memiliki asuransi BPJS kesehatan, indeks Pendidikan, dan persentase rumah tangga yang memiliki sanitasi layak, signifikan berpengaruh terhadap jumlah kematian bayi, anak balita dan ibu di Jawa. Selanjutnya, model GWMPIGR dengan fungsi pembobot kernel fixed Gaussian dan kernel fixed bisquare menghasilkan beberapa kelompok wilayah berdasarkan variabel prediktor yang signifikan mempengaruhi jumlah kematian bayi, anak balita, dan ibu di Jawa pada tahun 2019. Berdasarkan nilai AICc, model GWMPIGR dengan fungsi pembobot kernel fixed bisquare lebih baik dari model MPIGR dan model GWMPIGR dengan fungsi pembobot kernel fixed Gaussian dalam memodelkan data jumlah kematian bayi, anak balita dan ibu di Jawa pada Tahun 2019. =================================================================================================================================== The purpose of this study is to develop a mixed Poisson regression model to deal with overdispersion in the count data, namely, a Multivariate Poisson Inverse Gaussian Regression (MPIGR) model involving eksposur variables and more than two response variables. Furthermore, the MPIGR model was developed into the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model by including location effects in the model. Theoretical studies were conducted to obtain parameter estimators of the MPIGR and GWMPIGR models using the Maximum Likelihood Estimation (MLE) method with Newton-Raphson iterations method. The statistical test for hypothesis testing of the MPIGR and the GWMPIGR models were determined by using the Maximum Likelihood Ratio Test (MLRT) method. Simulation studies were conducted to evaluate the derivatives, program, and parameter estimation of the MPIGR model. The result of simulation study shows that the derivatives and the program of the MPIGR model is correct and good to deal with high overdispersion. Furthermore, the MPIGR and GWMPIGR models will be applied to determine the factors that affect the number of infant deaths, the number of under-five deaths, and the number of maternal deaths in Java in 2019. The results showed that in the MPIGR model, all predictor variables, namely the percentage of active integrated service post, the percentage of active family planning participants, the percentage of the population with BPJS health insurance, education index, and the percentage of household that has improved sanitation, had a significant effect on the number of infant, under-five, and maternal deaths in Java in 2019. The GWMPIGR model with Gaussian fixed kernel and bisquare fixed kernel weighting functions produce several regional groups based on significant variables that significantly affect the number of infant, under-five, and maternal deaths in Java in 2019. Based on the AICc value, the GWMPIGR model with a bisquare fixed kernel weighting function is better than the MPIGR model and the GWMPIGR model with a Gaussian fixed kernel weighting function in modeling data on the number of infant, under-5, and maternal deaths in Java in 2019

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019

Parameter Estimation and Hypothesis Testing of Multivariate Poisson Inverse Gaussian Regression

Multivariate Poisson regression is used in order to model two or more count response variables. The Poisson regression has a strict assumption, that is the mean and the variance of response variables are equal (equidispersion). Practically, the variance can be larger than the mean (overdispersion). Thus, a suitable method for modelling these kind of data needs to be developed. One alternative model to overcome the overdispersion issue in the multi-count response variables is the Multivariate Poisson Inverse Gaussian Regression (MPIGR) model, which is extended with an exposure variable. Additionally, a modification of Bessel function that contain factorial functions is proposed in this work to make it computable. The objective of this study is to develop the parameter estimation and hypothesis testing of the MPIGR model. The parameter estimation uses the Maximum Likelihood Estimation (MLE) method, followed by the Newton–Raphson iteration. The hypothesis testing is constructed using the Maximum Likelihood Ratio Test (MLRT) method. The MPIGR model that has been developed is then applied to regress three response variables, i.e., the number of infant mortality, the number of under-five children mortality, and the number of maternal mortality on eight predictors. The unit observation is the cities and municipalities in Java Island, Indonesia. The empirical results show that three response variables that are previously mentioned are significantly affected by all predictors

Formulasi Detergen Cair Ekstrak Buah Pedada (Sonneratia alba)

The pedada fruit extract (Sonneratia alba) contains saponin compounds which can be used as a natural foam in liquid detergents. The purpose of this study was to determine the effectiveness of pedada fruit extract as an additive to produce natural detergents that are environmentally friendly according to the Indonesian National Standard (SNI). This research consisted of preparation and extraction of pedada fruit with 96% ethanol, preparation of liquid detergent, and characterization the physical properties of liquid detergent. The formulation of liquid detergent used three treatments of adding pedada fruit extract, namely the addition of 5% (F1), 10% (F2), and 15% (F3). Data analysis used a completely randomized design (CRD) with one factor, namely the difference in the addition of pedada fruit extract to liquid detergent. The analysis was carried out in two repetitions. The results showed that the ethanol extract of pedada fruit was detected contain saponins. The treatment with ethanol extract of 5% (F1) is closest to the requirements of SNI 06-4075-1996 for liquid detergent with a pH value of 8.29 ± 0.04, viscosity of 37.7 ± 1.55 cPs, foam height of 2.5 cm, foam stability was 88.5±0.70%, specific gravity was 2.6±0.14  g/mL, and sedimentation volume was 0.975±0.021 mL

Formulasi Detergen Cair Ekstrak Buah Pedada (Sonneratia alba) : Liquid Detergent Formulation of Pedada Fruit Ethanol Extract (Sonneratia alba J. Smith)

Ekstrak buah pedada (Sonneratia alba) memiliki kandungan senyawa saponin yang bisa dimanfaatkan sebagai busa alami pada detergen cair. Penelitian ini bertujuan untuk menentukan efektivitas ekstrak buah pedada sebagai bahan tambahan untuk menghasilkan detergen alami yang ramah lingkungan dan sesuai dengan Standar Nasional Indonesia (SNI). Penelitian ini terdiri dari tahap preparasi dan ekstraksi buah pedada dengan etanol 96%, pembuatan detergen cair, dan karakteristik sifat fisik detergen cair. Formulasi detergen cair menggunakan tiga perlakuan penambahan ekstrak buah pedada yaitu perlakuan penambahan 5% (F1), 10% (F2), dan 15% (F3). Analisis data menggunakan rancangan acak lengkap (RAL) dengan satu faktor, yaitu perbedaan penambahan ekstrak buah pedada pada detergen cair. Analisis dilakukan sebanyak dua kali ulangan. Hasil penelitian menunjukkan bahwa ekstrak etanol buah pedada terdeteksi mengandung senyawa saponin. Perlakuan pemberian ekstrak etanol sebesar 5% (F1) paling mendekati syarat SNI 06-4075-1996 untuk detergen cair dengan nilai pH 8,29±0,04, viskositas 37,7±1,55 cPs, tinggi busa 2,5 cm, stabilitas busa 88,5±0,70%, bobot jenis 2,6±0,14 g/mL, dan volume sedimentasi  0,975±0,021 mL.The pedada fruit extract (Sonneratia alba) contains saponin compounds which can be used as a natural foam in liquid detergents. The purpose of this study was to determine the effectiveness of pedada fruit extract as an additive to produce natural detergents that are environmentally friendly according to the Indonesian National Standard (SNI). This research consisted of preparation and extraction of pedada fruit with 96% ethanol, preparation of liquid detergent, and characterization the physical properties of liquid detergent. The formulation of liquid detergent used three treatments of adding pedada fruit extract, namely the addition of 5% (F1), 10% (F2), and 15% (F3). Data analysis used a completely randomized design (CRD) with one factor, namely the difference in the addition of pedada fruit extract to liquid detergent. The analysis was carried out in two repetitions. The results showed that the ethanol extract of pedada fruit was detected contain saponins. The treatment with ethanol extract of 5% (F1) is closest to the requirements of SNI 06-4075-1996 for liquid detergent with a pH value of 8.29 ± 0.04, viscosity of 37.7 ± 1.55 cPs, foam height of 2.5 cm, foam stability was 88.5±0.70%, specific gravity was 2.6±0.14  g/mL, and sedimentation volume was 0.975±0.021 mL