Judul: Bayesian Conditional Negative Binomial  Autoregressive Model: A Case Study Of Stunting On Java
Abstrak: Sustainable Development Goals (SDGs) include stunting as one of its worldwide objectives, which requires an average annual reduction in the stunting rate of about 2.7%. Therefore, mapping stunting cases along with the underlying risk factors is crucial in Indonesia, especially on the island of Java, which has the largest population. The result can then be taken into consideration for policymaking to address stunting cases, particularly in Java and in Indonesia in general. This research aims to analyze stunting cases on the island of Java using the best models among GLM (Generalized Linear Model), GLMM (Generalised Linear Mixed Models), ICAR(Intrinsic Autoregressive), and CAR BYM (Besag York Mollie) with a negative binomial distribution. The data used in this study were obtained from the 2021 Health Profile of all provinces in Java, with predictors including the percentage of infants with exclusive breastfeeding, the percentage of complete basic immunization, the percentage of families with access to adequate sanitation, and the percentage of poverty rates. The study's findings reveal that the CAR BYM model has the best forecasts since it has the lowest DIC (Deviance Information Criterion) and MAD (Mean Absolute Deviance) values. The significant predictors are the percentage of families with access to adequate sanitation (X3) and the percentage of poverty rates (X4). A relative risk greater than one exists in 31.1% of Java's districts and cities, the majority of which are found in West Java and Central Java. Bogor district/city has the highest relative risk (RR) of stunting (RR = 5.833), followed by Bandung district/city (RR = 3.721), Tegal (RR = 3.291), and Brebes (RR = 2.252). Meanwhile, areas with low risk are found in districts and cities in the Special Region of Yogyakarta (RR = 0.610).
Keyword: negative binomial, Generalized Linear Model, Generalized Linear Mixed Model, conditional autoregressive, spatial, stunting

Judul: Factors Influencing Stunting Cases in West Java in 2021  Using Negative Binomial Spatial Regression
Abstrak: Stunting merupakan gangguan pertumbuhan dan perkembangan pada anak yang ditandai dengan tinggi badan anak berada di bawah standar. Provinsi Jawa Barat dengan angka prevalensi stunting 24,5 persen merupakan salah satu provinsi yang termasuk dalam 12 provinsi prioritas untuk menjalankan rencana aksi nasional percepatan penurunan stunting. Kasus stunting merupakan data cacah dan kejadiannya jarang terjadi. Analisis yang dapat digunakan adalah regresi Poisson. Asumsi yang harus dipenuhi pada regresi Poisson adalah equidispersi namun sulit dipenuhi karena kondisi overdispersi. Salah satu cara mengatasi overdispersi adalah menggunakan analisis regresi binomial negatif. Kasus stunting di Provinsi Jawa Barat terindikasi adanya efek spasial sehingga perlu digunakan pemodelan spasial. Penelitian ini bertujuan menentukan peubah yang memengaruhi kasus stunting di Provinsi Jawa Barat tahun 2021 dengan menggunakan regresi spasial binomial negatif. Hasil penelitian menunjukkan terdapat overdispersi. Uji efek spasial menunjukkan tidak terdapat heterogenitas pada data, terdapat dependensi spasial pada robust lag, dan terdapat dependensi spasial pada beberapa peubah penjelas. Hasil penelitian menunjukkan model terbaik dengan nilai AIC terkecil adalah spatial autoregressive binomial negatif. Peubah yang berpengaruh signifikan terhadap banyaknya stunting adalah persentase bayi berusia kurang dari enam bulan mendapat ASI, persentase tempat pengelolaan makanan memenuhi syarat, dan persentase bayi berat badan lahir rendah., Stunting is a growth and development disorder in children characterized by the child's height being below standard. With a stunting prevalence rate of 24.5 percent, West Java province is one of the provinces included in the 12 priority provinces for carrying out the national action plan to accelerate stunting reduction. Stunting cases are count data, and their occurrence is rare. The analysis that can be used is Poisson regression. The assumption that must be fulfilled in Poisson regression is equidispersion, but it isn't easy to fulfill because of the overdispersion condition. One way to overcome overdispersion is to use negative binomial regression analysis. The case of stunting in West Java Province indicated a spatial effect, so it was necessary to use spatial modeling. This study aims to determine the variables that affect stunting cases in West Java Province in 2021 using negative binomial spatial regression. The results showed that there was overdispersion. The spatial effect test indicated that there is no heterogeneity in the data, there is a spatial dependency on RLM lag, and there are spatial dependencies on several variables. The results showed that the best model with the smallest AIC value was a spatial autoregressive negative binomial. Variables that have a significant effected on the number of stunting are the percentage of infants aged less than six months who are breastfed, the rate of places where food management meets the requirements, and the percentage of babies with low birth weight.
Keyword: binomial negatif, overdispersi, spasial, stunting

Judul: Penentuan energi aktivasi ion untuk melewati membran teflon pada berbagai larutan elektrolit
Abstrak: Kaseno (1999) menyatakan bahwa dalam teknologi pemisahan, membran adalah bahan yang dapat memisahkan dua komponen dengan cara yang spesifik yaitu menahan atau melewatkan salah satu komponen lebih cepat dari komponen lainnya. Kemampuan pemisahan yang dimiliki oleh membran untuk melewatkan suatu senyawa kimia atau molekul diakibatkan oleh adanya perbedaan sifat fisika atau kimia antara membran dengan senyawa kimia.
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