In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided.

No abstract

No abstract

Inflasi merupakan indikator makro ekonomi yang penting bagi sebuah negara. Inflasi yang rendah dan terkendali merupakan harapan bagi semua bangsa di dunia. Penelitian ini bertujuan untuk memodelkan inflasi di Pulau Jawa dengan mengguankan pendekatan model spasial durbin. Data yang digunakan adalah data sekunder BPS selama periode 2015-2018. Peubah yang digunakan untuk menduga inflasi adalah tingkat kemiskinan dan UMK. Pada penelitian ini diperoleh nilai , yang berarti model Spasial Durbin yang dibangun mampu menjelaskan variasi pada Inflasi sebanyak 64,92%, pada inflasi terdapat pengaruh spasial autoregresive. Kemudian, ada pengaruh signifikan peubah tingkat kemiskinan dan UMK terhadap inflasi, namun secara spasial keduanya tidak berpengaruh signifikan.

The combination of the lasso variable selection method and the lasso group results in a variable selection method that can overcome problems in both the lasso method and the lasso group. This combination can also be applied to the cases of nonorthogonal groups as required in the lasso group method. This paper discusses the lasso sparse method with the coordinate descent approach as proposed by Friedman, et.al (2010). The real case raised in this study is the tendency of the sexual behaviour of young women in Bengkulu province. Because there are 15 explanatory variables for X, a variable selection process is needed to obtain the simplest model. The results were selected as many as 3 explanatory variables so that the remaining 12 explanatory variables can then be modelled in logistic regression. The substance of the unselected regression parameters is young women with more high school education, living in rural areas, rarely reading newspapers/magazines, rarely accessing the internet> 1 time a month, and strongly agree with virginity more likely to engage in sexual activity compared with educational characteristics, residence, newspaper reading intensity, internet access, and other virginity attitudes.