The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP  distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population .  The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP  distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population .  The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.

The phenomenon of hot mudflow in Sidoarjo is interesting to be investigated further. Regarding the cause, the disaster occurred due to drilling errors resulting in the Lapindo mudflow which resulted in gas emissions causing health problems, especially those related to the respiratory tract, namely respiratory tract infections (ARI). Risk factors that can affect the incidence of ARI in general are socio-demographic, biological, housing and density factors and pollution. Therefore, this study aims to obtain a model for classifying ARI patient data in the Jabon, Tanggulangin, and Porong sub-districts, Sidoarjo district with the variables that contribute to the classification. The nonparametric approach Multivariate Adaptive Regression Spline (MARS) was chosen because several previous studies stated that this method resulted in a higher classification accuracy than other classification methods. In addition, MARS is a classification method that is able to form a model with causal interactions to produce the best MARS model obtained from a combination of Maximum Interaction (MI), Basis Function (BF), and Minimum Observation (MO) values. The results of modeling with MARS there are three variables that contribute to the grouping, namely the percentage of the distance between the house and the source of the Lapindo mudflow, the number of activities outside the house, and the number of house ventilation. The overall model classification accuracy is 97,4 percent with a GCV value of 0,096 and an R2 of 82,9 percent

Curah hujan merupakan salah satu faktor iklim yang berpengaruh di berbagai bidang sehingga pemerintah membangun stasiun hujan untuk mengukur curah hujan di lokasi tertentu di Indonesia yang dianggap memiliki potensi. Akan tetapi curah hujan di luar daerah stasiun hujan tidak diketahui secara pasti, sehingga perlu dilakukan prediksi curah hujan dengan menggunakan analisis deret waktu dengan metode Box-Jenkins yang dikenal dengan Autoregressive Integrated Moving Average (ARIMA), maupun analisis krigging untuk melihat kebergantungan spasial lokasi. Identifikasi model dilakukan dengan meihat plot ACF dan PACF data. Data yang digunakan adalah data curah hujan di Bogor periode 10 harian dari bulan Januari 2013 - Desember 2014 sehingga diperoleh model deret waktu terbaik untuk 12 stasiun yang terdiri dari ARIMA(1,1,1), ARIMA(1,1,0), dan ARIMA(3,1,0). Krigging dilakukan untuk memprakirakan 5 waktu ke depan.

ABSTRAK Pinjam meminjam menjadi bagian penting dari roda pembangunan. Pembangunan ekonomi yang merupakan bagian dari pembangunan nasional adalah salah satu usaha demi tercapainya masyarakat yang adil dan makmur. Bank sebagai penyedia dana kredit bagi masyarakat tidak selalu berjalan lancar, ada kalanya debitur tidak memenuh kewajiban sesuai dengan waktu yang disepakati (wanprestasi). Untuk meningkatkan pelayanan kepada nasabah yang merupakan PNS, Bank Jatim memberikan kredit multiguna berjangka yang dapat digunakan sebagai biaya pendidikan, kredit pemilikan rumah, pembelian kendaraan, keperluan konsumsi lainnya yang tidak bertentangan dengan hukum dan lain sebagainya. Metode penelitian yang digunakan menggunakan pendekatan deskriptif kuantitatif dengan analisis forcasting plafond kredit menggunakan software minitab pada data PT. Bank Jatim Cabang RSU Dr. Soetomo Surabaya pada jumlah dana yang direalisasi tahun 2018-2021. Dengan menggunakan analisis time series atau deret waktu, para nasabah kredit multiguna pada tahun 2021 cenderung memilih jangka waktu 5 tahun untuk melunasi kreditnya. Kata kunci: kredit, kredit multiguna, kredit macet, time series