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Coronary Artery Bypass Graft (CABG) post-operative readmission has a high incidence rate compared to other health cases. This particular case contributes to an increase in morbidity and hospital costs of patients. Therefore, an appropriate prediction model is needed while the model can be beneficial to the health financing institutions. There are many risk factors that will be used to predict CABG post-operative readmission. Of the many risk factors observed, some factors that have a significant influence on construction the Logistic Regression model will be determined. This model is developed to generate probabilities which are then called Created Readmission Risk Scores (CRRS).

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Corresponding author: sri_math@sci.ui.ac.idAbstract. Nurse scheduling system in a hospital is being modeled as a preemptive goal programming problem that is solved by using LINGO software with the objective function to minimize deviation variable at each goal. The scheduling is done cyclically, so every nurse is treated fairly since they have the same work shift portion with the other nurses. By paying attention to the hospital's rules regarding nursing work shift cyclically, it can be obtained that numbers of nurse needed in every ward are 18 nurses and the numbers of scheduling periods are 18 periods where every period consists of 21 days.

Tujuan dari penelitian ini adalah untuk mengetahui sistem penggajian yang digunakan saat ini pada yayasan Al-Akmal. Untuk mengetahui kekurangan yang ada pada sistem penggajian dan merancang sistem penggajian terkomputerisasi yang sesuai di Yayasan Al-Akmal sehingga dapat membuat laporan penggajian yang akurat dan efisien. Metode penelitian yang digunakan pada perancangan sistem informasi penggajian ini adalah studi lapangan yaitu dengan pengamatan langsung, wawancara dengan pihak terkait dan juga melakukan dokumentasi untuk mendapatkan informasi yang dibutuhkan. Selain itu peneliti juga melakukan penelitian dengan menggunakan metode kepustakaan berdasarkan referensi dari berbagai diskusi serta berbagai media yang memuat informasi mengenai segala sesuatu yang menyangkut informasi yang dibutuhkan, analisa kebutuhan, perancangan, implementasi, pengkodean dan pengujian. Setelah dilakukan pengujian secara implementasi ternyata aplikasi ini dapat membantu memenuhi kebutuhan akan suatu proses penyimpanan data-data karyawan Yayasan Al-Akmal serta dapat membuat sistem yang lebih baik dalam hal penggajian. Kata Kunci: Perancangan, Aplikasi, Penggajian

Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.

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