Lung diseases have a significant impact on respiratory health, causing various symptoms and posing challenges in diagnosis and treatment. This research presents a methodology for classifying lung diseases using chest X-ray images, specifically focusing on COVID-19, pneumonia, and normal cases. The study introduces an optimal architecture for convolutional neural network (CNN) and long short-term memory (LSTM) models, considering evaluation metrics and training efficiency. Furthermore, the issue of imbalanced datasets is addressed through the application of some image augmentation techniques to enhance model performance. The most effective model comprises five convolutional blocks, two LSTM layers, and no augmentation, achieving an impressive F1 score of 0.9887 with a training duration of 91 s per epoch. Misclassifications primarily occurred in normal cases, accounting for only 3.05% of COVID-19 data. The pneumonia class demonstrated excellent precision, while the normal class exhibited high recall and an F1 score. Comparatively, the CNN-LSTM model outperformed the CNN model in accurately classifying chest X-ray images and identifying infected lungs. This research provides valuable insights for improving lung disease diagnosis, enabling timely and accurate identification of lung diseases, and ultimately enhancing patients’ outcomes.

It is important for Indonesia as a country with agricultural bases to develop crop insurance. Until now, Indonesia has not had any insurance for horticultural crops other than for corn. This paper discusses horticultural multicrop insurance products based on revenue risk that can be triggered by low prices, low yields, or a combination of both. In designing multicrop insurance products, it is important to model the variability of revenue risk through the implementation of copula toward crop yield and price and to estimate indemnity of the revenue-based multicrop insurance. The analysis employed Gumbel and Clayton copulas to model the dependency structure between crop yield and price of multicrops. Each marginal variable was modeled by using the ARIMA model. The results showed that multicrop revenue insurance tends to reduce the price of agricultural insurance in Indonesia, and thus this program has the potential to have good acceptance in agricultural insurance.

According to regulations, life insurance companies must meet several requirements related to the company's financial health, one of which is a technical reserve. Technical or premium reserves are funds that the insurance companies must prepare. These funds will cover financial losses experienced by someone who applies for the claim. Thus, the author will use the Gross Premium Valuation (GPV) method in this research. Premium reserves can be determined using two approaches: retrospective and prospective reserves. In this research, the author will determine the prospective reserves with GPV for single decrement insurance and single life n-year continuous term life. The distribution of deaths used in determining the probability of death is the Indonesian life table IV and the de-Moivre assumption with parameter (ɷ=111). Different assumptions for the death probability distribution will result in different premium reserve values, so we can see a difference in premium reserves resulting from the two death probability distributions. This research uses data from male policyholders aged 40 years who followed continuous term life insurance for 20 years. Benefits will be paid right at the time of death, with a discrete method of premium payments made at the beginning of each year for 10 years.

In Bayesian statistics, prior specification has an important role in determining the quality of posterior estimates. We use expert judgments to quantify uncertain quantities and produce appropriate prior distribution. The aim of this study was to quantify the uncertainty of life insurance claims, especially on the policy owner’s age, as it is the main factor determining the insurance premium. A one-day workshop was conducted to elicit expert judgments from those who have experience in accepting claims. Four experts from different insurance companies were involved in the workshop. The elicitation protocol used in this study was The Sheffield Elicitation Framework (SHELF), which produces four different statistical distributions for each expert. A linear pooling method was used to aggregate the distributions to obtain the consensus distribution among experts. The consensus distribution suggested that the majority of policy owners will make a claim at the age of 54 years old.

Membentuk portofolio optimal adalah metode yang dapat membantu para investor meminimalkan risiko dan mengoptimalkan keuntungan. Beberapa model untuk portofolio optimal termasuk Single Index Model (SIM), Treynor Black Model (TBM), dan Black-Litterman Model (BLM). SIM didasarkan pada pengamatan bahwa harga sekuritas berfluktuasi sejalan dengan indeks pasar. Pada TBM, seorang investor dapat melihat bahwa model ini kurang fokus pada nilai beta tetapi lebih berfokus pada risiko tidak sistematis. BLM menggabungkan elemen data historis dan pandangan investor untuk membentuk prediksi baru tentang portofolio sebagai dasar pemodelan. Prediksi pandangan dalam penelitian ini menggunakan pendekatan time series ARIMA dan GARCH. Tujuan dari penelitian ini adalah untuk membentuk tingkat pengembalian portofolio optimal dengan menggunakan SIM, TBM, dan BLM berdasarkan pandangan tunggal investor serta kombinasi pandangan beberapa investor dengan pendekatan ARIMA dan GARCH.