Profile of Cholelithiasis Underwent Laparoscopic Cholecystectomy Patients at The Aloei Saboe Hospital

Introduction: Cholelithiasis is one of the critical health problems. Modern lifestyles can allow gallstone disease in Indonesia to become a health problem that needs attention. The research objective is to know the profile of patients with cholelithiasis who performed laparoscopic cholecystectomy in Aloei Saboe Hospital.Method: The research design is a retrospective descriptive study. The population of this study was cholelithiasis patients who underwent laparoscopic cholecystectomy and were treated from January 2020 - December 2021, totaling 234 people. The number of samples is 86 people. We were using a purposive sampling data analysis technique, namely univariate analysis.Results: Regarding the distribution of patients based on sex, the most results were obtained from females (70 people, 81.4%), the largest age group is 46-55 years old (23 people, 26.7%), and the majority of patients did not have a history of diabetes mellitus (76 people, 88.4%).Conclusion: The distribution of cholelithiasis patients who underwent laparoscopic cholecystectomy in the Aloei Saboe Hospital is most common in women aged 46-55, and most patients have no history of diabetes mellitus. This finding may offer a primary data reference for further research adding the number of variables to determine the risk factors for cholelithiasis

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time