Journal of Enhanced Studies in Informatics and Computer Applications

Estimate Suicidal Rate in Indonesian based on Time Window using Linear Regression

Javier Fajri Zachary, Mochammad Anshori, Wahyu Teja Kusuma — Wed, 31 Jul 2024 08:47:03 +0000

The global phenomenon of suicide should be a serious source of worry. Suicide rates are still relatively high in Indonesia. According to the research, there are many different reasons why people commit suicide. Anyone can commit suicide, whether they are young children, teenagers, or adults. Preventive action is one way to avoid this. The prevalence of suicide can be used to gauge the level of preventive action. You may gauge how active you are in implementing the most effective prevention by looking at the predicted suicide rate in the future. Linear regression is one technique for predicting suicide rates. The time window (tw) method is also used to prepare the data because it is in time series form. The best regression model was tw = 5 with MSE = 0.001147, RMSE = 0.033869, and R2 = 0.981643 obtained for all rates. The model with tw = 3, which has errors of MSE = 0.001547, RMSE = 0.039334, and R2 = 0.969458, is the most accurate one for the female rate. Finally, with errors MSE = 0.00318, RMSE = 0.056392, and R2 = 0.973341, we arrive at tw = 5 for the male rate

Logistic Regression's Effectiveness in Feature Selection with Information Gain in Predicting Heart Failure Patients

Mochammad Anshori, M. Syauqi Haris, Arif Wahyudi — Wed, 31 Jul 2024 08:54:55 +0000

Heart failure is a chronic illness that obstructs blood flow, which is necessary for the body to circulate oxygen. Patients with heart failure have a poor chance of survival, as evidenced by the high death rate. The hospital's infrastructure and medical facilities determine the degree of patient safety, and the patients' medical records play a significant role in ensuring that they receive the right care. As a result, a system that uses specific data to forecast the safety of heart failure patients is required. Machine learning, a computer-based approach, is one way to get around this. The logistic regression algorithm has been used to generate predictions in earlier studies. The approach for feature selection from the dataset that is suggested in this study is information gain. You can filter features that are significant to the dataset in this way. In addition, selection can enhance machine learning efficacy by decreasing the dimensions of the data. Five features—time, serum creatinine, ejection fraction, age, and serum sodium—are the outcome of information gain. After that, predictions were made using logistic regression, and a data sharing ratio of 70% training data and 30% test data resulted in an accuracy of 0.8556. This demonstrates how feature selection with Information Gain can improve the accuracy of the logistic regression model and is a very effective method.

Design of an Inventory Information System at ITSK Soepraoen Using the Waterfall Method

Nugroho Teguh Yuono, M. Syauqi Haris, Risqy Siwi Pradini — Wed, 31 Jul 2024 08:55:46 +0000

In the current digital era, the use of information technology has become an urgent need for various institutions, including educational institutions. ITSK Soepraoen has integrated information systems in several units to increase operational efficiency. However, inventory data collection is still done manually using Microsoft Excel, which has proven to be less efficient and often causes delays in data processing. This research aims to design a website-based inventory information system at ITSK Soepraoen using the Waterfall method. This system is expected to facilitate data collection and management of inventory items as well as increase accuracy, transparency and efficiency in data processing. The research method used is the Waterfall approach which consists of four stages: requirements definition, system and software design, implementation, and testing. The result of this research is a lo-fi mockup of an inventory system that is well received by users with an acceptance rate of 92.75%. This percentage is relatively high so it can be concluded that the user accepts the design that has been created and for the next stage this inventory system can be fully implemented.

THE DISCRIMINANT ANALYSIS FUNCTION WAS IMPLEMENTED TO PREDICT THE PRESENCE OF DIABETES

Herry Prasetyo Wibowo, Mochammad Anshori, M. Syauqi Haris — Wed, 31 Jul 2024 08:56:34 +0000

Diabetes is a condition blood sugar concentrations are high and there is something wrong with insulin inside the body. A hormone called insulin controls the equilibrium of blood sugar concentration in humans. Diabetes has high-risk health, such as CKD, CVD, skin disease or even blindness. The reason people suffer from diabetes is caused of bad consumption habits. Some symptoms of diabetes are frequent urination and feeling hungry too quickly. Diabetes is sometimes difficult to diagnose, which is why it is also referred to as the silent killer. A preventive way is an early prediction of diabetes disease. This is very important to do. In this study, the discriminant analysis algorithm is used along with machine learning techniques. In this study, machine learning techniques are used. Its name is discriminant analysis algorithm. Two popular versions are linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This method is used because it is suitable for high-dimensional data and the discriminant analysis algorithm has minimal parameters. The discriminant analysis algorithm uses few parameters and this method appropriate for high-dimensional data. We'll compare the two approaches to find a way to demonstrate their dependability. Both approaches would be contrasted. Based on the result, QDA has the best performance. QDA can produce accuracy = 93.7%, TPR = 93.7%, precision = 94.3%, recall = 93.7% and F-measure = 93.9%. FPR of QDA is the lowest one, it is 1.02%. It means QDA has a small error in making predictions. Overall, based on the result QDA is the proven and proper method for detecting diabetes disease

Development of an Alokon Stock Management Information System Using a Lean Development Approach

Risqy Siwi Pradini, Agus Widodo — Wed, 31 Jul 2024 08:57:15 +0000

This study explores the development of an Alokon Stock Management Information System using the Lean Development approach to enhance the efficiency and effectiveness of stock management for contraceptive devices and drugs (alokon) distributed by the Women's Empowerment, Child Protection, and Family Planning Service in Indonesia. Alokon plays a critical role in family planning and reproductive health services. The research aims to address challenges such as inaccurate stock data, distribution delays, and the inability to meet demand promptly, which can disrupt health services. The Lean Development approach, emphasizing principles such as eliminating waste, strengthening learning, and delivering results quickly, was employed to create a responsive and adaptive system tailored to user needs. The resulting system incorporates features for managing alokon data, monitoring stock levels, and generating reports to ensure adequate stock availability and timely distribution. Evaluation of the system's usability was conducted using the System Usability Scale (SUS), involving ten respondents from the primary user groups. The average SUS score was 75, indicating a good level of usability. Users reported the system as comfortable and easy to use, although feedback highlighted areas for further improvement.