INSIDE - Jurnal Sistem Informatika Cerdas

Clustering Analysis of Results of Students' Industrial Work Practice Activities Using the K-Means Method

Wed, 07 Aug 2024 12:39:38 +0700

Praktik Kerja Industri (PRAKERIN) is an important program in the Vocational High School curriculum that provides students with real work experience before they enter the industrial world. Apart from technical skills, this program also develops soft skills such as teamwork, communication, and problem solving. However, evaluating PRAKERIN results often faces challenges in identifying and classifying student performance objectively and systematically. With data mining technology, it is possible to analyze the results of students' abilities after participating in PRAKERIN activities. In conducting this research, the K-Means clustering method was used to group students' competency abilities. With the K-Means clustering technique, it is hoped that teachers can adjust the learning model according to students' abilities. Based on the grouping results, it was found that grouping with 3 clusters was the most optimal grouping result with the smallest Davies Bouldin Index (DBI) value, namely 0.160. The application of the K-Means method in grouping student data based on English language ability scores can produce 3 groups of students who are competent, quite competent, and less competent.

Implementation of Deep Learning in Diagnosing Stroke Disease Based on Clinical Data Parameters

Sat, 10 Aug 2024 11:32:28 +0700

Stroke is a medical condition that requires rapid and accurate diagnosis to increase the chances of patient recovery and reduce the risk of long-term complications. This research investigates the application of deep learning techniques to diagnose stroke based on clinical data parameters. It develops and applies deep learning models that use various neural network architectures, such as convolutional neural networks (CNN). The dataset used is an open dataset in analyzing the dataset that includes patient clinical information such as hypertension, cardiac history, married status, employment level, body mass index (BMI), smoking and glucose. The model was trained using a dataset consisting of thousands of medical records of patients with stroke and without stroke. Model evaluation was conducted using performance metrics such as accuracy, precision, recall, and F1-score to assess the effectiveness in classification with an accuracy value of 95.05%. The results showed that the deep learning approach significantly improved the accuracy and speed in detecting stroke compared to conventional diagnosis methods. These findings suggest that the integration of deep learning in clinical diagnostic systems can improve early stroke detection and provide a solid basis for better clinical decisions.

Obesity Risk Prediction Using Random Forest Based on Eating Habit Parameters

Agung Muliawan, Difari Afreyna Fauziah, Eko Afrianto — Sat, 10 Aug 2024 11:39:21 +0700

Obesity is a global health problem associated with multiple chronic diseases, so early detection and risk prediction are important for prevention efforts. As obesity is one of the major health problems that can lead to various chronic diseases, accurate modelling can help in prevention and early intervention efforts. This study aims to develop an obesity risk prediction model using Random Forest technique, which is based on individual eating habit parameters. The dataset used is taken from an open dataset that has variables of eating habits, which includes variables such as frequency of consumption of high-calorie foods, eating patterns, and types of food. The data was processed and analysed with the Random Forest algorithm, an ensemble learning method known to be effective in handling datasets with high dimensionality and non-linear relationships between features. The developed Random Forest model showed good performance with a prediction accuracy of 81.76%. This accuracy indicates that the model can effectively distinguish individuals with high risk of obesity from those with low risk based on their eating habit parameters. The results of this study demonstrate the potential of Random Forest as a useful tool in identifying obesity risk, which can assist in data-driven health prevention and intervention strategies.

Bank Mini Semarak: School Education Fee Payment Information System using V – Model

Eko Afrianto, Ferry Wiranto, Agung Muliawan — Sat, 10 Aug 2024 16:57:49 +0700

SMK PGRI 5 Jember is a vocational high school located in Jember, Indonesia. Established with a mission to provide quality vocational education, SMK PGRI 5 Jember has become a well-known institution in the region. The school offers various vocational programs aimed at equipping students with practical skills and knowledge that are directly applicable to the workforce. The advancement of technology in the education sector has revolutionized various administrative processes, including payment systems. Traditionally, SMK PGRI 5 Jember, like many other educational institutions, has relied on manual payment systems for tuition fees, which involve students or their guardians making payments directly at the school office or through bank transfers. An online payment system offers numerous advantages that address the challenges associated with manual payment processes. Firstly, it enhances the efficiency of the payment process by allowing transactions to be completed quickly and easily from anywhere, at any time, without the need for physical presence at the school. This is particularly beneficial for parents or guardians who may have difficulty visiting the school during working hours. The V-Model emphasizes the importance of testing and validation at each stage of the software development lifecycle, ensuring that the system meets the specified requirements and works as intended

Development of Science Laboratory Information System (case study: SMA Negeri Grujugan Bondowoso)

Difari Afreyna Fauziah; Agung Muliawan — Sat, 10 Aug 2024 20:59:54 +0700

This research aims to develop a science laboratory information system at SMA Negeri Grujugan to improve the efficiency and effectiveness of laboratory management. The science laboratory at this school experiences several obstacles in terms of equipment inventory management, lab schedule management, and recording experimental results. To overcome these problems, this research proposes the creation of a web-based information system that can integrate various important functions in laboratory management. The methodology used in the development of this system includes requirements analysis, system design, implementation, and testing. The test results show that this information system is able to improve recording accuracy and simplify the laboratory administration process, as well as get positive feedback from users. The developed science laboratory information system provides solutions to various existing problems by providing features such as inventory management, practicum scheduling, and reporting of experimental results. With this system, it is expected that the management of science laboratories at SMA Negeri Grujugan will be more structured and efficient, and can improve the quality of science learning at the school.