Sinkron : jurnal dan penelitian teknik informatika
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GridSearch and Data Splitting for Effectiveness Heart Disease Classification
No full textCardiovascular disease (CVD) is a major global health issue that affects death rates significantly. This research aims to improve the early detection and diagnosis of cardiovascular illness by utilizing machine learning methods, particularly classification algorithms. According to estimates from the World Health Organization (WHO), cardiovascular disease (CVD) caused 17.9 million deaths globally in 2019, or 32% of all fatalities. The treatment and prognosis of cardiovascular illness are greatly improved by early detection and diagnosis. Classification, in particular, machine learning, has become a prominent tool for solving problems connected to heart disease. The main objective of this project is to assess how well Grid Search and various data-sharing methods classify cardiac disease. SVM, Random Forest Classifier, Logistic Regression, Naïve Bayes, Decision Tree Classifier, KNN, and XGBoost Classifier are just a few machine learning methods. The UCI heart disease dataset, which contains information from 303 heart disease patients and 165 healthy participants, is used for the evaluation. Performance parameters like recall, accuracy, precision, and F1 score are considered to evaluate the algorithms' efficacy. The investigation's expected outcomes are intended to increase doctors' ability to diagnose cardiac disease more accurately. Moreover, these results may aid in creating more complex classification models for diagnosing cardiac conditions
Multilayer Perceptron Performance Analysis in Liver Disease Classification
No full textLiver disease is a liver disease caused by viruses, alcohol, lifestyle and others. Someone often does not realize or is late to know liver disease so that when examined liver disease is severe, it would be better if treatment is done early by knowing the symptoms suffered. Data mining can help diagnose liver disease more easily, especially to help doctors determine whether patients suffer from liver disease or not, with symptoms almost close to liver disease. The process of diagnosing liver disease is carried out by a classification process and the result is that the patient suffers from liver or not. This research uses a data mining classification method using an artificial neural network method, namely Multilayer Perceptron. The Indian Liver Patient Dataset (ILPD) used in this study was obtained from the UCI Machine Learning Repository. The division of the data set over the training data and test data is done by Cross Validation. Performance measurement of the method uses confusion matrix. Based on the research conducted, it was found that the application of Multilayer Perceptron resulted in varying accuracy based on testing with different Fold values with the highest accuracy of 83.70% when the Fold was 7, and the lowest accuracy of 80.57% when the Fold was 3. Then the average accuracy of all Fold tests is 82.13
Problems in The Adoption of Agile-Scrum Software Development Process in Small Organization: A Systematic Literature Review
No full textAgile methods are becoming increasingly popular in modern corporate strategies, which represents a paradigm change in project management techniques. The concept of pragmatic agility has become essential for enterprises to manage the complexities of ever-changing contexts. However, some organizations—especially small ones with limited resources—face unforeseen difficulties while implementing Agile-Scrum software development. In order to clarify the challenges small businesses, encounter throughout this adoption process, this study combines ideas from fifteen studies into a thorough and systematic analysis of the literature. The issues that have been discovered may be categorized into four primary areas: technology, people, process, and organization, and agile techniques. Organizations are able to anticipate obstacles by using a comprehensive understanding provided by the methodical examination and classification of situations. This proactive approach is essential to preventing unfavorable outcomes, as those seen in the past when implementation errors were made worse by culture problems, insufficient support from upper management, and waning consumer cooperation. This research provides small firms with a navigational aid by synthesizing lessons from the literature, enabling them to plan an Agile-Scrum adoption process that is more smoothly executed. Organizations may enhance their preparation, protect themselves from frequent traps, and ultimately maximize the transformative potential of Agile techniques in their developmental undertakings by adopting these insights
Simulation and Modelling of Pre-emptive Priority CPU Scheduling Algorithm
No full textA model is a representation of an idea, thing or object in a simplified form. Model contains information about a system that is created with the aim of studying the actual system. Simulation is an imitation of system using a computer model. In this journal simulation is done by using OS-SIM, an operating system simulator. Process scheduling in an important part in operating systems. Several scheduling algorithms exist in the field. Shortest job first, round robin, first come first serve, priority and all of their variants. In this journal discussion about pre-emptive priority scheduling algorithm is presented thoroughly. Pre-emptive priority scheduling algorithm is an algorithm based on priority. The higher the number of the priority, the higher the priority. Five processes are available and given. Each with burst time, priority and different arrival times. Simulation and modelling with OS-SIM are discussed to understand this algorithm more easily. Some statistics numbers in the system are calculated automatically by the OS-SIM. Some screen shot pictures of the simulator are given to describe the model. It is concluded that for these processes the average turnaround time is 42/5 = 8.4 ms and for average waiting time is 28/5=5.6 ms and the total burst time is 14 ms
Development of Android-Based Smart System for Gingivitis Diagnosis Using Certainty Factor
No full textGingivitis is a gum disease that causes bleeding, swelling, redness, discharge, changes in normal contours, and although health authorities take this seriously, sometimes some patients consider it normal. This study aims to educate the public about the importance of understanding the condition of their bodies, especially the most vulnerable teeth. Lack of time to consult an expert leads to this disease being neglected. Therefore, it is necessary to develop a consultation application in the form of an expert system. The built system adopts the deterministic factor method. The certainty factor works by reading the entire data submitted by the expert and giving the result as a percentage of confidence that the patient has gingivitis. The experts used in this system are dental experts. Data obtained from direct experts and consultations resulted in new knowledge in the form of the percentage of trust patients suffering from gingivitis. The data collected are symptoms and solutions obtained from experts. This research provides a new service for patients suffering from gingivitis without the need to see a specialist directly. Based on the testing data provided to the patient and based on the patient's condition at that time, the test results of the system reached a confidence level of 98.74%. So that the results of consultation are obtained in the form of information about the disease and the solutions needed
Clothing Recommendation System Using the K-Nearest Neighbor Method
No full textThe world of fashion and the way we interact with it has been transformed by advances in information and communication technology. Clothing recommendation applications have become increasingly common, helping people choose clothes that suit their style and preferences. This study suggests using the KNN Method as a basis for building a more intelligent and personalized clothing recommendation system. To address the growing need for accurate clothing recommendations that match users' preferences, The goal of this research is to create a clothing recommendation system that can help users choose more appropriately because advances in technology have made it possible to gather and examine user data more thoroughly. In this study, the clothing recommendation system was implemented using the KNN Method. We ran simulations by setting the clothing dataset's parameter K value from 3 to 11. The simulation results show that the system's performance reaches its peak at parameter value K=8. We measured the system's accuracy, precision, and recall at this K value in order to assess its performance. The results show that the clothing recommendation system uses the KNN Method. A clothing recommendation system based on the KNN Method with the parameter K=8 has proven successful in classifying clothes with an accuracy of 83,67%
Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method
No full textRice, as a key element of Indonesia's food security, plays a crucial role in agricultural ecosystems. Despite its high economic value, rice plants are susceptible to various diseases that can reduce productivity and harvest quality. Farmer's limited knowledge about disease types, identification, and proper handling poses a serious challenge to sustainable agriculture. Previous studies highlight farmers' inadequate understanding of pests and diseases in rice plants, leading to a high dependency on pesticides. Furthermore, lack of training data and a shallow understanding of rice diseases present significant challenges in disease management efforts. This research aims to develop an Android-based Smart Farm application. This application utilizes image processing and artificial intelligence technologies to assist farmers in identifying leaf diseases in rice plants. Requirements analysis involves literature review and field observations around Bandung Regency. It can be concluded; Smart Farm application has been successfully developed with three functional and two non-functional requirements. Validation testing indicates a 100% functionality rate and an 80% accuracy in disease detection. Nevertheless, further attention is required to enhance accuracy by providing more training data and improving image quality. The implications of this research extend to enhancing farmers' knowledge, reducing pesticide dependency, and supporting sustainable agriculture in the future
Hybrid Analysis of Road Service Level Determination Decision Support System
No full textTraffic congestion on the highway is one of the problems often faced by road users, as on the highway traffic in the city of Padang often experiences congestion due to the growth of several highways that are relatively smaller than the growth of traffic volume, causing a decrease in road service levels. This study aimed to determine the level of road services in several traffic lanes in the city of Padang. Data analysis using Hybrid Decision Support System (HDSS) modeling by combining the Analytical Hierarchy Process (AHP) method with the Weighted Aggregated Sum Product Assessment (WASPAS) This method is included in the Multi-Criteria Decision Making (MCDM) group. The AHP method can consistently determine the weight value of each criterion, and the WASPAS method can analyze alternative data to obtain decision results by ranking with the Weighted Sum Model (WSM) and Weighted Product Model (WPM) processes. The ranking results show that there are three types of road services in the city of Padang, namely B, C, and D with an accuracy rate of 0.6947%, this the results of this study using HDSS modeling can provide a better analysis process
Web Program Testing Using Selenium Python: Best Practices and Effective Approaches
No full textA critical component of software development is testing programs on websites to make sure they run, are secure, and meet user expectations. This article explores efficient methods and best practices for utilizing Selenium with the Python programming language to perform program testing on websites. This study shows the outcomes of Selenium WebDriver-based automated testing on two webpages: https://demoqa.com/text-box and https://demoqa.com/login. Testing was done to assess Selenium's dependability and performance when it comes to completing text fields and completing the login procedure. The goal of the study is to evaluate Selenium WebDriver's dependability and effectiveness in basic testing jobs while spotting any issues that may arise. Using the appropriate locator to fill in text boxes efficiently using Selenium WebDriver ensures that the operation proceeds without major hiccups. The effective completion of the login procedure on the https://demoqa.com/login page further demonstrates the dependability of Selenium WebDriver for handling increasingly complicated interactions, including login. According to the analysis's findings, Selenium WebDriver is a dependable and efficient solution for test automation that performs consistently and steadily under a range of conditions. The research's conclusions highlight how crucial it is to use automation technologies in order to ensure software quality and boost testing effectiveness. Software engineers can detect issues more rapidly and completely test the functionality of applications with Selenium WebDriver, which enhances the overall quality of software developmen
Uncovering Blockchain's Potential for Supply Chain Transparency: Qualitative Study on the Fashion Industry
No full textWith the capacity to increase security and transparency, blockchain technology is being used as an interesting subject of investigation in the fashion industry. This underscores the importance of this current research endeavour. In terms of supply chain transparency, the fashion industry faces considerable barriers, thus requiring new approaches such as blockchain that can address issues such as child labour, unethical payment practices, and environmental impact. Main objective of this research is to identify how blockchain technology can improve transparency, accountability, and compliance with ethical standards. However, knowledge of the specific ways in which blockchain technology can improve transparency in the fashion supply chain, including the drivers and barriers, needs to be improved. The research method is described through a qualitative approach that includes in-depth interviews, participatory observation, and document analysis to collect data from various stakeholders in the industry, including manufacturers, distributors, and consumers. Explanation provides an overview of how the researcher collected and analysed data to achieve the research objectives. Blockchain increases transparency through the provision of verifiable and durable product records and fosters consumer-brand trust. Blockchain facilitates accountability and compliance with environmental and ethical standards, according to key findings. Research detected significant barriers, including exorbitant costs for implementation, limited knowledge of technology, and difficulties in fostering collaboration among relevant parties. Results of this study have far-reaching consequences, providing valuable insights to fashion industry stakeholders on how to overcome barriers to blockchain adoption. Long-term benefits of enhanced supply chain transparency and strategic recommendations ensure a smooth implementation process