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PREDICTING GEN-Z PERSONALITY ON TWITTER BASED ON BIG FIVE MODEL WITH KNN AND SVM
Get PDFGeneration Z is a group that is very connected to digital technology, especially social media such as Twitter. Their widespread presence on these platforms creates a unique opportunity to understand their behavioural patterns and personalities. However, research on personality prediction on social media is still limited and focused on certain platforms or different age groups. Personality prediction can help to find out someone's personality by just looking at tweets on social media. This research aims at two things: first, to build a Gen-Z personality prediction model on Twitter based on the Big Five Personality Model with the K-Nearest Neighbor (KNN) algorithm and Support Vector Machine (SVM). Second, test and compare the performance of previously generated personality prediction models with various evaluation metrics. The research results show that the KNN algorithm has an accuracy rate of 0.73%, precision of 0.73%, recall of 0.73%, and score of 0.72%. Based on the test results, the SVM algorithm obtained the best accuracy, which received an accuracy of 0.78%, precision of 0.82%, recall of 0.78%, and F1-score of 0.78%. This research contributes in two ways: first, scientifically, by understanding Gen-Z personalities on Twitter, and second, by developing new prediction methods and insights into Gen-Z behaviour. Second, practically, by helping with communication and marketing strategies, product/service development and social interventions for Gen-Z
CLASSIFICATION OF HEART DISEASE USING THE K-NEAREST NEIGHBOR ALGORITHM AND LOGISTIC REGRESSION
Get PDFHeart disease is a major cause of death in the world, including in Indonesia, with increasing rates and death rates that carry a huge burden on health and society. Lack of awareness of early signs contributes significantly to this challenge. This study aims to prevent heart disease through early diagnosis using K-Nearest Neighbor (K-NN) and Logistic Regression algorithms. The database, obtained from Kaggle.com, includes 15 clinical units for cardiac diagnosis. The test shows that the K-NN method with k = 3 achieves the highest performance on the experimental data (30%), with 90% precision, 93% precision, 87% recall, and 90% f1 - score. In comparison, Logistic Regression and sigmoid achieved 86% precision, 83% precision, 90% recall, and 86% f1-score on the same experimental data. These results show that K-Nearest Neighbor is better than Logistic Regression as a classification algorithm for heart disease database. Applying these findings to the web-based Streamlit system is expected to improve the efficiency and timeliness of heart disease screening
COMPARATIVE ANALYSIS OF AUTOMATION FUNCTIONAL TESTING TOOLS PERFORMANCE FOR PLAYSTORE APPS WITH DIA METHOD
Get PDFThe complexity of smartphone applications presents challenges for developers, who must ensure flawless functionality despite limitations such as budget and time constraints. Manual testing is time-consuming, prompting a shift towards automated testing methods to ensure efficiency and reliability. In this context, researchers are evaluating the efficacy of three leading test automation frameworks—Robot Framework, Katalon Studio, and UI Path—against key performance parameters. Using the Distance to the Ideal Alternative (DIA) method on playstore apps. The main performance parameters used as a reference are automated testing progress and tools usability. Katalon Studio emerges as the top performer, securing the top rank with a remarkably close to the alternative ideal positive distance (Ri) value of 0.00001. UI Path occupies the second position with a Ri value of 0.00135, while Robot Framework trails behind with a Ri value of 0.00295. This research contributes to the understanding of the performance of different automation frameworks in the context of functional testing, providing valuable insights for developers and organizations seeking to optimize their testing processes. The findings underscore the significance of Katalon Studio's exceptional performance and highlight opportunities for improvement in UI Path and Robot Framework. Additionally, implementing a robust monitoring and evaluation framework is crucial for tracking the ongoing performance and optimizing the efficiency of these automation frameworks
WEB-BASED INFORMATION SYSTEM PREDICTION OF VEHICLE THEFT VULNERABILITY IN JAYAPURA USING REGRESSION ANALYSIS
Get PDFVehicle theft in Jayapura Regency is quite high and there is no application to assist the police in making estimates or predictions of the number of theft cases that will occur in the next year. In 2022, cases of theft in Jayapura district will start to increase. to make these predictions the authors designed and built a system that can predict the number of these cases in building this application the authors use the Regression Analysis method this process can help the police predict the number of cases in the coming year. The development method used is SDLC, linear regression analysis and using the PHP programming language, the database uses MYSQL, Sublime Text. This research was conducted because there was no system that could assist the staff of the Resort Police (Polres) of Jayapura Regency. From this research, a system for predicting the level of vulnerability to motorized vehicle theft has been successfully built at the Jayapura District Police with data processed for attendance data using face region, reporting data using barcodes, queue data using counters and digital archive data helping the police store important documents
COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE AND GREY RELATIONAL ANALYSIS FOR BEST ADMINISTRATION STAFF SELECTION
Get PDFThe best administrative staff are individuals who are able to maintain the smooth operation of the organization with high efficiency and precision. One of the main problems is subjectivity in assessment that can cause dissatisfaction among employees. Sometimes, assessments are based more on personal relationships than objective performance, thus creating a sense of unfairness. The purpose of this study, using a combination of LOPCOW and GRA in determining the best administrative staff to develop a holistic and data-driven evaluation approach for the optimal administrative staff selection process. This process involves a comprehensive assessment based on various criteria, including work efficiency, accuracy, multitasking ability, and excellence in communication and problem solving. LOPCOW provides a strong objective basis by considering significant changes in performance data through logarithmic percentage changes, while GRA helps in identifying and understanding the relationship of similarities and differences between alternatives based on given criteria. By integrating these two methods, organizations can combine the advantages of LOPCOW's objectivity with the power of GRA's relational comparison analysis, resulting in a more comprehensive and accurate performance evaluation. The results of the ranking of the selection of the best administrative staff show that the first best administrative staff was obtained by Staff Name AH with a GRG value of 0.1666, the second best administrative staff was obtained by Staff Name RW with a GRG value of 0.1569, the third best administrative staff was obtained by Staff Name ES with a GRG value of 0.1266
KNOWLEDGE MANAGEMENT SYSTEM PENGOLAHAN SAMPAH MENGGUNAKAN SOCIALIZATION, EXTERNALIZATION, COMBINATION, INTERNALIZATION MODEL
Get PDFGarbage is an environmental problem that cannot be avoided, changes in human lifestyles cause an increase in the volume of waste, various ways are carried out to overcome the increase in the volume of waste, one of which is the Reduce, Reuse, Recycle (3R) technique which plays an important role in waste processing and can change waste. to be artistic and economical, to share knowledge about waste management requires a container that can accommodate and share knowledge. In this study, a Knowledge Management System (KMS) was developed using the Knowledge Management Life Cycle (KMSLC) method and capturing knowledge using the Sosialization Externalization Combination Internalization (SECI) model. The results of this study are web-based applications that can accommodate, add and share knowledge in the form of tacit and explicit and change the knowledge formed from the results of individual interactions into documented knowledge which is expected to help organizations manage all knowledge and develop it so that it can improve the abilities and knowledge of members organization for waste management
MENGUKUR KEPUASAN MAHASISWA DALAM MENGGUNAKAN APLIKASI MUSIC STREAMING MENGGUNAKAN METODE AHP
Get PDFOnline streaming applications are currently very popular among the public, especially students. Because of this user interest, the author wanted to conduct research. This research uses the AHP method to measure students' level of satisfaction with the use of music streaming applications. The research evaluation criteria included quality, service, price and payment. Questionnaires are used to determine student preferences and assessment of related criteria. The collected data was analyzed using the AHP method and the application priorities were compared. These findings will help developers and users improve quality and user experience. The research was conducted using the Analytical Hierarchy Process (AHP) methodology on students in the Bekasi City area with a population of 7058 people and obtained a sample size of 379 respondents using the Slovin formula. The research results show that Spotify is the most popular music streaming application among users, especially students. Followed by applications such as Joox and YouTube
KONTEN PROMOSI ANEKA PRODUK PALA BERBASIS SOSIAL MEDIA BAGI KELOMPOK WANITA TANI CITA MANDIRI
Get PDFThe ineffectiveness of word-of-mouth marketing techniques and the low ability of partners (Cita Mandiri Women Farmers Group) in utilising digital media have made nutmeg products not optimally marketed. The service activity aims to improve the ability of partners to create promotional content so that they can do marketing through social media. Service activities are carried out in three stages, namely 1) The preparation stage by conducting observations and identifying partner problems. 2) The implementation stage by conducting training and assistance in creating promotional content and online marketing techniques through social media. 3) Evaluation stage. Involving the Wanita Tani Cita Mandiri group in community service activities with structured training programmes, ongoing mentorship, and partnerships with online communities, this group can develop digital skills, create more creative promotional content, and expand their market reach. With the implementation of this solution, Wanita Tani Cita Mandiri can optimise the potential of social media as a marketing tool, increase their income, and overall make a positive contribution in the economic and social empowerment of the local community. In conclusion, through collaboration, training, and innovation, this group can make social media-based promotional activities a driving force to achieve economic independence and sustainability
SOCIAL MEDIA COMMENTS FOR GOVERNMENT INSTITUTION VIDEO CLASSIFICATION USING MACHINE LEARNING
Get PDFYouTube is a social media site that is quite familiar and is used as a means of disseminating video-based information. With a fairly high number of users, YouTube can become a communication medium for audiences, including government agencies. The user’s responses in comments reflect the nuance of the presented video. This research aims to determine the best algorithm for classifying video types based on user comments. Several machine learning algorithms used to carry out classification are Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression. K-Fold Cross Validation was chosen as a method to evaluate the performance of classification algorithms based on the accuracy values. of these algorithms in classifying YouTube videos based on comments. The first experiment with the highest ratio of training and test data for each algorithm was obtained at a ratio of 90:10, with respectively 78.99%, 86.21%, 84.01%, 72.72%, and 79.31%. In the second experiment with k-fold cross validation using a ratio of 90:10, the highest accuracy for each algorithm was obtained at a value of k = 10, which was respectively 74.39%, 81.34%, 78.05%, 85.21%, and 72.15%. From these results, it can be concluded that the most suitable algorithm for classifying YouTube videos based on comments is the Random Forest algorithm with a training and test data ratio of 90:10 and SVM with 10-cross-fold validation. These results show that a larger portion of data for learning has a positive impact on algorithm performance
APPLICATION OF OWASP ZAP FRAMEWORK FOR SECURITY ANALYSIS OF LMS USING PENTEST METHOD
Get PDFLearning Management System (LMS) is an application currently popular for online learning. The presence of LMS offers better prospects for the world of education, where its highly efficient use allows learning anywhere and anytime through the internet or other computer media. This study focuses on analyzing the security of the Learning Management System (LMS) on the domain e-learning.ibm.ac.id using the Pentest method with the Owasp Zap Framework. Security is a crucial step that needs to be considered by IBM Bekasi in protecting data and information from hacker threats. In this study, the method used is Pentest. Pentest is a series of methods used to test the security of a system by conducting literature studies, searching for data information, and domain information, followed by testing using Owasp Zap to find security-related vulnerabilities. The results of the testing using the Pentest method involve several stages of testing and scanning. The first step is checking domain information using Whois Lookup tools and then scanning using ZenMap on e-learning.ibm.ac.id. In this domain information search, the domain status serverTransferProhibited and clientTransferProhibited was found. The next stage is Vulnerability Analysis, where scanning is performed on the domain e-learning.ibm.ac.id using Owasp Zap tools. Based on the results from Owasp Zap scan, 16 vulnerabilities were found, with the breakdown being 2 high risk, 3 medium risk, 6 low risk, and 5 informational. In the exploitation stage using SQLMap, errors were found in the tested parameters, preventing injection