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Kelvin Kelvin
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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

 1 
Overview of Text Based Personality Prediction Using Deep Learning Kelvin, Kelvin; Utomo, Yesun
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11550

Abstract

Text-Based Personality Prediction (TBPP) has garnered increasing attention in recent years, particularly within the frameworks of the Myers-Briggs Type Indicator and the Big Five Personality Model. This study presents a comprehensive systematic review of TBPP methodologies, focusing specifically on research published since 2017. Leveraging Google Scholar, a meticulous selection process was employed to identify and analyze papers meeting relevance criteria. The selected studies were analyzed for research design, data collection methods, preprocessing techniques, and modeling approaches. Notably, the study identifies prevalent Natural Language Processing methods utilized in TBPP, such as Recurrent Neural Networks, Convolutional Neural Networks, Long Short-Term Memory networks, ensemble methods, and pre-trained models like BERT. Results indicate that combining knowledge graphs with Bi-LSTM models achieved the highest accuracy for Big Five traits at 71.5%, while a BERT-CNN-RNN ensemble reached 85% accuracy for MBTI. The synthesized findings offer valuable insights into the current landscape of TBPP, with the aim of informing both researchers and practitioners. Furthermore, the study provides recommendations for future research directions, emphasizing the importance of refining methodologies and addressing challenges to foster continued innovation in personality prediction within the TBPP domain.
Computer Resource Utilization Analysis for Microsoft Excel and Python in Data Processing Kelvin, Kelvin; Wahab, Wahidin; Wulandari, Meirista
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11736

Abstract

Data analysis is essential for gaining insights and making informed decisions. A crutial step in data analysis is data processing, which involves preparing and filtering raw data to ensure accuracy, consistency, and structure. While Microsoft Excel is commonly used for data processing, it is susceptible to human errors and has limitations in handling large datasets. Python provides an alternative by automating data processing through scripts executed by the interpreter. The superior software for data processing is obtained by comparing the computer resource utilization based on statistical theory approach, Wilcoxon signed-rank test. This test is appropriate because it does not require the assumption of a normal distribution, providing flexibility in comparing computer resource utilization between Microsoft Excel and Python. Microsoft Excel and Python proceed *.csv and *.xlsx files, then Task Manager recorded the data of computer resource utilization for each processing step. The Wilcoxon signed-rank test analyzes the data and evaluating two hypotheses. H0 (there is no any significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing) and H1 (there is significant differences in computer resource utilization between Microsoft Excel and Python are calculated for each data processing). The sum of ranks in Wilcoxon test are compared to the critical value from the Wilcoxon distribution table to determine the accepted hypothesis. Based on the Wilcoxon test results, hypothesis H1 is accepted, indicating a significant difference in computer resource utilization between Microsoft Excel and Python.
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