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All Journal IJoICT (International Journal on Information and Communication Technology) TIN: TERAPAN INFORMATIKA NUSANTARA
Marastika Wicaksono Aji Bawono
Telkom University
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Measuring Employee Performance by Competence and Self-efficacy Marastika Wicaksono Aji Bawono; Chandra Fitra Arifianto
TIN: Terapan Informatika Nusantara Vol 4 No 3 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i3.4159

Abstract

Telecommunication companies that always update themselves, demand optimal employee performance. Increased employee competence can be associated with self-efficacy of self-efficacy in doing work to be able to make increased performance achievements. The purpose of this study was to determine the effect between variables, namely competence, self-efficacy and employee performance. This study uses a quantitative approach using a sample of 206 employees at a telecommunications company. The analytical technique used in this research is path analysis with the help of the partial least square–structural question model (PLS-SEM) program. The results showed that there was an influence between the three variables: competence, self-efficacy and employee performance. Competence affects self-efficacy and employee performance. While self-efficacy affects employee performance and competence affects employee performance through self-efficacy.
Machine Learning Sentiment Analysis in Cyber Threat Intelligence Recommendation System Marastika wicaksono aji bawono aji bawono; Sachlany Kasman; Stevani Dwi Utomo
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.849

Abstract

The use of the digital world is increasing every day. Attacks and data theft occur on various websites, both government-owned and commercial and banking sites. Therefore, this research aims to identify the threats of frequently occurring viruses in a country. There is a considerable amount of news explaining cybercrime incidents. The problem of this research is that unstructured data such as articles and technical reports are difficult to analyze and identify the types of cybercrime attacks. Previous research attempted to semantically extract unstructured cyber threats, but there were shortcomings in previous research. The novelty of this research is the development of a Cyber Threat Intelligence (CTI) machine learning model to identify the types of virus attacks or cybercrimes that frequently occur in e-commerce transactions, so that they can take rescue actions for incident handling in the digital world using tactics, techniques, and procedures (TTP). The method involves using machine learning, taking Cyber Threat Intelligence (CTI) documents as input regarding cybersecurity threat handling steps, and then processing the data using AI TF-IDF and Bags of Words for the identification of steps, tactics, techniques, and procedures required for each frequently occurring security incident.
Co-Authors Chandra Fitra Arifianto Sachlany Kasman Stevani Dwi Utomo