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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) People and Behavior Analysis (PBA)
Vivin Sulistyowati
Sebelas Maret University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics Social Sciences

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Natural Language Processing (NLP) Technology for Chatbot Website Agreianti Puspitasari; Astrid Noviana Paradhita; Yohanes Wien Tineka; Vivin Sulistyowati; Ni Komang Septia Noriska; Haryanto
Jurnal Penelitian Pendidikan IPA Vol 10 No SpecialIssue (2024): Science Education, Ecotourism, Health Science
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10iSpecialIssue.8241

Abstract

Chatbot technology is one form of application of Natural Language Processing, NLP is one of the branches of science Artificial Intelligence that studying communication between humans and computers through natural language. the purpose of this research will be carried out development of a chatbot application for information about the corporate, making it easier for visitors to find the information needed. This application built with a Natural Language Processing approach. This chatbot application uses medote text mining as a medium of reasoning. System modelling used for this application is using the Unified Model Language. The chatbot that will be developed is a website chatbot. The website chatbot was chosen by the company because one of the platforms that can be used well for export marketing is the website. This chatbot development uses Pyton, JavaScrib, Object notion (Json), Html + CSS. The application used in coding is Virtual Studio Code. Pyton is used to train a chatbot model that uses Natural Language Procecing (NLP). This Chatbot model is trained with the Neural Network Algorithm method, which is an algorithm model that is close to the human brain, which can provide stimulation or stimulation, process, and provide output to find relationships between data sets. JavaScrib is used to create chatbot data sets.
Implementation of TOPSIS Method to Assist the Process of Accepting New Employees in the Company Astrid Noviana Paradhita; Yeni Fajariyanti; Muhammad Tafdhil Amanda; Agreianti Puspitasari; Vivin Sulistyowati
People and Behavior Analysis Vol. 3 No. 1 (2025): January - April Volume
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/pba.v3i1.3135

Abstract

Human resources play an important role in ensuring that all business processes in a company run smoothly. Human resources in a company must have competencies and skills that match the company’s needs. The employee recruitment process is the first gateway that must be carried out comprehensively to obtain reliable human resources. However, the company’s recruitment process is still at risk of bias and takes a long time. This study was conducted to identify a strategy for recruiting new employees who are competent according to the qualifications required by the company. This study was developed by applying a preference ordering technique based on similarity to the ideal solution (TOPSIS). The data criteria used in the study were based on a selection process commonly used in one of the state-owned companies in Indonesia, which included the results of a general intelligence test (TIU), a national insight test (TWK), a field ability test (TKB), and an interview test. The weighting used was TIU 30%, TWK 10% TWK, 20% TKB, and 40% for the interview test. The study results showed that the TOPSIS method can help companies find employees with the competencies that best match the qualifications needed. TOPSIS can also increase the efficiency of the recruitment process time by up to 70%. TOPSIS can help companies find the best 20 candidates in just 7 days, whereas, in the recruitment process, without TOPSIS, the company needs 21 to 30 days. Using TOPSIS, companies can streamline the recruitment process, reduce bias, and reduce maintenance costs.
Co-Authors Agreianti Puspitasari Agreianti Puspitasari Astrid Noviana Paradhita Astrid Noviana Paradhita Haryanto Muhammad Tafdhil Amanda Ni Komang Septia Noriska Yeni Fajariyanti Yohanes Wien Tineka