cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 52 Documents
 2   3   4   5   6 
Search results for , issue "Vol 9, No 1 (2025)" : 52 Documents clear
Chatbot Adoption Model in Determining Student Career Path Development: Pilot Study Ahmed, Mohamed Hassan; Abdullah, Rusli; Jusoh, Yusmadi Yah; Azmi Murad, Masrah Azrifah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3798

Abstract

A career decision is incredibly essential in one's life. It shapes one's future role in society, influences professional development, and can lead to success and fulfillment. Making a sound and consistent career decision based on skills and interests is critical for personal and professional development. Since generative AI is an emerging and revolutionizing technology industry in the market, which is very good in generating contents, providing consultancies and answering questions in humanly fashion, integrating AI chatbots into the career planning process can help students to get more accurate and personalized advice for their future career. This pilot study emphasized the student’s adoption of chatbot technology for career selecting processes utilizing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model with four additional constructs which influence the student’s career selection, namely: Perceived Student’s External Factors (PEF), Perceived Student’s Interest (PSN), Perceived Career Opportunities (PCO) and Perceived Self-Efficacy (PSF). An online survey was conducted, and 37 responses were received and analyzed. The measurement model produced a promising result, and the discriminant validity, construct reliability and validity of the model were confirmed with a Cronbach’s alpha (α) above 0.70 threshold and AVE over 0.5 cut-off for most of the constructs including the four above mentioned latent variables. However, the Price Value (PPV) and Facilitating Conditions (PFC) UTAUT2 constructs produced alpha () of 0.680 and 0.611 respectively which is still adequate since their AVE is above the 0.5 threshold. Consequently, their interpretation and conclusions should be approached with caution.
Optimising iCadet Assignment through User Profiling Fei, Yap Peak; Ting, Choo-Yee; Abdul-Rashid, Hairul A.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3470

Abstract

Industry Cadetship programme is a programme that assigns penultimate year students to companies matching their profiles, bridging academic learning and industry skills.  Manual data analysis for assignments is time-intensive, prompting this study’s objectives: (i) propose an algorithm to optimize student-company assignment by using the student and company profiles, (ii) propose a method for the assignment of lecturers to company, and (iii) use similarity measure techniques to recommend companies with similar characteristics. Data was collected from a university's student, company, and lecturer datasets. To assign students to companies, the Haversine, OpenStreetMap, and NetworkX were used to calculate the shortest geographical distance between the students and the companies; evaluated based on mean, variance, standard deviation, and utilization rate. For the lecturer assignment, cosine similarity was applied to measure the similarity between domain descriptions and company or lecturer information after performing Voyage AI embeddings. Lecturers are assigned to companies based on the highest domain similarity scores. The performance was evaluated using accuracy, precision, recall, and F1- score.  Findings showed embedding techniques significantly enhanced the matching process, with accuracy improved from 0.464 to 0.6071, precision increased from 0.417 to 0.5058, recall saw an equal rise from 0.464 to 0.6071, and the F1-score advanced from 0.417 to 0.5264. Longer descriptive inputs further improved performance, with accuracy rising from 0.6154 to 0.7692, precision from 0.5744 to 0.7751, recall remaining steady at 0.7692, and F1-score increasing from 0.5807 to 0.7484. This work can be extended to explore job portal dataset by aligning profiles with geography and specialization.

Page 6 of 6 | Total Record : 52

 2   3   4   5   6 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue