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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
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Articles 632 Documents
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Network Infrastructure Design Of Gigabit Passive Ethernet Technology At Dehasen University, Bengkulu Khairil, Khairil; Jumadi, Juju; Beti, Ila Yati
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1711

Abstract

The development of information technology has significantly increased the demand for reliable and stable internet network infrastructure, especially in higher education institutions. Universities require efficient network systems to support academic activities, digital learning, research, and administrative services. This research aims to design a network infrastructure using Gigabit Passive Ethernet Network (GPEN) technology at Universitas Dehasen Bengkulu. The previous network infrastructure relied on wireless radio point-to-point connections which often experienced interference caused by obstacles such as buildings, trees, and weather conditions. The proposed GPEN-based infrastructure is expected to provide a more stable and efficient network connection compared to the previous system. The research method used is Research and Development (R&D), which includes observation, literature study, and laboratory testing. Network design is implemented using MikroTik devices such as NetPower, GPEN11, and GPeR to extend Ethernet connectivity. The results of this study show that the GPEN network implementation provides better bandwidth stability, reduces interference problems, and improves overall network performance. Therefore, the GPEN-based infrastructure can be considered an effective solution for improving campus network connectivity at Universitas Dehasen Bengkulu
The Use of Apriori Method in Forecasting the Number of New Students Elfianty, Lena; Fredricka, Jhoanne; Alinse, Rizka Tri
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1709

Abstract

The development of information technology has significantly influenced many sectors, including education. Higher education institutions are required to manage and analyze data effectively in order to support decision-making processes. One of the challenges faced by universities is predicting the number of new students each academic year. The uncertainty in the number of applicants can affect academic planning, facility preparation, and marketing strategies carried out by the institution. This study aims to apply the Apriori method to analyze new student admission data in order to discover patterns and relationships within the data that can be used as a basis for forecasting the number of new students in the following academic year. The research method used includes data collection through observation, interviews, and literature study. The data used in this study are historical data of new student registrations from previous years.The analysis process is carried out using the Apriori algorithm to identify frequent itemsets and association rules based on support and confidence values. The results of the study indicate that the Apriori method is capable of identifying patterns and relationships among variables in the new student registration process. The information generated from this analysis can assist universities in developing more effective strategies for student recruitment and admission planning. By implementing a data mining approach using the Apriori method, educational institutions are expected to utilize their existing data to generate valuable information that supports strategic decision making and improves forecasting accuracy for new student admissions

Page 64 of 64 | Total Record : 632

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