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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JPTK: Jurnal Pendidikan Teknologi dan Kejuruan
Wihidayat, Endar Suprih
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Computer Science & IT Engineering Other

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A Comparison between the Use of Cisco Packet Tracer and Graphical Network Simulator 3 as Learning Media on Students’ Achievement Sari, Liska Mey Ika; Hatta, Puspanda; Wihidayat, Endar Suprih; Xiao, FENG
Jurnal Pendidikan Teknologi dan Kejuruan Vol 24, No 1 (2018): (May)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.156 KB) | DOI: 10.21831/jptk.v24i1.16042

Abstract

Usually in studying the practice of computer networks, it is encountered several obstacles such as (1) limited computer networks design tools, (2) limited learning time to design computer networks and (3) technical difficulties for finding the solutions of errors. To overcome those barriers as proposed in this study, computer network simulators were used. Computer network simulators were expected to help students designing and simulating networks planned to be applied to computer network practices. This study used two simulators to compare its effectiveness in assisting the students to learn computer networks, which were Cisco Packet Tracer and Graphical Network Simulator 3 (GNS3). This study was aimed to determine the difference of the influence of network simulators to (1) learning achievement, and (2) learning achievement improvement. The quasi-experiment method was used and data were collected through conducting testing before and after the utilization of the simulators. Based on the testing results it was concluded that (1) different effect of using Cisco Packet Tracer and GNS3, the average grade achievement in the class using GNS3 and using Cisco packet tracer were 76.67 and 70 respectively, and (2) improved learning achievement using GNS3 for around 35%, higher than using Cisco Packet Tracer.
Sentence embedding to improve rumour detection performance model Anggrainingsih, Rini; Wihidayat, Endar Suprih; Widoyono, Bambang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp115-121

Abstract

Recently, most individuals have preferred accessing the most recent news via social media platforms like Twitter as their primary source of information. Moreover, Twitter enables users to post and distribute tweets quickly and unsupervised. As a result, Twitter has become a popular platform for disseminating false information, such as rumours. These rumours were then propagated as accurate and influenced public opinion and decision-making. The issue will arise when a decision or policy with substantial consequences is made based on rumours. To avoid the negative impacts of rumours, several researchers have attempted to detect them automatically as early as feasible. Previous studies employed supervised learning methods to identify Twitter rumours and relied on feature extraction algorithms to extract tweet content and context elements. However, manually extracting features is time-consuming and labour-intensive. To encode each tweet's sentence as a vector based on its contextual meaning, we proposed utilising Bidirectional Encoder Representation of Transformer (BERT) as a sentence embedding. We then used these vectors to train some classifier models to detect rumours. Finally, we compared the performance of BERT-based models to feature engineering-based models. We discovered that the suggested BERT-based model improved all parameters by around 10% compared to the feature engineering-based classification model.
Co-Authors Feng Xiao Puspanda Hatta Rini Anggrainingsih Sari, Liska Mey Ika Widoyono, Bambang