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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Applied Research In Computer Science and Information Systems
Mery Oktaviyanti Puspitaningtyas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Analisis Kualitas Website PT Takdir Jaya Abadi Menggunakan Metode Webqual 4.0 Dan Importance Performance Analysis Syahrul Aziz; Mery Oktaviyanti Puspitaningtyas; Yumi Novita Dewi
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 2 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i2.4473

Abstract

PT Takdir Jaya Abadi's website functions as a complete company information media, which includes information about company profiles and the industry sector currently engaged in, namely the optical disc replica services field. Since it was created in 2020, an evaluation has never been carried out, so the company does not know the quality of the website. Hence, it is necessary to evaluate PT Takdir Jaya Abadi's website. This study aims to determine whether the website meets user expectations using the Webqual 4.0 method and Importance Performance Analysis (IPA). The data obtained from 65 respondents showed a conformity analysis of 96% which stated that the user was not satisfied. The development of the gap analysis was -0.166, which means that the quality level is still not in line with user expectations. The results of the quadrant analysis that is a priority for website improvement are quadrant I with the attributes IF1, IF2, IT2, IT3 and IT4. These attributes are regarding up to date information, accurate information, a sense of security when accessing, a sense of security when conducting information search activities, and the safety of users' personal information.
Enhancing Text Classification Performance: A Comparative Study of RNN and GRU Architectures with Attention Mechanisms Yulita Ayu Wardani; Mery Oktaviyanti Puspitaningtyas; Happid Ridwan Ilmi; Onesinus Saut Parulian
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.187

Abstract

Text classification plays a crucial role in natural language processing, and enhancing its performance is an ongoing area of research. This study investigates the impact of integrating attention mechanisms into a recurrent neural network (RNN) based architectures, including RNN, LSTM, GRU, and their bidirectional variants (BiLSTM and BiGRU), for text sentiment analysis. Three attention mechanisms Multihead Attention, Self Attention, and Adaptive Attention are applied to evaluate their effectiveness in improving model accuracy. The results reveal that attention mechanisms significantly enhance performance by enabling models to focus on the most relevant parts of the input text. Among the tested configurations, the LSTM model with Multihead Attention achieved the highest accuracy of 68.34%. The findings underscore the critical role of attention mechanisms in overcoming traditional RNN limitations, such as difficulty in capturing long-term dependencies, and highlight the potential for their application in broader text classification tasks.
Co-Authors Happid Ridwan Ilmi Onesinus Saut Parulian Syahrul Aziz Yulita Ayu Wardani Yumi Novita Dewi