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All Journal Jurnal Masyarakat Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Aris Sugiharto
Universitas Diponegoro
Computer Science & IT Engineering

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Analisis Pengaruh Kualitas Website Shopee Terhadap Kepuasan Pengguna Berdasarkan Metode WebQual 4.0 (Studi Kasus : Mahasiswa Pengguna Shopee di Universitas Diponegoro) M. Risqi Amirul Adieb; Aris Sugiharto; Satriyo Adhy
Jurnal Masyarakat Informatika Vol 15, No 1 (2024): May 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.1.59737

Abstract

The proliferation of e-commerce business actors creates fierce online buying and selling competition therefore e-commerce needs to maintain its existence. One of the main factors that encourage customers to conduct e-commerce transactions is the user satisfaction level in website acceptance of the e-commerce website's service quality. This research purposes to analyse the quality factors of the Shopee website that influence the level of user satisfaction based on the WebQual 4.0 method and the additional variable of user satisfaction. The results of data analysis and research obtained that the contribution of the influence provided by the WebQual 4.0 dimensions simultaneously on user satisfaction in the acceptance of the Shopee website is 76,2%, in terms of the WebQual 4.0 dimensions simultaneously with a significant influence on user satisfaction, usability that positively and significantly influences user satisfaction, information quality which positively and significantly influences user satisfaction, and service interaction quality with a positive and significant influence on user satisfaction.
BERT Model Fine-tuned for Scientific Document Classification and Recommendation Muhammad Deagama Surya Antariksa; Aris Sugiharto; Bayu Surarso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6789

Abstract

The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems.
Co-Authors Bayu Surarso M. Risqi Amirul Adieb Muhammad Deagama Surya Antariksa Satriyo Adhy