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All Journal G-Tech : Jurnal Teknologi Terapan JUSIFOR : Jurnal Sistem Informasi dan Informatika
Setyaji, Wahyu Candra
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Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Library & Information Science

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Pengaruh Gadget Terhadap Minat Belajar Siswa SMK Muhammadiyah 5 Kepanjen Dengan Metode TAM Setyaji, Wahyu Candra; Ratnasari, Novia; Widaningrum, Anisa Hudi; Wahyudi, Farid; Rahmawati, Zurriat Nyndia
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.7182

Abstract

Technology is developing at a rapid pace, and this significantly affects our daily lives. Gadgets have transformed our lifestyle, particularly in the field of education. One of the aspects impacted is students' learning interest. Students tend to focus more on subjects they are interested in, and less on those they are not. Learning interest is influenced by both internal and external factors, with gadget use being one of the external factors. The purpose of this study is to determine the extent to which gadgets influence students' learning interest at SMK Muhammadiyah 5 Kepanjen, Malang Regency. The data used in this research is primary data collected through questionnaires. The population consists of 275 students, with a sample of 163 tenth-grade students from all vocational programs at SMK Muhammadiyah 5 Kepanjen. The findings indicate that gadgets have a significant influence on students' learning interest, as evidenced by a significance value (sig.) lower than the probability value (0.001 < 0.05) and a t-calculated value greater than the t-table value (10.745 > 1.975).
TikTok Live Chat Analysis Using NLP for Sales and Spam Detection: A Systematic Literature Review Setyaji, Wahyu Candra; Ratnasari, Novia; Widaningrum, Anisa Hudi; Septarina, Amalia Agung
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7889

Abstract

The growing popularity of social commerce platforms such as TikTok Shop Live offers an interactive shopping experience through live streaming and chat features. However, many messages in the chat are irrelevant to sales, often containing spam or meaningless comments. This study examines the effectiveness of Natural Language Processing (NLP) methods in distinguishing between sales-related and spam chats to improve seller–consumer interactions. A systematic literature review was conducted on 13 articles focusing on spam detection and chat classification. The findings reveal that classical methods such as Naïve Bayes with TF-IDF achieve an accuracy of 85–90%, while K-Nearest Neighbor (KNN) and Logistic Regression are effective for simple cases with an accuracy of 80–87%. Deep learning methods deliver higher performance, with Long Short-Term Memory (LSTM) achieving 90–93% due to its strength in recognizing sequential patterns and informal language, while Convolutional Neural Networks (CNN) reach competitive accuracy rates of 88–91%. Transformer-based models, particularly Multilingual BERT, yield the highest accuracy (93–95%) because of their ability to capture contextual meanings in informal Indonesian texts. These findings confirm that a combination of classical and modern NLP methods is effective in supporting automated TikTok Live chat detection systems and can be further developed into real-time applications to enhance interaction quality, data-driven decision-making, and consumer satisfaction.
Co-Authors Rahmawati, Zurriat Nyndia Ratnasari, Novia Septarina, Amalia Agung Wahyudi, Farid widaningrum, anisa hudi