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All Journal International Journal of New Media Technology Jurnal ULTIMATICS
Farica Perdana Putri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementasi Algoritma Apriori untuk Rekomendasi Kombinasi Produk Penjualan Andre Setiawan; Farica Perdana Putri
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (709.773 KB) | DOI: 10.31937/ti.v12i1.1644

Abstract

Analyzing and systematically extracting essential information from recording transactions is important for a business, including online stores. Sometimes, some online stores offer a product package that is not suitable for the customer. It happens because they did not process the data transaction to observe the association between products on a package. A web-based recommendation system was build using the CodeIgniter framework with PHP programming language. The system developed using Market Basket analysis that can determine the combination of products. Apriori algorithm used as a technique to analyze the relationship between products based on the data transaction. The lift ratio value generated from the rule is 1.18, which means that the rule has the power of relationships between items. We evaluate the system using USE questionnaire with usefulness results is 90.83%, ease of use 89.09%, ease of learning 95%, and satisfaction 90.94%, which strongly agree in every aspect.
DistilBERT with Adam Optimizer Tuning for Text-based Emotion Detection Farica Perdana Putri
IJNMT (International Journal of New Media Technology) Vol 10 No 1 (2023): IJNMT
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v10i1.3170

Abstract

Emotion detection (ED) refers to identifying individual emotions or feelings, such as happiness, sadness, disappointment, fear, etc. The classic machine learning technique still relies on feature engineering, which makes it difficult to convey the meaning of words. Deep learning-based algorithms have recently been shown to be beneficial for emotion detection because they require only a simple feature creation process. Transfer learning is an approach that uses data similarities, data distribution, models, tasks, and other factors to apply knowledge learnt in one domain to a new domain. This study is to shed light on the fine-tuned models' efficacy in detecting emotions from the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset. In order to optimize the model, we conducted the hyperparameters tuning on Adam optimizer in DistilBERT. The experiment examined the moment estimators and learning rate of Adam optimizer. The effect of the parameters to training and validation accuracy were presented and analyzed.
Co-Authors Andre Setiawan