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Mustaqim, Tanzilal
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Computer Science & IT

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Aspect Based Sentiment Analysis of Product Review Using Memory Network Ismet, Hilya Tsaniya; Mustaqim, Tanzilal; Purwitasari, Diana
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.34094

Abstract

Abstract. Purpose: Consumer opinion is one of the essential keys that affect the success of a product. Sentiment analysis of consumer opinion is needed to find out information about customer satisfaction for companies in the decision-making process. The traditional sentiment analysis process extracts a complete sentiment from a single sentence. However, it does not consist of only one sentiment in one sentence. The total number depends on the number of aspects that make up the sentence. Therefore, a sentiment analysis process is needed to pay attention to aspects.Methods: This research focuses on product reviews from Indonesian e-commerce on several aspects of sentiment. Uses fastText word embedding to avoid Out of Vocabulary in datasets and Gated Recurrent Units for aspect spread detection. Sentiment classification on aspects using the Memory Network method.Result: The experiment results showed that aspect-based sentiment classification predictions had an accuracy of 83% compared to 78% overall classification predictions for review texts, indicating that aspect-based sentiment analysis can improve model performance on product review classification predictions.Novelty: Most product reviews analysis use document-level classification to extract and predict sentiment reviews, aspect-based analysis can be applied to product reviews for better sentiment understanding, using Memory Network to store important information explicitly on aspects and polarity.
Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image Mustaqim, Tanzilal; Fatichah, Chastine; Suciati, Nanik
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.37350

Abstract

Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of mAP@.5 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.
Co-Authors Chastine Fatichah Diana Purwitasari Ismet, Hilya Tsaniya Nanik Suciati