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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Seminar Nasional Informatika (SEMNASIF) JURNAL ISTEK Proceeding of the Electrical Engineering Computer Science and Informatics
Winarko, Edi
Computer Science And Electronics Department, Faculty Of Mathematics And Natural Sciences Universitas Gadjah Mada, Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm Ramos Somya; Edi Winarko; Sigit Priyanta
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4021

Abstract

In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
Content-based product image retrieval using squared-hinge loss trained convolutional neural networks Arif Rahman; Edi Winarko; Khabis Mustofa
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5804-5812

Abstract

Convolutional neural networks (CNN) have proven to be highly effective in large-scale object detection and image classification, as well as in serving as feature extractors for content-based image retrieval. While CNN models are typically trained with category label supervision and softmax loss for product image retrieval, we propose a different approach for feature extraction using the squared-hinge loss, an alternative multiclass classification loss function. First, transfer learning is performed on a pre-trained model, followed by fine-tuning the model. Then, image features are extracted based on the fine-tuned model and indexed using the nearest-neighbor indexing technique. Experiments are conducted on VGG19, InceptionV3, MobileNetV2, and ResNet18 CNN models. The model training results indicate that training the models with squared-hinge loss reduces the loss values in each epoch and reaches stability in less epoch than softmax loss. Retrieval results show that using features from squared-hinge trained models improves the retrieval accuracy by up to 3.7% compared to features from softmax-trained models. Moreover, the squared-hinge trained MobileNetV2 features outperformed others, while the ResNet18 feature gives the advantage of having the lowest dimensionality with competitive accuracy.
Co-Authors Agus Harjoko Agus Harjoko Anak Agung Istri Ngurah Eka Karyawati Ani Dijah Rahajoe Aniati Murni Arymurthy Arif Rahman Arif Rahman Azhari Azhari Azhari, Azhari Dian Widiyanto Chandra Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edy Winarno Jumadi Jumadi Khabib Mustofa Khabis Mustofa Kristoko Dwi Hartomo Mila Chrismawati Paseleng Moh. Edi Wibowo Muhammad Davi Ramos Somya Retantyo Wardoyo Sigit Priyanta Sri Yulianto Joko Prasetyo Wibowo, Moh. Edi Wiranto Wiranto