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All Journal G-Tech : Jurnal Teknologi Terapan
Raymond Oetama
Universitas Multimedia Nusantara, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Image Error Detection: A Systematic Literature Review Raymond Oetama; David Tjahjana; Iwan Prasetiawan; Catherine Anastasia
G-Tech: Jurnal Teknologi Terapan Vol 7 No 3 (2023): G-Tech, Vol. 7 No. 3 Juli 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v7i3.2494

Abstract

The advancement of technology, as well as the creation of new techniques and methodologies for image analysis, is rapid. However, image detection may face some errors. Image error detection will be discussed in this comprehensive literature review. Throughout the papers, this work attempts to learn about the types of images used, algorithms that are frequently used, techniques that are frequently used, and metrics used to test the correctness of the suggested approach. The most commonly used image type is medical images such as Magnetic Resonance Imaging, the algorithm that is widely used is a Convolutional Neural Networks based algorithm. The method that is widely used is a machine learning-based method, and the measurement that is widely used is a Peak Signal Noise Ratio measurement method to measure the accuracy of the algorithm.
Enhancing Marketing Strategies for Office Stationery Stores Using Equivalence Class Transformation Algorithms Ega Silfa Yuliana; Raymond Oetama
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3441

Abstract

Office stationery stores facilitate operations for offices, schools, and businesses. However, the store owner must overcome the challenge of selecting the right products for promotion. This research aims to tackle this issue by comparing the performance of the Equivalence Class Transformation algorithms in discovering association rules using Support, confidence, and lift ratios. The findings reveal that the algorithm generates association rules based on Support, confidence, and lift values. Ten critical rules are identified, shedding light on algorithm effectiveness. Ultimately, this study underscores the significance of refining marketing approaches for brick-and-mortar stationery businesses and the value of data-driven methods in aiding decision-making. In the early promotion catalog phase, priority is assigned to rules with solid agreement by the Shop Owner, guiding the selection of featured items based on robust product associations.
Co-Authors Catherine Anastasia David Tjahjana Ega Silfa Yuliana Iwan Prasetiawan