Implementation Of Ratcliff/Obershelp And Cosine Similarity Methods On Image For Detection

Deri Lianda — Thu, 12 Jun 2025 08:23:36 +0000

Image is a tool for communicating in conveying certain messages from the image maker, so that images can be used as language in the technical field. Important pictures are stored by the user on a storage medium. The problem that occurs is that an image can be copied for personal gain, for example forging scanned images on important documents that can be misused. This study uses the Ratcliff/Obershelp and Cosine Similarity methods to detect similarities and differences in image images. So that with the implementation of the Ratcliff/Obershelp and Cosine Similarity methods for detecting similarities and differences in images, the image similarities can be known.

Effectiveness And Efficiency Of LLM Models Vs Traditional Machine Learning In Sentiment Analysis Of Indonesian Language Product Reviews

Galih Setiawan Nurohim, Budi Al Amin, Heribertus Ary Setyadi — Sat, 05 Jul 2025 08:07:28 +0000

This research aims to conduct a comparative analysis of the performance and efficiency of several machine learning models in the task of sentiment analysis on Indonesian language customer reviews. In the digital business era, a quick and accurate understanding of customer opinions is a strategic asset for making decisions, from product development to marketing strategy. Four models were evaluated: two Transformer-based models (agufsamudra/indo-sentiment-analysis and ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa), Naive Bayes, and K-Nearest Neighbors (KNN) on a dataset of 5,400 product reviews. The evaluation metrics used are Accuracy, Precision, Recall, and F1-Score. The results show that the Naive Bayes model and the Transformer model 'agufsamudra/indo-sentiment-analysis' achieve the highest performance with an F1-Score and accuracy of around 95%, significantly outperforming other Transformer models (90%) and KNN (47%). The crucial finding of this research is that the performance of the classical Naive Bayes model is equivalent to the state-of-the-art Transformer model. From an accounting and business perspective, this implies that solutions with much higher computational efficiency (Naive Bayes) can provide a more optimal Return on Investment (ROI) for large-scale implementation of customer sentiment monitoring systems.

Jurnal Media Computer Science

Implementation Of Ratcliff/Obershelp And Cosine Similarity Methods On Image For Detection

Effectiveness And Efficiency Of LLM Models Vs Traditional Machine Learning In Sentiment Analysis Of Indonesian Language Product Reviews