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All Journal Knowledge Engineering and Data Science Smart Techno (Smart Technology, Informatic and Technopreneurship)
Suhana, Rizka
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Computer Science & IT Engineering

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Sentiment Analysis of YouTube Comments for the Jumbo Movie Trailer Using IndoBERT Zamakhsyari, Fardan; Suhana, Rizka; Ramadhani, Irfan; Priyo Santoso, Dwi
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.198

Abstract

The film industry in Indonesia has experienced significant growth, from cinematography to animation. Along with this growth, public opinion has also varied, from assessments of the storyline to the production process. To assess public sentiment on social media, a system is needed that can accommodate this process. This study aims to analyse public sentiment towards the trailer for the animated film ‘Jumbo,’ which was released on the YouTube platform. Using an NLP approach, two fine-tuned IndoBERT models were compared: ‘Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis’ and ‘rikidharmawan/finetuning-sentiment-model-indobertweet-v2’. The data to be processed was obtained from 1,468 YouTube comments through a crawling process using the YouTube API. The data was then analysed using both models to classify the comments into positive, neutral, and negative sentiments. Evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The evaluation results show that ‘Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis’ is superior, with an accuracy of 57.2% and a higher average F1-score compared to ‘rikidharmawan/finetuning-sentiment-model-indobertweet-v2,’ which has an accuracy of 51.3%. This research contributes to the selection of sentiment analysis models for Indonesian-language data, particularly in the domains of social media and the film industry.
Fish Image Classification using Transfer Learning Method withAdaptive Learning Rate Suhana, Rizka; Mahmudy, Wayan Firdaus; Budi, Agung Setia
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The diversity of fish species in coral reef ecosystems is one of the indications in determining health in coral reef ecosystems. Many Indonesian Fisheries and Marine Research and Development Agency experts carefully classify fish images. A reliable technique for performing image classification is Convolutional Neural Network (CNN). Transfer learning appears and adopts part of CNN, namely the modified convolution layer. The paper aims to solve the fish classification problem using the pre-trained model of Mobilenet V2. The model has a low computational process and does not use too many memory resources when training image data. The research image data used is 49,281 data of various sizes and 18 types of fish. The image is entered into the transformation process (random rotation, random resize crop, random horizontal flip) on the training and test data to produce varied data. After the transformation process, the image data is entered into the training process using the Mobilenet V2 architecture. Testing the Mobilenet V2 architectural model obtained an accuracy score of 99.54%, which is reliable in classifying fish images.
Co-Authors Agung Setia Budi Agung Setia Budi, Agung Setia Irfan Ramadhani, Irfan Priyo Santoso, Dwi Wayan Firdaus Mahmudy Zamakhsyari, Fardan