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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Sistem Cerdas Building of Informatics, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Jurnal Sistem Cerdas

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Sentiment Analysis ChatGPT Using the Multinominal Naïve Bayes Classifier (NBC) Algorithm Sri Rahayu, Dwi; Novita, Rice; Khairil Ahsyar, Tengku; Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.388

Abstract

Chatbots have become one of the popular solutions for improving customer service. One well-known chatbot is ChatGPT, a language model developed by OpenAI. As time goes by and more and more people use ChatGPT, sentiment analysis is needed about users' opinions about the ChatGPT service. Therefore, it is necessary to carry out sentiment analysis of the ChatGPT service on Twitter to find out how users respond to this chatbot service. In this research, the results showed positive sentiment of 57%, negative sentiment of 29% and neutral sentiment of 14%. Topics for each sentiment were also obtained and sentiment prediction results from 40% of the test data with results of 96% positive, 3.5% negative and 0.5% neutral with a test accuracy of 63%.
Classification of Beef and Pork with Deep Learning Approach Akhiril Anwar Harahap; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.393

Abstract

Beef is one of the most consumed meats in Indonesia. However, the high price of beef has led to rogue traders mixing pork with beef. This condition occurs due to the lack of public knowledge about the difference between the two meats. To maintain food safety in Indonesia and especially in Riau province, the Livestock Service Office of Riau province conducts market surveys. There are several methods that are usually used to check the content of beef or pork, including Rapid Test Kit and Elisa. Both methods are time consuming and costly. One other solution that can be used is the artificial intelligence method, namely deep learning. In this research, a classification approach using deep learning is used to distinguish between beef and pork in the form of a web application. This research compares Convolutional Neural Network algorithm with Inception-V3 and Inception-Resnet-V2 architecture with hyperparameter optimization. From several experiments that have been carried out, the best model is the Inception-Resnet-V2 architecture with an experimental scenario using a learning rate of 0.001, and an optimizer Adam with an accuracy of 96.50%, Precision 96.48%, Recall 96.55% and F1-Score 96.50%. By using this model, web-based applications can be developed using the flask framework well and can perform classification accurately.
Comparison of Service and Ease of e-Commerce User Applications Using BERT Yuda, Afi Ghufran; Novita, Rice; Mustakim; Afdal, M.
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.403

Abstract

The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
Sentiment Analysis on the Impact of Artificial Intelligence (AI) Development to Determine Technology Needs Abror, Naufal; Novita, Rice; Mustakim; Afdal, M.Afdal
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.404

Abstract

Artificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Andriawan, Ahmad Rizky Anisa Putri Auliani, Sephia Nazwa Azzahra, Aura Dwinnie, Zairy Cindy F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Khairil Ahsyar, Tengku Kurniawan, Viki M Afdal M. Afdal Muhammad Iqbal Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Suci Maharani Rahayu, Dwi Sri Ramadani, Faradila Rozanda, Nesdi Evrilayan Sasmita, Indah Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly