In facing the industrial era 4.0, college graduates in information and computer science are required to adapt to the developments and needs of current industrial technology. The link and match between the world of education and industry is the key to optimizing the absorption of skilled labor. To answer these challenges, the Information systems undergraduate study program at Institut Teknologi Telkom Purwokerto, SUHU, and T-Lab held a Webinar Series "Link & Match of Information Technology between Academics and Industrial Needs" with the topic of Digital Business Roadmap: Exploring creative digital business ideas. This webinar activity is carried out using a mentoring method as well as sharing new knowledge with participants about Digital Business Transformation and how to explore creative digital business ideas. The result is that participants can understand digital business transformation and then explore creative digital business ideas to be developed in a business proposal.
Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E-marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace.
Korean culture in the last two decades has shaken the whole world, including Indonesia, almost all millennial children talk about Korean culture, whether from dramas, films, songs, fashion, lifestyle, industrial products have begun to penetrate the lives of Indonesian people. One of the trends of public interest is Korean film drama. However, in the expansion of Korean film dramas in Indonesia, there must still be a negative perspective, so an analysis of the sentiments of Indonesian people's opinions regarding Korean culture is currently on the rise. This sentiment analysis data is taken from comments about Korean dramas that are widely written on Twitter. From the many opinions about Korean dramas, a classification is needed according to the existing sentiments so that it will be easy to get the tendency of opinions written on Twitter towards Korean dramas whether they tend to have positive, neutral or negative opinions. In the analysis of opinion sentiment using a Naive Bayes approach taken from Twitter Social Media. The application of the Nave Bayes algorithm in grouping positive, neutral and negative sentiments based on Korean drama commentary review data collected. The purpose of this study is to analyze public opinion and sentiment on Korean dramas on Twitter based on 100 data taken, using Orange tools for the sentiment analysis process. The results given show a percentage value of 69% by calculating the Naive Bayes algorithm
Sebagai salah satu objek penelitian yang dikembangkan untuk klasifikasi spesies tanaman melalui proses identifikasi citra daun. Seringkali citra daun yang digunakan tidak dalam kondisi yang ideal untuk dikaji karena banyaknya gangguan terutama bila pengujian dilakukan menggunakan objek sketsa yang tentunya memiliki tingkat keserupaan yang jauh dari bentuk alaminya. Sehingga diperlukan adanya perbaikan struktur citra sketsa daun agar mempermudah klasifikasi tanaman dengan metode penerjemahan citra. Metode yang dapat digunakan untuk mengubah menerjemahkan citra adalah dengan memanfaatkan model generatif pada kerangka kerja <em>deep learning.</em> Metode yang diusulkan pada model generatif ini adalah dengan memanfaatkan jaringan permusuhan model antara model generatif dengan arsitektur U-Net dan model diskriminatif dengan arsitektur PatchGANs yang lebih dikenal dengan metode CoGANs. Kerangka kerja <em>deep learning </em> tersebut dilatih selama 50 <em>epoch</em> kemudian dianalisis secara kualitatif dan kuantitatif. Pada hasil kualitatif, semakin lama pembelajaran maka hasil penerjemahan akan semakin baik. Sedangkan pada penilaian kuantitatif model generatif melalui MAE <em>loss function</em> menghasilkan nilai 0,0926 (± 0,0068) yang menunjukkan bahwa model generatif tersebut menghasilkan penerjemahan citra yang mampu mendekati citra aslinya
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.