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All Journal Journal of Information Systems Engineering and Business Intelligence IT JOURNAL RESEARCH AND DEVELOPMENT Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Alifia Revan Prananda
Department Of Information Technology, Faculty Of Engineering, Universitas Tidar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion Alifia Revan Prananda; Irfandy Thalib
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.1-8

Abstract

Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion:  According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows      that the decision tree provides the best performance.
Toward Better Analysis of Breast Cancer Diagnosis: Interpretable AI for Breast Cancer Classification Alifia Revan Prananda; Eka Legya Frannita
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.11563

Abstract

Recently, some countries have been distressing with the increasing number of breast cancer cases. Those cases were extremely increased in every year. Practicaly, the increasing number of patients was caused by the manual examination. Recently, some researchers have been done in the development of AI method for solving this problem. However, AI itself still has limitation since it worked in the black-box approach which was difficult to be trusted. Thus, to overcome those problems, we proposed a method that was able to classify breast ultrasound images into two classes (benign and malignant) and able to explain how the prediction was made. Our proposed method consisted of four processes i.e., pre-processing step, development of CNN model, interpretable step and evaluation. In this research work, our proposed method performed into 780 breast ultrasound images divided into three classes (133 normal, 210 malignant, and 437 benign). In the training process, our proposed method obtained training accuracy of 0.9795, training loss of 0.0675. The validation process obtained validation accuracy of 0.8000 and validation loss of 0.5096. While, in the testing process, our proposed method achieved accuracy of 0.7923. In the interpretable process using LIME, the LIME result is covered by doctor visualization. It was indicated that LIME was suitable enough in visualizing the important features of breast cancer severity. Regarding to the results, our proposed method has a potensial to be implemented as an early detection method for classifying malignancy of breast cancer in order to help the doctor in the screening process
Toward Adaptive Manufacturing Development: Implementation of Artificial Intelligence for Identifying Leather Defects Alifia Revan Prananda; Eka Legya Frannita
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 10 No 2 (2023): List of the Accepted Article for Future Issues
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v10i2.4329

Abstract

Artificial intelligence was the powerful approach that was proven to be impactful for solving several problems. In the leather inspection cases, artificial intelligence also contributed some research works that effected for leather inspection process. In this research, we employed NasNet architecture conducted by using fine-tunning transfer learning method to distinguish the types of leather defects. We used 3600 images that was distributed into six classes which are folding marks, grain off, growth marks, loose grains, pinhole and non-defective. Our proposed solution successfully achieved accuracy for training data is 0.9788 with loss of 0.0198. While the maximum accuracy in validation data is 0.8059 with loss of 0.2126. In the testing data, our experiment obtained accuracy of 0.8603 with loss of 0.1603. These results indicated that our proposed solution was suitable to recognize the characteristics of leather defects and suitable to distinguish them.
Co-Authors Eka Legya Frannita Irfandy Thalib