cover
Contact Name
Adiwijaya
Contact Email
adiwijaya@telkomuniversity.ac.id
Phone
+6282217633999
Journal Mail Official
jdsa@telkomuniversity.ac.id
Editorial Address
Telkom University Jl. Telekomunikasi Terusan Buah Batu Indonesia, 40257, Bandung, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
Journal of Data Science and Its Applications
Published by Universitas Telkom
ISSN : -     EISSN : 26147408     DOI : https://doi.org/10.34818/jdsa
Core Subject : Science,
JDSA welcomes all topics that are relevant to data science, computational linguistics, and information sciences. The listed topics of interest are as follows: Big Data Analytics Computational Linguistics Data Clustering and Classifications Data Mining and Data Analytics Data Visualization Information Science Tools and Applications in Data Science
Articles 5 Documents
Search results for , issue "Vol 3 No 2 (2020): Journal of Data Science and Its Applications" : 5 Documents clear
Classification of Personality based on Beauty Product Reviews Using the TF-IDF and Naïve Bayes (Case Study : Female Daily) Novia Russelia Wassi; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.61

Abstract

A person's personality is an important parameter to determine the character of each person and also as an assessment in various ways. In this day and age personality can not only be known from psychological tests, but also can be known in various ways. One way is through reviews presented in electronic media. In this study, a person's personality was classified into three "Big Five" personality groups, namely: Openness, Conscientiousness, and Extraversion using the Naïve Bayes method and TF-IDF as Feature Extraction. The results of the classification that have been done get 81% accuracy with preproccessing scenarios using Stemming and Stopword, TF-IDF unigram, and BernoulliNB classifier type.
Comparative Analysis of Support Vector Machine-Recursive Feature Elimination and Chi-Square on Microarray Classification for Cancer Detection with Naïve Bayes Talitha Kayla Amory; Adiwijaya Adiwijaya; Widi Astuti
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.62

Abstract

Cancer is a world-famous deadly disease. According to the World Health Organization (WHO), cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. One well-known technique for cancer detection is the DNA microarray technique. DNA microarray technology provides an opportunity for researchers to analyze thousands of gene expression profiles at the same time to determine whether a person has cancer or not. However, one of the problems in DNA microarray data is the large number of features that require feature selection. In overcoming these problems, this study will use the feature selection Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Chi-Square and use the Naïve Bayes classification method. The accuracy results from using feature selection with those that are not will be compared. The accuracy between using the two feature selection methods will also be compared to find which feature selection method is better when combined with the Naïve Bayes classification method. To get an overall picture of the performance comparison, this study also considers precision, recall, and F1-score. The best accuracy results obtained were 100% lung cancer data with SVM-RFE and Chi-Square, 99.6% ovarian cancer with SVM-RFE, 93.7% breast cancer with SVM-RFE, and 90% colon cancer with SVM- RFE.
Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network Iyon Priyono; Adiwijaya Adiwijaya; Annisa Aditsania
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.52

Abstract

Cancer is a deadly disease caused by abnormal growth of tissue cells that are not controlled in the body. In 2018, according to Globocan data, the number of cancer sufferers has increased from the previous years which was 18.1 million people, with a mortality rate of 9.6 million. In recent years, cancer prediction using DNA microarrays data can help medical experts in analyzing whether a person has cancer or not. DNA microarray data have very large and complex gene expression, therefore a dimensional reduction method is needed. Then, the dimension reduction results will be used for classification into types of cancer or not. In this paper, Principal Component Analysis (PCA) is used as a feature extraction to reduce dimension and Functional Link Neural Network as a classifier. Based on the simulation, the average of accuracy using the FLNN and PCA about 76.08%. Keywords: cancer detection, Microarray data, Functional Link Neural Network, Principal Component Analysis.
Aspect Based Sentiment Analysis on Beauty Product Review Using Random Forest Anggitha Yohana Clara; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.58

Abstract

Cosmetics and beauty products (including skincare) are the products used as body care or face care and used to accentuate the body alure. A product could give diverse sentiment to the consumers including positive and negative sentiment. Many consumers of beauty products are sharing their reviews to help other consumers to find the right products to buy and to give feedback to the brand of the beauty product itself. The number of reviews is inversely proportional to the lack of opinion identification towards product’s aspects. Hence, a study has been conducted to analyze beauty products reviews as toner, serum, sun protection, and exfoliator. The analysis process is conducted aspect based to determine sentiment towards aspect of beauty products based on the reviews. The result is addressed to people using skincare and beauty product brands in deducting consumer’s opinion. The solution to this problem is by using Random Forest with hyperparameters tuning as classification method, and TF-IDF and n-gram as feature extraction methods. The multi-aspect sentiment analysis in this study obtained highest accuracy for 90.48%, precision for 87.27%, recall for 70.13%, and F1-Score for 71.77%.
Movie Recommendation Using Conversational Mechanism and Knowledge Based Filtering Marendra Septianta; Z. K. Abdurahman Baizal; Kemas Muslim Lhaksmana
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.49

Abstract

Conversational recommender system created for helping users in searching information in a domain by using conversational mechanism. These systems help user to get recommendation by selecting items that most suitable to user’s preference by asking user needed. The recommendations generated by eliciting user’s experience e.g. his favourite movies, actor and director and then gives the item that match their interest. There are many methods to get the suitable recommendation that match the user’s preference. In this paper, we use ontology which represents knowledge to get result of recommendation that fit to user preference by using knowledge-based filtering to determine the user’s need. Our system has been implemented for movie domain. We test our system performance by studying user's perception.

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