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 1 (2020): Journal of Data Science and Its Applications" : 5 Documents clear
Forecasting Number of Passengers of TransJakarta using Seasonal ARIMAX Method Maftukhatul Qomariyah Virati; Diory Paulus Pamanik; Setia Pramana
Journal of Data Science and Its Applications Vol 3 No 1 (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.2020.3.45

Abstract

TransJakarta is one of the most common public transportation modes used by the public in Jakarta. Every day there are more than 300.000 people who use TransJakarta . The number of TransJakarta buses is still limited, so to optimize services, we should know when the number of users in peak time and when the number of users in low time. In addition to providing comfort to customers, maintenance for TransJakarta buses can also be optimized, thereby reducing incident and unwanted events. This study investigates the pattern of the number of TransJakarta passengers differs on weekends, weekdays, and holidays. Also, this study predict how many TransJakarta passengers in the future, by using SARIMAX method, which is SARIMA method with X - factor. In the implementation, the study is conducted using R application with the addition of x-factor in the form of dummy variable for tap-in data in holiday period.The predicted result being produced is not too far away with the actual figure with the best model is SARIMA(0,0,0)(2,1,0)[7] with x-factor and the error analys is MSE = 162402173, MAPE = 2.6122 and MASE = 0.211698.
Identification of Pedestrians Attributes Based on Multi-Class Multi-Label Classification using Convolutional Neural Network (CNN) Wrida Adi Wardana; Indah Agustien Siradjuddin; Arif Muntasa
Journal of Data Science and Its Applications Vol 3 No 1 (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.2020.3.43

Abstract

The usage of computer vision in identifying pedestrians attributes has received a great attention, especially in the visual surveillance systems. For instance, searching for system based on the attributes. Attributes Identification using Convolutional Neural Network architecture is presented in this article, since the architecture can perform feature learning. CNN consist of convolution layer, ReLU, Pooling, and Fully-connected. There are three experiment scenarios are conducted based on the number of convolution layers, to determine the effect of layers on CNN performance. Three different CNN architectures were trained and tested using a PETA dataset with 35 attributes. The highest accuracy achieved is 75.66% based on number of convolutional layers. The conducted experiments showed that more numbers of convolution layers used would produce the better CNN's performance.
Implementation of Minimum Redundancy Maximum Relevance (MRMR) and Genetic Algorithm (GA) for Microarray Data Classification with C4.5 Decision Tree Irne Mabarti
Journal of Data Science and Its Applications Vol 3 No 1 (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.2020.3.37

Abstract

Cancer is one of the highest causes of death in various countries, even an increase in mortality rates happens every year. On the other hand, bioinformatics technology will be beneficial for predicting cancer, one of the methods that can be considered in predicting cancer is the classification of microarrays data. Microarray data is data containing many gene expressions that describe DNA cells. Microarray data has enormous dimensions. The dimension reduction method used in this study is the Minimum Redundancy Maximum Relevance (MRMR), the optimization method used is the Genetic Algorithm (GA) method, and the last method is C4.5 aimed at classifying gene data. In this study, there were two trials. The first trial used the Minimum Redundancy Maximum Relevance (MRMR) method combined with Genetic Algorithm (GA) as an optimization method and the C4.5 classification method, and the trial resulted in an average accuracy of 79%. While the second trial using the Genetic Algorithm (GA) method for feature selection and the C4.5 classification method produces an average accuracy of 78%.
Snakebite Classification Using Active Contour Model and K Nearest Neighbor Chiara Janetra Cakravania; Dody Qori Utama
Journal of Data Science and Its Applications Vol 3 No 1 (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.2020.3.38

Abstract

Indonesia is categorized as one of tropical countries that have a high risk of snakebites. This surely may endanger rural citizens’ lives for there are still many snakes found in rural areas. The main cause of death from snakebite cases is by reason of the venom squirted from snake’s canine teeth. Others causes are errors in identifying the bite marks visually. There are anatomical differences between puncture wounds from venomous and non-venomous snakes. This study established a snakebite identification system using Active Contour Model and K Nearest Neighbor (KNN) methods. By performing some tests related to the parameters used in the method, the highest accuracy value on K Nearest Neighbor method was obtained by using the correlation distance rule, the K value = 3, without using distance weight in the classification system.
Sentiment Analysis on Movie Reviews using Information Gain and K-Nearest Neighbor Novelty Octaviani Faomasi Daeli; Adiwijaya Adiwijaya
Journal of Data Science and Its Applications Vol 3 No 1 (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.2020.3.22

Abstract

The huge resources need effectiveness and efficiency, it can be processed by machine learning. There have been many studies conducted using machine learning method and produced quite good performance in sentiment analysis. Some machine learning methods that are often used in general are Naive bayes (NB), K-nearest neighbor (KNN), Support vector machine (SVM), and Random forest methods. Mostly, KNN did not achieve better performance than other machine learning methods in sentiment analysis. In this study, the Polarity v2.0 from Cornell movie review dataset will be used to test KNN with Information gain features selection in order to achieve good performance. The purpose of this research are to find the optimum K for KNN and compare KNN with other methods. KNN with the help of Information gain feature selection becomes the best performance method with 96.8% accuracy compared to the NB, SVM, and Random forest while the optimum K is 3.

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