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 20 Documents
FORECASTING NUMBER OF PASSENGERS OF TRANSJAKARTA USING SEASONAL ARIMAX METHOD Virati, Maftukhatul Qomariyah; Pamanik, Diory Paulus; Pramana, Setia
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 | Full PDF (547.796 KB) | 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.
SNAKEBITE CLASSIFICATION USING ACTIVE CONTOUR MODEL AND K NEAREST NEIGHBOR Cakravania, Chiara Janetra; Utama, Dody Qori
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 | Full PDF (829.651 KB) | 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.
IDENTIFICATION OF PEDESTRIANS ATTRIBUTES BASED ON MULTI-CLASS MULTI-LABEL CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) Wardana, Wrida Adi; Siradjuddin, Indah Agustien; Muntasa, Arif
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 | Full PDF (1457.996 KB) | 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 Mabarti, Irne
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 | Full PDF (759.504 KB) | 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%.
SENTIMENT ANALYSIS OF MOVIE REVIEW USING NAïVE BAYES METHOD WITH GINI INDEX FEATURE SELECTION Purnomoputra, Riko Bintang; Adiwijaya, Adiwijaya; Novia Wisesty, Untari
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.412 KB) | DOI: 10.34818/jdsa.2019.2.36

Abstract

In movie reviews, there is information that determines whether the movie is good or bad. Sentiment analysis is used to process information to determine the polarity of the sentence. With unstructured reviews and a lot of data attributes so that it requires much time and computational capabilities that become a problem in the classification process. To process a lot of data selection features becomes a solution to reduce dimensions so it accelerate the classification process and reduce the occurrence of misclassification. The first Gini Index Text feature selection used to classify documents and successfully enhanced the classifier performance. Multinomial Naïve Bayes (MNNB)  is a popular classifier used for document classification however, will the Gini Index Text feature selection able to improve MNNB classification performance. Therefore in this study the author aims to use the Gini Index Text (GIT) for text feature selection with MNNB classifier to classify movie review  into positive and negative classes. The data used is IMDB dataset that contains reviews in English sentences, the data will be divided into two parts, training data is 90% and data testing is 10%. The test results prove that the Gini index as a selection feature can increase accuracy where accuracy without feature selection is 56% and with feature selection of 59.54% with an increase of 3.54%.
TOURISM RECOMMENDER SYSTEM USING ITEM-BASED HYBRID CLUSTERING METHOD (CASE STUDY: BANDUNG RAYA REGION) Arvianti, Qisti R; Baizal, Z. K. Abdurahman; Tarwidi, Dede
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.042 KB) | DOI: 10.34818/jdsa.2019.2.35

Abstract

The recommender system can be used to provide recommender for an item based on the highest. Therefore, the information recommended by the system can be as needed. The recommender system can help tourists to determine their travel choices, especially for tourists in the city of Bandung. In the recommender system there are two commonly used methods, namely collaborative filtering and content-based filtering methods. However, both methods still have drawbacks among them, in content-based filtering methods cannot recommend various items. While the collaborative filtering method cannot recommend items that have not been rated at all or cold start problems. The collaborative filtering method also cannot recommend to new users because new users do not have history. With the shortcomings of the two methods, the item-based clustering hybrid method (ICHM) is proposed to combine the two methods. The analysis was carried out by comparing the Mean Absolute Error (MAE) on several tests that have been carried out. In a cluster of 30 and c coefficient of 0.9, the average MAE value obtained is 0.2459 in cold start problems and 0.2488 in non cold problems. The smaller the MAE value is generated, that means the higher the level of accuracy.
PUBLIC RESPONSE ANALYSIS TOWARD POVERTY REDUCTION PROGRAM IN INDONESIA 2014-2018 THROUGH TWITTER DATA Nur Rohman, Mohammad Ilham; Mariyah, Siti
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (890.585 KB) | DOI: 10.34818/jdsa.2019.2.34

Abstract

Poverty reduction is the main priority of national development, it aims to encourage the improvement of the welfare of the poor in order to enjoy increasingly quality economic growth . But, every government program really needs an evaluation and how the response from the public. Social media monitoring can be used to understand and provide real-time feedback about policy reform. Therefore, government are expected to consider using similar technology in the current evaluation and operational framework. So they need tools to analyze the public response to poverty reduction programs in Indonesia by using Big Data through twitter data. With this research, it is expected that the public response from social media twitter can be a critique and suggestion for the government in establishing and implementing future poverty reduction policies. This research conducted to use twitter data on poverty reduction policies in Indonesia, such as: ?Bedah Kemiskinan Rakyat Sejahtera(BEKERJA)? Program, ?Padat Karya Tunai? Program, and ?Program Keluarga Harapan?. Data are sourced from all community tweets related to the programs. There are analyzed using the text mining method. The results of this study indicate that in general the public response received and supported three poverty reduction programs in Indonesia for the 2014-2018 period. So public response can be used as an evaluation and input to the government.
SENTIMENT ANALYSIS ON MOVIE REVIEWS USING INFORMATION GAIN AND K-NEAREST NEIGHBOR Daeli, Novelty Octaviani Faomasi; 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 | Full PDF (628.828 KB) | 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.
VISUALIZING LANGUAGE LEXICAL SIMILARITY CLUSTERS: A CASE STUDY OF INDONESIAN ETHNIC LANGUAGES Nasution, Arbi Haza; Murakami, Yohei
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1160.257 KB) | DOI: 10.34818/jdsa.2019.2.23

Abstract

Language similarity clusters are useful for computational linguistic researches that rely on language similarity or cognate recognition. The existing language similarity clustering approach which utilizes hierarchical clustering and k-means clustering has difficulty in creating clusters with a middle range of language similarity. Moreover, it lacks an interactive visualization that user can explore. To address these issues, we formalize a graph-based approach of creating and visualizing language lexical similarity clusters by utilizing ASJP database to generate the language similarity matrix, then formalize the data as an undirected graph. To create the clusters, we apply a connected components algorithm with a threshold of language similarity range. Our interactive online tool allows a user to dynamically create new clusters by changing the threshold of language similarity range and explore the data based on language similarity range and number of speakers. We provide an implementation example of our approach to 119 Indonesian ethnic languages. The experiment result shows that for the case of low system execution burden, the system performance was quite stable. For the case of high system execution burden, despite the fluctuated performance, the response times were still below 25 seconds, which is considered acceptable.
UNDERSTANDING PUBLIC ATTITUDE TOWARDS POLITICAL CANDIDATE THROUGH CONVERSATIONAL NETWORK IN WEST JAVA REGIONAL ELECTION Putra, Rimba Pratama; Fakhrurroja, Hanif; Alamsyah, Andry
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (444.177 KB) | DOI: 10.34818/jdsa.2019.2.21

Abstract

Social media is a very important part of the political campaign strategy. By using information about various policies as well as public opinion, will provide rich information in political issues during elections. The problem is how political attitudes in social media relate to the results of the election winners. In this paper, we proposed a methodology of social network analysis to measure conversational network activity. As a case study, we select the Pilkada Jawa Barat 2018 for the reason most populous province in Indonesia. We get the conversation in online social network service Twitter and collected 70335 tweets from June 20 to June 26, 2018. Our findings indicate that the network properties of each candidate is in accordance to the real count and the candidate that appear most often are "@ridwankamil", the name of the winner of the regional elections in the Pilkada Jawa Barat 2018. We summarize all the conversations of each candidate and our results show there are high correlations with the results of the election winners. Because the higher the conversations network of each candidate, the greater the possibility of winning the election.

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