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Adiwijaya
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adiwijaya@telkomuniversity.ac.id
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jdsa@telkomuniversity.ac.id
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Telkom University Jl. Telekomunikasi Terusan Buah Batu Indonesia, 40257, Bandung, Indonesia
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Journal of Data Science and Its Applications
Published by Universitas Telkom
ISSN : -     EISSN : 26147408     DOI : https://doi.org/10.34818/jdsa
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol 1 No 1 (2018): Journal of Data Science and Its Applications" : 5 Documents clear
Classifying Electronic Word of Mouth and Competitive Position in Online Game Industry Bram Manuel; Dodie Tricahyono
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.9

Abstract

The number of online review in online game industry growing significantly along with growing rateof internet adoption. With abundant number of data, one can acquire limitless insight, for example,information regarding of electronic word-of-mouth (e-WOM) whom greatly affecting consumerbehavior and business performance. Knowledge of e-WOM can be used as competitive intelligenceto deal with industrial competition. Therefore, this research answers how to classify e-WOM, whatare e-WOM aspects emerge in MMOFPS game, and how does comparison of e-WOM positivitybetween the three MMOFPS Game used as research objects. Dataset are constructed from Reviewpage of Steam website for respective games with total 499 reviews used as sample data. Then theanalysis conducted using Orange and Indico API as tools. Therefore, we found several noun wordsfrequently used as opinion target and we also found out that in aspect-level comparison, Game 2gain the highest e-WOM positivity value in community aspect and Game 1 gain the highest e-WOMpositivity value in general aspect. Thus, each respective game developer can manage to furtherdevelop their strategies from the information of their competitive position in the industry
Designing Interface of Mobile Parental Information System based on Users’ Perception Using Kansei Engingeering Ana Hadiana
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.7

Abstract

Users’ psychological emotion plays important role in designing an interface of software including application of information system. This research attempted to implement Kansei Engineering Type I (KEPack) as a method to analyze kinds of emotional factor related to user interface for mobile Parental Information System. This research used Kansei Words to explore users’ requirements based on psychological factors. Eighteen words were used for Kansei Words that have relationship with Parental Information System. Ten samples of mobile information system were selected as specimens considered suitable for designing interface of Parental Information System. Data questionnaires collected from thirty respondents were processed using multivariate statistical analysis such as Factor Analysis (FA) and Partial Least Square (PLS). This research found that the two important emotional factors i.e funny and informative have to be considered for designing user interface for mobile Parental Information System.
Ensemble Based Gustafson Kessel Fuzzy Clustering Achmad Fauzi Bagus Firmansyah; Setia Pramana
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.6

Abstract

Fuzzy clustering is a clustering method whcih allows an object to belong to two or more cluster by combining hard-clustering and fuzzy membership matrix. Two popular algorithms used in fuzzy clustering are Fuzzy C-Means (FCM) and Gustafson Kessel (GK). The FCM use Euclideans distance for determining cluster membership, while GK use Fuzzy Covariance Matrix that considering covariance between variables. Although GK perform better, it has some drawbacks on handling linearly correlated data, and as FCM the algorithm produce unstable result due to random initialization. These drawbacks can be overcame by using improved covariance estimation and cluster ensemble, respectively. This research presents the implementation of improved covariance estimation and cluster ensemble on GK method and compare it with FCM-Ensemble.
Transition Strategies of Change Management For the Succesful Implementation of Data Warehouse of Higher Education in Indonesia Ade Rahmat Iskandar; Ari Purno
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.2

Abstract

Data Warehouse of Higher Education offers enormous advantages in efficiency, productivity, cost reduction and information integration system. This paper conducted to support success transition of implementation of Data Warehouse of Higher Education in Indonesia (that is called ‘Sistem Pangkalan Data Pendidikan Tinggi or PD-DIKTI in Indonesian) that had been implemented since 2013 ago. However, Data Warehouse of Higher Education implementations are complex, with many encountering difficulty and even failure. Transition of Data Warehouse implementation has been identified as critical success factor. A model that is used to manage the transition of Data Warehouse of Higher Education is Bridge’s Model, where this model is included to top ten leading transition for managing change in the world. The paper is going to summerize the results of a selected relevant articles both of the success implementation new IT technology and how to handle resistance to make transition hoped. In addition, we need to look at the transition models. The paper is hopefully able in changing the better equipped management to get satisfactory decision for all involved in implementing new IT system. This paper also conducted with Action research to get the information trusted.
Mapping Organization Knowledge Network and Social Media Based Reputation Management Andry Alamsyah; Maribella Syawiluna
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.3

Abstract

Knowledge management are important aspects in an organization, especially in ICT industry. Having more control of it is essentials for the organization to stay competitive in the business. One way to assess the organization knowledge capital is by measuring employee knowledge network and their personal reputation in social media. Using this measurement, we see how employee build relationship around their peer networks or clients virtually. We also able to see how knowledge network support organization performance. The research objective is to map knowledge network and reputation formulation in order to fully understand how knowledge flow and whether employee reputation have higher degree of influence in organization knowledge network. We particularly develop formulas to measure knowledge network and personal reputation based on their social media activities. As case study, we pick an Indonesian ICT company which actively build their business around their employee peer knowledge outside the company. For knowledge network, we perform data collection by conducting interviews. For reputation management, we collect data from several popular social media. We base our work on Social Network Analysis (SNA) methodology. The result shows that employees knowledge is directly proportional with their reputation, but there are different reputations level on different social media observed in this research.

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