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JournalISSN: 2087-1244

ComTech 

Bina Nusantara University
About: ComTech is an academic journal published by Bina Nusantara University. The journal publishes majorly in the area(s): Information system & Information technology. It has an ISSN identifier of 2087-1244. It is also open access. Over the lifetime, 727 publications have been published receiving 1260 citations. The journal is also known as: Computer, Mathematics and Engineering Applications & Computer, mathematics and engineering applications.


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Journal ArticleDOI
31 Dec 2016-ComTech
TL;DR: This research aimed to produce an automatic text summarizer implemented with TF-IDF (TermFrequency-Inverse Document Frequency) algorithm and to compare it with other various online source of automatictext summarizer.
Abstract: The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.

137 citations

Journal ArticleDOI
Nina Nurdiani1
01 Dec 2014-ComTech
TL;DR: In studies that have problems related to specific issues, requiring a non-probability sampling techniques one of which is the snowball sampling technique is useful for finding, identifying, selecting and taking samples in a network or chain of relationships.
Abstract: Field research can be associated with both qualitative and quantitative research methods, depending on the problems faced and the goals to be achieved. The success of data collection in the field research depends on the determination of the appropriate sampling technique, to obtain accurate data, and reliably. In studies that have problems related to specific issues, requiring a non-probability sampling techniques one of which is the snowball sampling technique. This technique is useful for finding, identifying, selecting and taking samples in a network or chain of relationships. Phased implementation procedures performed through interviews and questionnaires. Snowball sampling technique has strengths and weaknesses in its application. Field research housing sector become the case study to explain this sampling technique.

97 citations

Journal ArticleDOI
31 Dec 2017-ComTech
TL;DR: In this article, a multivariate regression analysis was conducted to understand the factors affecting the acceptance of Bitcoin technology in Indonesia, which adopted the model of Unified Theory of Acceptance and Use of Technology (UTAUT), which took into account four influencing factors.
Abstract: This research intended to understand the factors affecting the acceptance of Bitcoin technology in Indonesia. It adopted the model of Unified Theory of Acceptance and Use of Technology (UTAUT), which took into account four influencing factors. Those were performance expectancy, effort expectancy, social influence, and facilitating conditions. The factors of gender and age were assumed to moderate the relations between those four factors and use and behavioral intention. The empirical data for those factors were collected by questionnaires from 49 respondents. The statistical significance of the relationships was evaluated by multivariate regression analysis. The result is a model that matches the data with R2 = 0,678. It demonstrates a high level of fitness. The analysis suggests that the performance expectancy factor and the social influence factor greatly affect the behavioral intention to use Bitcoin with the values of t-statistic of 3,835 (p-value = 0,000) for the former factor and 1,948 (0,059) for the latter factor. However, the social influence factor has less profound effect on the behavioral intention.

29 citations

Journal ArticleDOI
01 Dec 2011-ComTech
TL;DR: The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape.
Abstract: Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape.

22 citations

Journal ArticleDOI
01 Jun 2016-ComTech
TL;DR: In this paper, the authors applied cluster analysis or also known as clustering on poverty data of provinces all over Indonesia, which can be used to identify more quickly and efficiently on poverty chart of all provinces in Indonesia.
Abstract: The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government and non-government organizations, which involved in poverty problems, needed a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis was kmeans algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS) or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study are presented in the form of grouping of clusters' members visually. Cluster analysis in the study can be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations.

19 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202325
202212
202012
201912
201810
201731