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Communications in Science and Technology
ISSN : 25029258     EISSN : 25029266     DOI : -
Core Subject : Education,
Communication in Science and Technology [p-ISSN 2502-9258 | e-ISSN 2502-9266] is an international open access journal devoted to various disciplines including social science, natural science, medicine, technology and engineering. CST publishes research articles, reviews and letters in all areas of aforementioned disciplines. The journal aims to provide comprehensive source of information on recent developments in the field. The emphasis will be on publishing quality articles rapidly and making them freely available to researchers worldwide. All articles will be indexed by Google Scholar, DOAJ, PubMed, Google Metric, Ebsco and also to be indexed by Scopus and Thomson Reuters in the near future therefore providing the maximum exposure to the articles. The journal will be important reading for scientists and researchers who wish to keep up with the latest developments in the field.
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Articles 6 Documents
Search results for , issue " Vol 2 No 1 (2017)" : 6 Documents clear
Heuristics Miner for E-Commerce Visitor Access Pattern Representation Wardhani, Kartina Diah Kesuma; Yunanto, Wawan
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.21

Abstract

E-commerce click stream data can form a certain pattern that describe visitor behavior while surfing the e-commerce website. This pattern can be used to initiate a design to determine alternative access sequence on the website. This research use heuristic miner algorithm to determine the pattern. σ-Algorithm and Genetic Mining are methods used for pattern recognition with frequent sequence item set approach. Heuristic Miner is an evolved form of those methods. σ-Algorithm assume that an activity in a website, that has been recorded in the data log, is a complete sequence from start to finish, without any tolerance to incomplete data or data with noise. On the other hand, Genetic Mining is a method that tolerate incomplete data or data with noise, so it can generate a more detailed e-commerce visitor access pattern. In this study, the same sequence of events obtained from six-generated patterns. The resulting pattern of visitor access is that visitors are often access the home page and then the product category page or the home page and then the full text search page.
A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System Setya Budi, Rizki Firmansyah; -, Sarjiya -; Hadi, Sasongko Pramono
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.41

Abstract

The paper contains a literature review to obtain an optimization method that potentially can be used to optimize power plant expansion of Jawa-Madura-Bali (Jamali) power system in 2015-2050. An optimization model that can represent auction process and direct appointment of IPP by considering the long term period (multi-period framework) and multi-objective function (economical, reliable, and environmentally friendly), is needed. Based on the literature review that has been done, it is obtained the method that potentially can be used for the Jamali optimization is game theory with multi-period, bi-level and multi objective optimization method. Game theory is used to represent the auction process and direct appointment of IPP. Multi-period is used to represent the long term period from 2015-2050. Multi-objective optimization method is used to represent the aspects of cost, reliability, and CO2 emission which are considered in the optimization process
Automated localisation of optic disc in retinal colour fundus image for assisting in the diagnosis of glaucoma Listyalina, Latifah; Nugroho, Hanung Adi; Wibirama, Sunu; Oktoeberza, Widhia KZ
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.43

Abstract

Optic disc (OD), especially its diameter together with optic cup diameter can be used as a feature to diagnose glaucoma. This study contains two main steps for optic disc localisation, i.e. OD centre point detection and OD diameter determination.  Centre point of OD is obtained by finding brightness pixel value based on average filtering.  After that, OD diameter is measured from the detected optic disc boundary.  The proposed scheme is validated on 30 healthy and glaucoma retinal fundus images from HRF database.  The results are compared to the ground truth images.  The proposed scheme obtains evaluation result (E) for healthy and glaucoma images is 0.23 and 0.21, respectively.  These results indicate successful implementation of automated OD localisation by detecting OD centre point and determining OD diameter.
Electroencephalography (EEG)-based epileptic seizure prediction using entropy and K-nearest neighbor (KNN) Ibrahim, Sutrisno Warsono; Djemal, Ridha; Alsuwailem, Abdullah; Gannouni, Sofien
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.44

Abstract

Epilepsy is known as a brain disorder characterized by recurrent seizures. The development a system that able to predict seizure before its coming has several benefits such as allowing early treatment or even preventing the seizure. In this article, we propose a seizure prediction algorithm based on extracting Shannon entropy from electroencephalography (EEG) signals.  K-nearest neighbor (KNN) method is used to continuously monitor the EEG signals by comparing with normal and pre-seizure baselines to predict the upcoming seizure. Both baselines are continuously updated based on the most recent prediction result using distance-based method. Our proposed algorithm is able to predict correctly 42 from 55 seizures (76 %), tested using up to 570 hours EEG taken from MIT dataset. With its simplicity and fast processing time, the proposed algorithm is suitable to be implemented in embedded system or mobile application that has limited processing resources. 
Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering Agastya, I Made Artha; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.46

Abstract

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.
Modified Adaptive Support Weight for Stereo Matching Irijanti, Etik
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.47

Abstract

Local stereo matching algorithms are very popular in the recent years. The adaptive support weight algorithms can give high accuracy results such as global methods. This paper proposed a support aggregation approach for stereo matching that computed support weight in sparse support window mask. The improvement from the previous work is that the new support weight can reduce the computation into the fourth of the earlier work and help to reach the optimum correspondence. It means sparse support weight affects the time computation that is needed in stereo matching and optimizes the disparity. This support weight is used to accomplish the stereo matching evaluation using this method.

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