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Knowledge Engineering and Data Science
Published by Universitas Negeri Malang
ISSN : -     EISSN : 25974637     DOI : 10.17977/um018
Core Subject : Science, Engineering,
Computer Science & IT Engineering
KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1 (2018)" : 5 Documents clear
Capital Letter Pattern Recognition in Text to Speech by Way of Perceptron Algorithm Novan Wijaya
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.302 KB) | DOI: 10.17977/um018v1i12018p26-32

Abstract

Computer vision is a data transformation retrieved or generated from webcam into another form in means of determining decision. All kinds of transformations are carried through to attain specific aims. One of the supporting techniques in implementing computer vision on a system is digital image processing as the objective of digital image processing is to transform digital-formatted picture so that it can be processed in computer. Computer vision and digital image processing can be implemented in a system of capital letter introduction and real-time handwriting reading on a whiteboard supported by artificial neural network mode “perceptron algorithm” used as a learning technique for the system to learn and recognize the letters. The way it works is captured in letter pattern using a webcam and generates a continuous image that is transformed into digital image form and processed using several techniques such as grayscale image, thresholding, and cropping image.
SQL Logic Error Detection by Using Start End Mid Algorithm Jevri Tri Ardiansah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Okazaki Yasuhisa
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.845 KB) | DOI: 10.17977/um018v1i12018p33-38

Abstract

Data base is an important part of a system and it stores data to be manipulated. A language called SQL (Structured Query Language) is used for manipulating those data to make needed information. There are two types of error which make SQL more difficult in practical implementation. They are syntax error and logic error. The difference between them is that syntax error can be detected by compiler so it is easy to learn by its warning. But compiler does not show error warning if logical error was occurred. It makes logic error is more difficult to understand than syntax error. To help data base's user to learn SQL in practical implementation, web based SQL compiler that be able to detect syntax and logic error is developed by using Start End Mid algorithm.
Network Traffic Time Series Performance Analysis Using Statistical Methods Purnawansyah Purnawansyah; Haviluddin Haviluddin; Rayner Alfred; Achmad Fanany Onnilita Gaffar
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.856 KB) | DOI: 10.17977/um018v1i12018p1-7

Abstract

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.  
Decision Support System Determination of Main Work Unit In WPP-711 Using Fuzzy TOPSIS Hozairi Hozairi; Yaser Krisnafi
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.672 KB) | DOI: 10.17977/um018v1i12018p8-19

Abstract

Decision-making to determine the working units for being prioritized to be developed in order to improve fishery monitoring in WPP-711 is imperative. The Ministry of Maritime Affairs and Fisheries should make no mismatch decision-making through long-term calculation and analysis. The problem of determining the priority of working units is a complex problem, thus it is required to find an appropriate method to avoid a missmatch decision. TOPSIS is a decision-making method capable of solving multi-criteria problems. TOPSIS working principle determines the alternative by considering the shortest distance from the positive ideal alternative and furthest from the ideal negative solution. To improve the performance of TOPSIS, this research is integrated with Fuzzy logic with the aim of giving the right numeric value preference. From the test of 11 alternatives of 6 criteria, the priority of development of fishery monitoring in FMA 711 is: Pontianak Working Unit= 0.917, Batam Working Unit = 0.791 Natuna Working Unit = 0.685 and Tanjung Pinang Working Unit = 0.607. Furthermore,  the ranking result will be used as the basis for determining the strategy in increasing the monitoring of WPP-711 to minimize State losses due to the illegal fishing within Indonesia’s WPP-711 Regions.
Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data Fachrul Kurniawan; Binti Umayah; Jihad Hammad; Supeno Mardi Susiki Nugroho; Mochammad Hariadi
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.654 KB) | DOI: 10.17977/um018v1i12018p20-25

Abstract

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.

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