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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 54 Documents
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Search results for , issue "Vol 16, No 2: April 2018" : 54 Documents clear
The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety Abrham Debasu Mengistu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8419

Abstract

This paper presents the effects of segmentation techniques in the identification of Ethiopian coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of these regions very in color shape and texture. We investigated various segmentation techniques for efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans segmentation techniques are considered. For classification of the varieties of Ethiopian coffee beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is achieved when BPNN is used on FCM segmentation technique.
Trend of NFC Technology for Payment Transaction Edi Purnomo Putra; Fifilia Fifilia; Hanny Juwitasary
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8441

Abstract

Implementation of NFC Technology was developed to facilitate instant payment transaction such as purchase tickets, goods, or service to become one of the digital wallet besides to using credit cards. The high rate of mobile devices usage in Indonesia make many companies take advantage of this device as a media in electronic payment transactions, a lot of banking and financial product in Indonesia used this technology such as E-money, T-cash, Flazz, Ooredoo, Dompetku, XL-Tunaiku, Doku Wallet, Skye card, Artajasa MYNT and many more. The technique that used in this research to prove the hypotheses was Structural Equation Model (SEM) using Smart PLS software, we will used questionnaire and literature review method to found trend of NFC use. The population from which to draw conclusion was defined as all individual capable use NFC Technology for payment transaction .In this study, we will investigate NFC use in Indonesia, The proposed research model was develop by TAM Framework with some dimension from NFC study iterature that is Mobile Payment Knowledge, Technology, Trust, Behaviour and Compatibility into the model, in order to determine the significant factor that influence of NFC use in Indonesia.
Personal Security Tracking based on Android and Web Application Angry Ronald; Violitta Yesmaya; Muhammad Danaparamita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7625

Abstract

Nowadays, the frequency of abduction is increasing significantly. Information technology provide many social media which is causing the leaked of personal information where abductor can find out the information. Using social media, people can share location, life event, and anything related about their life event, these become the useful information for the abductor. To anticipate this, a lot of personal tracking device is developed, but the response is too late. Because, the family or relative of the victims will notice that abduction had happen after the victim is lost. According to the problem, this research will focus on develop the personal tracking device which trigger by the victims who own the phone based on android and web application. Whenever the abduction happens, the victim just need to click the button and the system will send a message to the relatives and family in their contact also trigger the notification to the local security. Software development life cycle that will be used is scrum which is divided into several processes like backlog, sprints, scrum meetings, and demos. The result of this research is android application for user and web application for managing application.
Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy Farag K. Abo-Elyousr; L. S. Nassrat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.5103

Abstract

Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability.
Bayesian Segmentation in Signal with Multiplicative Noise Using Reversible Jump MCMC Suparman Suparman; Michel Doisy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7510

Abstract

This paper proposes the important issues in signal segmentation. The signal is disturbed by multiplicative noise where the number of segments is unknown. A Bayesian approach is proposed to estimate the parameter. The parameter includes the number of segments, the location of the segment, and the amplitude. The posterior distribution for the parameter does not have a simple equation so that the Bayes estimator is not easily determined. Reversible Jump Markov chain Monte Carlo (MCMC) method is adopted to overcome the problem. The Reversible Jump MCMC method creates a Markov chain whose distribution is close to the posterior distribution. The performance of the algorithm is shown by simulation data. The result of this simulation shows that the algorithm works well. As an application, the algorithm is used to segment a Synthetic Aperture Radar (SAR) signal. The advantage of this method is that the number of segments, the position of the segment change, and the amplitude are estimated simultaneously.
Predicting the Spread of Acacia Nilotica Using Maximum Entropy Modeling Budi Arif Dermawan; Yeni Herdiyeni; Lilik Budi Prasetyo; Agung Siswoyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.6894

Abstract

Acacia nilotica planted in Baluran National Park aims to prevent the spread of fire from savanna to teak forest became developed into invasive and led to a decrease in the quality and quantity of savannas. Therefore, it is required to predict the spread of A. nilotica to minimize the impacts of invasion on savanna area. The study aims to identify environmental factors which affect spread of A. nilotica. Furthermore, the spread of A. nilotica is predicted using Maximum Entropy. Maximum Entropy is efficient model since it uses presence-only data while the most of other models use presence and absence data. The experimental results reveal six environmental factors, including elevation, slope, NDMI, NDVI, distance from the river, and temperature were identified affecting the spread of A. nilotica. The most dominant environmental factors were elevation and temperature with 40% and 39.6% contributions. Maximum Entropy performed well in predicting the spread of A. nilotica, it was indicated by AUC value of 0.938.
Real Time Face Recognition Based on Face Descriptor and Its Application I Gede Pasek Suta Wijaya; Ario Yudo Husodo; I Wayan Agus Arimbawa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7418

Abstract

This paper presents a real time face recognition based on face descriptor and its application for door locking. The face descriptor is represented by both local and global information. The local information, which is the dominant frequency content of sub-face, is extracted by zoned discrete cosine transforms (DCT). While the global information, which also is the dominant frequency content and shape information of the whole face, is extracted by DCT and by Hu-moment. Therefore, face descriptor has rich information about a face image which tends to provide good performance for real time face recognition. To decrease the dimensional size of face descriptor, the predictive linear discriminant analysis (PDLDA) is employed and the face classification is done by kNN. The experimental results show that the proposed real time face recognition provides good performances which indicated by 98.30%, 21.99%, and 1.8% of accuracy, FPR, and FNR respectively. In addition, it also needs short computational time (1 second).
Improved Key Frame Extraction using Discrete Wavelet Transform with Modified Threshold Factor Hussein Ali Aldelfy; Mahmood Hamza Al-Mufraji; Thamir R. Saeed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7692

Abstract

Video summarization used for a different application like video object recognition and classification. In video processing, numerous frames containing similar information, this leads to time consumption and slow processing speed and complexity. By using key frames reducing the amount of memory needed for video data processing and complexity greatly. In this paper key frame extraction of Arabic isolated word using discrete wavelet transform (DWT) with modified threshold factor is proposed with different bases. The results for different wavelet basis db, sym and coif show the best result for numbers of key frames at the threshold factor value (0.75).
Data Cleaning Service for Data Warehouse: An Experimental Comparative Study on Local Data Arif Bramantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7669

Abstract

Data warehouse is a collective entity of data from various data sources. Data are prone to several complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure data quality. Data cleaning service involves identification of errors, removing them and improve the quality of data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology, involved stages and services within data warehouse environment. It also provides a comparison through some experiments on local data with different cases, such as different spelling on different pronunciation, misspellings, name abbreviation, honorific prefixes, common nicknames, splitted name and exact match. All services are evaluated based on the proposed quality of service metrics such as performance, capability to process the number of records, platform support, data heterogeneity, and price; so that in the future these services are reliable to handle big data in data warehouse.
Fast and Accurate Spelling Correction Using Trie and Damerau-levenshtein Distance Bigram Viny Christanti M.; Rudy Rudy; Dali S. Naga
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.6890

Abstract

This research was intended to create a fast and accurate spelling correction system with the ability to handle both kind of spelling errors, non-word and real word errors. Existing spelling correction system was analyzed and was then applied some modifications to improve its accuracy and speed. The proposed spelling correction system is then built based on the method and intuition used by existing system along with the modifications made in previous step. The result is a various spelling correction system using different methods. Best result is achieved by the system that uses bigram with Trie and Damerau-Levenshtein distance with the word level accuracy of 84.62% and an average processing speed of 18.89 ms per sentence.

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