Yogyakarta, Indonesia

University of Technology, Yogyakarta

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Fitra R.,Biro Klasifikasi Indonesia | Prasetyo F.A.,Biro Klasifikasi Indonesia | Rudiyanto R.,Biro Klasifikasi Indonesia | Herawan T.,University of Technology, Yogyakarta
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Indonesian shipping generally used heavy fuel oil (HFO) or Marine diesel oil (MDO) as diesel engine fuel. Exhaust gas emission produced by fossil fuel such as SOX, NOX, CO2, and PM, contributed total global emission during 2007–2012 period as much as 3% of CO2, NOX for 12%, and SOX for 13%. To comply Emission Control Area (ECA), and high fuel efficiency is Dual fuel, where this method is combined from fuel oil with gas such as LNG or CNG. This paper analyzes dual fuel conversion in container ship 368TEU. Payback period and Rate Of Interest (ROI) have been adopted as method adopted for analyzing investment. From payback period, the results show that if dual fuel 80:20 is faster than dual fuel 70:30, and single fuel, where in 9th years get profit 257.743, and 10th years for single fuel and dual fuel 70:30. From the ROI method, the results show that for each methhod is 18.65% for dual fuel 80:20, 17.43% for dual fuel 70:30, and 16% for single fuel. © Springer International Publishing AG 2017.


Ariani M.,Prof Dr Moestopo University | Zulhawati,University of Technology, Yogyakarta
International Journal of Security and its Applications | Year: 2016

This study aimed to examine the effect of the ease of the transaction, the consumer interest, and the level of system security against acts of fraud on online shopping. Ease of transaction is measured from the transaction speed, high accuracy, high volume transaction, highly correlated, and ease of access are high. Measurement of consumer interests is including motivation, perception, learning, and memory. System security level measured from the privacy, integrity, autentication, availability, and access control.Sample selection is done by using purposive sampling method. The research data were collected from students of the Faculty of Economics, University of Trisakti. The samples used were 100 accounting students from semesters 1 to 9. The analysis technique used is multiple regression in SPSS version 23. The results showed that the factors such as the ease of transactions, consumer interest, and the security level of the system is partially measured by the transaction speed, high accuracy, high volume transaction are highly correlated. Meanwhile, ease of access is high, motivation, perception, learning and memory does not have a significant effect on the action of cheating but the privacy, integrity, autentication, availability. The access control can influence the actions of fraud significantly. Influence ease of transactions, consumer interest, and the security level of positive and significant impact on fraud actions simultaneously. © 2016 SERSC.


Herawan T.,University of Technology, Yogyakarta | Hassim Y.M.M.,University Tun Hussein Onn Malaysia | Ghazali R.,University Tun Hussein Onn Malaysia
International Journal of Intelligent Information Technologies | Year: 2017

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC. Copyright © 2017, IGI Global.


Heriana O.,Indonesian Institute of Sciences | Soesanti I.,University of Technology, Yogyakarta
Proceeding - 2015 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2015 | Year: 2015

Breast cancer was a disease with the condition of the breast tissue became abnormal due to the development of cancer cells in the breast area. One method of breast cancer nondestructive detection was by through shooting the indicated breast cancer by using an infrared camera. The emission variations of infrared radiation on the image captured showed the level of cancer. The results of infrared camera imaging called as thermography image was processed in computing algorithm to classify the cancer in breast areas according to the characteristics of each image. The image feature extraction was obtained through the calculation of fractal dimension of the image by using the box counting algorithm. Image classification process was done by using the Fuzzy C-Means algorithm to determine the level of the breast cancer size based on the T component of the TNM system, namely T0, T1, T2 and T3 to the 22 image data to obtain the value of parameter cluster centers in Fuzzy C-Means. The test results showed that the feature extraction of breast thermography image using box counting fractal method gave the different value between normal breast and inflammatory cancer breast tissues. Normal breast tissue (T0) had a fractal dimension average less than T1, there was 1.161525 with deviation standard value was 0.593625. Breast with tumor T1 had a fractal dimension average less than T2, there was 1.45455 with deviation standard value was 0.4645. Breast with tumor T2 had a fractal dimension average less than T3, there was 1.6596 with deviation standard value was 0.2925, and breast with tumor T3 had a fractal dimension average 1.81294 with deviation standard value was 0.20199. The classification of tumor size using Fuzzy C-Means in 3 and 4 clusters with the use of 64×64 pixel box size in box counting process was more consistent than the use of 32×32 pixels box size. © 2015 IEEE.


Abdullah Z.,University of Malaysia, Terengganu | Herawan T.,University of Technology, Yogyakarta | Deris M.M.,University Tun Hussein Onn Malaysia
Lecture Notes in Electrical Engineering | Year: 2014

Least association rule refers to the rule that only rarely occur in database but they might reveal some interesting knowledge in certain domain applications. In certain medical datasets, finding these rules is very important and required further analysis. In this paper we applied our novel measure known as Definite Factor (DF) with SLP-Growth algorithm to mining the Definite Least Association Rule (DELAR) from a benchmarked medical datasets. DELAR is also highly correlated and evaluated based on standard Lift measure. The result shows that DF can be used as alternative measure in capturing the interesting rules and thus verify its scalability. © Springer Science+Business Media Singapore 2014.


Abdullah Z.,University of Malaysia, Terengganu | Herawan T.,University of Technology, Yogyakarta | Deris M.M.,University Tun Hussein Onn Malaysia
Lecture Notes in Electrical Engineering | Year: 2014

Indirect pattern can be considered as one of the interesting information that is hiding in transactional database. It corresponds to the property of high dependencies between two items that are rarely appeared together but indirectly occurred through another items. Therefore, we propose an algorithm for Mining Indirect Least Association Rule (MILAR) from the real dataset. MILAR is embedded with a scalable least measure called Critical Relative Support (CRS). The experimental results indicate that MILAR is capable in generating the indirect least association rules from the given dataset. © Springer Science+Business Media Singapore 2014.


Abdullah Z.,University of Malaysia, Terengganu | Herawan T.,University of Technology, Yogyakarta | Deris M.M.,University Tun Hussein Onn Malaysia
Lecture Notes in Electrical Engineering | Year: 2014

Finding the interesting rules from data repository is quite challenging weather for public or private sectors practitioners. Therefore, the purpose of this study is to apply an enhanced association rules mining method, so called SLP-Growth (Significant Least Pattern Growth) proposed by [11, 36] to mining the interesting association rules based on the student admission dataset. The dataset contains the records of preferred programs being selected by post-matriculation or post-STPM students of Malaysia via Electronic Management of Admission System (e-MAS) for the year 2008/2009. The results of this study will provide useful information for educators and higher university authority personnel in the university to understand the programs' patterns being selected by them. © Springer Science+Business Media Singapore 2014.


Marlina E.,University of Technology, Yogyakarta
International Journal of Smart Home | Year: 2016

The enactment of the region of Mount Sewu as a Global Geopark Network (GNN) on 19 September 2015 has given a responsibility for governments and communities to develop this region with a proper concept. The karst of Mount Sewu surrounding the Gunungkidul Regency, Wonogiri Regency and Pacitan Regency is one of the most well-known karst regions in Java Island for its uniqueness. Geoheritage and social-cultural wealth in this region becomes the base of its enactment as GGN. This study presents geotourism as a strategy of geosite empowerment towards the tourism sustainability in Gunungkidul regency. It is found that the Geotourism development comes to be an appropriate strategy in developing 13 geosites in Gunungkidul for fulfilling the directive of the policy on the local and national tourism development, demands of the development trend towards the tourism interest among community and the directive of GGN in accordance with the concept as outlined in UNESCO. The directive of the development of this geotourism has been formulated through a synthesis of various analyses including the analysis of government, analysis of potential tourist attraction, and SWOT analysis. The dialogue from a variety of analyses required to produce the formula of geotourism development of various analyses is needed to guarantee the production of a formulation of the proper and synergic directive for the geotourism development analysis necessary to guarantee the formulation of appropriate and synergic Geotourism development with various existing development guidelines. © 2016 SERSC.


Suyitno,University of Technology, Yogyakarta | Sutiyoko,Manufacturing Polytechnic of Ceper
Procedia Engineering | Year: 2012

The objective of this research is to investigate the characteristics of gray cast iron in lost foam casting with the pouring temperature and the thickness variation. The Observed characteristics are fluidity, porosity and surface roughness. The pouring temperatures were 1300 - 1400 °C. The pattern was made from a polystyrene foam with 9 kg/m3of density. The pattern was designed with 10 mm of width and the thickness variation are 2 - 6.5 mm. A quartz sand with 50 of AFS was vibrated with an amplitude of 0.5 mm and a frequency of 23 Hz. The composition of the gray cast iron is 3.35 %C, 2.29 %Si, 0.449 %Mn and 0.189 %P. The Fluidity of gray cast iron increases with the pouring temperature. Normalizatied of the fluidity can estimate the fluidity at the different of the thickness. The porosity and surface roughness increase with the pouring temperature. © 2012 Elsevier B.V.


Hermawan A.,University of Technology, Yogyakarta
International Journal of Applied Engineering Research | Year: 2016

The appropriateness of choosing the major for the Senior High School students is considered as the most important part of the academic process to be considered in order to determine the right major for their further study in the highest level. The presence of back propagation neural network with momentum and adaptive learning rate make it possible for the system itself to determine the appropriate major for the Senior High School students based on their academic potential test result with the level of accuracy about 97.5%. In order to be able to do so, the system iterates in the amount of 28.356. The objective of the present study is to perform the dimensional reduction to decrease the iteration times and to improve the accuracy of choosing the major. The researcher had tried out and examined the neural network by reducing the input variable. A series of simulation had been conducted by the researcher using Matlab software. Upon completion the simulation the researcher found out that neural network was convergent when general competence variable was reduced with the constant accuracy about 97.5%, while the iteration had decrease in the amount of 1043. © Research India Publications.

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