http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Suharjito,Marimin (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
Suharjito,Marimin 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1
The high complexity of the supply chain network and the characteristics of products made supply chain management of agricultural products were more susceptible to the risks emergence of loss. Therefore, it is required to develop a mechanism for price negotiation which distributes the risks fairly for each stakeholder in the supply chain. In addition it is necessary to identify and evaluate supply chain risks in order to avoid continuing problems that can occur at any point in the supply chain network. The objectives of this study were to describe the model of identification and evaluation for corn supply chain risk, to formulate a fair pricing mechanism for corn supply chain using risk balancing model. Risk identification was conducted using fuzzy Analytical Hierarchy Process (AHP) approach and risk evaluation was done by using fuzzy logic with data input form the opinion of several experts maize supply chain. A fairly pricing model at farmer level was developed by using stakeholder dialogue approach based on a balanced fuzzy risk utility preference that was faced by all stages of the supply chain. In addition, fuzzy risk utility optimization was used to get a consensus of the supply chain stakeholder dialogue, where basic risk utility function was derived using fuzzy regression approach. Risk mitigation for each stage of supply chain was developed using fuzzy inferences based on the risk that has been evaluated. Based on the verification results, the model could identify the level of risks for each party of the supply chain and the action that must be taken for minimizing its impacts using appropriate strategies. The model could shift the risks from the farmer to the other parties to determine the fair benefit distribution on the price negotiation.
-
Choosing Mutual Fund based on Purpose, Risk Level and Inventor’s Profile using Neutral Network
Anton Chandra,Bambang Heru Iswanto,Suharjito 보안공학연구지원센터 2014 International Journal of Software Engineering and Vol.8 No.11
Investment in financial asset these days are easily to be done. The principle in investment is higher return, higher risk. An investment with the very high return, contain very high level of risk. Otherwise if you invest in the financial asset that contain low risk level, then the expected return will be low. Therefore decision of choosing the right investment instrument is very important because it is related to risk, individual readiness in its implementation and suitability of investor profile itself. This thesis propose a machine learning method to develop a decission support system for investment manager in determining the suitable investment instrument for the individual client based on financial objective , risk level and investment period. Machine learning was choosen because it was known of its ability in recognizing the complex pattern based on learning process using the given data set. Artificial neural network multilayer perceptron (MLP) with two layers could be used as a classificator model to predict the mutual fund investment type that was suitable for investor based on investment purpose , risk level and investor profile. Experiment using 2-layers artificial neural network, 9 unit input, 16 hidden units and 4 output units that was trained with 50 data points consists of 9 vestor input component and 4 vector target components. The architecture and the data in the network could done the classification with accuration 93.33 percents.
-
Optimization Weighted Matrix of Non-Negative Matrix Factorization for Image Compression
Robin,Suharjito 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3
Many methods have been applied in image compression and Non-negative matrix factorization (NMF) is one of some approach which could be applied in image compression. Non-Negative Matrix Factorization (NMF) was a realtively new approach to decompose data into two factors with non-negative entries. This paper shows that the NMF method can be applied in image compression using a model of 2x2 pixels weighted matrix rather than using the model of 200x200 pixels, 10x10 pixels and 4x4 pixels weighted matrix. This paper also shows that the 2x2 Weighted matrix model example of ((65536, 1), (1, 65536)) was the best weighted matrix for NMF image compression. Finally, this research proved that by using the weighted matrix with higher determinant value could gain smaller size compressed image.
-
API Fusion Tables and Google Maps Integration for GIS Thematic Mapping Visualization
Risma Ekawati,Suharjito 보안공학연구지원센터(IJSEIA) 2016 International Journal of Software Engineering and Vol.10 No.2
There are many methods that can be used to develop a Geographic Information System (GIS) as a representation of the data visualization on a digital map. One alternative that can be used is by using the Google service pack: Fusion Tables and Maps. This research described the development of a prototype for the visualization of GIS to web-based thematic map that was integrated with Google services. The proposed prototype features the upload and conversion of data from excel file XLS and XLSX format into Fusion Tables and displayed the data in a bar chart to explain the map of the area with different color degradation. This research also indicated that the results of upload process and data conversion can be carried out quicker than XLS excel file format as well as a high success rate of the process of merging two tables in Fusion Tables. This indicated that this concept can be implemented on a real thematic digital map system. Therefore, the proposed prototype in this article can be used to facilitate publication in the form of thematic maps on the data owned by the government or companies with using integrated GIS system efficiently. The final results of this research as shown by thematic map visualization systems were able to facilitate data analysis and assist in making decisions.
-
Brain Medical Image Retrieval Using Non-Negative Matrix Factorization and Canny Edge Detection
Ali Akbar Lubis,Suharjito 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.4
Disease conditions in the human brain can be detected by using medical image analysis. Content Based Image Retrieval of medical images can be used as an alternative to recognize the medical images of the human brain. In CBIR, feature extraction and recognition methods was an important feature because of medical image was different from the general image. In this study medical images that contain clinical information will be used as feature extraction using a canny edge detection and recognition features using non-negative matrix Factorization (NNMF). The purpose of this paper was to describe the use of canny edge detection and NNMF in CBIR. So CBIR could provide information on brain diseases and abnormalities. The results showed that the detection of disease in the human brain can be done by using both methods with good results if done preprocessing using histogram equalization.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
