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RISS 인기검색어
- 검색키워드
- 저자 : Eka Miranda (검색결과 4 건)
- 무료
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Forest Classification Method Based on Convolutional Neural Networks and Sentinel-2 Satellite Imagery
Eka Miranda,Achmad Benny Mutiara,Ernastuti,Wahyu Catur Wibowo 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4
The objective of this study is to develop a classification method based on convolutional neural network (CNN) and Sentinel-2 satellite imagery including the spectral feature, spectral index and spatial feature together as an input to answer forest monitoring problem. This research also used contextual information on Indonesia National Standard Agency’s document for Land cover classification as a baseline for feature extraction to get the appropriate classifier feature. The test set was located in Semarang, Central Java, Indonesia. The research workflow consists of defining forest class based on Indonesia National Standard Agency for Land cover classification, extracting optical image features based on contextual information of the forest class definition, extracting image features from the Sentinel-2 satellite image, and classifying image object features using CNN classifier. Image segmentation produced 1,211 segments/objects by using eCognition software. Subsequently, these objects were used as a dataset. Overall accuracy was used to evaluate the performance of the classification result. The result showed the classification method results in this study yielded high overall accuracy (97.66%) when using CNN with the image features like NDVI, Brightness, GLCM homogeneity and Rectangular fit. Small improvement of overall accuracy was also achieved when it was compared to GBT with an overall accuracy of 95.50%.
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Detection of Cardiovascular Disease Risk’s Level for Adults Using Naive Bayes Classifier
Eka Miranda,Edy Irwansyah,Alowisius Y. Amelga,Marco M. Maribondang,Mulyadi Salim 대한의료정보학회 2016 Healthcare Informatics Research Vol.22 No.3
Objectives: The number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults. Methods: The process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3. Results: The evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease. Conclusions: The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.
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Management Report for Marketing in Higher Education Based On Data Warehouse and Data Mining
Rudy,Eka Miranda 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.4
Elements of globalization in the world of education has expanded and developed, and made an Higher Education institutions market has been developed as a global phenomenon, so that Higher Education institutions aware to show their existence in global and high competition. The objective of this study was to apply data warehouse and data mining techniques that can be used by universities to obtain relevant information about their current condition, and to track the institution's development, which is required by the management to monitor organization performance in marketing area, and to support information in decision making process. The research method began with the collection of data and information, analyzes the current condition in marketing area, design the data warehouse model and mining the data. The obtained results were the data warehouse model and evaluation model using data mining technique to support the management in marketing decision-making process.
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Mediana Aryuni,Suko Adiarto,Eka Miranda,Evaristus Didik Madyatmadja,Albert Verasius Dian Sano,Elvin Sestomi 한국지능시스템학회 2023 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.23 No.2
In the field of medical data mining, imbalanced data categorization occurs frequently, whichtypically leads to classifiers with low predictive accuracy for the minority class. This studyaims to construct a classifier model for imbalanced data using the SMOTE oversamplingalgorithm and a heart disease dataset obtained from Harapan Kita Hospital. The categorizationmodel utilized logistic regression, decision tree, random forest, bagging logistic regression,and bagging decision tree. SMOTE improved the model prediction accuracy with imbalanceddata, particularly for minority classes
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