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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Hypertension Drug Suitability Evaluation Based On Patient Condition with Improved Profile Matching Hari Soetanto; Sri Hartati; Retyanto Wardoyo; Samekto Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp453-461

Abstract

The accuracy of the type or dosage of drugs by doctors is important. The types and doses of medicines given by the doctors should match the illness suffered by the patient as well as consider the patient's health condition. In hypertension disease, the error rate of drug dosage by medical personnel is quite high, reaching 34%. Meanwhile, the administration of the type and dosage of drugs appropriate to the patient's condition required the knowledge of high medical personnel and experienced medical personnel. In this research, we developed the model of drug suitability evaluation with hypertension patient's health condition using Profile Matching method. The proposed model evaluates the patient's health condition based on the parameters provided by the expert and produces recommendations on the type of drug. To optimize the Profile Matching method, in this research we applied interpolation weighting method which calculates the proximity level of the patient profile with drug profile more accurately. Based on the experiment, the proposed model has an accuracy value of 87%, precision 87.11% and recall of 85.44%. It proves that the proposed method can provide recommendations on the right type of hypertension medication. Also, the interpolation weighting method is proven to increase the accuracy. 
Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map Erick Alfons Lisangan; Aina Musdholifah; Sri Hartati
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp373-380

Abstract

Recently, clustering algorithms combined conventional methods and artificial intelligence. FSC-SOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster centers are processed twice i.e. searching for data with high density at FSC then updating the cluster centers at SOM. FSC-SOM was tested using 10 datasets that is measured with F-Measure, entropy, Silhouette Index, and Dunn Index. The result showed that FSC-SOM can improve the cluster center of FSC with SOM in order to obtain the better quality of clustering results. The clustering result of FSC-SOM is better than or equal to the clustering result of FSC that proven by the value of external and internal validity measurement.
E-Referral System Modeling Using Fuzzy Multiple-Criteria Decision Making Gandung Triyono; Sri Hartati; Reza Pulungan; Lutfan Lazuardi
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp475-486

Abstract

Currently, the quality of health services in Indonesia is getting better. However, the referral system still has some problems. The first is the difficulty in determining the referral hospital by the condition of the patient. The second is the different parameters used to determine the referral hospitals between countries. Based on those problems, it is necessary to improve the ability of the current referral system. This study developed a reference system model for assessing the suitability of the patient's background with the referral hospital. Some of the methods used are restful on web service technologies for information exchange between primary health care doctors and referral hospitals, Fuzzy Multiple-Criteria Decision Making (FMCDM) to determine the ranking of referral hospitals that fit the patient's background. The result of this study is an intelligent system model to get the referral hospital that fit the patient's background.
Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network Lilik Sumaryanti; Aina Musdholifah; Sri Hartati
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp182-190

Abstract

The increased of consumer concern on the originality of rice  variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30%  for Cilosari.