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Journal : Proceeding of the Electrical Engineering Computer Science and Informatics

Forecasts Marine Weather on Java Sea Using Hybrid Methods: TS-ANFIS Deasy Alfiah Adyanti; Ahmad Hanif Asyhar; ian Candra Rini Novitasari; Ahmad Lubab; Moh. Hafiyusholeh
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.866 KB) | DOI: 10.11591/eecsi.v4.1114

Abstract

Indonesia is an archipelago. Consequently, themajorities are working around the sea such as a fisherman.While the number of activities at sea are increasing more accident occurred are rising. This research presents marine weather prediction system using Hybrid Methods TS-ANFIS(Adaptive Neuro Fuzzy Inference System – Time Series) in orderto anticipate bad weather and reduce risk. This method use bothocean current and wave height at Java Sea particularly on Gresikin order to forecast ocean current velocity and wave height. Inputvariables used in this paper are data at (t), an hour before (t-1),and two hours before (t-2) and obtained next hour, next 6 hours,next 12 hours, and next day prediction as output. The resultsindicate that ocean current speed attain 16.97327 cm/s; 13.22302cm/s; 10.21107 cm/s; 14.09871 cm/s with mean error is about0.12993; 1.5758; 1.3182; 0.82613 while wave height reach 0.45554m; 0.48286 m; 0.46395 m; 0.54571 m with mean error is about0.0012247; 0.018619; 0.046584; 0.060206. Therefore, it was safe tosailing on 1st January 2016.
Implementation of Winnowing Algorithm for Document Plagiarism Detection Nurissaidah Ulinnuha; Muhammad Thohir; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar; Ahmad Zaenal Arifin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (375.112 KB) | DOI: 10.11591/eecsi.v5.1599

Abstract

Plagiarism prevention efforts are being evolved in various sector. Designing and developing plagiarism checker applications is the purpose of this paper. Specifically by knowing the percentage of similarity between the original document and the test document. Winnowing algorithm is used because it can detect plagiarism in documents up to sub-section of the document. In this paper using three validators consisting of computational mathematicians, software engineering experts, and users to test the feasibility of the application. Experiment using several scenarios, the result of the equation using winnowing algorithm is 90.12%.
Optimal ANFIS Model for Forecasting System Using Different FIS Deasy Adyanti; Dian Candra Rini Novitasar; Ahmad Hanif Asyhar; Fajar Setiawan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.341 KB) | DOI: 10.11591/eecsi.v5.1617

Abstract

Adaptive Network Based Fuzzy Inference System (ANFIS) using time series analize is one of intelligent systems that can be used to predict with good accuracy in all fields like in meteorology. However, some research about forecasting has less emphasis on the structure of the FIS ANFIS. Thus, in this paper, the optimization of the ANFIS model for predicting maritime weather is carried out by analyzing the appropriate initialization determinations of the three fuzzy Inference structures ANFIS which includes FIS structure 1 (grid partition), FIS structure 2 (subtractive clustering) and FIS structure 3 (fuzzy c-means clustering). In this paper, the variable input used are two hours (t-2) and one hour (t-1) before, and data at that time (t), and the output of this system is the prediction of next hour, six hours, twelve hours and next day of variable ocean currents velocity (cm/s) and wave height (m) using the three FIS ANFIS approaches. Based on the smallest goal error (RMSE and MSE) of the three FIS ANFIS approaches used to predict the ocean currents speed (velocity) and wave height, the model is best generated by subtractive clustering. It can be seen that subtractive clustering produces the smallest RMSE and MSE error values of other FIS structure.
Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier Ahmad Zoebad Foeady; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar; Muhammad Firmansjah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.455 KB) | DOI: 10.11591/eecsi.v5.1630

Abstract

Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately.