Gangguan mental adalah kondisi yang mempengaruhi pikiran, perasaan, suasana hati dan perilaku manusia pada semua usia termasuk anak-anak. Untuk mendapatkan kesimpulan, penelitian ini menggunakan metode Dempster Shafer. Setiap data gejala memiliki nilai keyakinan sebagai nilai awal untuk mendapatkan kesimpulan dalam metode Dempster Shafer. Tes yang digunakan dalam penelitian ini adalah black box, perhitungan teoritis, akurasi, laboratorium dan kuesioner. Hasil tes black box yang dilakukan oleh 3 responden mahasiswa menunjukkan sistem berjalan dengan baik. Perbandingan perhitungan teoritis dan perhitungan pada sistem memberikan hasil yang sama. Pengujian laboratorium dan pengujian akurasi dari 40 kasus memberikan hasil yang sama sekitar 95%. Selain itu, aplikasi ini juga telah diuji oleh para pakar dan masyarakat langsung untuk menilai apakah sistem sudah berjalan dengan baik atau tidak. Berdasarkan parameter MOS (Mean Opinion Score), sistem telah bekerja dengan baik dengan skor 4,44 dari skala 5.
This entitled of research "Expert System Diagnosis Eye Diseases Using MethodCertainty Factor Based Android" is proposed due to the increased spread eye diseases thatis leads to blindness in Indonesia it is happened because of the limited facilities of eyehealth services in health centers and hospitals, as well as lack of eye doctor who canexamine the eye, making eye left untreated and can cause serious damage to the eye.Therefore, many people are already suffering from the early symptoms of blindnessletting untreated without properly treatment. The purpose of this study is to develop anexpert system that is capable of providing information about eye diseases along withsuggested solutions of the disease suffered by imitating the analysis of an eye doctor.The system is built using certainty factor method which is an algorithm that isimprove the uncertainty idea of an expert. This method is used to describe the level ofconfidence of experts on issues being addressed. This system is built by Ionic frameworkfor Android applications with PHP, HTML5 and AngularJS.The system is able to provide information about eye diseases through the processof diagnosis, that can be used for early treatment of eye diseases. The whole function ofthis expert system application already worked properly without any mistakes. Anddiagnostics accuracy rate of the system is 75% in the diagnosis process of 15 diseases and52 symptoms.
This paper describes a novel method for extracting features of batik images. This method is called enhanced microstructure descriptor (EMSD). EMSD is the enhanced model of micro-structure descriptor (MSD) which proposed by Guang-Hai Liu. Different with MSD that uses only edge orientation similarity for creating micro-structure map and then utilises this map along with color values; EMSD adds a new micro-structure map that is based on color similarity and then utilises this map along with edge orientation values. The combination of MSD and the additional micro-structure descriptor is used as feature extractor in EMSD. This method is tested on 300 batik images, Corel datasets with 5,000 images and 10,000 images. We also compared EMSD to MSD and multi-textons histogram (MTH), which EMSD performance is superior than the other two.
Songket is one of Indonesia's cultural heritage that is still present today. One of the most famous songket woven fabrics is the Lombok songket. Lombok songket has diverse, unique, and beautiful motifs. However public knowledge of Lombok songket motifs is still minimal and the difference between one motif with another is still unknown. The lack of digitalized data collection is one reason for this. Therefore, we need a system that can classify the Lombok songket automatically. In this study, a system was developed based on texture features and shape features using Linear Discriminant Analysis (LDA). The GLCM method is used in the texture feature extraction process and the Invariant Moment method is used in the feature extraction process. The total data used in this study is 1000 images from 10 Lombok songket motifs which are divided into training data and test data. The highest accuracy is obtained on the Invariant Moment and GLCM feature with an image resolution of 300x300 pixels using the most effective feature that is equal to 96.67%.
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