Prediksi penyakit melalui warna urin menggunakan mobile android = Predict disease with urine color using mobile android

Andreas, Andreas (2014) Prediksi penyakit melalui warna urin menggunakan mobile android = Predict disease with urine color using mobile android. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Tugas akhir ini membahas pengembangan applikasi yang mendeteksi warna urin dan menampilkan daftar kemungkinan penyakit berdasarkan warna tersebut. Pendeteksian penyakit melalui warna urin biasanya jarang dilakukan. Pendeteksian biasanya dilakukan dengan mendeteksi zat-zat yang ada pada urin menggunakan dipstick. Kekurangan dari pendeteksian ini adalah tidak semua orang mengetahui penyakit yang mungkin dideritanya berdasarkan hasil dipstick. Aplikasi dikembangkan dengan menggunakan Eclipse, OpenCV, dan SQLite. Untuk pendeteksian warna, digunakan HSV color model dan Histogram Equalization untuk meningkatkan kualitas dari gambar. Uji coba Aplikasi dilakukan menggunakan pendekatan uji coba black-box. Aplikasi ini dapat mendeteksi warna urin dengan tingkat keakuratan sekitar 60% dan pengulangan pendeteksian antara 1 atau 2 kali pengulangan. Pencahayaan ruangan tidak terlalu berpengaruh terhadap pendeteksian urin yang memiliki warna yang pekat. Aplikasi ini memberikan informasi kepada pengguna mengenai kemungkinan penyakit berdasarkan warna urin. / This final project discusses the development of an application that detect the color of urine and show the list of possible disease based on the color. Urine detection using color is rarely used. Usually, people do urine detection by detecting the substances from the urine using dipstick. The weakness of this detection is that not everyone understand what disease they may have based on dipstick result. The application is developed using Eclipse, OpenCV, and SQLite. For color detection, HSV color model and Histogram Equalization are used to enhance the quality of image. Application testing is performed using black-box testing. This application can detect the urine color with accuracy around 60% and handles around 1 or 2 detection repetition. The room illumination does not influence to the detection of urine that has a thick color. This application give information to user about the possible disease based on urine color.

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