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All Journal Elektrika Jurnal Sosial dan Teknologi Journal Of World Science

Kurniawan Nugroho, Andi

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Author-ID : 2182211

Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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RANCANG BANGUN AUTOMATIC RAIN RECORDING (ARR ) BERBASIS DEVELOPMENT BOARD MIKROAVR128 Kurniawan Nugroho, Andi; Susilo, Edy; Setyati Budiningrum, Diah
Elektrika Vol. 9 No. 1 (2017): April 2017
Publisher : Universitas Semarang

The right use of rain can provide many benefits. For this reason, mapping of rainfall areas is needed. This is intended to determine the level of disaster preparedness for each region. In mapping, a tool is needed to calculate the rainfall that falls. In the market there are only manual measuring devices, while rainfall gauges are not available which can measure automatically and directly but additional equipment is needed separately. In addition, the measurement results cannot be seen by the public. Therefore we need a tool that works automatically that can display measurement results directly and can be seen directly by the public. Thus the community can see and measure rainfall that falls. In this plan Automatic Rain Recording is used by using a proximity sensor placed on top of a measuring cup and the sensor is connected to the AVR128 Development Board as the control center to convert sensor data into ADC (Analog to Digital Converter) data. This tool is connected with a microSD with a capacity of 8Gbyte to store rainfall level data. AVR128 will store every 2 minutes with a total of 275,368 data and 382 days of storage capability.Keywords : rainfall, ARR, ADC, AVR128, mikroSD

KLASIFIKASI POLA IMAGE PADA PASIEN TUMOR OTAK BERBASIS JARINGAN SYARAF TIRUAN ( STUDI KASUS PENANGANAN KURATIF PASIEN TUMOR OTAK ) Heranurweni, Sri; Destyningtias, Budiani; Kurniawan Nugroho, Andi
Elektrika Vol. 10 No. 2 (2018): Oktober 2018
Publisher : Universitas Semarang

Nowadays medical science has developed rapidly, diagnostic and treatment techniques have provided life expectancy for patients. One way of examining brain tumor sufferers is radiological examination that needs to be done, including MRI with contrast. MRI brain images are useful for seeing tumors in the initial steps of diagnosis and are very good for classification, erosions / destruction lesions of the skull. Smoothing image processing, segmentation with otsu method and feature extraction are carried out to facilitate the training and testing process. This study, will apply texture analysis with the parameters contrast, correlation, energy, homogenity to distinguish the texture of brain tumor images and normal so as to produce a standard gold value based on existing texture characteristics. Training and testing of texture features using backpropagation method of artificial neural networks with variations in learning rate values so that it is expected to obtain a classification of the image conditions of patients with brain tumors. The data used are 29 brain images that produce classification accuracy of 96.55%.Keywords : MRI images, brain tumors, textur, backprogation

Co-Authors Budiani Destyningtias Carles Siagian, Carles Cinta Vinski, Natasha Edy Susilo, Edy Endang Jayati, Ari Hardjo, Marhaen Ibnu Hajar, Andi Pujowaskito, Prihati Quintosa, Jaime Rodriguez Schroeter, CA Setyati Budiningrum, Diah Sri Heranurweni Susanti Pada Vinski, Deby Susanti Vinski, Deby Trofimova, Svetlana Vinski, Deby Susanti Pada Vinski Vinski, Natasha Cinta Xaverius Kaparang, Stevanus

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