Identifikasi Nominal Uang Kertas Untuk Tuna Netra Berbasis Mikrokontroller Dengan Sistem Suara

Uang merupakan alat yang dipakai untuk transaksi jual dan beli yang sudah digunakan seluruh manusia di penjuru dunia. Sehingga menjadikan uang sebagai barang pokok untuk setiap orang, tidak terkecuali para penyandang disabilitas seperti halnya tunanetra, kelemahan tunanetra dalam melihat dan mengidentifikasi uang dapat menyebabkan tertukar, salah ambil, bahkan bisa tertipu saat melakukan transaksi jual beli. Tujuan  dari  penelitian ini adalah untuk membantu para penyandang tunanetra dalam mengetahui nominal uang kertas dengan menggunakan sistem suara yang di identifikasi oleh sensor warna TCS230.Dalam penelitian ini menggunakan metode kualitatif dengan melakukan studi pustaka, pembuatan perangkat, integrasi sistem,pengujian, dan analisa sistem, serta beberapa percobaan dengan sensor warna TCS230 . Hasil Identifikasi nominal uang kertas tersebut kemudian dibaca dengan menggunakan sensor warna TCS230 yang terhubung dengan mikrokontroler atmega 328 dan  loudspeaker. Dengan alat identifikasi ini akan dapat diketahui nilai uang kertas rupiah dengan cara meletakan uang kertas rupiah diatas sensor TCS230 maka otomatis sensor membaca nilai RGB , apabila sesuai dengan range yang telah di tentukan olehATMega328 maka nominal uang kertas rupiah akan ditampilkan di layar LCD dan loudspeaker akan mengeluarkan suara yang sama berupa nilai mata uang kertas rupiah tersebut .Sehingga diharapkan alat ini dapat mempermudah tunanetra dalam mengetahui nominal uang kertas sehingga menguranggi terjadinya penipuan nominal uang terhadap tunanetra

A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images

Parasite Detection on thick blood smears is a critical step in Malaria diagnosis. Most of the thick blood smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, blood component segmentation using intensity slicing and morphological operation, blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick blood smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection

Incorporating Index of Fuzziness and Adaptive Thresholding for Image Segmentation

Binary Segmentation of an image played an important role in many image processing application. An image that was having no bimodal (or nearly) histogram accompanied by low-contrast was still a challenging segmentation problem to address. In this paper, we proposed a new segmentation strategy to images with very irregular histogram and had not significant contrast using index of fuzziness and adaptive thresholding. Index of fuzziness was used to determine the initial threshold, while adaptive thresholding was used to refine the coarse segmentation results. The used data were grayscale images from related papers previously. Moreover, the proposed method would be tested on the grayscale images of malaria parasite candidates from thickblood smear that had the same problem with this research. The experimental results showed that the proposed method achieved higher segmentation accuracy and lower estimation error than other methods. The method also effective proven to segment malaria parasite candidates from thickblood smears image

Segmentation of Malaria Parasite Candidate from Thickblood Smear Microscopic Images using Watershed and Adaptive Thresholding

Segmentation of malaria parasite on thick blood smear is a critical intermediate step in automation process of malaria detection. Most of the thick blood smear have low quality that characterized by high noise, the low-intensity difference between background and foreground, and the presence of artifacts. This situation makes the segmentation process becomes difficult. In this paper we proposed a new segmentation strategy for microscopic images of malaria parasite obtained from thick blood smear using watershed and adaptive thresholding. The proposed method consists of two main stages: image enhancement and segmentation. Enhancement process used Low-pass filtering and contrast stretching. Meanwhile, the segmentation used combination watershed segmentation and adaptive thresholding. The performance was evaluated on 253 parasite candidates, cropped from 22 thick blood smear microphotographs. The experimental results showed that the average segmentation accuracy of the proposed algorithm was 95.2%. Further analysis showed that the nucleus and cytoplasm of the malaria parasite were successfully extracted, thus the method is suitable for being used on detection of malaria parasites