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Listyalina, Latifah
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Journal : Journal of Electrical Technology UMY

 1 
Detection of Cervical Cancer Based on Learning Vector Quantization and Wavelet Transform Dharmawan, Dhimas Arief; Listyalina, Latifah
Journal of Electrical Technology UMY Vol 3, No 3 (2019): September
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.3357

Abstract

Cervical cancer has became the common women dsease in the world. Mostly, cervical cancer has been already known lately, because it is very dificult to detect this in early stage. In this work, a computer based software using Learning Vector Quantization (LVQ) has been designed as the early cervical cancer detection aid tool. There are six methods before the detection is performed, namely preprocessing, contrast stretching, median filtering, morphology operation, image segmentation, and Wavelet Transform based feature extraction. In tihis work, 73 cervical cell images that consist of 50 normal images and 23 cancer images are used. 35 normal images and 14 cancer images are used to train the LVQ. Then, 23 normal images and 9 cancer images are used in the testing process. Our results show 88,89 % cancer image can be detected correctly (sensitivity), 100 % normal image can be detected corerctly (specificity), and 95,83 % for overall detection (accuracy).
Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort Listyalina, Latifah; Yudianingsih, Yudianingsih; Dharmawan, Dhimas Arief
Journal of Electrical Technology UMY Vol 4, No 2 (2020): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v4i2.8590

Abstract

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.
Identifying Glucose Levels in Human Urine via Red Green Blue Color Compositions Analysis Listyalina, Latifah; Dharmawan, Dhimas Arief; Utari, Evrita Lusiana
Journal of Electrical Technology UMY Vol 4, No 1 (2020): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet umy.v4i1.8538

Abstract

Diabetes mellitus (DM), a metabolic disorder caused by the lack of the insulin hormone, has become a health problem quite severe and is the most common endocrine disease. Recently, diagnosing diabetes could be carried out through monitoring the glucose level in human blood taken from the patient's finger or arm. On the other hand, a non-invasive blood sugar detector with a benedict test on human urine is an alternative to monitor blood sugar without injuring the body. The test output can be determined from the colour of the colour change of urine. However, manual evaluations on the urine colour are prone to human subjectivity. In this paper, we present a computational method to determine the blood sugar level based on the colour of the given urine automatically. The proposed method identifies the blood sugar level by taking into account the colour intensity on the red, green, and blue (RGB) channels of the urine colour. In the experimental parts, the proposed method is capable of classifying the urine sample correctly. Hence, our approach may be beneficial for practical applications.
Retinal Blood Vessel Segmentation as a Tool to Detect Diabetic Retinopathy Dharmawan, Dhimas Arief; Listyalina, Latifah
Journal of Electrical Technology UMY Vol 3, No 2 (2019): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.3253

Abstract

The retina is an important part of the eye for humans. Inbesides its main function as part of the sense of sight, in the worldmedically, the retina after an image can be used to detect a numberdiseases, such as diabetic retinopathy. To detect a number of diseases,Retinal digital images taken using a digital fundus camera are used.In detecting diabetic retinopathy, digital images are neededsegmented retina. Nevertheless, automatic segmentation of digital imagesthe retina is a complex work, given the presence of artifactsas well as noise on the retinal digital image, evenly illuminated, intensitylow, low contrast, and varying lengths of retinal blood vessels.In this research, a blood vessel segmentation software system has been designed through three stagesimage processing, namely (i) preprocessing, (ii) improving image quality, (iii) andsegmentation of retinal blood vessels. With three image processing stages, the performance value is obtained, i.e. 84.62.
Performance Analysis of Lung Cancer Diagnosis Algorithms on X-Ray Images Dharmawan, Dhimas Arief; Listyalina, Latifah
Journal of Electrical Technology UMY Vol 2, No 2 (2018)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.2232

Abstract

Among several types of cancer, lung cancer is regarded as one of the most common and serious. In this respect, early diagnosis is required and beneficial to reduce mortalities caused by this type of cancer. Such diagnosis is typically performed by doctors through manual examinations on X-Ray images. However, manual examinations are labor extensive and time consuming. In this paper, we conduct a study to analyze the performance of some computer-based lung cancer diagnosis algorithms. The algorithms are built using different feature extraction (gray-level co-occurrence matrix, pixel intensity, histogram and combination of the three) and machine learning (Multi-layer Perceptron and K-Nearest Neighbor) techniques and the performance of each algorithm is compared and analyzed. The result of the study shows that the best performance of lung cancer classification is obtained by the computer algorithm that uses the combined features to characterize lung cancer and subsequently classifies the features using Multi-layer Perceptron.
Detection of Optic Disc Centre Point in Retinal Image Latifah Listyalina; Dhimas Arief Dharmawan
Journal of Electrical Technology UMY Vol 3, No 1 (2019): March
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.3150

Abstract

Glaucoma and diabetic retinopathy (DR) are the two most common retinal related diseases occurred in the world. Glaucoma can be diagnosed by measuring optic cup to disc ratio (CDR) defined as optic cup to optic disc vertical diameter ratio of retinal fundus image. A computer based optic disc is expected to assist the ophthalmologist to find their location which are necessary for glaucoma and DR diagnosis. However, many optic disc detection algorithms available now are commonly non-automatic and only work in healthy retinal image. Therefore, there is not information on how optic disc in retinal image of unhealthy patient can be extracted computationally. In this research work, the method for automated detection of optic disc on retinal colour fundus images has been developed to facilitate and assist ophthalmologists in the diagnosis of retinal related diseases. The results indicated that the proposed method can be implemented in computer aided diagnosis of glaucoma and diabetic retinopathy system development.
Segmentation of the Electrocardiography Images as a Tool to Identify Heart Diseases Latifah Listyalina; Dhimas Arief Dharmawan
Journal of Electrical Technology UMY Vol 3, No 4 (2019): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.3466

Abstract

The heart is a very vital organ. Cardiac examination can be done periodically by using an electrocardiograph. So that the heart's condition can be known. One of the optimization in helping the detection of heart disease automatically by using computer assistance. Automatic detection can be done by image processing methods as input, especially ECG images that have been segmented. In this study, ECG image segmentation is carried out through several stages, such as grayscalling, contrast enhancement, and segmentation. The hope, the results of this study can be used as input for automatic detection of heart disease.
Implementation of GLCM for Features Extraction and Selection of Batik Images Dhimas Arief Dharmawan; Latifah Listyalina
Journal of Electrical Technology UMY Vol 2, No 1 (2018)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.2128

Abstract

Batik is a craft that has high artistic value and has been a part of Indonesian culture (especially Java) for a long time. Batik cloth in Indonesia has various types of batik textures, batik cloth colors, and batik fabric patterns that reflect the regional origins of the batik cloth. Regarding the image of batik, the texture feature is an important feature because the ornaments on the batik cloth can be seen as different texture compositions. Besides batik motifs, also influenced by the shape characteristics that become parts of each batik motif. This research will add insight and knowledge to understand batik patterns based on the characteristics of batik motifs, namely texture. There are five batik motifs used, namely inland solo batik, semarang coastal batik, sidhomukti batik, parangklithik batik, and sidhodrajat batik. Initially preprocessing is done by cropping and grayscalling. Of the five image motifs, a cropping process is carried out for each motif. The next step is feature extraction. The features of GLCM were selected in this study. From the features contained in the GLCM, in this study four features were chosen, namely contrast, energy, correlation, and homogeneity. The final step is the selection or selection of features. The result of the feature selection of the four features carried out feature extraction are energy and homogeneity.
Co-Authors Adetya, Nais Pinta Agus Qomaruddin Munir Andri Saputra Ariyanto, Ipan Azmi, Desvika Arnila Bagus Seno Wulung, Raden Chandra, Hekin Irfan Desty Ervira Puspaningtyas Dharmawan, Dhimas Arief Dwi Wibowo, Midarto Eko Susilo Ellianto, Mario Sariski Dwi Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Hanung Adi Nugroho Ikhwan Mustiadi Irawadi Buyung, Irawadi Mario Sarisky Dwi Ellianto Mario Sarisky Dwi Ellianto Muh. Wahyu Syabani Muhammad Ikhwan Novi Irawati Nurwijayanti Oktoeberza, Widhia KZ Pambudi, Wisnu Pani Satwikanitya Putrama, Fala Putri, Naimah Ratnaningsih, Wahyu Ratri Retno Utami RR. Ella Evrita Hestiandari Sabdullah, Mursid Sarisky Dwi Ellianto, Mario Sarisky, Mario Satwikanitya, Pani Sugeng Winardi Suharyanto Sunu Wibirama Suwarno, Yuli Syahbani, Muhammad Wahyu Uma Fadzilia Arifin Uma Fadzilia Arifin, Uma Fadzilia Utarianingrum, Ratna Wahyu Ratnaningsih Wijaya, Nurhadi Wisnu Pambudi Yudianingsih Yudianingsih Yudianingsih, Yudianingsih