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"Berdasarkan Survey Demografi Kesehatan Indonesia (SDKI) tahun 2007 Angka Kematian Ibu (AKI) Indonesia 228/ 100.000 KH dan AKB 34/1000 KH. Salah satu dari tujuan pembangunan kesehatan di Indonesia adalah tercapainya Millenium Development Goals (MDG?s) tahun2015, yaitu terjadinya penurunan AKB 23/1000 KH, mengurangi jumlah AKI saat hamil dan melahirkan menjadi 102/100.000 KH, melalui Inisiasi Menyusu Dini (IMD). Penelitian bertujuan mengidentifikasi hubungan IMD dengan tinggi fundus uteri postpartum hari ketujuh. Variabel penelitian terdiri dari variabel independen utama IMD dan variabel kontrol (umur, paritas, pendidikan, pekerjaan, mobilisasi dini dan ASI eksklusif 7 hari, variabel dependen adalah TFU. Penelitian kohort prospektif ini menggunakan sampel 78 responden, masing-masing kelompok 39 responden. Data dianalisis secara univariat, bivariat menggunakan chi square dan multivariat dengan regresi logistik. Hasil penelitian, usia terbanyak 20-30 tahun 71,8%, pendidikan responden terbanyak pendidikan tinggi 73%, paritas responden terbanyak primipara 60,3%, status pekerjaan adalah tidak bekerja 82,1%, responden dengan TFU normal 61,5%. Ratarata waktu yang diperlukan bayi untuk IMD adalah 61,1 menit. Hasil analisis multivariat, ibu yang memberikan ASI eksklusif sampai 7 hari mempunyai peluang mendapatkan proses TFU normal 29,8 kali lebih tinggi, dibanding yang tidak menyusui ekslusif (95% CI : 4,921-138,131) setelah dikontrol variabel mobilisasi dini, IMD, pendidikan dan paritas.

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Indonesian Health and Demographic Survey 2007 indicate that a high level the point of Maternal Mortality Rate (MMR) is 228/100.000 life births. While Infant Mortality Rate (IMR) of 34/1000 life births. One of the MDG?S purposes 2015 are to increase maternal health and decrease IMR down to ¾ of the MMR for both of pregnant and delivery women to become 102/100.000 life births by Early Initation of Suckling.

This research is purpose to identify the relationship between early initiation and the impact of fundus uteri at a postpartum women in seventh day. The variable of this research consist of independent variable which are early initation and control variable (age, parity, education, work, early mobilization and exclusive breastfeeding up to seventh day). While dependent variable is the high impact fundus of a postpartum women in seventh day. The research of this prospective kohort use 78 responder as a samples, with each group are exsposure group and control group which amount to 39 responder. The data which have been gathered will be analysed by univariate, bivariate analyse use chi square and multivariat with double logistics regression.

From the result of univariate analyse, the most age is around 20-30 year 71,8%, the most responder education is to higher education 73%, the most responder parity is to primipara 60,3%, work status of responder is a housewife 82,15%, women with a normal high uteri fundus counted 61,5%, the avarage time for a baby to do early initation is around 61,1 minute. The Result of multivariate analyse shows that the opportunity of a mother who gives exclusive breastfeeding up to seventh day has a better involution process 29,8 higher times than a mother without exclusive breastfeeding (95% CI: 4,921-138,131) after controlled with early mobilization variable, early initation, parity and education. Sugested to a stakeholder or health worker especially for midwife should be doing this early initation program as a part of professional practice midwifery."

"Tujuan tesis ini adalah untuk mengetahui proporsi, karakteristik, dan faktor risiko retinopati diabetik pada responden diabetes melitus di puskesmas Jakarta Timur dan Jakarta Selatan. Desain penelitian berbasis populasi, studi deskriptif-analitik dengan metode potong lintang. Kriteria inklusi adalah pasien diabetes melitus berusia > 18 tahun yang dilakukan pemeriksaan foto fundus di puskesmas kecamatan Jakarta Timur dan Jakarta Selatan. Dilakukan cluster random sampling dan didapatkan 17 kecamatan intervensi yang dilakukan pemeriksaan foto fundus. Dilakukan consecutive sampling dengan pemberitahuan secara aktif kepada responden. Responden diperiksa foto fundus tanpa dilatasi dan retinopati digrading dengan menggunakan klasifikasi NSC (National Screening Committee). Responden diperiksa tajam penglihatan, tekanan darah, lingkar pinggang, lingkar panggul, pemeriksaan laboratorium, dan dilakukan wawancara terpimpin untuk evaluasi faktor risiko. Jumlah total sampel dari penelitian ini adalah 419 responden dengan proporsi retinopati diabetik adalah 49 responden (11.7%). Pada analisis multivariat, faktor risiko independen untuk DR adalah usia ≥ 60 saat datang (OR 0.46; 95% CI, 0.24-0.89), durasi DM ≥ 5 tahun (OR 1.43; 95% CI, 0.79-2.59), keturunan DM (+) (OR 1.89; 95% CI, 0.98-3.63), GDP ≥ 126mg/dl (OR 2.06; 95% CI, 0.95-4.44), penyakit komplikasi (+) (OR 1.41; 95% CI, 0.78-2.57), gangguan penglihatan ringan (OR 1.81; 95% CI, 0.84-3.88), lingkar pinggang berlebih (OR 0.39; 95% CI, 0.20-0.73). Responden dengan retinopati diabetik cenderung memiliki indeks massa tubuh normal, tanpa obesitas sentral, dengan lingkar pinggang normal. Berdasarkan data yang didapatkan, satu dari 10 responden diabetes melitus di puskesmas Jakarta Timur dan Jakarta Selatan memiliki retinopati diabetik. Faktor risiko independen yang berkaitan dengan retinopati diabetik adalah usia ≥ 60 tahun dan lingkar pinggang berlebih.

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The purpose of this study was to describe the proportion, characteristics, and risk factors of diabetic retinopathy in diabetic population at primary health care (PHC) in East Jakarta and South Jakarta. Population-based cross sectional study, analytic ? descriptive. Method: Diabetic individuals > 18 years were screened for diabetic retinopathy with single field nonmydriatil 45o retinal photograph at PHC in East Jakarta and South Jakarta and retinopathy was graded in NSC (National Screening Committee) system. We had cluster random sampling for 34 PHC and 17 were selected and performed retinal photography for DR screening. Consecutive sampling was performed with active announcement for diabetic patients in PHC within the scope of the study. All participants underwent guided interview and examination including uncorrected visual acuity, blood pressure, waist-hip circumference, body mass index, and collection of blood samples. Results : We had 419 diabetic person who participated in this study. The overall proportion of DR was 49 (11.7%). In logistic regression analysis, independent risk factors for DR were age ≥ 60 years (OR 0.46; 95% CI, 0.24-0.89), diabetic duration ≥ 5 years (OR 1.43; 95% CI, 0.792.59), related to diabetes mellitus (OR 1.89; 95% CI, 0.98-3.63), fasting blood glucose ≥ 126mg/dl (OR 2.06; 95% CI, 0.95-4.44), complications of diabetes (OR 1.41; 95% CI, 0.78-2.57), mild visual acuity disturbance (OR 1.81; 95% CI, 0.84-3.88), excessive waist circumference (OR 0.39; 95% CI, 0.20-0.73). Person with DR tend to have normal body mass index, without central obesity, with a normal waist circumference. Conclusion : One in 10 adults with diabetes at PHC in East Jakarta and South Jakarta has diabetic retinopathy. The independent association of DR with established risk factors were age more than or equal to 60 years old and excessive waist circumference."

"Macular edema is a kind of human sight disease as a result of advanced stage of diabetic retinopathy. It affects the central vision of patients and in severe cases lead to blindness. However, it is still difficult to diagnose the grade of macular edema quickly and accurately even by the medical doctor's skill. This paper proposes a new method to classify fundus images of diabetics by combining Self-Organizing Maps (SOM) and Generalized Vector Quantization (GLVQ) that will produce optimal weight in grading macular edema disease class. The proposed method consists of two learning phases. In the first phase, SOM is used to obtain the optimal weight based on dataset and random weight input. The second phase, GLVQ is used as main method to train data based on optimal weight gained from SOM. Final weights from GLVQ are used in fundus image classification. Experimental result shows that the proposed method is good for classification, with accuracy, sensitivity, and specificity at 80%, 100%, and 60%, respectively."

"World Health Organisation (WHO) has predicted 300 million peoples will suffer of diabetic in 2025. Long-term diabetics can lead to diabetic retinopathy that can cause blindness in developing countries. One of the abnormalities of diabetic retinopathy is exudate. Exudates are classified into two categories, i.e. hard and soft exudates. This paper proposes feature extraction based on texture for distinguishing hard, soft and non-exudates. The green channel of the original images is enhanced by CLAHE and followed by median filtering and thresholding in red channel to detect and remove the optic disc. The enhanced image is segmented based on clustering to obtain the region of interest of exudates. Feature extraction based on texture is conducted by using GLCM and lacunarity. Results show that classification based on NaïveBayes algorithm achieves accuracy, specificity and sensitivity of 92.13%, 96% and 87.18%, respectively."

"Bayi prematur adalah bayi yang lahir dengan usia kehamilan kurang dari 37 minggu yang memiliki sistem saraf dan organ-organ yang belum sempurna sehingga lebih beresiko mengalami berbagai masalah kesehatan. Salah satu masalah kesehatan yang dapat terjadi adalah pada organ mata yang merupakan organ penting dalam perkembangan bayi. Retinopathy of Prematurity (ROP) merupakan salah satu penyakit mata yang terjadi pada bayi prematur yang disebabkan oleh pembentukan pembuluh darah retina yang tidak normal. Proses diagnosis yang dilakukan oleh dokter mata belum bisa mengatasi kenaikan jumlah kasus ROP, sehingga disini penulis menggunakan pendekatan deep learning untuk melakukan klasifikasi tingkat keparahan ROP pada citra fundus retina. Metode deep learning yang digunakan adalah Convolutional Neural Network (CNN) dengan arsitektur ResNet50. Data yang digunakan pada penelitian ini merupakan data sekunder yang diperoleh dari online database Kaggle berupa 90 data citra fundus retina yang terbagi atas 38 citra bukan penderita ROP, 19 citra penderita ROP Stage 1, 22 citra penderita ROP Stage 2, dan 11 citra penderita ROP Stage 3. Pada tahap persiapan data, dilakukan perbaikan kontras citra menggunakan Contrast Limited Adaptive Histogram (CLAHE) dan image masking. Kemudian dilakukan resize citra menjadi ukuran 224×224. Data kemudian diaugmentasi menggunakan teknik flip horizontal dan rotation agar data menjadi lebih banyak yang kemudian dibagi menjadi 80% data training dan 20% data testing. Dari 80% data training, diambil 20% untuk data validation. Training model dilakukan menggunakan model dengan arsitektur ResNet50 dengan hyerparameter model yaitu batch size 64, learning rate 0.001, dan epoch sebanyak 30, fungsi optimasi Adam (Adaptive moment estimation), dan fungsi loss categorical cross entropy. Proses modelling dilakukan sebanyak 5 kali percobaan dan berhasil memperoleh nilai rata-rata kinerja training model sebesar 99.714% dan 92.85% pada akurasi training dan akurasi validation-nya, selain itu diperoleh nilai 0.01864 dan 0.18434 pada loss training dan loss validation. Sedangkan rata-rata kinerja testing model berhasil memperoleh akurasi testing sebesar 97.352%, testing loss sebesar 0.0986374, dan AUROC sebesar 0.0955. Selain melakukan evaluasi kinerja, peneliti juga akan menggunakan GradCAM untuk menampilkan visualisasi ciri-ciri yang dianggap penting untuk nantinya membantu dokter dalam mengevaluasi ROP.

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Premature infants are babies born with a gestational age of less than 37 weeks, and they have underdeveloped nervous systems and organs, making them more susceptible to various health issues. One of the health problems that can occur involves the eye, which plays a crucial role in the baby's development. Retinopathy of Prematurity (ROP) is one of the eye diseases that affects premature infants and is caused by abnormal blood vessel formation in the retina. The current diagnostic processes performed by ophthalmologists have not been effective in addressing the increase in ROP cases. Therefore, in this study, the author employs a deep learning approach to classify the severity of ROP in retinal fundus images. The deep learning method utilized is the Convolutional Neural Network (CNN) with the ResNet50 architecture. The research data consists of 90 retinal fundus images obtained from the online database Kaggle, comprising 38 images of non-ROP cases, 19 images of ROP Stage 1, 22 images of ROP Stage 2, and 11 images of ROP Stage 3. In the data preparation phase, the image contrast is enhanced using Contrast Limited Adaptive Histogram (CLAHE) and image masking techniques. Subsequently, the images are resized to 224×224 dimensions. Data augmentation is performed using horizontal flip and rotation techniques to increase the dataset, which is then split into 80% training data and 20% testing data. From the 80% training data, 20% is further allocated for validation data. The model is trained using the ResNet50 architecture with hyperparameters set to batch size 64, learning rate 0.001, and 30 epochs. The optimization function used is Adam (Adaptive Moment Estimation), and the loss function is categorical cross-entropy. The modeling process is repeated five times, and the average performance of the training model is achieved at 99.714% for training accuracy and 92.85% for validation accuracy, with training and validation losses of 0.01864 and 0.18434, respectively. As for the average performance of the testing model, the testing accuracy is 97.352%, the testing loss is 0.0986374, and the AUROC (Area Under the Receiver Operating Characteristic) is 0.0955. In addition to evaluating the model's performance, the researcher also employs GradCAM to visualize important features, which can assist doctors in evaluating ROP cases."

"Retinal detachment (RD), atau ablasi retina, adalah kondisi ketika retina neurosensori terlepas dari lapisan dasarnya, yaitu epitel pigmen retina (EPR), karena kehilangan kerekatan. RD bisa menjadi kondisi yang serius jika tidak segera ditangani, seperti gangguan penglihatan hingga kebutaan permanen. Di Indonesia, diperkirakan terdapat 17.500—25.000 kasus baru setiap tahunnya. Namun, dengan jumlah dokter yang terbatas, pendeteksian RD secara konvensional mungkin tidak dapat dilakukan dengan cepat. Dengan memanfaatkan metode machine learning, khususnya deep learning, yang kini berkembangan pesat, dapat dilakukan pendeteksian RD melalui citra fundus mata menggunakan Convolutional Neural Network (CNN) dengan arsitektur ResNeSt. Terdapat masalah keterbatasan jumlah data pada kelas RD sehubungan dengan perlindungan privasi pasien yang membatasi akses terhadap data medis. Untuk meningkatkan jumlah data, dilakukan augmentasi data dengan GAN untuk menghasilkan data baru berupa citra sintetis untuk kelas RD. Dilakukan pula percobaan dengan menerapkan Contrast Limited Adaptive Histogram Equalization (CLAHE) sebagai tahap preprocessing sebelum augmentasi dengan GAN dengan tujuan meningkatkan kualitas citra yang masuk sebagai input dari GAN. Lebih lanjut, penelitian ini menguji tiga skenario dengan dua rasio splitting data, yaitu 6:2:2 dan 6:1:3. Skenario 1 menjalankan model ResNeSt tanpa preprocessing CLAHE dan augmentasi GAN pada data input. Skenario 2 menjalankan model ResNeSt dengan augmentasi GAN pada data input. Sementara itu, skenario 3 menjalankan model ResNeSt dengan menerapkan preprocessing CLAHE dan augmentasi GAN pada data input. Untuk splitting data dengan rasio 6:2:2, skenario 1 menghasilkan nilai rata-rata accuracy 89,9%, sensitivity 76,3%, specificity 94,3%, dan loss 52,4%, skenario 2 menghasilkan nilai rata-rata accuracy 92,3%, sensitivity 88,2%, specificity 94,8%, dan loss 18,6%, sedangkan skenario 3 menghasilkan nilai rata-rata accuracy 95,9%, sensitivity 94,4%, specificity 96,8%, dan loss 9,8%. Sementara itu, untuk splitting data dengan rasio 6:1:3, skenario 1 menghasilkan nilai rata-rata accuracy 91,3%, sensitivity 78,6%, specificity 94,9%, dan loss 27,9%, skenario 2 menghasilkan nilai rata-rata accuracy 94%, sensitivity 90,2%, specificity 96,3%, dan loss 17,9%, sedangkan skenario 3 menghasilkan nilai rata-rata accuracy 97,9%, sensitivity 97%, specificity 98,4%, dan loss 5,4%. Didapatkan bahwa performa model terbaik adalah ketika menggunakan skenario 3 dengan rasio splitting data 6:1:3.

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Retinal detachment (RD), also known as retinal ablation, is a condition where the neurosensory retina separates from its underlying layer, the retinal pigment epithelium (RPE), due to the loss of adhesion. RD can become a serious condition if not promptly treated, potentially leading to vision impairment, even permanent blindness. In Indonesia, an estimated 17,500–25,000 new cases of RD occur annually. However, with a limited number of doctors, conventional detection methods for RD may not be performed swiftly enough. Leveraging machine learning, particularly deep learning, which has rapidly advanced, RD detection can be facilitated through fundus imaging using Convolutional Neural Network (CNN) with ResNeSt architecture. A significant challenge arises due to the limited amount of data available for the RD class, as patient privacy regulations restrict access to medical data. To address this, data augmentation is applied using Generative Adversarial Networks (GAN) to generate synthetic images for the RD class. Additionally, experiments were conducted by applying Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing step before GAN augmentation, aiming to enhance the quality of the images inputted into the GAN. This study further evaluates three scenarios with two data splitting ratios, 6:2:2 and 6:1:3. Scenario 1 involved training the ResNeSt model without CLAHE preprocessing or GAN augmentation. Scenario 2 involved training the ResNeSt model with GAN augmentation. Scenario 3 involved training the ResNeSt model with both CLAHE preprocessing and GAN augmentation. For the 6:2:2 data splitting ratio, Scenario 1 achieved an average accuracy of 89.9%, sensitivity of 76.3%, specificity of 94.3%, and loss of 52.4%. Scenario 2 achieved an average accuracy of 92.3%, sensitivity of 88.2%, specificity of 94.8%, and loss of 18.6%. Meanwhile, Scenario 3 achieved an average accuracy of 95.9%, sensitivity of 94.4%, specificity of 96.8%, and loss of 9.8%. For the 6:1:3 data splitting ratio, Scenario 1 achieved an average accuracy of 91.3%, sensitivity of 78.6%, specificity of 94.9%, and loss of 27.9%. Scenario 2 achieved an average accuracy of 94%, sensitivity of 90.2%, specificity of 96.3%, and loss of 17.9%. Meanwhile, Scenario 3 achieved an average accuracy of 97.9%, sensitivity of 97%, specificity of 98.4%, and loss of 5.4%. The best model performance was observed in Scenario 3 with a 6:1:3 data splitting ratio."

"Retinopati hipertensi merupakan penyakit yang timbul pada retina akibat komplikasi dari hipertensi atau tekanan darah tinggi. Pemeriksaan gejala retinopati hipertensi penting untuk dilakukan supaya penanganan yang tepat dapat diberikan. Gejala retinopati hipertensi terdapat pada pembuluh darah di retina sehingga diagnosis dapat dilakukan melalui citra fundus retina. Penelitian ini memanfaatkan model Data-Efficient Image Transformer (DeiT) untuk mengklasifikasikan citra fundus retina menjadi dua kelas, yaitu kelas retinopati hipertensi dan kelas normal. Data yang digunakan dalam penelitian ini diperoleh dari empat database open-source, yaitu DRIVE, JSIEC, ODIR, dan STARE. Preprocessing berupa resize dan Contrast Limited Adaptive Histogram Equalization (CLAHE) diterapkan untuk menyeragamkan ukuran citra dan meningkatkan kontras citra. Generative Adversarial Network (GAN) digunakan untuk menghasilkan citra sintetis guna mengatasi masalah keterbatasan jumlah data serta meningkatkan variasi data yang dapat dipelajari oleh model DeiT. Penelitian ini menganalisis pengaruh metode GAN terhadap kinerja model DeiT dengan menggunakan metrik evaluasi accuracy, sensitivity, dan specificity. Analisis dilakukan dengan membandingkan tiga skenario: skenario A menggunakan data asli, skenario B menggunakan data hasil augmentasi GAN, dan skenario C menggunakan preprocessing CLAHE dan data hasil augmentasi GAN. Skenario A menunjukkan kinerja yang cukup baik dengan nilai rata-rata accuracy, sensitivitiy, dan specificity sebesar 94%, 97,7%, dan 84,6% untuk rasio pembagian data 70:30, serta 95,7%, 97%, dan 92,8% untuk rasio pembagian data 80:20. Skenario B mengungguli skenario sebelumnya dengan nilai rata-rata accuracy, sensitivitiy, dan specificity sebesar 96,4%, 97,2%, dan 95,7% untuk rasio pembagian data 70:30, serta 97,5%, 97,9%, dan 97,1% untuk rasio pembagian data 80:20. Pada skenario C, diperoleh nilai rata-rata accuracy, sensitivitiy, dan specificity sebesar 95,7%, 95%, dan 96,2% untuk rasio pembagian data 70:30, serta 95,5%, 94,9%, dan 96,4% untuk rasio pembagian data 80:20. Hasil penelitian menunjukkan bahwa penerapan metode GAN berhasil meningkatkan kinerja model DeiT, khususnya pada nilai specificity. Dari ketiga skenario yang diuji, skenario B yang memanfaatkan data sintetis hasil augmentasi GAN tanpa preprocessing CLAHE memberikan hasil yang paling unggul.

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Hypertensive retinopathy is a disease that occurs in the retina due to complications from hypertension or high blood pressure. Examination of hypertensive retinopathy symptoms is important to ensure appropriate treatment can be performed. The symptoms of hypertensive retinopathy are found in the blood vessels of the retina, allowing diagnosis to be performed through retinal fundus images. This study uses the Data-Efficient Image Transformer (DeiT) model to classify retinal fundus images into two classes: hypertensive retinopathy and normal. The data used in this study were obtained from four different open-source databases: DRIVE, JSIEC, ODIR, and STARE. Preprocessing in the form of resizing and Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to standardize the image size and enhance the image contrast. Generative Adversarial Network (GAN) was used to generate synthetic images to address the problem of limited data availability and increase the variety of data that can be learned by the DeiT model. This study analyzes the impact of the GAN method on the performance of the DeiT model using evaluation metrics of accuracy, sensitivity, and specificity. The analysis was conducted by comparing three scenarios: scenario A using the original data, scenario B using GAN-augmented data, and scenario C using CLAHE preprocessing and GAN-augmented data. Scenario A showed fairly good performance with average accuracy, sensitivity, and specificity values of 94%, 97.7%, and 84.6% for a 70:30 data split ratio, and 95.7%, 97%, and 92.8% for an 80:20 data split ratio. Scenario B outperformed the previous scenario with average accuracy, sensitivity, and specificity values of 96.4%, 97.2%, and 95.7% for a 70:30 data split ratio, and 97.5%, 97.9%, and 97.1% for an 80:20 data split ratio. In scenario C, the average accuracy, sensitivity, and specificity values were 95.7%, 95%, and 96.2% for a 70:30 data split ratio, and 95.5%, 94.9%, and 96.4% for an 80:20 data split ratio. The results of the study indicate that the application of the GAN method successfully improved the performance of the DeiT model, particularly in terms of specificity. Out of the three scenarios tested, scenario B, which utilized GAN-augmented synthetic data without CLAHE preprocessing, yielded the best results."

"Retinopati Diabetik adalah salah satu penyakit pada retina disebabkan oleh komplikasi diabetes yang dapat berujung pada kebutaan. Retinopati Diabetik tidak bisa dideteksi langsung secara kasat mata karena tanda-tandanya berada di bagian syaraf retina. Dari beberapa penelitian yang telah dilakukan pendeteksian Retinopati Diabetik dimungkinkan dapat dilakukan dengan melakukan klasifikasi menggunakan data citra retina atau yang biasa disebut sebagai citra fundus. Dalam penelitian ini diterapkan metode segmentasi citra yaitu Watershed dan Efficient Graph-Based beserta metode klasifikasi yaitu K-Nearest Neighbor dan Support Vector Machine dalam pendeteksian Retinopati Diabetik. Dari hasil implementasi, metode untuk segmentasi Efficient Graph-Based menggunakan data citra fundus dari DIARETDB0 diperoleh nilai akurasi, recall, dan precision lebih tinggi dibandingkan dengan metode segmentasi Watershed.

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Diabetic Retinopathy is one of disease on retina because of Diabetic complication that can cause blindness. Diabetic Retinopathy cant detected directly from the eyes because sign of Diabetic Retinopathy itself is in the eyes nerve. From several research that has been done prove that Diabetic Retinopathy can be detected by using retinas image or usually called fundus image.

In this research use segmentation method that is Watershed and Efficient Graph-Based with classification method that is K-Nearest Neighbor and Support Vector Machine for detection of Diabetic Retinopathy. From the implementation result, the Efficient Graph-Based segmentation method using fundus image data from the DIARETDB0 obtained that the accuracy, recall, and precision score is higher than Watershed segmentation method."