ABSTRAKPenelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe) berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All.Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet. ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe) based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with parameters One agains all .Keywords: Diabetic Retinopathy, KNN, SVM, Wavelet.

Teknologi   internet   telah   menjadi   bagian   penting dan membawa perubahan besar dalam kehidupan manusia. Pengguna   internet   terus   bertambah signifikan   setiap   tahun nya   khususnya   di Indonesia kurang lebih mencapai   73.7 %   dari total populasi, angka ini merupakan hasil survey yang dilakukan oleh APJII (Asosiasi Penyelenggara Jasa Internet Indonesia) perioda 2019-2020 (Q2).  Namun penggunaan internet   juga memberikan dampak negatif bagi individu   maupun lingkungannya. Salah satu isu penting yang menjadi fokus dalam dua dekade terakhir adalah internet addiction disorder dan gadget addiction disorder.  Kecanduan internet muncul karena penggunaan internet   berlebihan hingga menyebabkan munculnya dampak negatif atau kecenderungan menimbulkan gejala penyalahgunaan. Pada studi ini kami melakukan survey terhadap 2014 orang responden dengan parameter yang bervariasi seperti sebaran usia dari anak-anak hingga manula, jenis pekerjaan, latar belakang pendidikan, sudah berapa lamakah memakai gadget, hingga durasi pengaksesan social media perhari. Semua parameter tersebut akan dianalisa dengan menggunakan chi square untuk menghitung korelasinya terhadap adiksi internet maupun gadget berdasarkan instrument IAT (Internet Addiction Test). Dari hasil pengujian, alat ukur IAT dinilai valid dan handal. Dari hasil pengukuran terhadap 2014 responden di Indonesia untuk kondisi adiksi internet/media sosial terjadi pada kategori anak-anak 0.16% (1 responden), remaja 73% (467 responden), dewasa 23% (147 responden), dan lansia 3.94% (23 responden). Sedangkan adiksi gadget terjadi pada kategori remaja sebanyak 75% teradiksi (382 responden), kategori dewasa sebanyak 23% teradiksi (117 responden), kategori lansia sebanyak 2% teradiksi (11 responden).

Cancer is a non-contagious disease that is the leading cause of death globally. The most common types of cancer with high mortality are lung and colon cancer. One of the efforts to reduce cases of death is early diagnosis followed by medical therapy. Tissue sampling and clinical pathological examination are the gold standard in cancer diagnosis. However, in some cases, pathological examination of tissue to the cell level requires high accuracy, depending on the contrast of the pathological image, and the experience of the clinician. Therefore, we need an image processing approach combined with artificial intelligence for automatic classification. In this study, a method is proposed for automatic classification of lung and colon cancer based on a deep learning approach. The object of the image that is classified is the histopathological image of normal tissue, benign cancer, and malignant cancer. Convolutional neural network (CNN) with VGG16 architecture and Contrast Limited Adaptive Histogram Equalization (CLAHE) were employed for demonstration of classification on 25000 histopathological images. The simulation results show that the proposed method is able to classify with a maximum accuracy of 98.96%. The system performance using CLAHE shows a higher detection accuracy than without using CLAHE and is consistent for all epoch scenarios. The comparative study shows that the proposed method outperforms some previous studies. With this proposed method, it is hoped that it can help clinicians in diagnosing cancer automatically, with low cost, high accuracy, and fast processing on large datasets.

ABSTRAKPengukuran tinggi dan berat badan manusia sekarang ini masih bersifat manual sehingga kurang efisien jika dilakukan untuk kebutuhan dengan jumlah objek yang banyak. Sebagai solusi, pada penelitian ini telah dirancang suatu sistem untuk mengukur Tinggi Badan (TB) dan Berat Badan (BB) manusia berbasis Morphological Image Processing (MIP). Proses dimulai dengan citra masukan berupa citra digital full body yang dapat diambil dengan smartphone kemudian dilanjutkan dengan operasi MIP terdiri dari proses dilasi, filling dan labeling. Hasil dari MIP adalah jumlah piksel tinggi objek yang dikonversi menjadi TB (cm). Sedangkan perhitungan BB (kg) diperoleh dari luas permukaan tubuh objek berbasis BSA dengan memodelkan ke bentuk tabung elips. Dari hasil pengujian, diperoleh performansi sistem maksimum yaitu Approximate Value 98.42% untuk TB dan 94.4% untuk BB. Nilai tersebut diperoleh dengan parameter nilai jarak pengambilan 306 cm dan strel (structure element) pada MIP adalah 2.Kata kunci: tinggi badan, berat badan, BSA, morphological image processing ABSTRACTMeasurement of human height and weight are still performed manual today so it have not efficiency if conducted to many objects. As a solution, this research has designed a system for measuring the human body height and weight based on morphological image processing (MIP). The process was started with an input image of a full body digital image that retrieved with a smartphone followed by a MIP operation consisting of dilation, filling, and labeling. The result of MIP is the number of high pixels of objects converted to height in cm. While weight calculation has been obtained from surface area of body object based on BSA by modelling it to ellipse tube. The system performance is obtained at maximum Approximate Value of 98.42% for height and 94.4% for weight. That value was obtained with 306 cm distance and 2 stucture element size of MIP. Keywords: height, weight, BSA, morphological image processing

The high rate of patients with tuberculosis (TB) with the graph showing a continual increase requires the research in any sector as the programs to eradicate tuberculosis. One of the applications is the Decision Support System (DSS) that helps the medical experts particularly doctors in diagnosing TB grade 1+. 2+ , and 3+ rapidly. Another problem is related to the imbalance between the number of patients and the number of medical practitioners in the condition of pandemic Corona Virus Disease (Covid-19) today. Hence, DSS is highly required and it can be used for the long-term management of Covid. In this study, the rapid classification of normal lung, tuberculosis lung, and Covid-19 lung based on the Chest X-Ray (CXR) image was proposed as the initial step of DSS implementation. The proposed image processing based CXR classification using Deep Learning Convolutional Neural Network (CNN) obtained the highest accuracy rate of 88.37%. This accuracy was obtained in the second scenario with the 208 CXR datasets. The small number of datasets used was related to the limited number of CXR Covid-19 images with good quality brightness. The proposed system developed is expected to help doctors in diagnose lung disease.