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Abstrak :
Pendeteksian wajah manusia merupakan proses dasar dari aplikasi yg lebih luas. Salah satu metodenya adalah pemanfaatan informasi warna. Sebuah model warna kulit yg merepresentasikan warna kulit manusia dibuat utk mensegmentasi skin region dan non skin region dlm gambar berwarna. Tiga persyaratan di terapkan utk menyeleksi skin region yg mempresentasikan wajah manusia (jumlah lubang dlm satu region, rasio tinggi terhadap lebar region dan koefesien korelasi siang antara region dengantemplete face).Makalah ini mensimulasikan deteksi wajah manusia dengan menggunakan segmentasi dan templete matching .Hasil simulasi menunjukan bahwa skin region yaitu jumlah lubang ,rasio tinggi terhadap lebar dan korelasi region dengan templete,ketiganya berperan cukup baik,sedangkan keberhasilan deteksi sangat tergantung pd proses sebelumnya yaitu segmentasi.

Abstrak :
Penelitian ini mengembangkan Computer Aided Diagnosis CAD radiografi paru anak dengan menggunakan metode segmentasi Particle Swarm Optimization PSO untuk membantu dokter mendeteksi paru yang abnormal. Metode PSO mencari abnormalitas berdasarkan nilai piksel. Metode PSO dikerjakan dengan dua variasi metode yaitu FCM Wienerfilter PSO dan FCM Adaphisteq PSO. Evaluasi dilakukan dengan menghitung ROC Receiver Operating Characteristics citra segmentasi tiap metode terhadap citra acuan evaluasi dokter. Metode FCM Wienerfilter PSO memiliki nilaiROC paling baik. Overall error metode ini yaitu 11.43 1.6 dibanding dengan metode FCM Adapthisteq PSO yaitu 28.57 1,6. Hal ini menggambarkan bahwa banyak kesalahan deteksi yang dilakukan pada metode FCM Adapthisteq PSO. Metode FCM Wienerfilter PSO ini memiliki nilai akurasi 88,57, sensitifitas 90,00, spesifitas 85,00, dan presisi 93,75 lebih tinggi dibanding dengan semua parameter ROC metode FCM Adaphisteq PSO yaitu akurasi 71,43, Sensitivitas 80,00, Spesifitas 50,00, dan Presisi 80.00. Hal ini membuktikan bahwa hasil deteksi metode FCM Wienerfilter PSO lebih banyak memiliki tingkat keberhasilan yang sesuai dengan evaluasi dokter dan lebih baik dalam mendeteksi citra abnormal. Pada citra abnormal nilai piksel metodeFCM wienerfilter PSO memiliki rentang 209-255, dan nilai piksel metodeAdapthisteq PSO memiliki rentang 206-255. ...... The study developed Computer Aided Diagnosis CAD children pulmonary radiography using Particle Swarm Optimization PSO segmentation method to help doctors detect abnormal lung. The PSO method searched abnormalities by value of the image pixel. PSO method used two variations method, namely FCM Wienerfilter PSO and FCM Adaphisteq PSO. The evaluation was done by calculating the ROC Receiver Operating Characteristics segmentation of each image against the reference image evaluation doctors. FCM Wienerfilter PSO method has better ROC value. Overall error of this method is 11.43 1.6 compared with the method of FCM Adapthisteq PSO is 28.57 1.6. This explain that many of the error detection on FCM Adapthisteq PSO method. ROC FCM Wienerfilter PSO results show the value of accuracy 88,57, sensitivity 90,00, specificity 85,00, and precision 93,75 is relatively higher than all parameter of ROC FCM Adaphisteq PSO method that isaccuracy 71,43, sensitivity 80,00, specificity 50,00, and precision 80.00. This proves that the results of the detection method of FCM Wienerfilter PSO has more success rates in accordance with doctor rsquo s evaluation and better at detecting abnormal image. Abnormal lung pixel values by the method of FCM wienerfilter PSO is 209 255, andAbnormal lung pixel values by the FCM Adapthisteq PSO methodis 206 255.

Abstrak :
Counting bacteria colonies uses Total Plate Count (TPC) method which is generally done by seeing, counting, naked eyes and time consuming. The aim of research is to make an image processing algorithm that is able to count the total bacteria colonies on the TPC method which is can be applied without any special equipment, and to provide informations about the best configuration in its application. This research was done by making an image processing algorithm based on gray-scale image segmentation, using MATLAB, and testing its accuracy in counting bacteria colonies. The result showed that the optimal threshold value for black background is between 0.67 and 0.79, and the optimal threshold value for white background was between 0.01 and 0.09. This experiment revealed that low value o f disk shape morphological structuring element could give the optimal detection of bacteria colonies. In conclutions, the total numbers of bacteria colonies which was counted by gray-scale image segmentation which one of image processing algorithm method was not statistically different compared to manual counting. Automation is one side o f technology that can help human doing something with easiness and simplicity. Digital image processing is one kind o f automation. Counting bacteria colonies using Total Plate Count (TPC) commonly done by counting bacteria colonies manually, it was very time consuming. The aim of this research is to make an image processing algorithm that able to count total bacteria colonies on the TPC method which can be applied without require special equipment, and to provide information about best configuration in its application. This research was done by making an image processing algorithm based on color image segmentation using MATLAB, and testing its accuracy in counting bacteria colonies. The results showed that the total numbers of bacteria colonies as the result o f counting by image processing algorithm based on color image segmentation were not different statistically compared to manual counting. A good detection o f bacteria colonies was depends on the configuration o f some parameters, such as color sampling, value o f color tolerance, shape and value o f morphological structuring element.

Abstrak :
Penelitian ini mengembangkan Uji Korelasi Computer Aided Diagnosis CAD radiografi paru anak dengan menggunakan metode segmentasi Markov Random Field MRF untuk membantu mendeteksi abnormalitas paru dengan kecenderungan infeksi. Metode MRF mencari abnormalitas berdasarkan nilai piksel citra. Metode MRF dikerjakan dengan empat variasi yaitu MRF tanpa filter, median filter MRF, wiener filter MRF dan adapthisteq MRF. ROC hasil segmentasi wiener filter relatif lebih tinggi dari tanpa filter. Hasil ROC wiener filter menunjukkan nilai akurasi akurasi akurasi 81,4 , sensitivitas 82,0 , spesifitas 80,0 , presisi 91,1 dan overall error 18,6 . Sedangkan ROC untuk tanpa filter maupun filter yang lain menunjukkan lebih rendah dari nilai ROC wiener filter. Namun perbedaan ROC untuk setiap jenis tingkat keberhasilan tidak lebih dari 5 , artinya keempat metode MRF masih dapat diimplementasikan. Nilai piksel paru abnormal dengan metode MRF tanpa filter, median filter MRF, dan adapthisteq MRF sama yaitu 205-255. Nilai piksel paru abnormal dengan metode wiener filter MRF yaitu 197-255. Citra paru belum dapat menentukan secara definitif penyakit infeksi paru pada anak. ...... This study developed a correlation test Computer Aided Diagnosis CAD radiographic of children pulmonary using segmentation Markov Random Field MRF method to detect lung abnormalities with infection trends. MRF method searched abnormalities by value of the image pixel. MRF method used four variations, namely MRF without a filter, median filter MRF, wiener filter MRF, and adapthisteq MRF. ROC segmentation results wiener filter is relatively higher than without a filter. ROC wiener filter results show the value of accuracy 81.4 , sensitivity 82.0 , specificity 80.0 , precision 91.1 and overall error of 18.6 . While the ROC for unfiltered and filter others show lower than the value of ROC wiener filter. However, differences in ROC for any kind of success rate is not more than 5 , meaning that all four methods MRF can still be implemented. Abnormal lung pixel value with MRF method without filter, median filter MRF, and adapthisteq MRF same namely 205 255. Abnormal lung pixel values by the method of wiener filter MRF is 197 255. Radiographic of children pulmonary can not definitively determine lung infections in children.

Abstrak :
Penelitian ini mengembangkan Computer Aided Diagnosis CAD radiografi paru anak dengan menggunakan metode segmentasi Deformable Models untuk membantu mendeteksi abnormalitas. Metode Deformable Models mencari abnormalitas berdasarkan nilai piksel citra. Metode Deformable Models dikerjakan dengan dua variasi yaitu median filter Deformable Models dan wiener filter Deformable Models. Nilai piksel paru-paru abnormal dengan segmentasi wiener filter Deformable models adalah 186-255 dan median filter Deformable Models adalah 191-255. Metode wiener filter Deformable models menghasilkan nilai ROC lebih tinggi dibandingkan metode median filter dengan nilai akurasi 78,5, sensitivitas 74,5, spesifitas 80, presisi 90,0 dan overall error 21,0. ...... This study developed a correlation test Computer Aided Diagnosis CAD radiographic of children pulmonary using segmentation Deformable Models method for detecting Abnormalities. Deformable models method searched abnormalities by value of the image pixel. Deformable models method used two variations, namely median filter Deformable Models and wiener filter Deformable Models. Abnormal result lung pixel values with segmentation Wiener filter Deformable models is 186 255 and median filter Deformable Models is 190 255. Wiener filter Deformable models method have ROC result relatively higher than median filter Deformable models with value of accuracy 78,5, sensitivity 74,5, specificity 80.0, precision 90,0 and overall error of 21,0.

Abstrak :
Salah satu aplikasi yang akan diterapkan pada sistem e-Livestock di Indonesia adalah aplikasi untuk mengenali rumpun ternak sapi berdasarkan data citra ternak sapi. Permasalahan yang dihadapi pada proses pengenalan rumpun ternak tersebut adalah pada saat proses segmentasi objek ternak sapi, karena pada umumnya data citra ternak sapi memiliki background yang cukup kompleks dan beragam. Sehingga sangat sulit untuk mengenali objek ternak sapi secara otomatis menggunakan mesin. Beberapa penelitian yang telah dilakukan mengungkapkan bahwa metode segmentasi secara interaktif yang berbasiskan graf cukup ampuh untuk melakukan segmentasi pada citra yang kompleks. Pada penelitian ini dilakukan proses segmentasi secara interaktif berbasiskan graf yang merupakan pengembangan dari metode Graph Cuts. Metode ini dapat memberikan hasil yang lebih akurat pada citra dengan background yang beragam dan komples dengan rata-rata nilai akurasi sebesar 90%, walaupun masih membutuhkan interaksi dari pengguna. Citra ternak sapi yang telah dilakukan proses segmentasi dapat diklasifikasikan jenis rumpunnya dengan akurat dengan rata-rata nilai akurasi sebesar 97,5%. ......One of the applications that will be applied to the e-Livestock system in Indonesia is an application to recognize the race of cattle beef from cattle beef image data. Problems faced in the process of recognizing cattle race is in the process of cattle object segmentation, because the cattle beef data image have a complex and diverse
background. So it is very difficult to recognize the cattle object fully automatic using the machine. Previous research and studies revealed that the interactive segmentation method which is based on graph was powerful enough to perform image segmentation with complex background. In this research, we develop the process of graph-based interactive segmentation which is the development of Graph Cuts methods. This method can provide more accurate results in imagery with complex and diverse background with 90% accuracy, although still requiring interaction from the user. Cattle image segmentation process that has been done can be accurately classified its race with an average accuracy of 97,5%.

Abstrak :
Penyakit mata kering adalah suatu kondisi yang bersifat multifaktorial kronis yang sering terjadi pada permukaan mata. Mata kering diklasifikasikan menjadi dua, yaitu mata kering defisiensi aqueous dan mata kering evaporatif. Penyakit mata kering evaporatif yang disebabkan oleh disfungsi kelenjar meibom sudah terjadi pada lebih dari 85% kasus penyakit mata kering. Disfungsi kelenjar meibom adalah kelainan difus dan kronis dari kelenjar meibom yang umumnya ditandai dengan adanya perubahan kualitatif atau kuantitatif dalam sekresi kelenjar. Area kerusakan pada kelenjar meibom dibagi menjadi 4 tingkat atau skala, yaitu normal (meiboscore 0), meiboscore 1, meiboscore 2, dan meiboscore 3. Proses dalam mendiagnosis penyakit mata kering masih dilakukan secara subjektif oleh tenaga medis, hal tersebut dapat mengakibatkan perbedaan dalam menilai tingkat disfungsi kelenjar meibom. Penulis menggunakan data science untuk mendiagnosis penyakit mata kering dengan melakukan pendekatan Artificial Intelligence (AI) yang di dalamnya terdapat metode deep learning. Pada penelitian ini, penulis melakukan klasifikasi pada data citra yang merupakan hasil segmentasi model U-Net dengan 4 kelas skala meiboscore menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur ResNet50. Data citra yang digunakan pada penelitian ini adalah sebanyak 417 data citra yang dilatih menggunakan ResNet50 dengan epoch sebanyak 30 dan learning rate sebesar 0,001. Pembagian data dilakukan dengan masing-masing data training, data testing, dan data validation sebanyak 80%, 20%, dan 10% dari data training. Dari hasil simulasi, diperoleh masing-masing nilai rata-rata akurasi dan AUC adalah 92,62% dan 0,99 dengan running time yang didapat selama 1,8 detik. ......Dry eye disease is a chronic multifactorial condition that often occurs on the ocular surface. Dry eye is classified into two, namely aqueous deficiency dry eye and evaporative dry eye. Evaporative dry eye disease caused by meibomian gland dysfunction already occurs in more than 85% of dry eye disease cases. Meibomian gland dysfunction is a diffuse and chronic disorder of the meibomian glands that is generally characterized by qualitative or quantitative changes in glandular secretions. The area of damage to the meibomian glands is divided into 4 levels or scales, namely normal (meiboscore 0), meiboscore 1, meiboscore 2, and meiboscore 3. The process of diagnosing dry eye disease is still done subjectively by medical personnel, which can lead to differences in assessing the level of meibomian gland dysfunction. The author uses data science to diagnose dry eye disease by taking an Artificial Intelligence (AI) approach in which there is a deep learning method. In this research, the author classifies image data which is the result of segmentation of the U-Net model with 4 classes of meiboscore scale using the Convolutional Neural Network (CNN) method with ResNet50 architecture. The image data used in this research is 417 image data trained using ResNet50 with 30 epochs and a learning rate of 0.001. Data division is done with each training data, testing data, and validation data as much as 80%, 20%, and 10% of the training data. From the simulation results, the average accuracy and AUC values are 92.62% and 0.99 respectively with a running time of 1.8 seconds.

Abstrak :
Coronavirus Disease 2019 (COVID-19) merupakan sebuah penyakit yang disebabkan oleh novel coronavirus SARS-CoV-2. Penyakit yang berasal dari Provinsi Hubei di China ini sudah menyebar ke seluruh dunia, menjangkiti banyak hingga seluruh negara di dunia. Sudah menginfeksi kurang lebih 400 juta jiwa di seluruh dunia pada pertengahan kuartal pertama tahun 2022. Mencegah penyebaran COVID-19 merupakan tindakan yang harus segera dilakukan, salah satu caranya adalah dengan pendeteksian sedini mungkin. Pendeteksian COVID-19 selain menggunakan metode kedokteran, dapat dipertimbangkan mengenai penggunaan artificial intelligence. Penelitian mengenai metode pendeteksian COVID-19 menggunakan citra X-Ray yang telah dilakukan oleh Dhita menuai hasil yang cukup sukses. Menambahkan penelitian tersebut, kami melakukan metode pendeteksian menggunakan citra CT Scan. Beberapa penelitian mengenai pendeteksian COVID-19 menggunakan citra CT Scan seperti Tang et al. meneliti mengenai segmentasi citra CT Scan terhadap daerah local lesi terindikasi COVID-19 atau Pneumonia. Rahimzadeh, Attar, and S. M. Sakhaei juga melakukan penelitian sebelumnya mengenai pengklasifikasian pasien COVID-19 menggunakan citra CT Scan dengan mendapatkan hasil 90% akurasi dengan menggunakan metode FPN. ......Coronavirus Disease 2019 (COVID-19) is a disease caused by the novel coronavirus SARS-CoV-2. This disease which originates from the Hubei Province in China has already spread throughout the world, reaching many if not all countries in the world. There have been more than 400 million people infected across the globe as of the first quarter of 2022. Prevention of the spreading of the disease is very important, and one of the best ways to do so is to detect its infection as soon as possible. Aside from asking a doctor, the task of detecting COVID-19 using artificial intelligence has been considered. The research done by Dhita to detect COVID-19 using X-ray images has been seen as a success. Adding to that, we attempt to detect COVID-19 using CT Scan images. A couple research papers about detecting COVID-19 using CT Scan images such as the ones done by Tang et al. tried to segment CT Scan images related to the lesions that indicate COVID-19 or Pneumonia. Rahimzadeh, Attar, and S. M. Sakhaei also conducted research related to classifying COVID-19 patients using CT Scan images and found success at 90% accuracy with an FPN model.

Abstrak :
The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.

Metode-metode mulitilevel thresholding bersifat sangat efisien untuk bi-level thresholding. Namun, metode-metode tersebut secara komputasional sangat mahal untuk digunakan dalam multilevel thresholding, karena pencarian threshold optimalnya dilakukan secara mendalam untuk mengoptimalkan fungsi objektifnya. Untuk mengatasi kelemahan ini, sebuah metode hybrid antara Genetic Algorithm (GA) dan Particle Swarm Optimization (PSO), yang disebut GA-PSO, berbasis multilevel thresholding disajikan dalam makalah ini. Algoritma GA-PSO digunakan untuk mencari nilai threshold yang optimal untuk memaksimalkan fungsi obyektif dari metode Otsu. Metode GA-PSO yang diusulkan diuji pada lima citra standar dan dibandingkan dengan algoritma Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Hasil penelitian menunjukkan efektivitas dalam pencarian threshold multilevel optimal dari algoritma yang diusulkan.