66 research outputs found
Sistem Penunjang Keputusan Dengan Menggunakan Metode Ahp Dalam Seleksi Produk
Full text linkDecision Support Sistem Is A Concept That Can Be Applied To Help Humans In Decision Making. In this research, the problem that will be faced is how the process of determining or selecting a product that is most interested in a mini market by adopting the concept of a decision support sistem. The method to be used is the Analytical Hierarchy Process (AHP). This method is able to provide results in the form of number results from the selection process, the results given later will also give the calculation results of the criteria. The Criteria Are Price, Taste, Product Design, Aroma And Benefits. Based on the results obtained that the price has a priority value of 30-50%. The purpose of this study is to help the mini market to see which products are interested. Then the benefits obtained are to assist the Mini Market Manager to provide products of interest so that there is no accumulation of products resulting in losse
"Heartbeat alone" - Calamine
No full text"Heartbeat Alone" is a song by the Australian artist Calamine and was produced as part of the Indie 100 research intensive project within the Independent Music Project (IMP). The IMP is an ongoing, interdisciplinary research arm within QUT. The song's author is Georgia Potter
Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques
Full text linkGround Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients.Ground Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients
Deep learning approach analysis model prediction and classification poverty status
Get PDFThe problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the coronavirus disease (COVID-19) pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-means method, artificial neural network (ANN), and support vector machine (SVM). The analytical model will be optimized using the pearson correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty
Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection
No full textDiagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.
 
Effectiveness of VGG19 in deep learning for brain tumor detection
Get PDFImage processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients
Machine Learning Analisis Klasifikasi dalam Penentuan Status Gizi Anak
No full textMalnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.Gizi buruk merupakan salah satu permasalahan yang terjadi pada kalangan anak-anak yang diakibatkan oleh faktor kurangnya asupan gizi. Fakta yang terjadi bahwa Indonesia telah menyumbang angka 36% dengan menjadikan negara peringkat ke lima dengan kasus malnutrisi terbesar di dunia. Berdasarkan hal tersebut maka dibutuhkan sebuah solusi dalam menekan laju angka pertumbuhan kasus gizi buruk. Penelitian ini bertujuan untuk melakukan analisis klasifikasi penentuan status gizi dengan menggunakan Artificial Neural Network (ANN). Proses analisis klasifikasi ANN nantinya akan memanfaatkan performa kinerja algoritma Multilayer Perceptron (MLP) yang di optimalkan dengan metode Pearson Correlation (PC) untuk menghasilkan keluaran optimal. Pada dasarnya metode PC mampu memberikan peran aktif dalam mengukur kinerja analisis Machine Learning (ML). Adapun dataset penelitian ini menggunakan data kasus gizi anak yang terjadi pada periode tahun sebelumnya. Dataset tersebut bersumber dari Rumah Sakit Umum Propinsi (RSUP) M. Djamil Padang. Berdasarkan pengujian yang telah dilakukan bahwa kinerja metode PC pada proses klasifikasi ANN mampu menyajikan pola analisis yang tepat dan akurat. Pola analisis tersebut juga telah mampu memberikan tingkat akurasi yang cukup baik sebesar 95%. Tidak hanya itu, penelitian ini juga mampu menyajikan pola analisis dengan model arsitektur ANN terbaik dalam klasifikasi status gizi. Berdasarkan keseluruhan hasil, maka penelitian ini dapat dijadikan sebuah solusi alternatif dalam penanganan masalah kasus gizi pada anak. Dengan hasil tersebut maka penelitian ini juga berkontribusi bagi Dinas Kesehatan Provinsi Sumatera Barat dalam menekan angka kasus gizi buruk yang terjadi pada periode tahun berikutnya
Application of Object Mask Detection Using the Convolution Neural Network (CNN)
No full textThe spread of Coronavirus Disease (Covid-19) is still a serious problem that we are currently facing. Spread occurred very quickly through the face-to-face interaction process. The face-to-face interaction process that occurs both in public spaces and in closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is to increase the use of masks in both public and closed spaces. On the basis of this, this study aims to develop an object detection process in image processing techniques. Object detection development using the convolution neural network (CNN) method to provide optimal output. CNN can process the input image, which is converted into a pixel matrix and then sent to the convolution layer. The research data set consists of 2000 images of masks and not masks. The images were obtained from open sources, github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used in the monitoring of public services for the community to increase the use of masks
Neural Network Backpropagation Identifikasi Pola Harga Saham Jakarta Islamic Index (JII)
Full text linkJakarta Islamic Index (JII) is an organization engaged in the economy with the aim to pay attention to stock movements every day. With the JII, people who do not understand about shares and their movements, will be easy to know and understand the movement of shares that occur at certain times. The problem in this research is that many investors are unable to predict the rise and fall of stock prices. The prediction process can be done with a backpropagation algorithm. The algorithm is a concept of computer science which is widely used in the case of analysis, prediction and pattern determination. The process starts from the analysis of the variables used namely interest rates, exchange rates, inflation rates and stock prices that occurred in the previous period. The variables used are continued in the formation of network patterns and continued in the process of training and testing in order to produce the best network patterns so that they are used as a process of identifying JII stock price movements. The results obtained in the form of the value of stock price movements with an error rate based on the MSE value of 11.85% so that this study provides information in the form of knowledge for making a decision. The purpose of the research is used as input for investors in identifying share prices. In the end, the benefits felt from the results of this study, investors can make an initial estimate before investing in JII.Jakarta Islamic Indeks (JII) adalah sebuah organisasi yang bergerak di bidang perekonomian dengan tujuan untuk memperhatikan pergerakan saham ditiap harinya. Dengan adanya JII, masyarakat yang tidak mengerti tentang saham serta pergerakannya, akan mudah mengetahui dan memahami pergerakan saham yang terjadi pada waktu tertentu. Permasalahan dalam penelitian ini, banyak para investor yang tidak mampu memprediksi kenaikan dan penurunan harga saham. Proses prediksi dapat dilakukan dengan algoritma backpropagation. Algoritma tersebut merupakan konsep ilmu pengetahuan bidang ilmu komputer yang banyak digunakan dalam kasus analisa, prediksi dan penentuan pola. Proses di mulai dari analisa variabel yang digunakan yakni suku bunga, kurs nilai tukar mata uang, tingkat inflansi dan harga saham yang terjadi pada periode sebelumya. Variabel yang digunakan di lanjutkan pada pembentukan pola jaringan dan diteruskan dalam proses melatih dan menguji guna menghasilkan pola jaringan yang terbaik sehingga digunakan sebagai proses identifikasi pergerakan harga saham JII. Hasil yang didapat berupa nilai pergerakan harga saham dengan tingkat kesalahan berdasarkan nilai MSE sebesar 11.85% sehingga, penelitian ini memberikan informasi dalam bentuk knowledge guna pengambilan sebuah keputusan. Tujuan dari penelitian dijadikan input bagi para investor dalam identifikasi harga saham hingga Pada akhirnya manfaat yang dirasakan dari hasil penelitian ini, para investor dapat melakukan estimasi awal sebelum berinvestasi pada JII
Determination of children's nutritional status with machine learning classification analysis approach
Get PDFMalnutrition is a problem that is often faced by every country around the world. Various facts show that malnutrition is of particular concern to many researchers. To can overcome this problem, every effort has been made such as developing analytical models in identification, classification, and prediction. This study aims to determine the nutritional status of children using the machine learning (ML) classification analysis approach. The methods used in the ML analysis process consist of cluster K-Means, artificial neural network (ANN), sum square error (SSE), pearson correlation (PC), and decision tree (DT). The dataset for this study uses data on child nutrition cases that occurred in the previous and was sourced from the provincial general hospital (RSUP) M. Djamil, Padang, West Sumatera. Based on the research presented, ML performance in the nutritional status classification analysis gave maximum results. These results are reported based on the level of precision with an accuracy of 99.23%. The results of the analysis can also present a knowledge-based nutritional status classification. This research can contribute to and update the analytical model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children
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