Analisis Faktor-Faktor yang Mempengaruhi Sektor Moneter Berdasarkan Jumlah Uang yang Beredar Pada Statistik Ekonomi dan Keuangan Indonesia (SEKI)

One of the impacts of the economic crisis that occurred in Indonesia was influenced by the monetary sector that moved the macroeconomy into the economy of the society such as currency rate of exchange and rate of interest policies applied in all banks in Indonesia. Analysis of the factors that influence the money supply is one step that can be used to analyze economic statistics and the financial condition of Indonesia. The analysis begins with finding the regression equation using a nonlinear regression model of several factors used such as foreign activities, bills to the central government, bills to the private sector, and the value of Gross Domestic Product (GDP). Other tests were also carried out such as the data normality test, heteroscedasticity test, and autocorrelation test to read data characteristics used in the 1999-2016 period using SPSS and then analyzed. Where the results obtained in the regression of the central government independent variables do not have a significant effect, the data normality test shows that the data is normally distributed, heteroscedasticity does not occur, and the data does not contain autocorrelation

Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine

Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs).  The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes

Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network

ABSTRACT Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%

PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION

Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees

LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT

The strategic role of the Bali Strait as a connection between the islands of Java and Bali is growing in line with the increase in the economy and tourism of the two islands. Therefore, it is necessary to have a further understanding of the condition of the waters in the Bali strait, one of which is ocean currents. This study aims to predict future ocean currents based on 30-minute data in the Bali Strait in the range of 16 May 2021 to 9 June 2021 obtained from the Perak II Surabaya Maritime Meteorological Station. In this study, the Long Short Term Memory method was used. The parameters used are hidden layer, batch size, and learn rate drop. Based on the parameters used, the results showed that the smallest MAPE value was 18.64% for U ocean current velocity data and 5.29% for V ocean current velocity data

Pengembangan aplikasi Plagiarism Checker of Sunan Ampel (PCSA) sebagai deteksi plagiasi karya ilmiah di UIN Sunan Ampel Surabaya menggunakan Algoritma Winnowing

Perkembangan internet semakin pesat hal ini berimbas kepada semakin banyaknya informasi yang tersedia. Hal ini memudahkan seseorang dalam melakukan penjiplakan suatu karya. Maraknya informasi yang tersedia secara online menjadikan kebiasaan copy–paste tanpa menyebutkan referensi menjadi mudah dan jamak dilakukan, sehingga karya ilmiah yang dibuat tanpa disadari menjadi hasil plagiasi dari karya ilmiah lain. Penelitian ini bertujuan untuk mendesain aplikasi pendeteksian plagiat pada karya ilmiah menggunakan algoritma Winnowing. Metode penelitian yang digunakan adalah penelitian dan pengembangan. Aplikasi pendeteksian plagiat yang dihasilkan berbasis desktop. Aplikasi yang dibangun berhasil mendeteksi kalimat yang sama antara berkas yang diuji dengan berkas yang ada pada repositori. Berdasarkan uji kelayakan baik dengan uji kelompok kecil maupun uji kelompok besar, aplikasi deteksi plagiasi yang dibangun merupakan aplikasi yang termasuk kualifikasi valid dan layak digunakan

Prediksi Kecepatan Arus Laut dengan Menggunkan Metode Backpropagation (Studi Kasus: Labuhan Bajo)

One factor that is very influential on the dynamics of the waters is the speed of ocean currents. The speed of the ocean currents has an impact on activities around the coast that is for tourists to get information about the condition of the movement of the sea. One of them is in Labuhan Bajo. Labuhan Bajo is a tourist area that has a variety of natural beauty where visitors increase every year. The influence of the west monsoon wind in Labuan Bajo is very large on the condition of sea movement, especially on ocean currents. Predictions about the speed of ocean currents are very important in marine activities, especially diving because it is an effort to prevent the occurrence of things that are not desirable because of the condition of the sea that is not conducive. In this study the method used in predicting the current speed is the Backpropagation method. By testing the hidden layer nodes and the learning rate on the Backpropagation method the best MAPE results are obtained from sharing 70% of training data with 100 hidden layer nodes and the learning rate of 0.1 is 7.59%. Whereas by sharing 80% of the best MAPE training data, there are 100 hidden layer nodes and the learning rate of 0.1 is 0.57%. Then from 90% of the data sharing training data obtained the best MAPE results in the hidden layer node 100 and a learning rate of 0.4 out of 6.65%, this shows that the Backpropagation method is very well used in predicting the speed of ocean currents

Implementation of The Open Jackson Queuing Network to Reduce Waiting Time

Waiting for service is a common thing in-hospital services. The more patients are waiting, the service delay increases, so waiting time in the queue gets longer. In health care in a hospital, a patient will queue several times in more than one queue in a hospital outpatient installation. The case study in this research is the queue system in the hospital's outpatient treatment, implementing an open Jackson queueing network to minimize waiting time. The workstations examined in this study were the registration, pre-consultation, and cardiology poly consultation, and pharmacy. The data is carried out for six days, counting the number of arrivals and departures with each point at intervals of 5 minutes. Applying the Jackson open queue network model, a recommendation was obtained for the hospital to increase employees' numbers. The registration workstation must have four servers; a poly cardiology workstation had three nurses and four doctors, while for pharmacy, had seven employees. With this personnel's addition, patients' total waiting time in the queuing system is approximately 12 minutes/patient. So, it can reduce waiting times in the queueing system that was initially 108 minutes/patient

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR