A criterion for robust estimation of location and covariance matrix is considered, and its application in outlier labeling is discussed. This method, unlike the methods based on MVE and MCD, is applicable to large and high-dimension data sets. The method proposed here is also robust and has the same breakdown point as the MVE-and MCD-based methods. Furthermore, the computational complexity of the proposed method is significantly smaller than that of other methods.
In this study, ground based data from spectroradiometer International Light type ILT900 combined with remotely sensed data from MODIS (Moderate Resolution Imaging Spectrometer) sensor of experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java were used as input data for rice crop estimation using regression analysis. We chose four spectral bands (1-4) of MODIS data and four spectral bands of spectroradiometer data with same (the most similar) wavelength with chosen MODIS data. In addition to the spectral reflectance measurements, we also measured rice production data from several 7 x 20 plot areas that contain different rice varieties and different fertilizer compositions. The data from spectroradiometer then used for estimating regression model based on two approaches, Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The evaluation on ground-based data shows that PCR and PLSR give good accuracy with r2 = 0.968 and 0.984 respectively.
Diabetes adalah penyakit yang terjadi ketika kandungan glukosa di dalam darah tinggi. Tes glukosa yang menghasilkan keakuratan tinggi harus dilakukan beberapa kali untuk mendeteksi diabetes di dalam tubuh. Beberapa indikator di dalam tubuh dapat menjadi titik awal untuk mendeteksi diabetes. Bagaimanapun juga, keterbatasan seorang tenaga medis dalam mendeteksi dalam jumlah data yang sangat besar dengan cara manual menjadi kendala. Salah satu solusi untuk gap tersebut adalah menggunakan komputer sebagai perhitungan matematika dalam metode pengelompokan K-Means dan Fuzzy C-Means. Pengelompokan terdiri dari kelompok diabetes dan non-diabetes. Pengujian untuk masing-masing metode dilakukan terhadap 9 data. Hasil pengujian terbaik metode K-Means adalah 73,438% dan untuk metode Fuzzy C-Means adalah 82,812%.
Jamu adalah obat tradisional dari tanaman herbal yang dianggap atau dipercaya secara turun-temurun dapat membuat bugar badan. Jamu merupakan alternatif lain masyarakat dalam mencari obat berbahan herbal. Akan tetapi bagi banyak orang masih sulit membedakan antara rimpang jahe dengan lengkuas dan kunyit dengan temulawak. Dengan permasalahan tersebut maka, perlu adanya pengenalan untuk masalah tersebut dengan klasifikasi menggunaakan metode support vector machine. Pembuatan aplikasi ini menggunakan bahasa pemrogaman Python untuk pengambilan parameter pembeda yang digunakan yaitu warna menggunakan metode Color Histogram, bentuk menggunakan metode Sobel serta tekstur menggunakan metode Gray Level Co-occurrence Matrix untuk rempah jahe, kunyit, lengkuas dan temulawak yang akan dievaluasi. Evaluasi model yang terbaik yaitu menggunakan metode support vector machine dengan metode pencarian parameter Randomized Search Cross Validation kernel rbf dengan train 83.9% dan test 77.6%.
This study aims to do a prediction of demand goods at a factory for 1 day ahead using double moving average method and comparing the forecasting results. Data source come from two different types of data which are complete data and clean data. Clean data was an optimal data that has been cleaned from outlier using boxplot method. The data source used in the calculation is simulation data for 945 days. Based on the test results, Shows the results of forecasting using complete data that is equal to 4692 with MAPE 6.88 while the results of forecasting use clean data that is equal to 4876 with MAPE 3.84. From these results, it can be concluded that forecasting using clean data is more accurate than forecasting using complete data because the smaller the error rate (MAPE) produced, the better the accuracy.
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