ANALYSIS OF VISIBLE INFRARED IMAGING RADIOMETER SUITE CAPABILITY FOR POPULATION ESTIMATION ON JAVA ISLAND

Population data, despite their significance, are often missing or difficult to access, especially in cities/regencies not belonging to the metropolitan areas or centers of various human activities. This hinders practices that are contingent on their availability. In this study, population estimation was carried out using nighttime light imagery generated by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The variable illuminated area was integrated with the population data using linear regression based on an allometric formula so as to produce a regression value, correlation coefficient (r), and coefficient of determination (r2). The average r2 between the illuminated area and the total population was 0.86, indicating a strong correlation between the two variables. Validation using samples of population estimates from three different years yielded an average error of 73% for each city and 7% for the entire study area. The estimation results for the number of residents per city/regency cannot be used as population data due to the high percent error, but for the population on a larger regional scale, in this case, the island of Java, they have a much smaller percent error and can be used as an initial picture of the total population.

Validation of The Cullender and Smith Method for Determining Pressure Loss in The Tubing in Gas Wells

The ability of the reservoir to deliver a certain quantity of gas depends both on the inflow performance relationship and the flowing bottom hole pressure. In order to determine the deliverability of the total well system, it is necessary to calculate all the parameters and pressure drops, one of which in the tubing. Calculation of pressure loss in the tubing is a very important parameter in the stability of fluid flow from the reservoir to the surface. The calculation of pressure loss in the tubing which is most widely used in the field is the Cullender and Smith Method. The purpose of this study is to validate why the Cullender and Smith method is most widely used in the field to determine the pressure loss in the tubing compared to other pressure loss in tubing methods. The methodology used in this study is calculating the pressure loss in the tubing with the Average Temperature and Deviation Factor Method, the Sukkar and Cornel Method, and the Cullender and Smith Method. After calculating the pressure loss in the tubing using each of these methods, then comparing the percent error of the calculation method with the results in the well. The data used in the calculation is the data from the MZ Field from 7 wells in the East Kalimantan area. The results of the average error percentage obtained from this study are the Average and Deviation Factor Method is 5.38%, the Sukkar and Cornell Method is 5.65%, and the Cullender and Smith Method is 3.83%. From this study, it can be said that the Cullender and Smith Method to be valid or the most accurate method for used in the field compared to other methods due to resulting the smallest percent error from the calculation.

Prediksi Kuota Pemesanan Bahan Bakar Pada SPBU dengan Metode Regresi Linear Berganda

Bahan bakar minyak menjadi komoditi penting dalam menjalankan roda perekonomian suatu negara, data Badan Pengatur Hilir Minyak dan Gas(BPH MIGAS) mencatat Indonesia menghabiskan 28,25 juta kiloliter selama tahun 2019, angka ini dihimpun dari seluruh Stasiun Pengisian Bahan Bakar Umum (SPBU) yang menjadi hilir distribusi BBM kepada masyarakat, namun disisi lain SPBU sering kehabisan stok karna kurangnya pengendalian terhadap stok, dampaknya adalah antrian panjang masyarakat di SPBU, bagi SPBU yang kehabisan stok jelas akan mengurangi pemasukan karna delay tidak ada penjualan selama proses pengiriman dari hulu ke hilir, maka dibutuhkan adanya sistem yang mampu membantu memprediksi berapa kuota yang harus dipesan sehingga kondisi out of stock tidak terjadi, untuk melakukan peramalan kuota bahan bakar digunakan metode regresi linier berganda yang terdiri dari variabel independent stok sisa (X1), stok masuk (X2) dan variabel dependent stok keluar (Y). Setelah dilakukan uji asumsi klasik dapat disimpulkan bahwa variabel independent (X1 dan X2) berpengaruh positif terhadap variabel dependent (Y). Dari hasil pengujian tingkat error menggunakan metode MAPE (Mean Absolute Percent Error) diperoleh tingkat error untuk peramalan pertalite selama seminggu sebesar 11,0% dan untuk tingkat error peramalan solar sebesar 13,2%.

Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error 456.963.281 and Mean Absolute Percent Error 10,0%. Moving Averge 4 was 206.771 tons with the Mean Absolute Deviation 27.333, Mean Squared Error 752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was 210.908 tons with Mean Absolute Deviation 26.890, Mean Squared Error 723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.

Comparison of Serum, Plasma, and Liver Zinc Measurements by AAS, ICP-OES, and ICP-MS in Diverse Laboratory Settings

Progress improving zinc nutrition globally is slowed by limited understanding of population zinc status. This challenge is compounded when small differences in measurement can bias the determination of zinc deficiency rates. Our objective was to evaluate zinc analytical accuracy and precision among different instrument types and sample matrices using a standardized method. Participating laboratories analyzed zinc content of plasma, serum, liver samples, and controls, using a standardized method based on current practice. Instrument calibration and drift were evaluated using a zinc standard. Accuracy was evaluated by percent error vs. reference, and precision by coefficient of variation (CV). Seven laboratories in 4 countries running 9 instruments completed the exercise: 4 atomic absorbance spectrometers (AAS), 1 inductively coupled plasma optical emission spectrometer (ICP-OES), and 4 ICP mass spectrometers (ICP-MS). Calibration differed between individual instruments up to 18.9% (p < 0.001). Geometric mean (95% CI) percent error was 3.5% (2.3%, 5.2%) and CV was 2.1% (1.7%, 2.5%) overall. There were no significant differences in percent error or CV among instrument types (p = 0.91, p = 0.15, respectively). Among sample matrices, serum and plasma zinc measures had the highest CV: 4.8% (3.0%, 7.7%) and 3.9% (2.9%, 5.4%), respectively (p < 0.05). When using standardized materials and methods, similar zinc concentration values, accuracy, and precision were achieved using AAS, ICP-OES, or ICP-MS. However, method development is needed for improvement in serum and plasma zinc measurement precision. Differences in calibration among instruments demonstrate a need for harmonization among laboratories.

Penerapan Metode Single Exponential Smoothing Pada Prediksi Penjualan Bed Sheet

Prediksi merupakan bagian dari awal suatu proses pengambilan suatu keputusan. Dalam kegiatan produksi, prediksi dilakukan untuk menentukan jumlah permintaan terhadap suatu produk dan merupakan langkah awal dari proses perencanaan dan pengendalian produksi. Permasalahan stok barang yang umum terjadi, seperti stok barang yang tidak terjual atau stok barang dengan merk tertentu menjadi kendala yang dihadapi dalam upaya untuk memenuhi kebutuhan pelanggan. Disamping itu, upaya dalam menghasilkan perencanaan dan pengendalian produksi yang baik juga merupakan salahsatu fungsi prediksi dalam kegiatan produksi. Pada penelitian ini diimplementasikan penggunaan metode Single Exponential Smoothing untuk memprediksi stok bedsheet dari berbagai merk berdasarkan data-data penjualan produk tersebut. Metode yang digunakan untuk menghitung kesalahan prediksi yang dihasilkan adalah metode Mean Absolute Percent Error(MAPE). Nilai prediksi ditentukan dari nilai alpha yang paling cocok dari perhitungan kesalahan prediksi hingga menghasilkan nilai yang paling kecil. Data yang digunakan merupakan data penjualan bed sheet periode Februari 2020 sampai dengan Mei 2020 dari 3(tiga) merk yang cukup diminati pelanggan pada tempat studi kasus. Dari hasil perhitungan yang dilakukan hasil perhitungan akurasi prediksi dari beberapa merk bed sheet tersebut mencapai 94.01%.

Maximum Total Point Motion of Five Points Versus All Points in Assessing Tibial Baseplate Stability

Maximum total point motion (MTPM), the point on a baseplate that migrates the most, has been used to assess the risk of tibial baseplate loosening using radiostereometric analysis (RSA). Two methods for determining MTPM for model-based RSA are to use either 5 points distributed around the perimeter of the baseplate or to use all points on the 3D model. The objectives were to quantify the mean difference in MTPM using 5 points vs. all points, compute the percent error relative to the 6-month stability limit for groups of patients, and to determine the dependency of differences in MTPM on baseplate size and shape. A dataset of 10,000 migration values was generated using the mean and standard deviation of migration in six degrees of freedom at 6 months from an RSA study. The dataset was used to simulate migration of 3D models (two baseplate shapes and two baseplate sizes) and calculate the difference in MTPM using 5 virtual points vs. all points and the percent error (i.e. difference in MTPM/stability limit) relative to the 6-month stability limit. The difference in MTPM was about 0.02 mm, or 4% percent relative to the 6-month stability limit, which is not clinically important. Furthermore, results were not affected by baseplate shape or size. Researchers can decide whether to use 5 points or all points when computing MTPM for model-based RSA. The authors recommend using 5 points to maintain consistency with marker-based RSA.

Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction

Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules

Assessment of Moisture Content in Sandy Beach Environments from Multispectral Satellite Imagery

A framework for estimating moisture content from satellite-based multispectral imagery of sandy beaches was tested under various site conditions and sensors. It utilizes the reflectance of dry soil and an empirical factor c relating reflectance and moisture content for specific sediment. Here, c was derived two ways: first, from in-situ measurements of moisture content and average NIR image reflectance; and second, from laboratory-based measurements of moisture content and spectrometer reflectance. The proposed method was tested at four sandy beaches: Duck, North Carolina, and Cannon Beach, Ocean Cape, and Point Carrew, Yakutat, Alaska. Both measured and estimated moisture content profiles were impacted by site geomorphology. For profiles with uniform slopes, moisture contents ranged from 3.0%-8.0% (Zone 1) and from 8.0%-23.0% (Zone 2). Compared to field measurements, the moisture contents estimated using c calibrated from in-situ and laboratory data resulted in percent error of 3.6%-44.7% and 2.7%-58.6%, respectively. The highest percent error occurred at the transition from Zone 1 to Zone 2. Generally, moisture contents were overestimated in Zone 1 and underestimated in Zone 2, but followed the expected trends based on field measurements. When estimated moisture contents in Zone 1 exceeded 10%, surface roughness, debris, geomorphology, and weather conditions were considered.

Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.