scholarly journals Prediksi Lebar Tajuk Pohon Dominan pada Pertanaman Jati Asal Kebun Benih Klon di Kesatuan Pemangkuan Hutan Ngawi, Jawa Timur

2018 ◽  
Vol 12 (2) ◽  
pp. 127 ◽  
Author(s):  
Ronggo Sadono
Keyword(s):  
Forest Management ◽  
Standard Error ◽  
Mean Squared Error ◽  
Management Unit ◽  
Seed Orchards ◽  
Root Mean Squared Error ◽  
Crown Width ◽  
Squared Error ◽  
Non Linear ◽  
Teak Tree

Penelitian ini bertujuan untuk menentukan model perkembangan lebar tajuk pohon dominan jati asal Kebun Benih Klon pada tegakan berkualitas baik.Penelitian dilakukan di Kesatuan Pemangkuan Hutan Ngawi pada petak tanaman jati asal Kebun Benih Klon bertumbuhan baik pada umur 615 tahun. Petak tanaman bertumbuhan baik ditentukan berdasarkan kriteria persentase keberhasilan tanaman, rata-rata tinggi pohon dan rata-rata diameter batang serta aksesibilitasnya. Pada petak yang memenuhi syarat bertumbuhan baik dipilih sebanyak 30 sampel pohon dominan dan tiap sampel diukur radius tajuk pada empat arah mata angin. Hasil pengukuran radius tajuk digunakan untuk menghitung rata-rata radius tajuk sebagai rata-rata kuadratik 4 arah pengukuran radius tajuk dan lebar tajuk sebagai dua kali rata-rata radius tajuk. Rata-rata aritmatik dari lebar tajuk 30 pohon dominan tiap petak pengukuran digunakan sebagai variabel respons dan umur tegakan sebagai variabel prediktor. Data pengukuran selanjutnya dipilah menjadi dua bagian, yaitu sebagian besar untuk pengembangan model dan satu bagian lagi untuk validasi model. Analisis regresi non linear dengan metode kuadrat terkecil digunakan untuk memilih 4 kandidat model penduga rata-rata lebar tajuk, yaitu model Sigmoid, Power, Schumacher dan Gompertz. Pemilihan model didasarkan atas nilai koefisien determinasi tertinggi dan standard error of the estimate terkecil serta signifikansi uji F dan uji T. Akhirnya, model terbaik diuji kelayakannya dengan kriteria root mean squared error, simpangan agregatif dan simpangan relatif. Model Gompertz adalah model terbaik untuk memprediksi perkembangan rata-rata lebar tajuk pohon dominan, yang dapat dituliskan dengan persamaan:CW = 6,585 Xe-0,705xe-0,091sagedan dapat menjelaskan 79% variasi data. Model tersebut lolos validasi dan layak digunakan untuk memprediksi rata-rata lebar tajuk pohon dominan jati asal Kebun Benih Klon pada tegakan berkualitas baik umur 6 tahun sampai dengan umur 15 tahun di Kesatuan Pemangkuan Hutan Ngawi.Predicting Crown-width of Dominant Trees on Teak Plantation from Clonal Seed Orchards in Ngawi Forest Management Unit, East JavaAbstractThis study aims to determine the model of crown width development of the dominant teak tree planted using seeds from clonal seed orchards. The research was carried out in Ngawi Forest Management Unit on the good quality teak compartment having stands age from 6 to 15 years old. The good quality compartments were determined based on higher stand density, taller average tree height, larger average stem diameter, and good accessibility. In a well-qualified compartment, 30 samples of the dominant tree were selected and each sample was measured for the crown radius in the four radii. The measured crown radius was used to calculate average crown radius as a quadratic mean of 4-crown radii and crown width as double of average crown radius. The arithmetic mean of the crown width of the 30 dominant trees in each measured compartment was used as the response variable and stand age as the predictor variable. The measurement data were then sorted into two parts, namely: mostly for model fitting and the remaining for model validation. Non-linear regression analysis with the least squares method was used to evaluate 4 candidate models of average crown width, namely: Sigmoid, Power, Schumacher, and Gompertz models. The model selection was based on the highest coefficient of determination and the smallest standard error of the estimate and the significance of F test and T test. The best model was eventually validated using the following criteria : root mean squared error, aggregate deviation, and relative deviation. Gompertz model was the best model to predict the average crown width development of dominant teak tree and expressed as:CW = 6.585 Xe-0.705xe-0.091xageand able to explain 79% variation of data. The model was passed based on statistical validation and it was feasible for predicting the average of crown width of dominant teak tree from clonal seed orchards on good quality stand aged 6 to 15 years in Ngawi Forest Management Unit.

Microscope Volume Segmentation Improved through Non-Linear Restoration

2012 ◽  
pp. 344-353
Author(s):  
Moacir P. Ponti
Keyword(s):  
Mean Squared Error ◽  
Imaging System ◽  
Volume Segmentation ◽  
K Value ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Non Linear ◽  
Before And After ◽  
Restoration Method ◽  
Noise Statistics

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

2021 ◽  
pp. 1-21
Author(s):  
Elsa Arrua-Duarte ◽  
Marta Migoya-Borja ◽  
Igor Barahona ◽  
Lena C. Quilty ◽  
Sakina J. Rizvi ◽  
...  
Keyword(s):  
Rating Scale ◽  
Mean Squared Error ◽  
Intraclass Correlation ◽  
Test Validity ◽  
Wilcoxon Test ◽  
Digital Version ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Digital Format ◽  
Paper And Pencil

Abstract Objective: The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Methods: 69 patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales (Kappa and Intraclass Correlation Coefficients), and reliability (Cronbach’s alpha and Guttman’s coefficient). We calculated the Comparative Fit Index and the Root Mean Squared Error associated with the proposed one-factor structure. Results: Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the Intraclass Correlation Coefficient for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F= 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and Root Mean Squared Error was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. Conclusion: The digital DARS is consistent in many respects to the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (3) ◽  
pp. 1-12
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

Microscope Volume Segmentation Improved through Non-Linear Restoration

2010 ◽  
Vol 1 (4) ◽  
pp. 37-46 ◽  
Author(s):  
Moacir P. Ponti
Keyword(s):  
Mean Squared Error ◽  
Imaging System ◽  
Volume Segmentation ◽  
K Value ◽  
Thresholding Method ◽  
Squared Error ◽  
Non Linear ◽  
Before And After ◽  
Restoration Method ◽  
Noise Statistics

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.

scholarly journals Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America

2020 ◽  
Vol 12 (18) ◽  
pp. 3098
Author(s):  
Jongmin Park ◽  
Barton A. Forman ◽  
Rolf H. Reichle ◽  
Gabrielle De Lannoy ◽  
Saad B. Tarik
Keyword(s):  
North America ◽  
Brightness Temperature ◽  
Mean Squared Error ◽  
Transfer Model ◽  
Vegetation Types ◽  
Large Variability ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
L Band ◽  
Soil Hydraulic

L-band brightness temperature (Tb) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in Tb observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, Tb estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily Tb observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed Tb were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed Tb with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled Tb due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

scholarly journals Combined Multilateration with Machine Learning for Enhanced Aircraft Localization

Proceedings ◽  
2020 ◽  
Vol 59 (1) ◽  
pp. 2
Author(s):  
Benoit Figuet ◽  
Raphael Monstein ◽  
Michael Felux
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Real World ◽  
Mean Squared Error ◽  
Accurate Estimate ◽  
Regression Technique ◽  
Gradient Boosting ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Using Data

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.

scholarly journals Forecasting Oil Price Using Web-based Sentiment Analysis

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4291 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Guan-Rong Zeng ◽  
Wen-Jing Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Sentiment Analysis ◽  
Mean Squared Error ◽  
Oil Price ◽  
Future Research ◽  
Price Forecasting ◽  
Web Based ◽  
Market Sentiment ◽  
Root Mean Squared Error ◽  
Strong Intensity ◽  
Squared Error

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.

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