Analisis Regresi untuk Memprediksi Tahanan Kapal Cepat

Alvin, Mohammad Haidar; Atok, Mohamad; Indiaryanto, Mahendra

doi:10.12962/j23373520.v9i1.51386

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Untuk menentukan optimum estimator dilakukan dengan cara melihat grafik perubahan Nilai Root Mean Square Error (RMSE) terhadap jumlah estimator yang digunakan dalam model AdaBoost. RMSE adalah penilaian akar kuadrat yang mengukur besarnya rata-rata kesalahan antara hasil prediksi dan data aktual [23]. Semakin rendah nilai RMSE, semakin baik kinerja sistem yang diuji.…”

Section: Analisis Jumlah Estimator Adaboostunclassified

Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

Iman

Putrada

Prabowo

et al. 2021

JETT

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One way to prevent the spread of the COVID-19 virus is to check body temperature regularly. However, checking body temperature manually by directing the thermogun at someone's face is still often found. This study implements the use of the AMG8833 thermal camera to detect a person's body temperature without making any contact. The AMG8833 is a general-purpose temperature detection camera so to be used as a temperature meter, its accuracy needs to be improved by regression. The purpose of this research is to improve the performance of AMG833 as a thermal camera with AdaBoost regression. AdaBoost is a type of ensemble learning that uses several decision tree models. For face detection, the system uses the Haar Cascade method. The test results show that the decision tree model produces an R-Squared value of 0.93 and an RMSE of 0.21. Meanwhile, AdaBoost succeeded in improving the performance of the regression model with a higher R-Squared value and a lower RMSE value with values of 0.95 and 0.18, respectively.

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Section: Analisis Jumlah Estimator Adaboostunclassified

Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

Iman

Putrada

Prabowo

et al. 2021

JETT

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“…Kurniadi uses the linear regression method in his research. Alvin [15], in his research, also uses the linear regression method in predicting the durability of highspeed ships. Similarly, research conducted by Rizky et al [16], made predictions to anticipate inventory in warehouses.…”

Section: Introductionmentioning

confidence: 99%

“…Also used linear regression method. In addition to using the linear method, research conducted by [11], [15], [16], [14] also utilizes applications built with specific programming languages. So to solve the problems experienced by PT.…”

Section: Introductionmentioning

confidence: 99%

Application design to help predict market demand using the waterfall method

Purba

2021

MATRIX

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Covid-19 has not been defeated make the economy unstable. The government increases purchasing power with a stimulus. The government's stimulus for PPnBM 0% of cars is a breath of fresh air. Sales rate increases. Even so, the ups and downs keep happening. Sales are unstable. In one to three months, five cars can be sold. It could also take 3 to 6 months, only 1 unit. Must make a strategy to avoid overcrowding of units. Like PT. Suka Fajar Ltd Medan. Companies engaged in the sale of Mitsubishi cars. As operating costs increase, management limits the acceptance of car shipments. This policy has fatal consequences. When consumers want to buy a particular car unit, it is not available. Incidents like this happened more than five times. The company's image is not good. Researched to design and build applications that can make predictions—collaborated with the linear regression. To be directed and sequential, the waterfall is used. Ensuring the application is suitable for testing with the black box. Research leads to the conclusion that the application is designed and built according to what is needed.

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Form Factor Prediction Based on Ship Model Test Data by Statistical Method

Widodo

Santoso²,

Erwandi³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The ship model resistance test is carried out on the towing tank with a certain scale to get the total resistance coefficient of the ship model. The value of the ship’s form factor is needed in extrapolation calculations to get the coefficient and total resistance value of a full-scale ship. The form factor can be obtained using the traditional method, namely testing the resistance of the ship model with a Froude Number between 0.1 – 0.2. This method is known as the Prohaska method. Another method or a more modern method is to use multiple linear regression statistics based on the ship model test. We used data from 41 displacement ship model tests conducted at the Hydrodynamics Technology Laboratory (LTH)-National Agency for Research & Innovation (BRIN). Data processing is done through statistical software using the OLS or Robust method which was previously done by transforming predictor variables to enlarge the variety of variables so that a good regression equation is obtained. The resulting form factor regression is a useful tool for predicting the ship’s Effective Power value.

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Analisis Regresi untuk Memprediksi Tahanan Kapal Cepat

Cited by 3 publications

References 8 publications

Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

Application design to help predict market demand using the waterfall method

Form Factor Prediction Based on Ship Model Test Data by Statistical Method

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