Klasifikasi Kecocokan Dan Keberhasilan Air Pada Generator Uap Turbin Menggunakan Metode NaÏve Bayes (Studi Kasus : PT Indah Kiat Pulp & Paper)

Yusvita, Chyndi (2022) Klasifikasi Kecocokan Dan Keberhasilan Air Pada Generator Uap Turbin Menggunakan Metode NaÏve Bayes (Studi Kasus : PT Indah Kiat Pulp & Paper). Other thesis, Universitas Islam Riau.

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Abstract

This company always need power electricity that uses generator turbine for operate production company and turbine need constant water as well as water quality in every area different and lacking from turbine this among other things, current fickle and things that cause changing water availability as well as capacity electricity generated _ from turbine this have power that is not too big. The water turbine is tool for change energy water potential becomes Becomes energy mechanic. Energy mechanic this then changed Becomes energy electricity by a generator. Steam generator turbine working for change energy potential Becomes energy mechanic. The falling force of the water pushing on the propeller causes turbine turn with replace function push wind for turning the propeller replaced by water for rotate turbine. Rotation turbine this is linked to the generator. each machine operated for constant and predictable water capacity water suitability. Some water parameters contained compound chemical with knowing the suitability of the water for can used into the turbine generator, due to the physical parameters of this water already no suitable for use so turbine no produce power good power and after test. because of that , in study this gurgle method nave bayes for give decision what are the physical parameters of the water ? enter suitable water qualification and then suitable water is also predicted for enter qualification successful on turbine generator with 80% training data and 20% testing data from all data, test accuracy that has been conducted use 5-fold cross validation with k=5 then could obtained conclude that app built with use method Naive Bayes already as expected with average value of accuracy by 95.18%.

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