Penerapan LearningVector Quantization Pada Kasus Emisi Gas Kendaraan Bermotor Kota Medan Untuk Pembangunan Berkelanjutan

Khairani, Desi (2023) Penerapan LearningVector Quantization Pada Kasus Emisi Gas Kendaraan Bermotor Kota Medan Untuk Pembangunan Berkelanjutan. Skripsi thesis, State Islamic University of North Sumatera.

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Abstract

Learning Vector Quantization (LVQ) is a pattern classification method with each unit representing a specific category or class. Learning Vector Quantization is a single layer net where each input layer is directly connected to an output neuron. Both are connected with a weight Some output units should be used in each class. Emissions or exhaust gases are fuels derived from the combustion of fossils such as oil, natural gas or coal that are wasted into the air. While motor vehicle gas emissions are the combustion system in the combustion engine, this combustion system will come out through the exhaust. So it is known that the accuracy of this Learning Vector Quantization method in the case of motor vehicle emission tests using emission test data reaches 80%. And in the work done by inputting some variable data and determining the output variable, normalizing the data, and working on the training stage until the end which produces output in the form of a class that meets the quality standards of moderate pollution.

Jenis Item:	Skripsi (Skripsi)
Uncontrolled Keywords:	Learning vector quantization, Motorized vehicle emissions, Accuracy results
Subjects:	300 Social sciences > 380 Commerce, communications, transport > 388 Transportation Ground transportation
Divisions:	Fakultas Sains dan Teknologi > Matematika > Skripsi
Pengguna yang mendeposit:	Mrs. Khoirunnisa Wardah Rizkia Surbakti
Date Deposited:	07 Dec 2023 13:58
Last Modified:	02 Jan 2024 18:31
URI:	http://repository.uinsu.ac.id/id/eprint/20999

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