KLASIFIKASI GAMBAR CATAT METER MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

HARYANTI, TIAS NOVIKA (2023) KLASIFIKASI GAMBAR CATAT METER MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. S1 thesis, Universitas Mercu Buana Bekasi.

Preview	Text 41518310010-TIAS NOVIKA HARYANTI-01 COVER.pdf Download (546kB) | Preview
Preview	Text 41518310010-TIAS NOVIKA HARYANTI-02 ABSTRAK.pdf Download (29kB) | Preview
	Text 41518310010-TIAS NOVIKA HARYANTI-03 BAB 1.pdf Restricted to Registered users only Download (73kB)
	Text 41518310010-TIAS NOVIKA HARYANTI-04 BAB 2.pdf Restricted to Registered users only Download (88kB)
	Text 41518310010-TIAS NOVIKA HARYANTI-05 BAB 3.pdf Restricted to Registered users only Download (114kB)
	Text 41518310010-TIAS NOVIKA HARYANTI-06 BAB 4.pdf Restricted to Registered users only Download (1MB)
	Text 41518310010-TIAS NOVIKA HARYANTI-07 BAB 5.pdf Restricted to Registered users only Download (26kB)
	Text 41518310010-TIAS NOVIKA HARYANTI-08 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (176kB)
	Text 41518310010-TIAS NOVIKA HARYANTI-09 LAMPIRAN.pdf Restricted to Registered users only Download (348kB)

Abstract

ABSTRAK Nama : Tias Novika Haryanti NIM : 41518310010 Program Studi : Teknik Informatika Judul Laporan Skripsi : Klasifikasi Gambar Catat Meter Menggunakan Convolutional Neural Network Pembimbing : Harni Kusniyati, M.Kom Listrik merupakan kebutuhan pokok manusia dalam menjalankan setiap kegiatan, yang pemakaiannya dapat diukur menggunakan KWH Meter. Di Indonesia pelanggan listrik dibagi menjadi dua yaitu Prabayar dan Pascabayar. Pelanggan listrik pascabayar memerlukan pencatatan angka yang tertulis pada KWH meter untuk mengetahui rupiah tagihan listrik yang harus dibayarkan. Dalam pelaksanaan pencatatan angka tersebut, tidak jarang ditemukan kendala seperti pagar rumah pelanggan yang terkunci sehingga petugas tidak berhasil memotret angka stan meter. Oleh karena itu PLN rutin melakukan validasi atas keseluruhan data pencatatan meter. Penelitian ini bertujuan untuk mempermudah proses validasi data catat meter yang berupa gambar, yang diklasifikasikan kedalam tiga kelas menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur Resnet34. Pada penelitian ini mendapat hasil tingkat akurasi tertinggi sebesar 97.50% dengan total loss 0.3%. Kata Kunci : Listrik, KWH, Klasifikasi, CNN, Resnet34. ABSTRACT Name : Tias Novika Haryanti NIM : 41518310010 Study Program : Informatics Engineering Title Thesis : Image Classification of Meter Reading Using Convolutional Neural Network Counsellor : Harni Kusniyati, M.Kom Electricity is a basic human need in carrying out every activity. Electricity usage can be measured using a KWH meter. In Indonesia, electricity customers are divided into two, namely Prepaid and Postpaid. Postpaid electricity customers need to record the numbers written on the KWH meter to find out the rupiah electricity bill that must be charged. In the implementation of recording these nu mbers, it is not uncommon to encounter obstacles such as a locked customer's fence and so on, so the meter reading results must be re-validated. This study aims to validate the meter record images which are classified into three classes using the Convolutional Neural Network (CNN) algorithm with the Resnet34 architecture. In this study, the highest accuracy level for the Resnet34 architecture was obtained, with an accuracy rate of 97.50% and cost rate of 0.3%. Keywords: Electricity, KWH, Classification, CNN, Resnet34

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO 23 004
NIM/NIDN Creators:	41518310010
Uncontrolled Keywords:	Listrik, KWH, Klasifikasi, CNN, Resnet34.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	siti maisyaroh
Date Deposited:	22 Sep 2023 05:29
Last Modified:	22 Sep 2023 05:29
URI:	http://repository.mercubuana.ac.id/id/eprint/81389

Actions (login required)

View Item