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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika
Yunanda, Sita Fara
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Implementasi Algoritma Lempel-Ziv-Welch (LZW) untuk Kompresi File Teks Yuliastuti, Gusti Eka; Sulaksono, Danang Haryo; Kusuma, Ris Fani; Yunanda, Sita Fara
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 4, No 1 (2023)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2023.v4i1.4864

Abstract

Abstrak−Saat ini data dan informasi merupakan hal yang sangat penting bagi masyarakat. Masyarakat membutuhkan informasi yang cepat melalui media digital. Semakin lama akan semakin banyak data dan informasi yang disimpan. Hal tersebut berdampak pada kurangnya ruang penyimpanan yang tersedia. Data dan informasi tersebut perlu dipadatkan ke dalam ukuran yang lebih kecil agar lebih efisien dalam hal penyimpanan. Untuk melakukan efisiensi penyimpanan data dan informasi tersebut, penulis melakukan kompresi dengan studi kasus file dokumen. Kompresi file dilakukan dengan menerapkan Algoritma Lempel-Ziv-Welch (LZW). Kelebihan dari Algoritma LZW yakni hasil kompresi baik dengan waktu yang lebih singkat. Hasil kompresi file dengan Algoritma LZW tidak akan mempengaruhi file secara keseluruhan, karena Algoritma LZW termasuk ke dalam jenis lossless dimana hasil file sebelum dan sesudah kompresi tidak akan berbeda secara signifikan.
Penerapan Metode CNN (Convolutional Neural Network) dalam Mengklasifikasi Uang Kertas dan Uang Logam Rewina, Anggita Eka; Sulistyowati, Sulistyowati; Kurniawan, Muchamad; N, Muhammad Dinarta; Yunanda, Sita Fara
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5128

Abstract

Banknotes and coins are valuable assets that are used as legal means of payment in everyday life. The value of these two types of money has been determined and is printed on each piece of banknote when used in transactions and trade. Even though currently banknotes can be recognized using technology such as ATM machines, these machines are only able to recognize the value of the largest currency owned by a country. Computers require digital images as input to display the information contained therein because computers do not have the ability of the human eye to directly recognize or calculate the objects they see. Therefore, techniques or methods are needed that aim to obtain information from digital images to facilitate human interpretation. This research aims to design a system for detecting banknotes in images using the Convolutional Neural Network (CNN) architecture, which is a form of deep learning. . The system also integrates image pre-processing using user-based manual annotation techniques in Python program code. Using the CNN method, a test was carried out to detect the nominal amount of money in the input image. Test results using 29 banknote dataset samples and 31 coin money dataset samples show that the two types of money are divided into two classes, namely paper and coins. From the training carried out on banknotes and coins, an average accuracy of 98% was obtained, showing good results. Repetition of the detection process also shows consistent output probabilities. However, there are several denominations of money that show high accuracy values, so it can be concluded that the labeling annotation method is thought to be less effective.
Co-Authors Danang Haryo Sulaksono, Danang Haryo Gusti Eka Yuliastuti Kusuma, Ris Fani Muchamad Kurniawan, Muchamad N, Muhammad Dinarta Rewina, Anggita Eka Sulistyowati Sulistyowati