Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sistem informasi dan informatika (SIMIKA) Mutiara: Multidiciplinary Scientifict Journal
Atthariq, Muhammad
Unknown Affiliation
Arts Computer Science & IT Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
KLASIFIKASI TINGKAT KESEGARAN DAUN BAWANG MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS PENGOLAHAN CITRA DIGITAL Novianti, Andi Fitri; Atthariq, Muhammad; Dini, Juliano Nufiansyach; Kaswar, Andi Baso; Lapendy, Jessica Crisfin
Jurnal Sistem Informasi dan Informatika (Simika) Vol 7 No 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3378

Abstract

Green onions, commonly used in Indonesian cuisine, have significant agricultural potential. Despite high production, their quality, particularly freshness, is traditionally evaluated visually, leading to inconsistent and subjective results. This study aims to develop an objective and accurate method for classifying the freshness of green onions using an Artificial Neural Network (ANN). Previous studies have employed ANN but have not specifically targeted the freshness classification of leeks. The proposed method utilizes the color and texture features of green onions.The research methodology includes image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification using ANN. A total of 300 images were acquired and categorized into three freshness levels: not fresh, less fresh, and fresh. During the training phase, 240 images were used, and 80 images were reserved for testing. The optimal feature combination identified includes HSV and LAB color features along with texture features (Contrast + Energy). The results demonstrated that the freshness classification of green onions achieved 100% accuracy in both training and testing phases. The training process, with 240 images, had a computation time of 142.684 seconds, while the testing process, with 80 images, took 35.648 seconds. These findings indicate that using ANN based on color and texture features is highly effective in determining the freshness level of green onions.
Income Smoothing Practices in Animal Feed Sub-Sector Companies Listed on the Indonesia Stock Exchange For the 2020-2022 Period Afiffah, Junita Nur; Martin, Boyke; Ramadhaningsih, Devi; Samuel, Binsar; Ramadhan, Yanuar; Atthariq, Muhammad
Mutiara: Multidiciplinary Scientifict Journal Vol. 1 No. 9 (2023): Mutiara: Multidiciplinary Scientifict Journal
Publisher : Al Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/mutiara.v1i9.93

Abstract

This study aims to analyze Income Smoothing practices in animal feed sub-sector companies with a focus on the financial implications of using the eckel index during the period 2020 – 2022. The sample used is animal feed sub-sector companies in the basic industrial and chemical sectors listed on the Indonesia Stock Exchange, there are 5 companies. Based on calculations using the eckel index derived from the financial statements of each company, This research shows that there are 4 companies that do profit smoothing and 1 company that does not do income smoothing.
Co-Authors Afiffah, Junita Nur Andi Baso Kaswar Andi Baso Kaswar Dini, Juliano Nufiansyach Lapendy, Jessica Crisfin Martin, Boyke Novianti, Andi Fitri Ramadhan, Yanuar Ramadhaningsih, Devi Samuel, Binsar