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All Journal Jurnal Mahasiswa Matematika AKSIOMA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK Jurnal Matematika Sains dan Teknologi Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Mathvision : Jurnal Matematika Jurnal Teknologi Informasi dan Terapan (J-TIT) Perdikan: Journal of Community Engagement Jurnal Abdi Insani MATHunesa: Jurnal Ilmiah Matematika KARANGAN Journal Jurnal Matematika dan Sains (JMS) Jurnal Pendidikan MIPA Karaton: Jurnal Riset dan Inovasi Sumenep Journal of Advanced Vocational Information and Communication Technology (JAVICT)
Ahmad Khairul Umam
Politeknik Elektronika Negeri Surabaya
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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PERBANDINGAN LEVEL 1 DAN 2 TRANSFORMASI WAVELET DISKRIT UNTUK DENOISING CITRA Ahmad Khairul Umam; Nirwana Haidar Hari; Joko Prasetyo; Richa Latifatin Nisa'; Elisa Dwi Aprilia
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 02 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The wavelet transform is an improvement of the Fourier transform. The Fourier transform changes the domain from the signal domain to the frequency domain, while the wavelet transform changes the signal domain to the scale (dilation) and translation domains. Discrete wavelet transformation (DWT) can be used to reduce noise in images. In this study, level 1 and 2 DWT methods for image denoising are compared. Lena and Mandrill grayscale test images of 512×512 pixels were used. The wavelets used are Haar, symlets, biorthogonal, coiflets, and Daubechies wavelets. We compare the PSNR value and computation time for Lena and Mandrill images using DWT level 1 and 2. From the research results, the highest PSNR values are for DWT level 2. As for the fastest computation times, they are for DWT level 1.
Face Tracker Audio for Saronen Music Using Augmented Reality on Social Media Ahmad Walid Hujairi; Khoironi Khoironi; Much Chafid; Ahmad Khairul Umam; Ahmed David Anugerah
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.425

Abstract

Saronen music is a traditional Madurese music commonly played at cultural and traditional events. Saronen music typically combines traditional instruments such as gamelan, trumpet, kenong, korca, large drums, and small drums, producing a unique sound characteristic of Madura. This research aims to preserve saronen music through the development of an interactive filter on Instagram and Facebook using augmented reality. The face tracker feature allows users to interact with each instrument using head movements or facial expressions. The development method, MDLC encompasses concept, design, material collection, design, testing, and distribution. Functional research results indicate the filter can run well on Android and iOS operating systems. Testing on 31 social media users yielded positive feedback from the majority, stating that the filter has clear instructions, a low error rate, is easy to use, satisfying, and attractive. This innovation is expected to be a means of preserving saronen music for the younger generation through social media.
Convolutional Neural Network-Based Approach For In-Ovo Embryo Classification Khoironi Khoironi; Ibram Maulana Akhsanul Qasasi; Ahmad Khairul Umam; Much Chafid; Ahmad Walid Hujairi; I Wayan Rangga Pinastawa; Musthofa Galih Pradana
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13752

Abstract

The egg hatching process is a crucial stage in chicken breeding, as the quality of the resulting embryos will greatly determine the productivity and success of the cultivation. However, monitoring the internal condition of eggs during incubation is still limited and is generally done manually using the candling method. Therefore, a non-invasive approach is needed to support the process of monitoring embryo development. This study aims to develop a chicken embryo classification system based on in-ovo images using a Convolutional Neural Network (CNN). This system utilizes internal egg images taken with a camera integrated in the incubator, so that the condition of the eggs can be automatically classified into categories of fertile, infertile, and developmental failure. The research stages include collecting in-ovo image data, preprocessing in the form of removing the background using the rembg library, and resizing the images to 224 × 224 pixels. The dataset was then divided into training data and test data with a ratio of 90%:10%. The CNN model was built with several layers, namely convolution, max pooling, dropout, flattening, and fully connected, then trained for 28 epochs with a batch size of 32. The results showed that the developed CNN model was able to achieve an accuracy of 90.5% in the 24th epoch and experienced a decrease in the 26th to 28th epochs. Testing also used images from different incubators than those used from the dataset showed that the model could classify egg conditions well.
Co-Authors Abdul Aziz, Moch. Ilham Agung Teguh Setyadi Agustien, Lusiana Ahmad Isro'il Ahmad Isro'il Ahmad Isroil Ahmad Walid Hujairi Ahmed David Anugerah Ainul Yaqin Ainur Taufikur Rahman Rahman Alfan, Aris Amanda Fatma Muamalah Anggita, Sharazita Dyah Anis Kurli, Anis Aris Alfan Bagus Edi Fathorrasi Bahtiar Arifianto, M. Mohamad Zaky Daniswara, Krisna Adilia Deny Fardiansyah Putra Dorik Prayogik F. Elisa Dwi Aprilia Faiq Muntashir Firdausy, Davana Yudista Hari, Nirwana Haidar Hasnah Aulia Zahra I Wayan Rangga Pinastawa Ibnu Cipta Ramadhan Ibram Maulana Akhsanul Qasasi Imro’atun Najiyah Indah Saniatur Rohma Irma Wulandari Joko Prasetyo Joko Prasetyo Joko Prasetyo Khansa Nadhif Shafa Khoironi Khoironi Khoironi Khoironi Khoironi Khoironi, Khoironi Krisna Adilia Daniswara Krisna Adilia Daniswara Lusiana Agustien Moh. Khairil Anwar Muamalah, Husnul Much Chafid Musthofa Galih Pradana Nihaya Alivia C. Dewi Nirwana Haidar Hari Pukky Tetralian B. N. Pukky Tetralian Bantining Ngastiti Pukky Tetralian Bantining Ngastiti Richa Latifatin Nisa' Satria Utama Disawa Supriyanto Taufiqurrahman Zaqiyatus Shahadah Zaqiyatus Shahadah