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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Informasi Interaktif DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Kepada Masyarakat
Arie Rachmad Syulistyo
Politeknik Negeri Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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An Experimental Study on Deep Learning Technique Implemented on Low Specification OpenMV Cam H7 Device Asmara, Rosa Andrie; Rosiani, Ulla Delfana; Mentari, Mustika; Syulistyo, Arie Rachmad; Shoumi, Milyun Ni'ma; Astiningrum, Mungki
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2299

Abstract

This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.
Klasifikasi Mutu Telur Burung Puyuh Berdasarkan Warna dan Tekstur Menggunakan Metode K-Nearest Neighbor (KNN) dan Fusi Informasi Sumari, Arwin Datumaya Wahyudi; Mawarni, Putri Indah; Syulistyo, Arie Rachmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021854393

Abstract

Kualitas produk merupakan faktor utama untuk menjamin keberlangsungan satu usaha peternakan. Perusahaan telur puyuh yang memiliki ribuan burung Puyuh seperti CV. NS Quail Farm mampu memproduksi ribuan telur dalam sehari karena seekor burung Puyuh mampu menghasilkan 250-300 butir telur per tahun. Penyeleksian ribuan telur-telur tersebut dilakukan secara tradisional oleh para pekerja peternakan sehingga kualitas telur-telur hasil seleksi bergantung pada perspektif masing-masing pekerja. Guna memperoleh telur hasil seleksi dengan kualitas yang sama, maka dibangun sebuah sistem pencitraan digital untuk pemilihan telur burung Puyuh berdasarkan fitur warna dan tekstur kulit telur menggunakan metode klasifikasi K-Nearest Neighbor (KNN) yang dikombinasikan dengan fusi informasi. 300 data citra telur burung Puyuh diolah menggunakan normalisasi Red, Green, Blue (RGB) dan Otsu thresholding guna memperoleh fitur warna dan fitur tekstur yang kemudian difusikan menjadi fitur terfusi tunggal sebagai masukan pengklasifikasi KNN. Dari hasil-hasil penelitian, disimpulkan bahwa sistem berhasil mengklasifikasikan mutu telur Baik, Sedang, dan Buruk dengan akurasi rata-rata sebesar 77,78%. Disamping itu, klasifikasi dengan fusi informasi mampu mengungguli klasifikasi tanpa fusi informasi sebesar 11,11% pada nilai  yang sama yakni 7 dan fusi informasi juga mampu mempercepat proses klasifikasi sebesar 0,22 detik dibandingkan terhadap klasifikasi tanpa fusi informasi.AbstractThe quality of product us a primary factor to ensure the sustainability of a farm business. A company which has thousands of quail such as CV. NS Quail is capable of producing thousand quail eggs in a day because a quail is able to produce 250-300 eggs per year. The selection of the eggs is carried out traditionally by the farm workers so that the quality of the selected eggs are depended on the perspective of each worker. In order to obtain the same quality of the selected eggs, a digital imaging system for quail egg selection based on color feature and texture feature using K-Nearest Neighbor (KNN) combined with information fusion is developed. 300 image data of quail egg was processed using Red, Green, Blue (RGB) and Otsu thresholding to obtain color feature and texture feature which then were fused to become single fused feature as the input to KNN classifier. From the research results, it is concluded that the system was managed to classify egg quality as good, medium, and bad with an accuracy of 77,78%. In addition, the classification with information fusion was able to outperform the classification without information fusion by 11.11% at the same  value of 7 and information fusion is also able to accelerate classification process by 0.22 seconds compared to that of without information fusion.
PELATIHAN PENGEMBANGAN KONTEN DIGITAL MENGGUNAKAN ALAT DESAIN BERBASIS WEB DAN MOBILE Arie Rachmad Syulistyo; Milyun Ni’ma Shoumi; Septian Enggar Sukmana; Cahya Rahmad; Ariadi Retno Tri Hayati Ririd; Anugrah Nur Rahmanto; Devi Zettyara
Jurnal Pengabdian kepada Masyarakat Vol. 11 No. 1 (2024): JURNAL PENGABDIAN KEPADA MASYARAKAT 2024
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v11i1.4764

Abstract

The internet and information systems are rapidly evolving, with technology becoming increasingly sophisticated worldwide. Consequently, entrepreneurs can now offer valuable information or conduct business from the convenience of their own homes. In this context, it is expected that PKK mothers, specifically those affiliated with the PKK group RW 07 in Purwodadi Village, Blimbing District, Malang City, should seize these opportunities by utilizing digital content. The PKM Community Service Team is encouraged to offer training in digital content creation in light of advancements in information technology. The team is currently seeking web-based and mobile design tools that can be readily utilised by PKK group mothers. The digital content produced can be employed to advertise activities within the RW community or their own merchandise. Following the implementation of community service, the PKM team evaluated the PKK group with 5 questions. The PKK group deems the presented material useful and the program beneficial for enhancing their skills. Overall, the PKK group expresses satisfaction with the program's implementation. Additionally, following observations made by the PKM Team, the information we offer has been utilized to produce captivating content for broadcasting announcements on social media platforms belonging to the PKK organization.
Co-Authors Adi Wibowo Annisa Puspa Kirana Anugrah Nur Rahmanto Ariadi Retno Tri Hayati Ririd Arwin Datumaya Wahyudi Sumari Astiningrum, Mungki Aura Kanza Caesaria Budiprasetyo, Gunawan Devi Zettyara Dwi Marhaendro Jati Purnomo Dwi Puspitasari Firdani Rianda Putra Luqman Affandi Luqman Affandi M. Alfin Zakariya Mamluatul Hani’ah Mawarni, Putri Indah Muhammad Febrian Rachmadi Mustika Mentari Putra, Firdani Rianda Rahmad, Cahya Ratsanjani, Muhammad Hasyim Ridwan Rismanto Rokhimatul Wakhidah Rosa Andrie Asmara Septian Enggar Sukmana Shoumi, Milyun Ni’ma Ulla Delfana Rosiani Vira Meliana Agustin Zakariya, M. Alfin