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All Journal Metodik Didaktik Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Otomasi Kontrol dan Instrumentasi TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Jurnal Abmas Fidelity : Jurnal Teknik Elektro EPSILON: Journal of Electrical Engineering and Information Technology Journal of Environment and Sustainability Education Journal of Mechatronics and Artificial Intelligence
Diky Zakaria
Universitas Pendidikan Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Physics Social Sciences Other

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Automated Bird Deterrent System: A Review Muhammad Fauzan Hernadi; Yusuf Haryo Timur; Roy Dongan Putra Manalu; Nabilah Khairunnisa; Diky Zakaria
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.1.95184

Abstract

Bird pests pose a significant threat to agriculture, causing extensive crop damage and economic losses. Traditional bird repellent methods, such as scarecrows and loud noises, often lose their effectiveness over time as birds adapt. This paper reviews the development and effectiveness of an automated bird repellent system, integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The study used a systematic literature review (SLR) methodology, analyzing 20 articles published between 2015 and 2024. Key findings show that automated systems, utilizing sensors and AI algorithms such as YOLO, DenseNet, and Mask R-CNN, significantly improve bird detection and repellent accuracy. The DenseNet model, in particular, achieved a detection accuracy of 99.65%. The review highlights the need for further research to optimize sensor placement and assess the long-term impacts of this technology on bird behavior and agricultural ecosystems. This comprehensive review underscores the potential of automated bird repellent systems to improve crop protection and sustainability in agriculture.
Analisis Sistematis Algoritma Convolutional Neural Network untuk Klasifikasi Gambar Bokeh dan Blur: Tinjauan Literatur Alif Chandra Wijaya; Arditya Baskara Mahbubi; Miftah Fauzi Januarta; Tasya Syabila; Diky Zakaria
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i4.3953

Abstract

The classification of bokeh and blur images is a challenge in Computer Vision, often addressed using Convolutional Neural Networks (CNNs). This study conducts a Systematic Literature Review (SLR) on 23 articles from Scopus, ScienceDirect, and Google Scholar, with inclusion criteria covering the 2014–2024 publication period, CNN as the primary method, and publication in peer-reviewed journals or conferences (60.87% from scientific journals). The analysis reveals that ResNet and VGG models achieve >90% accuracy, yet still face challenges related to dataset size, computational requirements, and the lack of statistical comparisons across models. This study identifies opportunities for further development through transfer Learning, lightweight models such as MobileNet, and more comprehensive statistical analysis to enhance image classification efficiency across various applications, including digital photography, medical imaging, and security systems.
Co-Authors Abdullah, Cep Ubad Adhi, Himmawan Sapta Adi Nugraha Adi Nugraha Afika, Afika Agung Satria Pamungkas Agus Ramelan Alif Chandra Wijaya Alimudinsyah Alrasyid Arasid, Wildan Arditya Baskara Mahbubi Arifin, Riyadhil Haqqy Arlya, Zaky Khairul Fajar Asradinto, Faris Fathan Dany Syauqi Nazhif Diana Eka Putri Elysa Nensy Irawan Endah Setyowati Fahrezi, Fauzan Muhammad Fatimah, Rafharum Fauzi Ahmad Muda Fauziah, Dini Fauzie Salman Galura Muhammad Suranegara Geralda Livia Nugraha Hadi Putri, Dewi Indriati Haffiyan Putra Pratama Hafiziani Eka Putri, Hafiziani Eka Hamzah, Muhammad Bilal Hamzah, Muhammad Bilal Bilal Hanopa Abdul Hidayah Hidayat, Endang Himmawan Sapta Adhi Ichwan Nul Ichsan Isma Widiaty Jamilah, Dewi Siti Jelita Permatasari Liptia Venica Maharani, Aisyah Aira Putri Makna A’raaf Kautsar Maria Bestarina Laili Miftah Fauzi Januarta Muchtar Ali Setyo Yudono Muhamad Ajis Muhamad Fajar Imanul Haq Muhamad Irsan Muhammad Bilal Hamzah Muhammad Faris Fathoni Muhammad Fauzan Hernadi Muhammad Husni Muttaqin Muhammad Rizalul Wahid Mumtaz, Auziah Nabilah Khairunnisa Nina Herlina Nugraha, Geralda L. Nugraha, Rifki Destrizal Nuur Wachid Abdul Majid Pamungkas, Agung Satria Reyhan Praditya Bagaskara Rezka Bunaiya Prayudha Roy Dongan Putra Manalu Saputra, Dede Irawan Sembiring, Vladio Sada Arihta Sri Subekti Steven, Michael Sunardi, Egi Tasya Syabila Taufik Ridwan Taufik Ridwan, Taufik Tri Seda Mulya Venus Lidzikri Adhitya Yohanes Adi Nugroho Yohanes Adi Nugroho Yusuf Haryo Timur