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All Journal Tanra : Jurnal Desain Komunikasi Visual Fakultas Seni dan Desain Universitas Negeri Makassar E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Abdidas Jurnal Citrakara Aksa : Jurnal Desain Komunikasi Visual
Siti Hadiati Nugraini
Universitas Dian Nuswantoro
Arts Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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STRATEGI PERANCANGAN MEDIA PROMOSI COFFEE SHOP “LUANG WAKTU COFFEE” KOTA TEGAL Irfan Nadhif Putra; Siti Hadiati Nugraini
CITRAKARA Vol. 7 No. 4 (2025): DESEMBER 2025
Publisher : Universitas Dian Nuswantoro

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Luang Waktu Coffee Tegal merupakan sebuah kedai kopi yang menjual berbagai macam produk minuman kopi, dan menyediakan makanan ringan maupun berat. Namun dengan semakin banyaknya coffee shop baru, media promosi yang kurang efektif membuat target pemasaran menurun relatif cukup besar dari tahun 2020 sampai 2023. Oleh karena itu Luang Waktu Coffee Tegal memerlukan perancangan media promosi yang efektif dan kreatif untuk meningkatkan omset. Metode penelitian yang digunakan adalah metode kualitatif, data diperoleh melalui observasi, wawancara, dokumentasi, dan studi literatur. Metode analisis SWOT digunakan dalam penelitian ini, serta menggunakan metode perancangan pemikiran desain. Hasil akhir perancangan adalah feed Instagram sebagai media utama dan media pendukung berupa story Instagram Ads , story Instagram , poster, X-Banner , pamflet, stiker, dan gantungan kunci, sehingga dapat menarik konsumen dengan jangkauan yang lebih luas serta dapat meningkatkan omset penjualan Luang Waktu Coffee Tegal.
Penentuan Metode Augmentasi pada CNN Berdasarkan Metode Optimasi Fox untuk Klasifikasi Citra Ikan Firman Wahyudi; Siti Hadiati Nugraini; Arief Soeleman; Ricardus Anggi Pramunendar; Pulung Nurtantio Andono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Automatic identification of fish species plays an important role in various fields such as conservation biology, fisheries management, and biological research. The Convolutional Neural Network (CNN) method has become an effective solution for automating this process using digital images. However, achieving high accuracy requires careful consideration of factors such as the quantity and quality of data, image preprocessing methods, feature extraction techniques, classification algorithms, and optimization strategies. This study addresses these challenges by proposing a CNN model optimized using the FOX optimization algorithm to select the most suitable augmentation methods. The results show that selecting appropriate augmentation techniques, such as K-means Color Quantization, Horizontal Flip, Voronoi, Elastic Transformation, and Contrast Normalization, can significantly improve the accuracy of fish species recognition, reaching up to 98.75 percent during the training phase. The proposed model also demonstrates strong generalization capabilities with a validation accuracy of 96.90 percent, indicating minimal overfitting. Although the training process is computationally intensive, this approach has proven to be highly effective for applications that require high accuracy and strong generalization capabilities, thus contributing to a better understanding and management of marine ecosystems in support of sustainable fisheries practices..
Co-Authors Ahmad Akrom Amartya Anugrah Indrawati Lestari Arief Soeleman Arni Ernawati Belinda Elsa Aulia Sinatrya Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Erisa Adyati Rahmasari Erna Zuni Astuti Firman Wahyudi Grace Victoria Adhiputri Irfan Nadhif Putra Khamadi Khamadi Khamadi Maulana Zaky Muhammad MY. Teguh Sulistyono Nadia Adzra Ammara Pulung Nurtantio Andono Ricardus Anggi Pramunendar Richard Emmerig Rudy Setyo Wibowo Samuel Williem Arthur Phillips Wibowo Wellia Shinta Sari Yoga Kharisma Putra Zara Maulidia Hikmah