[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-141418-113":53,"doc-detail-141418-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","automatic-colorization-of-grayscale-images-based-on-deep-learning-model-for-improved-visual-interpretation","Pewarnaan Otomatis Citra Grayscale Berbasis Model Deep Learning Untuk Peningkatan Interpretasi Visual","","Pewarnaan otomatis citra grayscale menghadapi tantangan hilangnya dimensi warna yang penting bagi interpretasi visual. Penelitian ini mengembangkan dan menilai model deep learning untuk mengotomatisasi transformasi dari citra grayscale menjadi citra berwarna. CNN dilatih menggunakan dataset gambar berwarna, dengan input grayscale dan output warna dalam ruang warna LAB melalui pemetaan intensitas piksel ke kanal a dan b. Pengujian pada lima sampel menunjukkan kemiripan visual dengan sumber asli. Evaluasi kuantitatif memakai MAE, PSNR, dan SSIM dengan rata-rata SSIM 0.9963, menunjukkan kualitas pewarnaan sangat baik. Temuan ini membuka peluang untuk restorasi foto bersejarah dan visualisasi data.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/automatic-colorization-of-grayscale-images-based-on-deep-learning-model-for-improved-visual-interpretation/141418/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/automatic-colorization-of-grayscale-images-based-on-deep-learning-model-for-improved-visual-interpretation/141418.png","ImageObject",300,407,{"name":89,"@type":90},"Theodora","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-25",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",5,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Mengapa pewarnaan otomatis citra grayscale dianggap masalah yang sulit?","Question",{"text":110,"@type":111},"Karena pemetaan dari satu piksel grayscale ke warna aslinya bersifat tidak unik (ill-posed), sehingga satu intensitas grayscale bisa memiliki banyak kemungkinan representasi warna.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana model deep learning pada penelitian ini melakukan proses pewarnaan?",{"text":115,"@type":111},"Model melatih CNN dengan input citra grayscale dan output citra berwarna, serta mempelajari pemetaan intensitas piksel ke kanal a dan b pada ruang warna LAB.",{"name":117,"@type":108,"acceptedAnswer":118},"Apa metrik evaluasi yang digunakan untuk mengukur performa model?",{"text":119,"@type":111},"Evaluasi kuantitatif menggunakan Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), dan Structural Similarity Index (SSIM), dengan SSIM rata-rata 0.9963.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},141418,1787655751,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Pewarnaan Otomatis Citra Grayscale Berbasis Model Deep Learning Untuk Peningkatan Interpretasi Visual  \n1*Alindro Septo Nugroho, 2Julian Sahertian, 3Rony Heri Irawan  \n1 2 3Teknik Informatika, Universitas Nusantara PGRI Kediri E-mail: *[1](1alindrosepto9@gmail.com)[alindrosepto9@gmail.com](1alindrosepto9@gmail.com), ~~[2](2juliansahertian@unpkediri.ac.id)[j](2juliansahertian@unpkediri.ac.id)~~[uliansahertian@unpkediri.ac.id](2juliansahertian@unpkediri.ac.id), [3](3Rony@unpkediri.ac.id)[Rony@unpkediri.ac.id](3Rony@unpkediri.ac.id)  \nPenulis Korespondens : Alindro Septo Nugroho  \nAbstrak—Pewarnaan otomatis citra grayscale menggambarkan tantangan besar dalam bidang pengolahan citra digital. Citra grayscale, meskipun mengandung informasi spasial yang melimpah, mengalami hilangnya dimensi warna yang penting, yang mungkin membatasi kemampuan interpretasi visualnya. Studi ini berorientasi pada pengembangan dan penilaian model deep learning yang ditujukanuntuk mengotomatisasi proses pewarnaan gambar grayscale. Metodologi yang diterapkan mencakuppelatihan jaringan saraf tiruan konvolusional (CNN) pada dataset gambar berwarna, dengan gambar grayscale sebagai input dan gambar berwarna sebagai output yang diinginkan. Pelatihan difokuskan padapembelajaran pemetaan yang rumit dari intensitas piksel grayscale menuju saluran warna a dan b dalam ruang warna LAB. Hasil pengujian yang melibatkan lima gambar sampel menunjukkan bahwa model dapat menghasilkan pewarnaan yang secara visual mirip dengan citra sumber aslinya. Evaluasi kuantitatif dengan metrik Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), dan Structural Similarity Index (SSIM) menunjukkan performa yang unggul, dengan nilai rata-rata SSIM sebesar 0.9963, menandakan kualitas pewarnaan yang Sangat Baik. Penemuan ini menekankan potensi besar deep learning dalam menghidupkan kembali informasi warna yang hilang dari gambar grayscale, membukapeluang untuk aplikasi kreatif dalam bidang restorasi foto bersejarah dan visualisasi data.  \nKata Kunci—Deep learning, Pewarnaan Citra, PSNR, SSIM, Grayscale.  \nAbstract—Automatic coloring of grayscale images represents a major challenge in the field of digital image processing. Grayscale images, although containing abundant spatial information, suffer from the loss of important color dimensions, which may limit their visual interpretability. This study is oriented towards the development and assessment of a deep learning model aimed at automating the coloring process of grayscale images. The applied methodology includes training a convolutional artificial neural network (CNN) on a dataset of color images, with grayscale images as input and color images as the desired output. The training is focused on learning a complex mapping from the intensity of the grayscale pixels to the a and b color channels in color space. Test results involving five sample images show that the model can produce coloring that is visually similar to the original source image. Quantitative evaluation with the metrics of Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) metrics show superior performance, with an average SSIM value of 0.9963, indicating “Excellent” coloring quality. This discovery highlights the great potential of deep learning in reviving lost color information from grayscale images, opening up opportunities for creative applications in the fields of historic photo restoration and data visualization. historic photos and data visualization.  \nKeywords—Deep learning, Image Colorization, PSNR, SSIM, Grayscale..  \nThis is an open access article under the CC BY-SA License.  \nI. PENDAHULUAN  \nCitra grayscale, atau monokrom, memiliki signifikansi historis dalam fotografi dan representasi visual. Namun, citra ini secara intrinsik kehilangan informasi warna yang krusialuntuk pemahaman kontekstual dan pengalaman visual mendalam. Mewarnai citra grayscale secara manual merupakan pekerjaan yang memakan waktu, m","cbCaitUspJas0fa8","https://ap.wps.com/l/cbCaitUspJas0fa8","pdf",679168,10,"Indonesian","# Pendahuluan\n## Latar belakang dan signifikansi citra grayscale\n## Tantangan pewarnaan manual dan sifat ill-posed\n## Perkembangan metode (transfer warna, CNN, autoencoder, GAN)\n## Studi terdahulu dan arah penelitian terbaru","[{\"question\":\"Mengapa pewarnaan otomatis citra grayscale dianggap masalah yang sulit?\",\"answer\":\"Karena pemetaan dari satu piksel grayscale ke warna aslinya bersifat tidak unik (ill-posed), sehingga satu intensitas grayscale bisa memiliki banyak kemungkinan representasi warna.\"},{\"question\":\"Bagaimana model deep learning pada penelitian ini melakukan proses pewarnaan?\",\"answer\":\"Model melatih CNN dengan input citra grayscale dan output citra berwarna, serta mempelajari pemetaan intensitas piksel ke kanal a dan b pada ruang warna LAB.\"},{\"question\":\"Apa metrik evaluasi yang digunakan untuk mengukur performa model?\",\"answer\":\"Evaluasi kuantitatif menggunakan Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), dan Structural Similarity Index (SSIM), dengan SSIM rata-rata 0.9963.\"}]","Pewarnaan Otomatis Citra Grayscale Berbasis Model Deep Learning Untuk Peningkatan Interpretasi Visual | PDF",15]