[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-127546-113":53,"doc-detail-127546-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","implementation-of-plant-image-quality-enhancement-on-camera-differences-for-photosynthesis-pigment-prediction","Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning","","Penelitian mengimplementasikan perbaikan kualitas citra tanaman menggunakan machine learning untuk mengatasi variasi hasil akibat perbedaan kamera dalam prediksi pigmen fotosintesis. Pigmen utama yang ditelaah meliputi klorofil, karotenoid, dan antosianin, yang pada praktiknya umumnya dianalisis dengan metode seperti KCKT dan spektrofotometer namun memerlukan waktu serta sumber daya tinggi. Aplikasi berbasis FP3Net dipilih karena biaya rendah dan aksesibilitas. Performa Fuzzy Piction dipengaruhi kondisi cahaya dan spesifikasi kamera, sehingga diterapkan algoritma 3D-TPS dan dievaluasi memakai SSIM serta MAE.",{"@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/implementation-of-plant-image-quality-enhancement-on-camera-differences-for-photosynthesis-pigment-prediction/127546/",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/implementation-of-plant-image-quality-enhancement-on-camera-differences-for-photosynthesis-pigment-prediction/127546.png","ImageObject",300,407,{"name":89,"@type":90},"Himbo","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",7,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Mengapa kualitas citra tanaman perlu diperbaiki saat menggunakan kamera berbeda untuk prediksi pigmen?","Question",{"text":110,"@type":111},"Karena perbedaan jenis dan spesifikasi kamera menghasilkan variasi warna pada citra, dipengaruhi jumlah pixel, jarak, dan iluminasi. Variasi ini memengaruhi akurasi aplikasi Fuzzy Piction dalam menghitung kandungan pigmen.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Apa peran algoritma 3D-TPS dalam penelitian ini?",{"text":115,"@type":111},"3D-TPS digunakan untuk menstandarkan kualitas/representasi warna citra pada aplikasi Fuzzy Piction di ruang warna sRGB agar warna lebih sesuai dengan objek asli. Perbaikan ini diharapkan meningkatkan akurasi prediksi pigmen.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana evaluasi performa dilakukan setelah perbaikan citra?",{"text":119,"@type":111},"Citra yang telah diperbaiki dievaluasi menggunakan metrik SSIM untuk kualitas citra, serta MAE untuk menilai kesalahan prediksi pigment. Hasil terbaik dilaporkan pada rentang nilai SSIM tertentu dan MAE yang rendah.","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},127546,1785939896,{"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},687207017582,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning  \nFelix Adrian Tjokro Atmodjo*1, Kestrilia Rega Prilianti2, Hendry Setiawan3  \nProgram Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Ma Chung, Malang 65151, Jawa Timur, Indonesia  \n[Email:](Email:1311810013@student.machung.ac.id)[1](Email:1311810013@student.machung.ac.id)[311810013@student.machung.ac.id](Email:1311810013@student.machung.ac.id), [2](2kestrilia.rega@machung.ac.id)[kestrilia.rega@machung.ac.id](2kestrilia.rega@machung.ac.id), [3](3hendry.setiawan@machung.ac.id)[hendry.setiawan@machung.ac.id](3hendry.setiawan@machung.ac.id)  \nAbstract. Implementation of Plant Image Quality Improvement based on Machine Learning on Camera Variation to Predict Photosynthetic Pigments. Pigments are natural dyes found in plants and animals. In photosynthesis, there are 3 essential pigments:  \nchlorophyll, cartenoid, and anthocyanin. Pigment analysis can be performed with High Performance Liquid Chromatography (HPLC) anda spectrophotometer. However, HPLC and spectrophotometers require high resources and time. Thus, the Fuzzy Piction Android application built using the FP3Net model is the best choice in pigment prediction since it is low on cost and accessible. However, the Fuzzy Piction produces different performance, which is affected by light conditions and camera specifications. The experiment used ten sample imagesfor Jasminum sp., P. betle, Syzygium oleina of green and red variations, and Graptophyllum pictum leaves with three smartphone cameras and three lighting levels.  \nImprovements using 3D-TPSproduced thebestSSIM values in the range of 0.9191–0.9797 for images Syzygium oleina of green and red variations leaves, and the predicted MAE value of pigment was 0.0296– 0.0492.  \nKeywords: 3D-TPS, plant leaves, pigment, image quality improvement  \nAbstrak. Pigmen merupakan pewarna alamiyangditemukanpada tumbuhan dan hewan.  \nDalamproses fotosintesis terdapat tiga pigmen yang penting, yaitu klorofil, kartenoid, dan antosianin. Analisis pigmen dapat dilakukan dengan Kromatorafi Cair Kinerja Tinggi (KCKT) danspektrofotometer. Namun, KCKT danspektrofotometer membutuhkansumber daya dan waktu yang tinggi. Sehingga, aplikasi Android Fuzzy Piction yang dibangunmenggunakan model FP3Net mejadi pilihan dalam prediksi pigmen dengan biaya murah dan mudah. Akan tetapi, aplikasi Android Fuzzy Piction menghasilkan kinerja yang berbeda-beda yang dipengaruhi oleh kondisi cahaya dan spesifikasi kamera. Dilakukan percobaan dengan mengambil sepuluh sampel citra daun dari empat varietas tanamanyaitu, pucuk merah, daun ungu, melati, dan sirih. Citra diambil dengan tiga kamera smartphone dan tiga tingkat pencahayaan yang berbeda. Perbaikan yang dilakukan menggunakan algoritma 3D-TPS menghasilkan nilai SSIMterbaik pada rentang 0.9191– 0.9797 untuk citra daun pucuk merahdan nilai MAE prediksipigmen sebesar 0.0296 – 0.0492.  \nKata Kunci: 3D– TPS, daun tanaman, pigmen, perbaikan kualitas citra  \n1. Pendahuluan  \nPigmen merupakan pewarna alami yang ditemukan pada tumbuhan dan hewan. Dalamproses fotosintesis terdapat tiga pigmen yang berperan yaitu klorofil, karotenoid, dan antosianin. Setiap pigmen memberikan informasi mengenai kondisi kesehatan tanaman. Metode analisa pigmen dapat dilakukan secara destruktif maupun non-destruktif menggunakan Kromatografi Cair Kinerja Tinggi (KCKT) dan Spektrofotometer yang membutuhkan waktu dan biaya tinggi ([1], [2]) . Selain itu, metode KCKT menyebabkan kerusakan pada daun yang diteliti, sehinggamencegah penelitian lain terkait aspek warna pada jaringan daun [2] . Pengukuran secara non destruktif menggunakan citra digital mencegah adanya kerusakan pada daun. Citra digital membuat pengukuran kuantifikasi pigmen menjadi lebih mudah, efektif, dan cukup akurat [3] .  \nSaat ini, hasil citra digital menggunakan kamera smartphone sudah mampu digunakan untuk menganalisis kandungan p","cbCaif5PINFrC6V6","https://ap.wps.com/l/cbCaif5PINFrC6V6","pdf",1474272,10,"Indonesian","# Pendahuluan\n## Latar belakang pigmen fotosintesis dan analisisnya\n## Kamera smartphone, pengaruh kualitas citra, dan kebutuhan standarisasi\n## Machine learning untuk perbaikan citra (3D-TPS)\n# Tinjauan Pustaka","[{\"question\":\"Mengapa kualitas citra tanaman perlu diperbaiki saat menggunakan kamera berbeda untuk prediksi pigmen?\",\"answer\":\"Karena perbedaan jenis dan spesifikasi kamera menghasilkan variasi warna pada citra, dipengaruhi jumlah pixel, jarak, dan iluminasi. Variasi ini memengaruhi akurasi aplikasi Fuzzy Piction dalam menghitung kandungan pigmen.\"},{\"question\":\"Apa peran algoritma 3D-TPS dalam penelitian ini?\",\"answer\":\"3D-TPS digunakan untuk menstandarkan kualitas/representasi warna citra pada aplikasi Fuzzy Piction di ruang warna sRGB agar warna lebih sesuai dengan objek asli. Perbaikan ini diharapkan meningkatkan akurasi prediksi pigmen.\"},{\"question\":\"Bagaimana evaluasi performa dilakukan setelah perbaikan citra?\",\"answer\":\"Citra yang telah diperbaiki dievaluasi menggunakan metrik SSIM untuk kualitas citra, serta MAE untuk menilai kesalahan prediksi pigment. Hasil terbaik dilaporkan pada rentang nilai SSIM tertentu dan MAE yang rendah.\"}]","Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning | PDF",15]