[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120782-id":3,"doc-seo-120782-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120782,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",52,"Teknologi","Aplikasi Android Pendeteksi Kualitas Beras Berbasis Machine Learning - Convolutional Neural Network (CNN) - Penelitian dan Pengembangan","Aplikasi Android dikembangkan untuk mendeteksi kualitas beras secara otomatis berbasis machine learning. Kualitas beras ditautkan pada ciri fisik yang memengaruhi penampilan dan mutu saat dimasak, sehingga diperlukan metode pendeteksian yang efisien dan mudah diakses. Pengembangan menggunakan metodologi agile dengan fleksibilitas terhadap perubahan kebutuhan dan masukan pengguna. Model machine learning dibangun menggunakan Convolutional Neural Network (CNN) dengan TensorFlow, Keras, serta arsitektur MobileNet, dilatih menggunakan dataset 1800 gambar (1440 data training) pada 25 epoch.","Aplikasi Android Pendeteksi Kualitas Beras Berbasis Machine Learning Menggunakan  \nMetode Convolutional Neural Network  \nFebriyanti Paramudita*1, Mulki Indana Zulfa2  \n1,2Teknik Elektro, Fakultas Teknik, Universitas Jenderal Soedirman, Indonesia  \n[Email:](Email:1 febriyanti.paramudita@mhs.unsoed.ac.id)[1](Email:1 febriyanti.paramudita@mhs.unsoed.ac.id)[ febriyanti.paramudita@mhs.unsoed.ac.id](Email:1 febriyanti.paramudita@mhs.unsoed.ac.id), [2](2 mulki_indanazulfa@unsoed.ac.id)[ mulki_indanazulfa@unsoed.ac.id](2 mulki_indanazulfa@unsoed.ac.id)  \nAbstrak  \nBeras merupakan makanan pokok dan bahan utama dalam berbagai produk pangan lainnya, yang memerlukantingkat kualitas tertentu. Kualitas beras meliputi sifat fisik yang memengaruhi penampilan dan menentukankualitasnya saat dimasak. Hal tersebut mendorong kebutuhan akan metode pendeteksian kualitas beras yang efisien. Penelitian ini menyajikan aplikasi android untuk pendeteksian kualitas beras berbasis machine learning. Metode pengembangan aplikasinya menggunakan agile yang memberikan fleksibilitas menangani perubahankebutuhan dan umpan balik dari pengguna selama proses pengembangan. Lebih lanjut model machine learning dibuat menggunakan metode Convolutional Neural Network (CNN) dengan library TensorFlow dan Keras sertaarsitektur MobileNet. Bahasa pemrograman yang digunakan adalah python dengan lingkungan simulasinya menggunakan Google Colab. Model yang sudah dibangun dilatih menggunakan dataset berupa 1800 gambar, 1440 diantaranya adalah data training dengan 25 epoch. Hasil simulasi menunjukkan training loss sebesar 0.0012, dengan nilai akurasi 99.44% .  \nKata kunci: agile, android, convolution neural network, machine learning  \nBuild and Design of an Android Application for Rice Quality Detection based on Machine Learning using Convolutional Neural Network (CNN)  \nAbstract  \nRice is a staple food and the main ingredient in various other food products, which require a certain level of quality. The quality of rice includes physical properties that affect its appearance and determine its quality when cooked. This prompted the need for an efficient rice quality detection method. This study presents an android application for detecting the quality of rice based on machine learning. The application development method uses agile which provides flexibility to handle changing needs and feedback from users during the development process. Furthermore, machine learning models are created using the Convolutional Neural Network (CNN) method with the TensorFlow and Keras libraries and the MobileNet architecture. The programming language used is python with the simulation environment using Google Colab. The model that has been built is trained using a dataset in the form of 1800 images, 1440 of which are training data with 25 epochs. The simulation results show a training loss of 0. 0012, with an accuracy value of 99.44%..  \nKeywords: agile, android, convolution neural network, machine learning  \n1. PENDAHULUAN  \nBeras merupakan makanan pokok dan bahan utama dalam berbagai produk pangan lainnya. Handani et all.(2021) menjelaskan bahwa permintaan beras akan cenderung meningkat sejalan dengan meningkatnya jumlah penduduk. Permintaan beras akan terus meningkat seiring dengan pertambahan jumlah penduduk. Permintaan beras di seluruh Indonesia terus meningkat sekitar 1,00 persen setiap tahun hingga tahun 2050 [1] . Oleh karenaitu, beras yang tersedia di pasaran harus memenuhi standar dan kualitas yang baik. Mutu beras dipengaruhi oleh beberapa ciri fisik seperti (1) ukuran dan bentuk butiran,(2) tingkat derajat sosoh,(3) kejernihan,(4) kebersihandan kemurniannya. Sebab beras dikonsumsi dalam bentuk butiran utuh, maka ciri-ciri fisik tersebut berperan penting dalam kualtasnya [2] .  \nKualitas beras dapat dikategorikan berdasarkan bentuk dan warna. Warna dan bentuk beras mempengaruhikualitasnya. Semakin putih, bersih, dan utuh beras, semakin baik kualitasnya. Penilaian tingkat keputihan dan kebersih","cbCaitvfrybGHK7u","https://ap.wps.com/l/cbCaitvfrybGHK7u","pdf",607116,3,1,9,"Indonesian","id",113,"# PENDAHULUAN\n## Latar Belakang Kualitas Beras dan Kebutuhan Deteksi\n## Tujuan Penelitian\n# METODE PENELITIAN\n## Model Deteksi dengan Machine Learning","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Mengembangkan aplikasi Android berbasis machine learning untuk mendeteksi kualitas beras.\"},{\"question\":\"Metode machine learning apa yang digunakan untuk membangun model?\",\"answer\":\"Model menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNet serta library TensorFlow dan Keras.\"},{\"question\":\"Bagaimana proses pengembangan aplikasi dilakukan?\",\"answer\":\"Pengembangan menggunakan metode agile untuk menangani perubahan kebutuhan dan umpan balik pengguna selama proses pengembangan.\"}]","Aplikasi Android Pendeteksi Kualitas Beras Berbasis Machine Learning - Convolutional Neural Network (CNN) - Penelitian dan Pengembangan | PDF",1785732010,14,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"android-app-for-rice-quality-detection-based-on-machine-learning-convolutional-neural-network-cnn-research-and-development","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/teknologi/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/android-app-for-rice-quality-detection-based-on-machine-learning-convolutional-neural-network-cnn-research-and-development/120782/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Apa tujuan penelitian ini?","Question",{"text":76,"@type":77},"Mengembangkan aplikasi Android berbasis machine learning untuk mendeteksi kualitas beras.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode machine learning apa yang digunakan untuk membangun model?",{"text":81,"@type":77},"Model menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNet serta library TensorFlow dan Keras.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana proses pengembangan aplikasi dilakukan?",{"text":85,"@type":77},"Pengembangan menggunakan metode agile untuk menangani perubahan kebutuhan dan umpan balik pengguna selama proses pengembangan.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,119,123,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":97,"slug":118},54,"Penelitian & Laporan","research-report",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":97,"slug":122},49,"Sastra","literature",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":124},"technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]