[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117579-id":3,"doc-seo-117579-113":30,"detail-sidebar-cat-0-id-113":84},{"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":20},117579,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",54,"Penelitian & Laporan","IMPLEMENTASI MACHINE LEARNING UNTUK SISTEM IRIGASI CERDAS","Penggunaan air yang tidak efisien dalam pertanian menurunkan produktivitas lahan, terutama ketika irigasi masih mengandalkan cara manual atau jadwal tetap. Penelitian ini mengembangkan sistem irigasi cerdas berbasis sensor tanah yang mengukur kelembaban, suhu, cahaya, dan curah hujan, lalu memprosesnya dengan algoritma Random Forest untuk memprediksi kebutuhan irigasi dalam tiga kategori. Dataset disusun dari simulasi 10.000 sampel tanpa penambahan noise dan dibandingkan terhadap beberapa algoritma lain. Hasil menunjukkan Random Forest mencapai akurasi di atas 96,4% serta peningkatan Recall melalui SMOTE pada data minoritas.","IMPLEMENTASI MACHINE LEARNING UNTUK SISTEM IRIGASI CERDAS  \nOleh: Raditya Artha Rochmanto1*), Septiantar Tebe Nursaputro2  \n1,2Program Studi Teknologi Rekayasa Elektronika, Jurusan Teknik Elektro, Politeknik Negeri Semarang, Semarang,  \nIndonesia  \n1,2Program Studi Teknik Elektronika, Jurusan Teknik Elektro, Politeknik Negeri Semarang, Semarang, Indonesia  \n[E-mail:](E-mail:1raditya.artha@polines.ac.id)[1](E-mail:1raditya.artha@polines.ac.id)[raditya.artha@polines.ac.id](E-mail:1raditya.artha@polines.ac.id)  \nAbstrak  \nPenggunaan air yang tidak efisien dalam pertanian seringkali menjadi salah satu penyebab utama menurunnyaproduktivitas lahan. Di era pertanian modern, implementasi Machine Learning (ML) dapat menjadi solusi untuk memantau kondisi tanah dan menentukan kebutuhan irigasi secara otomatis. Penelitian ini mengembangkan sistem berbasis sensor tanah dengan parameter kelembaban, suhu, cahaya, dan curah hujan yang diproses menggunakanalgoritma Random Forest. Data dikembangkan melalui simulasi sebanyak 10.000 sampel tanpapenambahan noise untuk menjaga representasi kondisi nyata. Model Random Forest dibandingkan dengan Decision Tree, SVM, dan Neural Network. Hasil pengujian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi di atas 96,4%. Selain itu dilakukanjuga perbandingan penggunaan antara baseline data denganjumlah data tiap kelas yang berbedajauh dengan metode penyeimbang data Synthetic Minority Oversampling Technique (SMOTE). Hasil pengujian menunjukkan meskipun penggunaan metode SMOTE menurunkan akurasi secara umum, tetapi dapat meningkatkan Recall yang menunjukkan bahwa model lebih sensitifpada data minoritas.  \nKata kunci: irigasi, machine learning, random forest  \nAbstract  \nInefficient water use in agriculture is often a major cause of declining land productivity. In the modern agricultural era, the implementation of Machine Learning (ML) can be a solution for monitoring soil conditions and automatically determining irrigation needs. This study developed a soil sensor-based system with parameters of humidity, temperature, light, and rainfall processed using the Random Forest algorithm. Data was developed through simulations of 10,000 samples without the addition of noise to maintain the representation of real conditions. The Random Forest model was compared with Decision Tree, SVM, and Neural Network. The test results showed that Random Forest provided the best performance with an accuracy above 96.4%. Furthermore, a comparison was conducted between the use of baseline data with a significantly different number of data per class using the Synthetic Minority Oversampling Technique (SMOTE) data balancing method. The test results showed that although the use of the SMOTE method decreased accuracy in general, it could increase Recall, making the model more sensitive to minority data.  \nKeywords: irigation, machine learning, random forest  \n1. Pendahuluan  \nPertanian merupakan sektor yang sangat bergantung pada ketersediaan air. Menurut Food and Agriculture Organization (FAO), sekitar 70% pemanfaatan air tawar global digunakanuntuk keperluan irigasi pertanian. Namun, sebagian besar sistem irigasi di negara berkembang masih mengandalkan metode manual atau berbasis jadwal tetap, yang berpotensi menyebabkan pemborosan air dan penurunan produktivitas tanaman. Kondisi inisemakin diperburuk oleh perubahan iklim yang mengakibatkan pola curah hujan tidak menentu, sehingga efisiensi penggunaan air menjadi salah satu isu strategis yang harus dipecahkan.  \nDi Indonesia, permasalahan irigasi juga cukup  \nsignifikan. Sistem irigasi tradisional tidak mampu menyesuaikan diri dengan kebutuhan tanaman yang dinamis, padahal setiap tanaman memiliki fase pertumbuhan dengan kebutuhan air yang berbedabeda. Oleh karena itu, diperlukan solusi berbasis teknologi yang mampu memantau kondisi tanah secara real-time sekaligus memprediksi kebutuhan air secara cerdas.  \nInternet of Things (IoT) menawarkan peluang besar dala","cbCaio0rKhvym6hH","https://ap.wps.com/l/cbCaio0rKhvym6hH","pdf",311142,9,1,6,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan gap penelitian\n## Tujuan dan kontribusi penelitian\n# Metode Penelitian\n## Perancangan arsitektur sistem\n## Pembuatan dataset simulasi\n## Pemilihan algoritma dan evaluasi","[{\"question\":\"Bagaimana penggunaan SMOTE memengaruhi hasil model?\",\"answer\":\"SMOTE menurunkan akurasi secara umum, tetapi meningkatkan Recall, sehingga model lebih sensitif terhadap data minoritas.\"}]","IMPLEMENTASI MACHINE LEARNING UNTUK SISTEM IRIGASI CERDAS | PDF",1785677088,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"machine-learning-implementation-for-smart-irrigation-system","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/id/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/id/document/machine-learning-implementation-for-smart-irrigation-system/117579/",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":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-17","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Bagaimana penggunaan SMOTE memengaruhi hasil model?","Question",{"text":76,"@type":77},"SMOTE menurunkan akurasi secara umum, tetapi meningkatkan Recall, sehingga model lebih sensitif terhadap data minoritas.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,91,95,99,103,107,109,113,117,121,125],{"id":87,"doc_module":4,"doc_module_name":46,"category_name":88,"show_sort_weight":89,"slug":90},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":89,"slug":94},48,"Cerita & Novel","story-novel",{"id":96,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":89,"slug":98},56,"Gaya Hidup","lifestyle",{"id":100,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":89,"slug":102},51,"Komik","comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":89,"slug":106},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":89,"slug":108},"research-report",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":89,"slug":112},49,"Sastra","literature",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":89,"slug":116},52,"Teknologi","technology",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":89,"slug":120},50,"Ujian","exam",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":89,"slug":124},57,"Umum","general",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":4,"slug":128},181,"Formulir","formulir"]