[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120474-id":3,"doc-seo-120474-113":31,"detail-sidebar-cat-0-id-113":85},{"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},120474,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",54,"Penelitian & Laporan","Machine Learning untuk Memprediksi Jumlah Penjualan, Stok dan Jumlah Tanam Hasil Pertanian Hidroponik - Analisis regresi linear vs support vector machine","Penelitian ini membahas kesulitan Hidroponik Tilung Farm dalam memprediksi data stok, transaksi, dan jumlah tanam yang optimal di Palangka Raya. Data diperoleh dari Agustus 2023 hingga Februari 2024 untuk tiga jenis tanaman dengan pola penanaman mingguan dan masa panen 30–35 hari. Pengolahan menggunakan machine learning berbasis regresi linear dan support vector machine, dengan pengumpulan data melalui studi pustaka, wawancara, serta observasi. Evaluasi memakai metrik MSE, MAE, dan MAPE menunjukkan regresi linear memberikan nilai kesalahan paling kecil sehingga lebih efektif untuk membantu efisiensi dan peningkatan keuntungan.","Machine Learning untuk Memprediksi Jumlah Penjualan, Stok dan Jumlah Tanam Hasil Pertanian Hidroponik  \nF Sesilia*1, V H Pranatawijaya2, R Priskila3  \n1-3Program Studi Teknik Informatika, Universitas Palangka Raya  \nE-mail: [frirasesilia@mhs.eng.upr.ic.id](frirasesilia@mhs.eng.upr.ic.id) *1, [viktorhp@it.upr.ac.id](viktorhp@it.upr.ac.id2)[2](viktorhp@it.upr.ac.id2), [ressa@it.upr.ac.id](ressa@it.upr.ac.id3)[3](ressa@it.upr.ac.id3)  \nAbstrak. Hidroponik Tilung Farm, usaha budidaya tanaman di Palangka Raya, sering mengalami kesulitan dalam memprediksi data stok, transaksi, dan jumlah tanam yang optimal. Sebelumnya, data ini hanya digunakan untuk melihat hasil penjualan. Penelitian ini bertujuan untuk memprediksi data tersebut dengan akurat menggunakan machine learning. Metode penelitian meliputi pengumpulan data dengan studi pustaka, wawancara, dan observasi. Data stok, transaksi, dan jumlah tanam diolah dengan machine learning menggunakan algoritmaregresi linear dan support vector machine. Hasil penelitian menunjukkan bahwa algoritmaregresi linear menghasilkan nilai terkecil untuk MSE, MAE, dan MAPE dalam memprediksi data stok, transaksi, dan jumlah tanam. Kesimpulannya, algoritma regresi linear lebih baik dalam memprediksi data tersebut dibandingkan dengan algoritma support vector machine. Penelitian ini membantu Hidroponik Tilung Farm dalam mengelola stok, transaksi, dan jumlahtanam secara optimal, sehingga meningkatkan efisiensi dan keuntungan.  \nKata kunci: penjualan; prediksi; machine learning  \nAbstract. Tilung Farm Hydroponics, a plant cultivation business in Palangka Raya, often experiences difficulties in predicting optimal stock data, transactions, and planting quantities. Previously, this data was only used to view sales results. This research aims to predict this data accurately using machine learning. Research methods include data collection by literature study, interviews, and observation. Stock, transaction, and number of planting data are processed using machine learning using linear regression algorithms and support vector machines. The research results show that the linear regression algorithm produces the smallest values forMSE, MAE, and MAPE in predicting stock, transaction, and number of planting data. In conclusion, the linear regression algorithm is better at predicting this data compared to the support vector machine algorithm. This research helps Tilung Farm Hydroponics in managing stock, transactions and planting quantities optimally, thereby increasing efficiency and profits.  \nKeywords: sales; prediction; machine learning  \n1. Pendahuluan  \nHidroponik Tilung Farm adalah salah satu tempat usaha budidaya tanaman dengan sistem hidroponik yang terletak di Palangka Raya, Kalimantan Tengah. Data yang digunakan dari tempat usaha ini adalah data set yang digunakan dari bulan Agustus 2023 sampai Februari 2024 dengan menanam 3 jenis tanaman, yaitu sawi dan pakcoy seribu bibit penanaman untuk seminggu dan habis dalam waktu 30-35 hari. Hidroponik Tilung Farm sering mengalami kesulitan dalam memprediksi data stok, data transaksi, dan jumlah tanam yang optimal. Transaksi yang tidak stabil dapat menyebabkan kesulitan dalam mengatur keuangan dan operasional usaha.  \nMachine learning dapat digunakan untuk memprediksi data stok, data transaksi, dan jumlah tanam yang optimal dengan bahasa hyton[1] . Bagaimana hasil prediksi perhitungan data stok tanaman, data transaksi dan jumlah tanam optimal berdasarkan algoritma regresi linear dan algoritma support vector machine[2] . Berdasarkan hasil prediksi stok tanaman, data transaksi dan jumlah tanam optimal denganalgoritma regresi linear dan support vector machine yang digunakan, manakah yang paling efektif untuk memprediksi stok tanaman, transaksi danjumlah tanam optimal hidroponik. Beberapa algoritma machine learning yang digunakan untuk prediksi data stok, data transaksi, dan jumlah tanam yang optimal antara lain regresi linier, algoritma ini menggunakan hubungan linear an","cbCaimrLD3OmybUh","https://ap.wps.com/l/cbCaimrLD3OmybUh","pdf",506605,9,1,12,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang masalah\n## Tujuan penelitian dan perumusan pertanyaan\n# Metode\n## Pengumpulan data\n## Deskripsi dataset dan variabel penelitian\n## Tahapan penelitian","[{\"question\":\"Bagaimana cara mengukur performa model prediksi?\",\"answer\":\"Kinerja diukur menggunakan metrik regresi seperti MSE, MAE, dan MAPE untuk membandingkan akurasi prediksi kedua algoritma.\"}]","Machine Learning untuk Memprediksi Jumlah Penjualan, Stok dan Jumlah Tanam Hasil Pertanian Hidroponik - Analisis regresi linear vs support vector machine | PDF",1785730275,18,{"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":80,"head_meta":82,"extra_data":84,"updated_unix":29},"machine-learning-to-predict-sales-stock-and-planting-quantity-in-hydroponic-farming-linear-regression-vs-support-vector-machine","",{"@graph":37,"@context":79},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/machine-learning-to-predict-sales-stock-and-planting-quantity-in-hydroponic-farming-linear-regression-vs-support-vector-machine/120474/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-18","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73],{"name":74,"@type":75,"acceptedAnswer":76},"Bagaimana cara mengukur performa model prediksi?","Question",{"text":77,"@type":78},"Kinerja diukur menggunakan metrik regresi seperti MSE, MAE, dan MAPE untuk membandingkan akurasi prediksi kedua algoritma.","Answer","https://schema.org",{"og:url":53,"og:type":81,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":83,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":86},[87,92,96,100,104,108,110,114,118,122,126],{"id":88,"doc_module":4,"doc_module_name":47,"category_name":89,"show_sort_weight":90,"slug":91},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":93,"doc_module":4,"doc_module_name":47,"category_name":94,"show_sort_weight":90,"slug":95},48,"Cerita & Novel","story-novel",{"id":97,"doc_module":4,"doc_module_name":47,"category_name":98,"show_sort_weight":90,"slug":99},56,"Gaya Hidup","lifestyle",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":90,"slug":103},51,"Komik","comic",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":90,"slug":107},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":90,"slug":109},"research-report",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":90,"slug":113},49,"Sastra","literature",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":90,"slug":117},52,"Teknologi","technology",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":90,"slug":121},50,"Ujian","exam",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":90,"slug":125},57,"Umum","general",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":4,"slug":129},181,"Formulir","formulir"]