[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122091-id":3,"doc-seo-122091-113":31,"detail-sidebar-cat-0-id-113":93},{"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},122091,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",54,"Penelitian & Laporan","Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan - Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian","Penelitian ini mengembangkan sistem prediksi waktu penyiraman optimal pada perkebunan dan pertanian dengan pendekatan machine learning untuk meningkatkan produktivitas. Metodologi meliputi pengambilan data dari automatic weather station (AWS) melalui ThingsBoard, pemrosesan berurutan dari JSON (postman) ke CSV dengan penyesuaian timestamp, lalu pra-pemrosesan berupa pembersihan data, pemilihan variabel, integrasi, dan pembentukan dataset bersih. Evaluasi memakai pembagian data latih-uji serta perbandingan logistic regression, random forest, dan decision tree, dengan pengujian kinerja menggunakan confusion matrix serta visualisasi hasil untuk meningkatkan interpetabilitas. Akurasi yang tinggi menunjukkan keandalan sistem dalam memberikan rekomendasi waktu penyiraman yang lebih presisi dan efisien.","Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan: Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian  \nDevelopment of Optimal Watering Time Prediction System in Plantation: A Machine Learning Approach for Improved Agricultural Productivity  \nMohammad F. Anggarda1,*, Iwan Kustiawan2, Deasy R. Nurjannah3, Nurul F. A.  \nHakim1  \n1Program Studi Pendidikan Teknik Otomasi Industri dan Robotika, Fakultas Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung,  \nJawa Barat 40154 Indonesia  \n2 Program Studi Teknik Elektro, Fakultas Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung, Jawa Barat 40154 Indonesia  \n3 Program Studi Teknik Elektronika Politeknik TEDC, Jl. Politeknik Jl. Pesantren No. 2, Cibabat, Kec. Cimahi Utara, Kota Cimahi, Jawa Barat 40513  \n*E-mail Penulis Korespondensi: [anggarda@upi.edu](anggarda@upi.edu)  \nABSTRACT  \nModern agriculture relies heavily on technology, especially in irrigation management and crop watering. Several previous studies have appliedfield data-based predictive techniques to improve crop yields. This research aims to develop a prediction system for optimal watering time in plantations and agriculture using a machine learning approach. The rigorous methodology includes data capture, pre-processing, model evaluation and testing, validation, and visualization. High accuracy demonstrates the system's reliability in determining optimal watering needs to improve resource efficiency and crop yields in agriculture. The data obtained from the automatic weather station (AWS) via thingsboard is processed sequentially, starting from data retrieval in json format using postman to transformation into csv files with proper timestamp adjustment. The pre-processing stage includes data cleaning, variable selection, data integration, and generating a clean dataset. In the evaluation stage, the dataset is divided into training data and test data, with the application and comparison of logistic regression, random forest and decision tree models applied as classifiers. Furthermore, the validation and results stage includes prediction, performance testing using the confusion matrix, and visualization of prediction results in the form of text and icons that aim to increase interpetability for users through Google Collaboratory. The results of this research provide an overview of the optimal watering time based on the dataset from the automatic weather station. Further analysis shows that the implementation of machine learning models significantly improves the prediction accuracy, proving the effectiveness of the system in providing more precise watering time recommendations to increase agricultural productivity. The main objective is to develop a machine learning-based watering time prediction system using data from the automatic weather station and evaluate various classifier algorithms to select the best model.  \nKeywords: Development of prediction system; optimal watering time; machine learning and agricultural productivity.  \nABSTRAK  \nPertanian modern sangat bergantung pada teknologi, khususnya dalam pengelolaan irigasi dan penyiraman tanaman. Beberapa penelitian terdahulu telah menerapkan teknik prediktif berbasis data lapangan untuk meningkatkan hasil panen. Penelitian ini bertujuan untuk mengembangkan sistem prediksi waktu penyiraman optimal pada perkebunan maupun pertanian dengan menggunakan pendekatan machine learning. Metodologi yang ketat mencakup pengambilan data, pra-pemrosesan, evaluasi dan pengujian model, validasi, dan visualisasi. Akurasi yang tinggi menunjukkan keandalan sistem dalam menentukan kebutuhan penyiraman optimal untuk meningkatkan efisiensi sumber daya dan hasil panen dalam pertanian. Data yang diperoleh dari automatic weather station (AWS) melalui thingsboard diproses secaraberurutan, mulai dari pe","cbCaipKu50at2fn2","https://ap.wps.com/l/cbCaipKu50at2fn2","pdf",1107692,5,1,13,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang produktivitas pertanian dan irigasi\n# Metodologi Sistem\n## Pengambilan data dari AWS melalui ThingsBoard\n## Pra-pemrosesan data dan pembentukan dataset\n# Evaluasi Model\n## Pembagian data latih dan uji\n## Perbandingan model classifier: logistic regression, random forest, decision tree\n# Validasi, Pengujian, dan Visualisasi\n## Confusion matrix untuk pengujian kinerja\n## Visualisasi prediksi dan peningkatan interpetabilitas","[{\"question\":\"Apa tujuan utama penelitian ini?\",\"answer\":\"Mengembangkan sistem prediksi waktu penyiraman optimal pada perkebunan dan pertanian menggunakan pendekatan machine learning serta mengevaluasi algoritma klasifikator untuk memilih model terbaik.\"},{\"question\":\"Data apa yang digunakan untuk membangun sistem prediksi?\",\"answer\":\"Data berasal dari automatic weather station (AWS) yang dikirim melalui ThingsBoard, kemudian diambil dalam format JSON dan diubah menjadi CSV dengan penyesuaian timestamp.\"},{\"question\":\"Model machine learning apa saja yang dibandingkan?\",\"answer\":\"Penelitian membandingkan logistic regression, random forest, dan decision tree sebagai classifier pada tahap evaluasi.\"}]","Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan - 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