The tomato plant is widely consumed by the community and is widely cultivated by farmers. Tomato plants are susceptible to disease attacks. Plant diseases cause a decrease in the quality and quantity of crops or agricultural produce. The idea of the 4.0 agricultural revolution emerged as a result of the 4.0 industrial revolution. Farmers are not ready to face increasingly rapid technological advances. It is important to identify the disease in tomato leaves correctly in the efficiency of disease management for efforts to control so that disease in tomato leaves does not develop. The main objective of the proposed method is to develop a technique for identifying foliar diseases in tomato plants by increasing the classification accuracy. The novelty of this research is a combination of several feature extractions to improve classification accuracy. The features used are the color feature, the Hu-Moment feature, and the firur haralick. In the classification process, the Random Forest algorithm and other classification algorithms are applied for comparison. In this study, the Random Forest method and the combination of extraction features have shown an increase in accuracy, the accuracy obtained is 96%.
<p>Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. <em>Gradient Boosted Trees</em> dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. <em>Gradient Boosted Trees</em> menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan <em>Gradient Boosted Trees</em> untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, <em>Gradient Boosted Trees,</em> K-NN-<em>Gradient Boosted Trees</em>dilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau <em>Gradient Boosted Trees</em> yaitu 98.17%.</p><p><em><strong>Abstract</strong></em></p><p><em><em>Poverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.</em></em></p>
Poverty is a problems faced by developing countries, as well as Indonesia. According to data from the Central Statistics Agency in 2018, more than half the distribution of the poor population in Indonesia is in Java, which is 13,340.15 million people. Somokerto village is one of the villages in the district of Magelang, Central Java, which receives government assistance in an effort to reduce poverty. But in the process of classifying citizens who are entitled to receive assistance is still done manually. Manual classification is considered inaccurate in obtaining the results of social assistance recipients. In overcoming this problem, we need a systematic calculation to get accurate results. In this case, the researcher uses data mining classification calculation by comparing 2 calculation methods, namely K-NN and Naïve Bayes. The reseachers use Rapidminer tools. The research stages are identification of problems, data collection, implementation K-NN, Implementation Naive bayes, data testing process to produce accuracy and compare the result. The results obtained are the accuracy of Naïve Bayes higher than K-NN, namely Naïve Bayes 89.04% and K-NN 87.67%. This figure is classified in the category of good classification. From the results of the study it can be concluded the Naïve Bayes algorithm is suitable to be applied in the calculation of recipients of social assistance.
Media sosial mengajak siapa saja yang tertarik untuk berpartisipasi dengan secara terbuka memberikan tanggapan, memposting komentar, dan berbagi informasi secara cepat dan tanpa batasan waktu. Potensi Media sosial bisa menghubungkan banyak orang dengan mudah dan gratis dalam satu waktu mendorong komunikasi dilakukan secara virtual dalam jaringan internet. Tidak dapat dipungkiri bahwa media sosial bisa diakses oleh siapapun, hampir semua orang memiliki paling tidak satu akun sosial media sebagai media untuk berkomunikasi secara online. Pengguna sosial media ini tidak hanya dari kalangan anak remaja tetapi anak usia SD sampai orang tua sudah banyak yang familiar dengan sosial media, pengguna sosial media mencapai angka 62% dari total populasi penduduk Indonesia menggunakan smartphone untuk menggunakan akun sosial media. Perkembangan yang pesat dan pemanfaatnya yang tergolong besar ternyata dapat menimbulkan dampak positif dan dampak negatif. Di era ini kebebasan dalam mengakses website apapun media sosial jenis apapun tentunya harus dibarengi dengan sikap hati-hati dan mawas diri sehingga jangan sampai apa yang dipost di media sosial menjadi boomerang kepada diri sendiri, dampak negatif dari sosial media adalah munculnya bentuk kejahatan baru dalam bentuk virtual dan perbuatan melawan hukum yang dilakukan tidak secara langsung yang kemudian muncul istilah cybercrime. Pentingnya perhatian dalam bersosial media menjadi topik yang menarik untuk dibahas dan disosialisasikan sebagai kontrol dan pengetahuan tentang UU ITE yang dapat berimbas kepada siapa saja yang tidak berhati-hati dan bijak dalam bersosial media.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.