Pengaruh Kondisi Cuaca Terhadap Serangan Hama Penggerek Batang Pada Tanaman Padi Di Desa Ciaruteun Ilir, Kec. Bungbulang, Kab. Bogor

Perubahan iklim yang tidak menentu saat ini berdampak pada berbagai sektor termasuk pertanian, dimana salah satu dampaknya adalah meningkatnya populasi hama. Saat ini Balai Proteksi Tanaman Pangan dan Holtikultura (BPTPH) Desa Ciaruteun Ilir, Kecamatan Bungbulang, Kab. Bogor di Jawa Barat mengalami kesulitan dalam mengamati dan mencegah serangan hama, terutama hama penggerek batang. Penelitian ini membahas mengenai prediksi serangan hama penggerek batang, terutama batang padi. Penelitian ini menitik beratkan pada penerapan peringatan dini serangan hama yang didasarkan pada data klimatologi berupa suhu, kelembaban, dan curah hujan. Dari uraian tersebut, peneliti membuat sistem prediksi serangan hama padi berbasis web menggunakan metode Naïve Bayes yang diterapkan berdasarkan nilai probabilitas. Nilai probabilitas digunakan untuk memprediksi peluang di masa depan berdasarkan pada pengalaman dimasa lalu, sehingga akan memudahkan pegawai BPTPH Desa Ciaruteun Ilir dalam menganalisis, mengidentifikasi dan memantau kemunculan serangan hama penggerek batang untuk diinformasikan kepada pertani. Berdasarkan data training yang berhasil diklasifikasikan 138 data training yang di uji pada data serangan hama metode Naive Bayes berhasil memprediksi adanya serangan hama dengan persentase keakuratan sebesar 96.76%

Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms

In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result