Ujian esai merupakan evaluasi pembelajaran dalam bentuk soal esai yang mempunyai jawaban lebih bervariasi dibandingkan soal pilihan ganda. Variasi jawaban tersebut memberikan kesulitan tersendiri bagi guru dalam menilai jawaban. Sistem penilaian esai dibangun untuk menjadi salah satu solusi yang dapat mempercepat dan mempermudah proses penilaian. Sistem penilaian esai pada penelitian ini dilakukan dengan mengukur kesamaan jawaban siswa dan kunci jawaban guru. Penelitian ini menggunakan algoritma winnowing. Algoritma winnowing adalah algoritma untuk mengukur kemiripan teks. Algoritma winnowing menghasilkan fingerprint yang akan mewakili teks jawaban pada perhitungan kemiripan dengan persamaan jaccard coeficient. Pengujian dilakukan untuk mengetahui kemampuan algoritma winnowing dalam memberikan penilaian esai dengan menggunakan perubahan nilai n-gram dan window dari algoritma winnowing. Hasil pengujian menunjukkan penggunaan nilai n-gram dan window pada metode winnowing berpengaruh pada kesamaan fingerprint yang ditemukan. Semakin banyak kesamaan fingerprint yang ditemukan, maka semakin tinggi nilai yang dihasilkan sistem. Akurasi penilaian sistem menunjukkan hasil yang lebih baik pada teks jawaban yang memiliki struktur kalimat jawaban yang sama dengan kunci jawaban.
Rainfall is one of the important information that widely used in various fields. Rainfall data involving location information is referred to as spatial rainfall data. Some of the model approaches to spatial rainfall data are the Vector Autoregressive (VAR) model, state-space, Markov chain stochastic model, and Geographically Weighted Regression (GWR). However, these models have not been able to produce predictions of the occurrence of no rain (zero value) or extreme values. Currently, theoretical modelling is mostly approached by artificial neural network (ANN) techniques. The purpose of this study is to model spatial rainfall data in East Java, Indonesia in 2020 with the ANN approach which is supported by several variables such as location and elevation information. The ANN used backpropagation and Rporp by combining the learning rate and layer which is then obtained the RMSE value. The results show that the best model has the smallest RMSE of 1.22 when the learning rate is 0.15 on 11 layers using Rprop algorithm.
The main purpose of this study was to identify the key factor of various environmental characteristics dynamics in Lesser Sunda island. This is significant to support effective and efficient conservation management planning that prioritized in the area. We retrieved the dataset for this study from a global database package. Moreover, a multivariate analysis for dimension reduction, such as Principal Component Analysis (PCA) was utilized. The result indicated that dimension of environmental characteristics in Lesser Sunda island be reduced to six dimensions by considering the eigen value. Moreover, the first two dimension that contribute most variance proportion suggested Sea Surface Temperature (SST), pH and distance to shore as the key determining factors of environmental changes in studied area. Therefore, these factors should be highly considered for marine conservation design in Lesser Sunda island.
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