Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.
Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.
Abstrak. Paperless merupakan bentuk ideal era informasi dengan kelebihan berupa efisien waktu, ramah lingkungan, manajemen dokumentasi yang baik, serta menjadi langkah penting meningkatkan citra organisasi dalam bidang lingkungan. Dalam konteks lingkungan, paperless adalah langkah nyata mengurangi penggunaan pohon untuk kertas. Konsep paperless sudah diusulkan pemerintah dan telah dijamin secara hukum sehingga berbagai sektor memulai untuk mengimplementasikan konsep paperless baik sektor pemerintahan, pendidikan, maupun industri. Sampai saat ini belum diketahui berapa banyak sektor yang mengimplementasikan aplikasi paperless, platform apa saja yang digunakan untuk mengembangkan aplikasi paperless, dampak dari penggunaan aplikasi paperless dan tantangannya bagi Indonesia. Oleh karena itu, penelitian ini bertujuan untuk mengetahui lebih detail tentang pemanfaatan aplikasi paperless baik dari sektor, platform, dampak, dan tantangannya bagi Indonesia. Data-data yang digunakan pada penelitian ini adalah artikel jurnal yang dipublikasikan pada jurnal-jurnal terakreditasi Sinta yang membahas tentang pengembangan aplikasi paperless pada sektor pemerintahan, pendidikan, dan industri mulai tahun 2010 hingga 2019. Data-data tersebut dianalisis menggunakan metode Systematic Literature Review (SLR). Hasil dari penelitian ini menunjukkan bahwa sektor yang paling sering mengembangkan aplikasi paperless adalah sektor pendidikan sedangkan platform yang dominan digunakan untuk mengembangkan aplikasi paperless adalah website. Dampak penggunaan aplikasi paperless memiliki dampak positif baik dari segi peformansi, penghematan anggaran, maupun permasalahan lingkungan yang dihasilkan oleh limbah kertas. Aplikasi paperless adalah jawaban di era digital dalam mendukung pelestarian lingkungan. Adapun tantangannya adalah bagaimana pemerintah membuat regulasi untuk mendukung aplikasi paperless di seluruh instansi dan memberikan dukungan dana kepada sektor-sektor yang penggunaan kertasnya tergolong banyak akan tetapi kekurangan dana dalam mengimplementasikan aplikasi paperless. Aplikasi paperless juga harus mudah digunakan dan pengguna harus diberikan pelatihan secara kontinyu agar aplikasi paperless dapat dimplementasikan dengan mudah. Kata kunci: paperless; systematic literature review; platform aplikasi; pendidikanAbstract. Going paperless is an ideal form of the information era with the advantages of being time-efficient, environmentally friendly, proper documentation management, and it is an important step to improve the perception of the organization in the environmental field. From the environmental perspective, paperless is a concrete step to reduce the use of trees for paper. The paperless concept has been proposed by the government and has been legally guaranteed, so various sectors have begun to implement the paperless concept such as in the government, education, and industry sectors. However, there has been limited research that studies how many sectors implement paperless applications, the platforms that are used to develop pap...
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