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.

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...

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60% and 95%. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites.

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Malaria is a disease caused by the plasmodium parasite and has caused many fatalities. In general, identifying malaria parasite infection can be done by visually observing thick and thin blood smears through microscopic devices. Identification of parasites in thick blood preparations has a higher level of difficulty than thin blood preparations. In thick blood preparations, various objects such as artefacts and noise have a structure similar to the structure of parasitic objects. This paper aims to develop a parasite detection method based on image processing in thick blood smears, consisting of two main stages. First is to improve image quality by applying contrast value stretching, converting green channels, and refining each image. Second is to segment the plasmodium parasite using global threshold Otsu and active contour followed by several morphological operations. The proposed method produces a high sensitivity of 98.06% with an average negative false rate of 1.4%. With the sensitivity level obtained, it can be interpreted that most of the parasitic objects have been detected correctly in one blood sample image.