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Virus Corona menjadi permasalahan internasional pada tahun 2020. Hal ini sangat berdampak bagi kehidupan masyarakat. Pemerintah Indonesia mengambil peran dalam menekan peningkatan jumlah penderita virus Corona dengan cara membatasi kegiatan masyarakat di luar rumah. Salah satu dampak yang signifikan dari Virus Corona adalah di sektor perekonomian. Oleh karena itu, perlu dilakukan analisis sentimen untuk menentukan kecenderungan opini masyarakat terhadap dampak virus Corona. Twitter merupakan salah satu platform yang digunakan oleh masyarakat untuk mengekspresikan kondisi terkini setelah virus Corona merambah. Tujuan dari penelitian ini adalah memperoleh analisis dokumen text untuk mendapatkan sentimen positif atau negatif masyarakat. Data yang digunakan merupakan dokumen tweet dari Twitter mengenai dampak virus Corona. Data yang terkumpul dibagi untuk digunakan sebagai data latih dan data uji proses klasifikasi. Metode yang digunakan untuk klasifikasi dalam penelitian ini adalah Metode Naive Bayes Classifier. Hasil klasifikasi dievaluasi menggunakan accuracy dan error rate dengan tujuan mengetahui keakuratan dokumen setelah diklasifikasi menjadi sentimen positif atau negatif. Hasil penelitian menunjukkan metode Naive Bayes mampu mengklasifikasi dokumen tweet dengan akurasi 67% dan error rate sebesar 33%. Percobaan dengan menggunakan 3 jumlah data berbeda (100, 200, dan 500) menghasilkan selisih nilai akurasi  yang tidak jauh berbeda yaitu 0,02. Hal ini menunjukkan metode Naive Bayes untuk klasifikasi data tweet terkait dampak virus Corona menghasilkan performa yang stabil. Nilai accuracy yang diperoleh cukup baik dan penelitian selanjutnya bisa dikembangkan dengan memperhitungkan unsur semantik pada dokumen tweet.

Balinese script writing, as one of Balinese cultural richness, is going to extinct because of its decreasing use. This research is a way to preserve it through collaboration between Computer Science and Language discipline, that focused on accuracy comparison of Latin-to-Balinese script transliteration method on mobile application as a ubiquitous learning media. From few research in this area, there are only two existing methods to be compared, i.e. each on Android mobile application that were called Belajar Aksara Bali (BAB), and Transliterasi Aksara Bali (TAB). The comparison was based on The Balinese Alphabet writing rules and examples document by Sudewa. Through the experiment, TAB has outperformed BAB since TAB has passed over 68% (103 of 151) cases, while BAB has passed over only 39% (59 of 151) cases. This research contributes on a comprehensive accuracy comparison analysis of Latin-to-Balinese script transliteration method, specifically on mobile application, since there is no such study. This research also contributes on those methods improvement possibility. In the future, this research can be used as a reference for improvement of any Latin-to-Balinese script transliteration method by taking care on thirteen kind of special words that were found during this comparison study.

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Blood cockle (Anadara granosa) is one of the marine biotas that can be used as a bioindicator of the pollution level of sea water. The nature of blood cockle stays in one place because of their slow movement and they are non-selective filter feeders which filter water in order to get food. The use of activated charcoal during soaking is to keep the food safety from blood cockle contaminated with heavy metals. The purpose of this study is to determine the effectivity test of soaking duration on blood cockle (Anadara granosa) and activated charcoal toward reducing metals lead (Pb). This study employed Randomized Complete Design (RCD) using three different soaking periods such as 30 minutes, 60 minutes, and 90 minutes. The samples were analyzed by Inductively Coupled Plasma Emission (ICPE). The parametric data was analyzed with One Way Anova test. The result of the study showed that the soaking duration among 30 minutes, 60 minutes, and 90 minutes in activated charcoal showed significantly different (P< 0.05) toward the levels of lead.

Mangrove menjadi salah satu ekosistem lahan basah yang berperan penting dalam menyerap karbon. Namun, secara alami ekosistem mangrove juga mampu mengemisikan gas rumah kaca kedalam atmosfer. Metana merupakan salah satu gas rumah kaca yang berdampak signifikan terhadap perubahan iklim. Penelitian tentang siklus metana telah dilakukan di ekosistem mangrove TAHURA Ngurah Rai Bali. Penelitian ini bertujuan untuk mengukur konsentrasi gas metana pada tiga zona ekosistem mangrove. Metode chamber tertutup digunakan dalam pengambilan sampel gas yang kemudian dianalisis dalam gas kromatografi dengan sensor flame ionization detector (FID). Karakter ekologi mangrove yang terdiri dari parameter struktur komunitas mangrove dan lingkungan diukur dari setiap plot kuadrat pengambilan sampel gas. Hasil penelitian menunjukkan konsentrasi gas metana tertinggi ditemukan pada zona darat dengan rata-rata 3,698 ± 0,986 mg. L-1. Walaupun demikian, konsentrasi gas metana pada dua zona lainnya tidak menunjukkan perbedaan yang signifikan dengan zona darat. Variabilitas konsentrasi gas metana tidak berbeda signifikan dengan kondisi struktur komunitas mangrove yang berbeda antar zona. Penelitian ini hanya menemukan variasi nilai potensial redoks (ORP) yang berhubungan signifikan dengan konsentrasi gas metana. Hasil penelitian mengindikasikan bahwa karakter ekologi mangrove yang cukup seragam di kawasan sehingga, tidak menimbulkan perbedaan yang signifikan pada konsentrasi gas metana antar zona. Namun, parameter kondisi substrat lainnya perlu dilibatkan dalam penelitian berikutnya.AbstractMangrove is one of the wetland ecosystems that play an important role in carbon sequestration and storage. However, the ecosystem also emits greenhouse gas into the atmosphere naturally. Methane has been considered as a significant effect on global warming. A preliminary study in a part of the carbon cycle was conducted on the mangrove ecosystem in Ngurah Rai Forest Park Bali. This study was aimed to determine methane gas concentration in three different mangrove zones. Gas samples were collected by closed chamber method and they were analyzed using gas chromatography embedded with the flame ionization detector (FID) sensor. Mangrove ecological parameters i.e. community structure and environmental condition were determined on each quadratic plot where gas samples were collected. The result showed that the highest methane concentration was found in the landward zone at 3,698 ± 0,986 mg. L-1. Even though, the methane concentration of the other zones had not significantly different from the landward zone. In addition, the mangrove community structure among the three zones was not different significantly. The oxidation-reduction potential was the only factor that had a significant correlation with methane concentration. Those results indicated that mangrove ecological conditions among zones were similar to each other,  hence the variation of methane concentration was not significant. Nevertheless, substrate abiotic characters need to be involved in greenhouse gas studies in the future.

Balinese script writing, as one of Balinese cultural richness, is going to extinct because of its decreasing use. This research is one of the ways to preserve Balinese script writing using technological approach. Through collaboration between Computer Science and Balinese Language discipline, this research focused on the development of a Latin-to-Balinese script transliteration robotic system that was called LBtrans-Bot. LBtrans-Bot can be used as a learning system to give the transliteration knowledge as one aspect of Balinese script writing. In this research area, LBtrans-Bot was known as the first system that utilize Noto Sans Balinese font and was developed based on the identified seventeen kinds of special word. LBtrans-Bot consists of the transliterator web application, the transceiver console application, and the robotic arm with its GUI controller application. The transliterator used the Model-View-Controller architectural pattern, where each of them was implemented by using MySQL database (as the repository for the words belong to the seventeen kinds of special word), HTML, PHP, CSS, and Bootstrap (mostly for the User Interface responsive design), and JavaScript (mostly for the transliteration algorithm and as the controller between the Model and the View). <em>Dictionary</em> data structure was used in the transliterator memory as a place to hold data (words) from the Model. The transceiver used batch script and AutoIt script to receive and trasmit data from the transliterator to the GUI controller, which control the Balinese script writing of the robotic arm. The robotic arm with its GUI controller used open-source mDrawBot Arduino Robot Building platform. Through the experiment, LBtrans-Bot has been able to write the 34-pixel font size of the Noto Sans Balinese font from HTML 5 canvas that has been setup with additional 10-pixel length of the width and the height of the Balinese script writing area. Its transliterator gave the accuracy result up to 91% (138 of 151) testing cases of The Balinese Alphabet writing rules and examples document by Sudewa. This transliterator result outperformed the best result of the known existing transliterator based on Bali Simbar font, i.e. Transliterasi Aksara Bali, that only has accuracy up to 68% (103 of 151) cases of the same testing document. In the future work, LBtrans-Bot could be improved by: 1) Accommodating more complex Balinese script with trade off to the limited writing area of robotic system; 2) Enhancing its transliterator to accommodating the rules and/or examples from the testing document that recently cannot be handled or gave incorrect transliteration result; enriching the database consists of words belong to the seventeen kinds of special word; and implementing semantic relation transliteration.

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