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Stephanus Antonius Ananda
Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya

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Aplikasi Sentiment Analysis terhadap Trend Cryptocurrency pada Platform Twitter Menggunakan Library Textblob sebagai Alat Bantu Berinvestasi Ricky Chandra; Kartika Gunadi; Stephanus Antonius Ananda
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Along with the rapid development of information technology, many digital communication tools make it easier for people to access or share information. Twitter is one of the social media that has more than 1.3 billion users and more than 500 million tweets or tweets every day. The uniqueness of Twitter limits the number of writings to 280 characters, making Twitter a social media that contains sentiments about something. The cryptocurrency alone gets 4.1 million hashtag exposures per hour and has 2225 unique tweets per hour on the Twitter platform. The number of tweets related to Cryptocurrency causes investors to lose in terms of time because they have to manually assess a tweet. To overcome this, an effort that can be realized is to classify sentiments. One of the Natural Language Processing methods that have been developed for sentiment classification is TextBlob. In this thesis, an application will be made with sentiment analysis features using the Textblob Library, request tweets data using the Tweepy API, visualization of tweets data in the form of pie charts, tables, and word clouds, features that display the market price and history of cryptocurrency coins using the CoinGecko API and YFinance. as well as tweets from selected accounts. TextBlob Library testing is done by classifying results with 100 data that have been labeled by 2 examiners who have more than 1 year of experience investing in cryptocurrencies, the results obtained are 35% of the data have similarities between the results with the second tester, the application is tested with Tweets data request according to keywords, as well as application testing to display visualizations of Tweets data. A correlation test was conducted between the price change of cryptocurrency coins in 24 hours with the results of the classification of Tweet data and the Tweet volume of several coins. The conclusion that can be drawn from the correlation test is that when Tweet volume increases from the previous day, there will be a trend where the coin will increase or decrease. The results of the application web page where the application can display tweet data according to keywords and display visualizations, as well as display the price and history of the cryptocurrency market according to the available input.
Monitoring Kadar Amonia dalam Akuarium Ikan Menggunakan Metode Verifikasi Warna RGB dengan Memanfaatkan ESP32-CAM Matius Bryant; Stephanus Antonius Ananda
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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The main problem that is often found is poor aquarium water quality during the maintenance period, which can result in an increase of ammonia levels in the aquarium. In this thesis, the specimen that will be used for testing is predatory fish, where the food of this fish is raw meat or live fish whose size is smaller than them. The leftover of this food can increase the production of ammonia in the aquarium water. An increase in ammonia production will result in an increase in the nitrogen cycle as well, where the cycle will produce more nitrogen which results in reduced oxygen in the water. The effect of ammonia on fish can vary from difficulty of breathing, loss of appetite, and over time it will cause death in fish. In this thesis, an IoT(Internet of Things)- based monitoring system for aquarium ammonia levels will be used. This problem has actually been handled in several previous studies, one of which was researched by Talanta, D. E. entitled "Arduino-based Design and Build of Arduino-based Ammonia & PH Water Control in Fish Cultivation", but was considered less successful because Talanta only used an MQ-155 sensor to detect ammonia gas. While in this thesis the automation system is used to monitor & control ammonia levels in the aquarium by using the Camera function of ESP32-CAM to take pictures of the test kit and will then be processed with Python by utilizing the OpenCV library to verify RGB color in order to determine the ammonia level in the water. Based on the results of the system testing that has been carried out, it can be concluded that the ammonia detection accuracy of this system is 66.7%, this is because the measurement range of the water test strips being used is quite large, ranging from 0-6 PPM. So, it cannot produce small results such as 0.25 PPM, because 0.25 PPM levels will be directly classified to 0.5 PPM levels. In addition, it can also be concluded from the experiment conducted for 4 days that the automatic water change system in this thesis has an accuracy rate of 87.5% (8 trials with 1 failure) in maintaining water parameters safe for fish.