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To detect the maturity level of oil palm Fresh Fruit Bunches (FFB) generally seen from the loose fruit that fell to the ground. This method is always used when harvesting swit coconuts. Even though this method is not always valid, because many factors cause the fruit to fall from the bunch. The manual harvesting process can result in the quality of palm oil being not optimal. For this reason, technology is needed that can ensure the maturity level of oil palm FFB. This study aims to detect the maturity of oil palm FFB based on digital images by applying a deep learning algorithm so that the maturity level can be classified into three categories, namely: raw, ripe, and rotten. The deep learning algorithm was chosen because there have been many studies that have proven its high level of accuracy. This research method starts from; preparation of data, designing architectural models and convolutional neural network parameters, testing models, testing images, and analyzing results. From the results of the study, it was found that the convolutional neural network algorithm can be applied to detect the maturity level of oil palm FFB with an accuracy value of 92% for test data, and 76% for model testing.

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In this digital era, the use of information technology and internet technology cannot be separated from digital services. Starting from product promotion media, recording customer data, determining the amount of revenue from product sales, and optimizing the value of revenue. Sales of digital service products owned by PT. XYZ needs to be evaluated to find out which products are most in demand by customers from each product offering that has been made. Therefore we need a system to calculate revenue from the number of customers who use the product for further promotion. The object of this research focuses on optimizing the value of income at PT. XYZ of the products they market, the results of the object will be used as an evaluation to determine a new strategy in carrying out promotions for products that are less attractive to customers. The data used in this study is customer data for January 2017-December 2021. The method used in this study uses a genetic algorithm to determine the optimization of the revenue value. For the optimization results, the genetic algorithm went well, because it resulted in a smaller comparison of error values ​​compared to values ​​that were not optimized. The error value in January 2019 with a non-optimized value was 35,498.8 and the optimized value got an error value of 32,364.9. The results of this study are used as a sales evaluation to increase promotions on digital services that are less attractive to customers. In addition, the results of the application of this genetic algorithm method can provide a better solution to increase income in the next period.

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With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.