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User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

Data Processing PT. Daya Cipta Karya Sempurna regarding the entry and exit of goods at PT. Daya Cipta Karya Sempurna still uses a semi-manual system where when the ordered goods arrive, the items will be recorded in the warehouse using Microsoft Excel. Likewise with the items that come out are also recorded in the warehouse using Microsoft Excel. In addition, this information only exists in the warehouse so that when other parts or want to know the inventory of existing goods, other parts cannot access it directly. The section must ask the warehouse to find the state of the desired supply and must wait to get the answer. As a solution to the problems above, it takes an inventory system at PT. Daya Cipta Karya Sempurna Power that can be used to manage goods group data, goods data, user data, incoming goods data and outgoing goods data as well as display reports

Hydroponics are now increasingly in demand, should be checked regularly so that hydroponic plants thrive. Likewise, in Siliwangi Indah Hydroponics Semarang, which still uses manual methods, namely the absence of a device that detects nutrient water temperature and pH automatically and the absence of tools that can normalize nutrients automatically. Arduino Uno R3 can be used to detect temperature automatically with a temperature sensor DS18B20, and detect pH with a pH sensor. Then the sensor readings are sent to the farmer's cellphone with SMS Gateway. The sensor readings are also used to divert the flow of nutrients from the spare reservoir, so that the nutrients remain normal. So this tool can automatically measure the nutrient water temperature and pH and replace the main nutrients to the reserve nutrients. And the assessment obtained from experts on this tool gets a value of 3.6 and from the expert gets a value of 3.7. Hydroponic nutrition in this study is maintained normally and farmers always know the nutritional state. So that the hydroponic plants thrive and have maximum yields.