Muhammad Siddik Hasibuan’s scientific contributions

The freshness of vegetables is a very important factor in maintaining the quality, taste and nutritional benefits of vegetables. When fresh vegetables are consumed, we can feel the softness, richer taste, and get optimal nutritional benefits. The research methodology used in this research is a quantitative method, the main focus of which involves the use of numerical data, statistical analysis, and quantitative measurements throughout the research process. This research adopts a structured research framework, starting from data collection using the Fresh and Stale Images of Fruits and Vegetables dataset from Kaggle. In this research, the model process that occurs in the CNN algorithm consists of 2 parts, namely feature learning and classification. Data that has undergone feature extraction will be processed in convolution layer 1, after going through the feature learning process in convolutional layer 1, the image data will then be processed again in the max pooling layer which aims to reduce the size of the image data, so that the designed CNN model can understand more details regarding data on fresh and non-fresh vegetables. The feature learning process is carried out in 4 layers, after passing through feature learning the image will be converted into a vector in the flatten layer with the aim that the results of feature learning can be used as input values at the classification stage.

Complexity of the data is a major problem in producing in-depth understanding. So that the help of artificial intelligence is needed, namely Natural Languange Processing (NLP) which is the key to the problem because it can allow us to process text more effectively and increase the opportunity to allow computer interaction with human language to be more natural. Sentiment analysis is one of the main applications in the automation process to find out a person's opinion on a subject based on the text they write, one of which is a comment on cars in Indonesia such as Toyota, Daihatsu, Honda and Suzuki. This research utilizes a quantitative methodology and uses a machine learning model, namely the Naive Bayes algorithm, to classify the text into positive or negative based on the calculation of the probability of words in the text. A corpus is also used as a dictionary/word pattern using InsetLexicon to analyze the sentiment of the text based on keywords with predetermined weights that will be pre-processing first. This was done to assess the performance of Naive Bayes analysis and the effectiveness of InsetLexicon in improving the accuracy of sentiment on text. The research results from Table. 21 that the total value of comments on the highest aspect of all aspects on 4 types of cars and each brand is the aspect of "Comfortable" or comfort around 91% with the acquisition of average Recall, Precision and F1-Score around 0.83, 0.85 and 0.87 and the overall accuracy of the dataset is around 71%.

This study aims to develop a vegetable freshness classification model using Convolutional Neural Networks (CNN). The methodology used includes collecting vegetable image datasets, image preprocessing, training CNN models, and evaluating classification accuracy. The dataset used consists of various types of vegetables with varying levels of freshness. The results of the study show that the developed CNN model is able to classify vegetable freshness with high accuracy, so it can assist in the automation process of determining vegetable freshness in the food industry. Model evaluation is carried out using accuracy metrics and mean squared error (MSE).