The decision of the Indonesian government to relocate the nation's capital outside Java to the North Penajam Paser Regency has sparked controversy and misinformation on social media platforms. While sentiment analysis studies have been conducted on this topic, no research has yet analyzed the issue of hoaxes related to the relocation of the national capital. This study aims to fill this gap by analyzing hoaxes related to the relocation of the Indonesian national capital on Twitter. The study utilizes data crawling, filtering with Hoax Booster Tools (HBT) ASE, data labeling, preprocessing, and TF-IDF weighting. The data is then classified using Support Vector Machine (SVM) and Random Forest (RF) algorithms, and the results of both algorithms are compared. The study found that 85% of tweets had a positive sentiment and 15% had a negative sentiment. Furthermore, the SVM algorithm outperformed the RF algorithm with an accuracy of 95.24% compared to 86.90%. This study contributes to the understanding of the hoax issues related to the relocation of the Indonesian state capital and provides recommendations for government policies to address community concerns.

In Indonesia, longan fruit is abundantly accessible. The Longan fruit comes in a number of kinds, and they vary in terms of their morphology, including the characteristics of their leaves. The varieties of longan fruit are to be categorized in this study based on the shape of the leaves. The author uses the RGB color extraction function, the Grey Level Co-occurrence Matrix (GLCM), and the Shape feature to get data for each cultivar. The accuracy value is then processed using the Back-Propagation Neural Network (BPNN) technique to determine the accuracy value that will be used as a determinant of the categorization of the Longan leaf image. The eccentricity and metric parameters are key components of the method. The BPNN algorithm demonstrated its usefulness for categorizing various kinds of longan fruit leaves during testing by obtaining an accuracy of 70%.

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