The high flow of information from online media in Indonesia makes it difficult for manual analysis to identify emerging themes and sentiments. News headlines, as the first element seen by the public, play a crucial role in shaping opinion, but their massive volume and diverse themes make it difficult for manual analysis to identify topics and their underlying sentiments. To address this challenge, this study analyzed 30,329 news headlines from the online news portal detik.com for the entire year 2024. A quantitative Natural Language Processing (NLP) framework was applied, consisting of data collection through web scraping, text preprocessing, transformer-based topic modeling using BERTopic, sentiment classification using IndoBERT, and a topic sentiment intersection analysis. Preprocessing included case folding, text cleaning, normalization of informal words, and tokenization. For lexicon-based labeling, stopword removal and stemming were applied, while transformer-based models utilized minimally processed text to preserve contextual information. Topic modeling was performed using BERTopic, while sentiment classification (positive, negative, and neutral) used the IndoBERT model. The main objective of this study was to evaluate the combined performance of the two models in mapping dominant issues and the sentiments contained in media reports. The results showed that BERTopic successfully identified 366 topics. An evaluation of the 10 most dominant topics yielded a coherence score of 0.5145, indicating a relevant topic clustering. The IndoBERT demonstrated high agreement with lexicon-generated sentiment labels, with an accuracy of 94.78%, a precision of 95.04%, a recall of 94.79%, and an F1-score of 94.81%. These findings confirm that the combination of transformer-based models is effective for in-depth analysis of discourse in Indonesian-language political news headlines from a major Indonesian online news portal (detik.com).

Indonesian Sign Language (BISINDO) is one of the visual-based alternative languages used by people with hearing impairments. There are hundreds of thousands of Indonesian vocabularies that sign language gestures can represent. However, because the number of deaf people in Indonesia is only seven million or 3% of the population, sign language has become unfamiliar and challenging for some normal or laypeople to understand. This study aims to classify and detect gestures in sign language vocabulary directly based on mobile. Classification learning techniques are needed to recognize variations in gestures, such as machine learning with supervised learning techniques. The development of this research uses the convolutional neural network method with the help of techniques from the single shot detector architecture as the object of detection and the MobileNet architecture for classification. The object is 32 gestural vocabularies from the lyrics of the song 'Bidadari Tak Bersayap' with a dataset of 17,600 images. Then the images are divided into two parts of the model based on the nature of the biased and non-biased data, amounting to 8 and 24 classes, respectively. The research results in a biased model prediction of 15 out of 16, while a non-biased model of 36 out of 48 correct predictions with a total accuracy of real-time based testing on mobile of 93.75% and 75%, respectively.

The clustering application can be used to develop a variety of tourism potential. Currently, halal tourism is a national income that increases every year and is a favorite for Indonesia. The development of halal tourism is supported by a majority population Muslim and as a halal tourist destination in the world. The objective of this study is to investigate the number of clustering with partitioning approach i.e. K-Means (KM) with two simulation scenarios. The characteristics similarity of this method refers to 11 indicators in 2017 Global Muslim Travel Index (GMTI). The output of this study is to display the information in the form of a map and make it easier for the public to determine which halal tourism destinations are high, medium, and low potential.