Tuberculosis (TB) is one of the major global health issues, especially in developing countries. Although newly diagnosed TB patients can recover with a high cure rate, there are still many TB patients dying due to late diagnosis. A common way to diagnose TB is through an examination of the chest X-ray image (CXR) of the patient. Due to its high sensitivity, this method is preferred to others, and is vital for screening and triage for TB patients. Unfortunately, this examination requires an experienced radiologist to read the CXR image. In developing countries like Indonesia, the number of radiologists is still lacking and not well distributed across regions. In order to expedite the diagnosis, an automated system for detecting TB from CXR images may be able to help physicians examine more CXR images faster. In this paper, we proposed texture analysis of CXR images of Indonesian patients. The images were divided into local patches and represented using a variety of feature combinations. We then attempted to classify these patches into as normal or TB lesion patches using Support Vector Machine (SVM). Our results show that the combination of Principal Component Analysis (PCA) with GLCM and Hogeweg texture features obtain the best overall results compared to the baseline, with an accuracy of 91.2%, sensitivity of 97.1%, and specificity of 87.2%.

COVID-19 (short for coronavirus disease 2019) is an emerging infectious disease that has had a tremendous impact on our daily lives. Globally, there have been over 95 million cases of COVID-19 and 2 million deaths across 191 countries and regions. The rapid spread and severity of COVID-19 call for a monitoring dashboard that can be developed quickly in an adaptable manner. Wikidata is a free, collaborative knowledge graph, collecting structured data about various themes, including that of COVID-19. We present COVIWD, a COVID-19 Wikidata dashboard, which provides a one-stop information/visualization service for topics related to COVID-19, ranging from symptoms and risk factors to comparison of cases and deaths among countries. The dashboard is one of the first that leverages open knowledge graph technologies, namely, RDF (for data modeling) and SPARQL (for querying), to give a live, concise snapshot of the COVID-19 pandemic. The use of both RDF and SPARQL enables rapid and flexible application development. COVIWD is available at http://coviwd.org.

Chatbot, as a virtual assistant used by a company, can provide benefits for its users. Users can speak directly to the chatbot via a short message, then the system spontaneously identifies the intent of the message and responds with relevant action. Unfortunately, the scope of chatbot knowledge is limited in handling messages by an increasingly varied user. The main impact of this variation is a change in the composition of the intent label. This research will focus on two things. First, topic modeling to find intents from user messages that do not include in any intent labels. Second, we apply topic modeling on the labeled dataset to reorganize existing intents by analyzing the output of the topic model. After being analyzed, two possible changing intent compositions are merging and splitting intents because there is noise during the actual dataset annotation process. The topic modeling used in this research consists of LDA as the baseline, state-of-the-art model Top2Vec, and BERTopic. The labeled data that we used is derived from one of the e-commerce in Indonesia and four public datasets. We evaluate the topic models by using metrics of coherence, topic diversity, and topic quality. The results showed that the BERTopic and Top2Vec topic models produced better evaluation scores than the LDA topic models for the two research focuses. There are also intent splitting and intent merging found by proportion threshold analysis.

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