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After facing the COVID-19 pandemic, national and local governments in Indonesia realized a gap in the distribution of health care and human health practitioners. This research proposes two unsupervised learning methods, K-Means and Fuzzy C-Means (FCM), for clustering a list of hospital data in Jakarta, Indonesia, which contains information about the number of its human health resources. The datasets used in this study were obtained from the website the Ministry of the Health Republic of Indonesia provided through the content scraping method. The result shows that implementing K-Means and FCM clustering results in the same number of clusters. Nevertheless, both results have different areas and proportions that can be observed by three distance metrics, such as Hamming, Euclidean, and Manhattan distance. By using the clustering result using the K-Means algorithm, the hospital list was separated into three clusters with a proportion of 84.82%, 14.66%, and 0.52% for clusters 0, 1, and 2, respectively. Meanwhile, using the FCM algorithm, the hospital list was separated into three clusters with a proportion of 17.80%, 73.82%, and 8.38% for clusters 0, 1, and 2, respectively. To the best of our knowledge, this is the first discussion of clustering healthcare facilities in Indonesia, especially hospitals, based on their health professionals., (© 2022 The Author(s). Published by Elsevier B.V.)

Recognizing emotions is vital in communication. Emotions convey additional meanings to the communication process. Nowadays, people can communicate their emotions on many platforms; one is the product review. Product reviews in the online platform are an important element that affects customers' buying decisions. Hence, it is essential to recognize emotions from the product reviews. Emotions recognition from the product reviews can be done automatically using a machine or deep learning algorithm. Dataset can be considered as the fuel to model the recognizer. However, only a limited dataset exists in recognizing emotions from the product reviews, particularly in a local language. This research contributes to the dataset collection of 5400 product reviews in Indonesian. It was carefully curated from various (29) product categories, annotated with five emotions, and verified by an expert in clinical psychology. The dataset supports an innovative process to build automatic emotion classification on product reviews., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

An opinion is a type of judgment or a person's point of view about something. Twitter is a popular social media platform that includes a lot of public opinions and would be a suitable location to mine data in text form. With its vast population and active Twitter user base, Indonesia has the potential to be a source of opinion data mining. An opinion may be processed and result in the form of a person's emotional response towards something, such as whether they like, hate, love, or are happy about it. Upon that basis, a dataset of Indonesian-language tweets conveying public opinion on various topics was formed. The fact that there are only limited publicly available emotions text datasets in the Indonesian language supports our basis in this research to form our emotion dataset. The gathered data was cleaned and normalized in the pre-processing stage to the necessary form for study on the task of classifying emotions in Indonesian. The data collected is annotated with six emotional labels: anger, fear, joy, love, sad, and neutral., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)