The rise of social media had opened up an easy and fast way to distribute pornographic content through it. Although the negative effects linked to porn consumption are still inconclusive, government had established regulation regarding porn creation, distribution, and ownership. Unfortunately, the regulation is not well run. Porn are freely distributed through social media without any reaction from the authorities. This reseach aims to understand the distribution pattern and to find key players in the distribution of porn in social media using Social Network Analysis (SNA) so that mitigative actions could be made. Result shows that porn were first published by popular ‘Publisher’ accounts, re-shared by other publisher accounts or ‘Retweeters’, and unidirectionally consumed by followers (‘Consumers’). Interpretation and research limitations were discussed.

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Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of unwanted data (noise, erroneous data, or unwanted data) or to study the anomaly phenomenon itself (unusual but interesting). In the absence of an anomaly-labeled dataset, an unsupervised Machine Learning approach can be utilized to detect or label the anomalous data. This research uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to separate between normal and anomalous weather data by considering multiple weather variables. Then, PCA is used to visualize the clusters. The experimental result had demonstrated that DBSCAN is capable of identifying peculiar data points that are deviating from the ‘normal’ data distribution.

The presence of the internet and the rapid growth of social media had given rise to the blossoming of hoax creation and distribution through it. A hoax can cause anxiety and reactivity to its readers and could harm a certain party. Thereby, it is important to detect and report hoaxes to stop its spreading as soon as possible. This research aims to utilize the K Nearest Neighbor (KNN) classification algorithm to detect whether a piece of news is a hoax or not. Experiments were done by using 74 hoaxes compiled from Indonesian hoax-debunking community websites and were being compared against 74 real news from various reputable news websites in Indonesia. The result showed that the model could give detection/classification accuracy up to 83.6% and that the model is prone to false positives detections. The characteristics of the resulted model and further research directions are then discussed.

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.