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All Journal Jurnal ULTIMA Computing Indonesian Journal of Electrical Engineering and Computer Science
Dareen Kusuma Halim
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Engineering

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The Development of an IoT-based Indoor Air Monitoring System Towards Smart Energy Efficient Classroom Moeljono Widjaja; Dareen Kusuma Halim; Rahmi Andarini
Ultima Computing : Jurnal Sistem Komputer Vol 14 No 1 (2022): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v14i1.2565

Abstract

Indoor air quality has become a crucial issue, specifically during COVID 19 pandemic. The good indoor air quality will lead to occupants’ comfort condition, thus affecting their productivity. Indoor air temperature and relative humidity are two essential components of thermal comfort. This paper presents the development of a temperature and relative humidity monitoring system for the classroom using the Internet of Things (IoT). This system consists of three main components: logger nodes, a gateway logger, and an interconnected cloud server. The logger node (ESP8266 / ESP32 microcontroller and DHT22 sensor) is a device at the edge of the IoT system and is placed at the monitoring location. The logger gateway is built on a Raspberry Pi 4, which serves as an intermediate server. It receives periodic data (temperature and humidity) from the logger nodes through the publish-subscribe MQTT protocol and sends it to the MongoDB Atlas cloud database. The logger gateway saves all received logs into the SQLite database as temporary local storage and then uploads the data to the MongoDB Atlas cloud at a predefined interval. The MongoDB data is then displayed on a monitoring dashboard using MongoDB charts. The logger node with the DHT22 sensor has been adjusted using a linear model and successfully tested to monitor indoor and outdoor air conditions with satisfactory results. The recorded data has also been successfully modeled using the Gaussian Mixture Model and a simple Fuzzy model. These models can capture the dynamic of air condition in the room and predict the state of the cooling system.
Federated learning for scam classification in small Indonesian language dataset: an initial study Michael Chen; Dareen Kusuma Halim
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp325-331

Abstract

Most digital phishing or scam trick users into fraudulent links and is more effective against users with low technology literacy, like in Indonesia. Machine learning is widely used for scam classification, but most require sending the messages to a centralized server. This induces privacy concern as messages might contain private data. Federated learning (FL) was proposed to allow user devices to train models locally without sending data to server. In this work, we examined the use of FL with gated recurrent unit (GRU) model for classifying scam messages in Indonesian language with small dataset. We provided two FL-based baseline models (FedAvg and daisy-chained algorithms) and a dataset for scam classification in Indonesian language. We examined the models based on these performance metrics; precision, recall, F1, selectivity, and balanced accuracy. Despite the performance, we pointed out characteristics of the FL algorithms and the hyperparameters for this use case as pointers towards fine-tuning these baseline models. Overall, the FL model with FedAvg algorithm performed better in all metrics except recall.
Co-Authors Michael Chen Moeljono Widjaja Rahmi Andarini