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Journal : International Journal Software Engineering and Computer Science (IJSECS)

Internet of Things (IoT) Integration for Real-Time Monitoring in Smart Cities Fajri, T. Irfan; Rahayu, Novi; Wasiran; Budiman, Yusuf Unggul; Hasti, Novrini
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3465

Abstract

The advancement of Internet of Things (IoT) technology has opened great opportunities for the implementation of real-time monitoring systems in supporting smart city management. This research aims to develop an IoT integration model that can monitor various urban aspects, such as traffic management, energy consumption, waste management, and air quality, in an efficient and integrated manner. The model is designed to collect, process, and analyze data from various IoT sensors scattered in urban areas, with a focus on delivering information in an integrated manner. urban areas, with a focus on delivering real-time information to the government and the public. The research methodology includes the development of development of an IoT-based system prototype that integrates hardware and hardware and software with the support of cloud computing architecture for data management. data management.
Internet of Things (IoT) Integration for Real-Time Monitoring in Smart Cities T. Irfan Fajri; Novi Rahayu; Wasiran; Yusuf Unggul Budiman; Novrini Hasti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3465

Abstract

The advancement of Internet of Things (IoT) technology has opened great opportunities for the implementation of real-time monitoring systems in supporting smart city management. This research aims to develop an IoT integration model that can monitor various urban aspects, such as traffic management, energy consumption, waste management, and air quality, in an efficient and integrated manner. The model is designed to collect, process, and analyze data from various IoT sensors scattered in urban areas, with a focus on delivering information in an integrated manner. urban areas, with a focus on delivering real-time information to the government and the public. The research methodology includes the development of development of an IoT-based system prototype that integrates hardware and hardware and software with the support of cloud computing architecture for data management. data management.
Analysis of Household Electricity Consumption Patterns Using K-Nearest Neighbor (KNN) Method Cut Susan Octiva; Sultan Hady; Dedy Irwan; T. Irfan Fajri; Novrini Hasti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3877

Abstract

The increasing demand for electricity in the household sector poses significant challenges to energy efficiency initiatives and environmental conservation efforts. Examining electricity usage patterns offers a pathway to uncover key determinants that influence consumption levels while formulating more effective strategies for energy management. This study attempts to evaluate electricity consumption patterns in the household sector using the K-Nearest Neighbor (KNN) algorithm. This approach is used to categorize consumption data based on attribute similarities among household units. The findings are expected to encourage more rational electricity usage practices, thereby reducing energy inefficiencies and strengthening efforts to conserve natural resources. Furthermore, the analysis aims to provide actionable insights for households to adopt sustainable habits and for policymakers to design targeted interventions that address peak demand periods and promote the use of energy-efficient technologies. By identifying specific behavioral and technological factors that contribute to high consumption, the results can serve as a basis for tailored programs aimed at minimizing waste and promoting long-term environmental management.