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The quality of boiler feedwater is a crucial factor in maintaining the operational reliability of ship machinery systems against the risks of corrosion and scale formation. This research focuses on the design and development of a real-time monitoring system based on the Internet of Things (IoT) to automatically determine water quality. The hardware development utilizes a NodeMCU ESP32 microcontroller that integrates four main sensors: a pH sensor, a DS18B20 temperature sensor, a Total Dissolved Solids (TDS) sensor, and a turbidity sensor. The methodology applied is an experimental systems engineering model, encompassing circuit design, software programming, and the implementation of simple artificial intelligence. The automatic decision-making process in this tool is designed using zero-order Sugeno fuzzy logic with input parameters of turbidity, salinity, and acidity level (pH). The obtained data is transmitted wirelessly to a Firebase database and visualized through a 0.96-inch OLED display. Functional tests on seven fluid characteristics including pure water, hard water, and muddy water show that this prototype can accurately classify water quality into Good, Fair, and Poor categories. The main contribution of this design is the provision of a preventive, efficient, and applicable boiler maintenance solution for the modern shipping industry.

The implementation of smoking prohibition policies in Islamic boarding schools continues to depend largely on manual monitoring methods, which often face challenges related to consistency and supervision range. This study aims to design an Internet of Things (IoT)-based cigarette smoke detection system as an alternative monitoring approach that is more effective, measurable, and sustainable. The system design combines an MQ-2 gas sensor with a NodeMCU ESP8266 microcontroller programmed through the Arduino IDE platform. When smoke levels detected by the sensor exceed the predetermined limit, the system automatically triggers a buzzer and LED as warning indicators while simultaneously sending monitoring data to cloud-based platforms such as Firebase or ThingSpeak for real-time observation through web interfaces. The research outputs include a comprehensive system design consisting of system architecture, electronic circuit schematics, flowcharts, and pseudocode that are systematically arranged to support future prototype development and implementation. Through this design, the proposed system is expected to provide an initial technological solution that can enhance the effectiveness of monitoring and enforcing smoke-free regulations within Islamic boarding school environments.

The increasing popularity of futsal in Jayapura City has created a growing demand for efficient access to information regarding available sports facilities. However, the lack of an integrated digital platform leads to inefficiencies in locating and booking futsal fields, often forcing users to rely on manual communication or generic navigation services that lack real-time data. This study aims to develop an Android-based application that optimizes the search for the nearest futsal field using the Greedy Algorithm (GA). The system integrates the Google Maps API, the Firebase Realtime Database, and Kotlin/Java to provide real-time field availability, route visualization, and online booking features. The research employed the Waterfall development model, with data collected through observation, interviews, and a literature review, involving 19 futsal fields in Jayapura City. System testing was conducted using Black-Box Testing and User Acceptance Testing (UAT). The results show that implementing the Greedy Algorithm increased route efficiency by 22%, reduced search time by 55%, and achieved 93.2% user satisfaction. All core functions passed functionality testing without errors. This study contributes both practically and theoretically by demonstrating the potential of heuristic-based optimization in sports facility management and promoting smart-city digitalization in urban sports infrastructure.