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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science
Zulfachmi, Zulfachmi
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Computer Science & IT Engineering

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Development of IoT-based Automatic Water Drainage System on Fishing Boat to Improve Operational Efficiency Zulfachmi, Zulfachmi; Zulkipli, Zulkipli; Rahayu, Vita; Saputra, Aggry; As Saidah, Muthiah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6222

Abstract

The profession of fishermen requires a reliable system to remove stagnant water from fishing boats, as manual drainage is time-consuming and inefficient. This study proposes an IoT-based automatic water drainage system without using an inverter or ultrasonic sensor, offering a cost-effective alternative. The system utilizes a water level sensor and a DC water pump, controlled via a smartphone application. The research model used is the Research and Development (R&D) model, through several stages, namely potential and problems, initial data needs, prototype creation, prototype validation, prototype revision, validation, implementation. Problems occur at the prototype stage, problems that must be revised include aspects of wiring, Power Suitability, Water Level Sensor Test, and the configuration of the relay used. The IOT-based automatic water drainage system can function based on the results of white-box testing including Hardware Implementation, Software Implementation, Implementation of Application Usage, and Automatic Drainage System Testing. This is indicated by the results of the Liquid Water Level Sensor Functionality test, DC Water Pump Functionality Test, Solar Panel and Battery Functionality Test, and IOT Functionality Test. IOT-based automatic water discharge systems on fishing boats are more efficient and cost-effective in the long run, although diesel engines offer more reliability under adverse weather conditions or in places with limited access to sunlight.
Systematic literature review of learning model using augmented reality for generation Z in higher education Zulfachmi, Zulfachmi; Rahim, Normala; Rizhan, Wan; Rahayu, Puji; Saputra, Aggry
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1109-1120

Abstract

Higher education is evolving with innovations aimed at enhancing the quality of learning, and one prominent innovation is the integration of augmented reality (AR) technology into the learning process. AR merges real-world and virtual elements in real-time, creating interactive and immersive educational experiences. This technology supports the display and interaction with virtual objects, enhancing engagement and comprehension among students. However, effective integration of AR in higher education faces challenges such as limited technological infrastructure, the need for skilled lecturers, and the adaptation of teaching methods to suit generation Z's learning preferences. Despite their technological proficiency, many educational institutions struggle to optimally implement innovations like AR. This systematic literature review aims to explore and identify an AR-based learning model suitable for generation Z in higher education. Findings suggest that AR technology can significantly enhance learning by offering engaging visualizations and interactive experiences, aligning well with generation Z's characteristics and learning styles. Effective AR implementation requires suitable platforms, such as mobile, desktop, wearable, and projection platforms, each offering unique benefits. By designing AR learning models that cater to generation Z, educational institutions can improve learning outcomes and experiences.
Co-Authors AS SAIDAH, MUTHIAH Puji Rahayu Rahayu, Vita Rahim, Normala Rizhan, Wan Saputra, Aggry Zulkipli Zulkipli