Chriswanda, Gregory V. (2025) Perancangan Prototipe Sistem Monitoring Berbasis IoT Atas Pertumbuhan Mikroalga Air Tawar Yang Dipaparkan Pada Arus Listrik Dc: Sebuah Studi Eksplorasi Awal. Other thesis, Institut Teknologi Sepuluh November.
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Abstract
Penelitian ini dilatarbelakangi oleh potensi stimulasi listrik dalam memengaruhi lingkungan kultur mikroalga, yang masih jarang dieksplorasi terutama pada konteks air tawar. Paparan arus searah (DC) diyakini dapat menyebabkan perubahan fisikokimia seperti migrasi ion dan pembentukan spesies reaktif, sehingga diperlukan sistem pemantauan untuk mengamati dampaknya terhadap pertumbuhan mikroalga. Untuk itu, dirancang sebuah prototipe sistem monitoring berbasis IoT yang memadukan akuisisi data sensor (tegangan dan arus) serta analisis pertumbuhan mikroalga melalui citra RGB. Metode pengolahan citra dikembangkan menggunakan pemisahan saluran hijau dan klasifikasi intensitas piksel untuk menilai kehijauan. Seluruh data disimpan otomatis melalui koneksi daring. Penelitian ini bersifat eksploratif dengan fokus pada perancangan sistem, bukan pada pembuktian biologis. Hasil menunjukkan bahwa sistem berhasil merekam perubahan intensitas hijau seiring waktu serta mendeteksi variabilitas arus akibat perubahan komposisi media, termasuk kemungkinan efek dari pupuk cair. Tantangan teknis seperti kegagalan kamera, deteksi objek non-alga, dan fluktuasi lingkungan masih menjadi perhatian untuk pengembangan lebih lanjut.
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This research is motivated by the potential of electrical stimulation to affect the culture environment of microalgae, a topic that remains underexplored, especially in freshwater contexts. Exposure to direct current (DC) is believed to induce physicochemical changes such as ion migration and the formation of reactive species, making it important to develop a monitoring system to observe its effects on microalgae growth. To address this, a prototype IoT-based monitoring system was designed, combining sensor data acquisition (voltage and current) with image-based analysis of microalgae growth using RGB imaging. The image processing method utilizes green channel separation and pixel intensity classification to estimate greenness. All data are stored online automatically. This is an exploratory study focused on system design rather than biological validation. Results show that the system successfully recorded green intensity changes over time and detected current variability in response to shifts in the culture medium, including the addition of liquid fertilizer. Technical challenges such as camera failure, non-algae object detection, and environmental fluctuations remain areas for future improvement.
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