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All Journal ComEngApp : Computer Engineering and Applications Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Health Information : Jurnal Penelitian
Prasetya, Nyoman Wira
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Computer Science & IT Dentistry Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Automated Continuous IoT-Based Monitoring System for Vaname Shrimp Cultivation Management Syauqy, Dahnial; Hanggara, Buce Trias; Purnomo, Welly; Putra, Widhy Hayuhardhika Nugraha; Prasetya, Nyoman Wira
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Shrimp cultivation in Indonesia has been increasing since the introduction of white leg shrimp or often known as vaname (Penaeus vannamei) from the South Pacific waters. The use of a cultivation model with a circular pond with a diameter of 10 meters has begun to attract shrimp farmers in the northern coastal areas of Java, including Tuban Regency. There are several water quality parameters that affects survival rate such as Dissolved Oxygen (DO), Temperature, and Total Dissolved Solids (TDS). Shrimp pond farmers in Tuban Regency have used digital measuring tools to monitor the environmental conditions. However, these measurements cannot be carried out continuously for 24 hours. This often causes delays in identifying problems that occur in ponds and eventually impacts on reducing biomass weight, resulting in not achieving harvest targets. In this study, a continuous monitoring system for water quality management was designed and implemented. The system consists of an IoT-based water quality monitoring device combined with a Shrimp Aquaculture Management Information System. Based on the system that has been built, it is found that the system has been able to acquire all sensor parameters and send them to the server. The results of calibration and prediction using linear regression show that the average data reading error is achieving 14% for DO sensors, and 1% each for temperature and TDS sensors. The aggregated data is presented in tabular and graphic formats so that daily monitoring and predictions can be carried out in ponds.
Pengembangan Aplikasi Deteksi Kecemasan Kesehatan dan Intervensi: Pengembangan Aplikasi Deteksi Kecemasan Kesehatan dan Intervensi Wulan Sari, Ni Made Ayu; Ryandini, Felicia Risca; Addini, Ragil Aidil Fitriasari; Prasetya, Nyoman Wira
Health Information : Jurnal Penelitian Vol 17 No 2 (2025): Mei-Agustus
Publisher : Poltekkes Kemenkes Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36990/hijp.v17i2.1679

Abstract

Ringkasan: Latar belakang: Kecemasan kesehatan merupakan masalah psikologis yang sering timbul pada penderita penyakit kronis, terutama usia lanjut. Penggunaan kuisioner konvensional Health Anxiety Inventory (HAI) membutuhkan kertas berlembar-lembar untuk 18 item pertanyaan dan memerlukan waktu lama dalam pengolahan data skoring. Tujuan: Mengembangkan aplikasi deteksi dan intervensi kecemasan kesehatan menggunakan Software Development Life Cycle (SDLC) untuk mempermudah pengukuran dan pengolahan data. Metode: Penelitian pengembangan menggunakan model SDLC dengan tahapan perencanaan, analisis, desain, pengembangan, implementasi, pengujian, dan pemeliharaan. Kuisioner HAI tervalidasi (Cronbach's alpha = 0,936) diintegrasikan dalam aplikasi dan diuji pada 23 responden di Balai Kelurahan dan Laboratorium Keperawatan Maret-Desember 2024. Hasil: Pengujian fungsionalitas menunjukkan semua komponen berfungsi baik, termasuk login, informed consent, pengisian HAI, terapi menulis dan membaca, serta download hasil. Evaluasi pengguna menunjukkan 52,2% responden sangat paham kemudahan aplikasi. Simpulan: Aplikasi berhasil dikembangkan dan dapat mengurangi penggunaan kertas, mempercepat pengolahan data, serta menyediakan database kecemasan kesehatan untuk mendukung pengambilan keputusan klinis. Saran: Penelitian selanjutnya dapat mengembangkan aplikasi dengan menambah fitur penanganan kecemasan di rumah.
Design of Water Monitoring System in Aquaponics Based on Arduino Nano and Raspberry Pi Prasetya, Nyoman Wira; Imansyah Harahap, Arya Rizky; Aulady, Fadhli; Wulandari, Inayah
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 1 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i1.23005

Abstract

The aquaponic system is an agricultural technology that can provide a lot of results in a limited space by recirculating water and nutrients from the symbiosis created between fish farming and hydroponic plant cultivation. To maximize the nutritional needs of water that will be supplied to plants and maintain good water conditions for fish life, it is necessary to check several parameters that can be measured in water periodically in fishponds in an aquaponic system so that farmers can provide appropriate actions in managing the system. aquaponic farming. Based on this background, a system was designed using the Internet of Things concept that can monitor water conditions in aquaponic fishponds, store water parameter data in a database, then data can be monitored via the Website. The system is designed using the Arduino Nano microcontroller board as a data processor which is equipped with the ESP8266-07 wifi module which is connected via a WiFi Local Area Network (LAN) network and connected to the Raspberry Pi 3 Model B as a gateway to the database using the MQ Telemetry Transport Protocol (MQTT). The water parameters that are measured in the designed system are: water temperature, water turbidity, dissolved solids, dissolved oxygen, and water acidity (pH). The results of the designed system show that this system can run as expected so that it can facilitate monitoring of water in aquaponic farming systems
A Robust Framework for Dissolved Oxygen Forecasting in Precision Aquaculture: A LightGBM Approach with Advanced Feature Engineering Prasetya, Nyoman Wira; Harianto, Richard Wijaya
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37617

Abstract

Accurate prediction of necessary water quality parameters such as Dissolved Oxygen (DO) is very critical in precision aquaculture and is essential for performance-based decision-making. This thesis fills the gap between reactive monitoring and predictive intelligence through the construction of a solid machine learning infrastructure. We convert high frequency multivariate time series data into a supervised learning problem by an advanced feature engineering process that generates temporal predictions including lag features and rolling window statistics. A Light Gradient Boosting machine (LightGBM) algorithm trained using the above-mentioned engineered dataset has an extreme predictive power. Results of single-variable interpretation analysis showed that short term data, especially the 5-minute rolling statistics of DO and turbidity variability, are the main driving factors for the model prediction. This research confirms that a feature-engineered LightGBM approach is a computationally efficient, but highly accurate approach to supporting the development of early warning systems in modern aquaculture as a computationally scalable approach.
Co-Authors Addini, Ragil Aidil Fitriasari Aulady, Fadhli Dahnial Syauqy Hanggara, Buce Trias Harianto, Richard Wijaya Imansyah Harahap, Arya Rizky Ni Made Ayu Wulansari Purnomo, Welly Ryandini, Felicia Risca Widhy Hayuhardhika Nugraha Putra Wulandari, Inayah