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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika (JUTIF) Jurnal Abdi Masyarakat Indonesia
Dian Pertiwi, Kharisma Monika
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Public Health

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Design and Implementation of an IoT-Based Smart Drip Irrigation System Using Takagi-Sugeno Fuzzy Logic for Melon Cultivation Nur Alif, Muhammad Sufi; Dian Pertiwi, Kharisma Monika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11424

Abstract

The melon plant (Cucumis melo L.), a species of the Cucurbitaceae family, requires precise water management to support optimal growth. This study developed an Internet of Things (IoT)-based innovative irrigation system employing Takagi-Sugeno fuzzy logic to regulate water supply for melon cultivation in a greenhouse. The system integrates a capacitive soil moisture sensor and a DS18B20 temperature sensor, both connected to an ESP8266 microcontroller, which controls a solenoid valve used in the drip irrigation method. Sensor data are transmitted in real-time to Firebase Realtime Database (cloud platform) for monitoring through a web-based interface. The solenoid valve opening duration ranges from 0 to 720 seconds per irrigation session, dynamically adjusted according to soil moisture and temperature inputs. Experimental results demonstrate that the proposed system effectively maintains soil moisture within the optimal range of 60%–80%. However, plant growth evaluation indicates that the system has not fully promoted healthy development, particularly in plant height and leaf width, likely due to additional factors such as soil conditions, humidity, and nutrient availability. Despite these limitations, the proposed smart irrigation system shows strong potential for further refinement to enhance water efficiency and support sustainable melon cultivation.
Pemberdayaan UMKM Kuliner melalui Pengembangan dan Pendampingan Website Menu Digital pada Kedai Sri Rejeki 888 di Sidoarjo, Jawa Timur Dian Pertiwi, Kharisma Monika; Prathama Hartawan Putra, Mahendra; Aryasatya Fairuz Firjatullah, Daniswara
Jurnal Abdi Masyarakat Indonesia Vol 6 No 1 (2026): JAMSI - Januari 2026
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2531

Abstract

Kedai Sri Rejeki 888 sebagai UMKM kuliner di Sidoarjo, Jawa Timur, menghadapi kendala operasional konvensional seperti keterbatasan visualisasi menu (hanya 7 item), update manual jarang, dan kunjungan harian rendah (23 orang) akibat minimnya pemasaran digital. Kegiatan pengabdian masyarakat ini memberdayakan mitra melalui pengembangan website menu digital berbasis Next.js dengan fitur pengelolaan mandiri, integrasi Google Maps, dan pendampingan pelatihan menggunakan pendekatan Agile partisipatif. Hasil menunjukkan peningkatan jumlah menu menjadi 19 item, frekuensi update rutin mandiri, kunjungan harian 39 orang, serta kepuasan pelanggan dari 78% menjadi 93%. Dampaknya, mitra berdaya saing lebih tinggi dengan efisiensi operasional dan visibilitas pasar yang ditingkatkan.  
Optimizing Automatic Irrigation Duration for Grapevines in Greenhouses Using Multiple Linear Regression Analysis Dian Pertiwi, Kharisma Monika; Alfarabi, Trenady
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5289

Abstract

Greenhouses offer a controllable microclimate for high‑value horticulture, yet manual irrigation and single‑sensor threshold rules remain inefficient and error‑prone for grapevine cultivation in tropical conditions. This study designs and implements an Internet‑of‑Things (IoT) automatic irrigation system that employs an interpretable multiple linear regression (MLR) model as the decision core, using air temperature and soil moisture—acquired via DHT11 and capacitive soil‑moisture sensors—to estimate irrigation duration in real time. The model is trained on greenhouse measurements and deployed for low‑latency edge inference to actuate valves with duration‑to‑volume conversion, enabling precise and adaptive water delivery. Experimental evaluation shows strong predictive performance (MSE = 0.15, MAPE = 1.44%, R² = 0.98), indicating high accuracy and reliable generalization for operational control. The primary contributions are: (i) a lightweight, explainable regression formulation tailored to tropical grapevines that outperforms single‑parameter baselines; (ii) an end‑to‑end, edge‑deployable IoT pipeline that reduces computational and energy costs while maintaining real‑time autonomy; and (iii) an engineering blueprint that is scalable and maintainable for smallholder contexts. The impact for Informatics/Computer Science lies in demonstrating a practical ML‑on‑the‑edge reference design—combining interpretable modeling, sensor fusion, and actuation—that advances sustainable computing for precision agriculture, improves resource efficiency, and supports robust, replicable deployment of smart‑irrigation systems in data and power‑constrained environments.
Co-Authors Alfarabi, Trenady Aryasatya Fairuz Firjatullah, Daniswara Nur Alif, Muhammad Sufi Prathama Hartawan Putra, Mahendra