Jurnal Nasional Teknik Elektro dan Teknologi Informasi

Prototype of Internet of Things-Based Automatic Hydroponic System

2025-01-21T13:29:02+07:00

The increase in food needs, including vegetables and fruits, corresponds with population growth. However, agricultural land is increasingly declining due to land conversion. This decline can threaten national food security. Utilizing hydroponic systems for plant cultivation is one of the efforts to adapt to land reduction, land degradations, and adverse impacts of global climate change. Unfortunately, hydroponic cultivation requires constant monitoring of plant nutrition. This research aimed to create an automatic hydroponic system that controlled the process of regulating nutrients to save growers time and energy. Through Internet of things (IoT) technology, automatic hydroponic cultivation can monitor plant life, temperature, humidity, water level in reservoirs, total dissolved solids (TDS), and pH of nutrient solutions. In addition, it can visually monitor plants through Android applications. The hydroponic system used for planting was the nutrient film technique, and the plant cultivated was lettuce. The system consisted of TDS sensors to measure TDS, analog pH sensors to measure the pH, the HC-SR04 ultrasonic sensors to measure the water level in the reservoir, DHT11 sensors, ESP32 microcontrollers, and ESP32-CAM to monitor plant growth remotely. Based on system testing results, the average of TDS increased from 600 ppm in the first week to 900 ppm in the fifth week, the average pH was 6.19, and the average water level in the reservoir was 20.89 cm. All test result parameters are at the designed values.

Digitized Cursive Handwriting for Determining FMS in Early School-Age Children

2025-01-21T13:29:03+07:00

Assessing fine motor skills (FMS) in early school-age children is crucial for insights into their school readiness. In many countries, including Indonesia, teachers assess FMS by observing handwriting, often with the aid of an educational psychologist. However, this approach can be subjective and prone to observer bias. This study aimed to classify children’s FMS based on their cursive writing abilities using a digitizer to capture data. The system recorded data in real-time as children wrote in cursive, capturing the stylus’s relative position on the digitizer board (including x, y, and z positions), and pressure values, which served as features in the classification process. The study involved 40 1st and 2nd-grade students from various elementary schools. The data recording process generated substantial raw datasets. The random forest algorithm, renowned for its effectiveness in analyzing large datasets, was employed for classification. The results demonstrated this method’s efficacy in identifying FMS, achieving an accuracy rate of approximately 97.3%. This study concludes that integrating a digitizer with the random forest classification method provides a reliable and objective approach to assessing FMS in children, reducing observer bias, and ensuring precise results. In the long term, this approach can significantly enhance the accuracy of FMS assessments, enabling better-targeted interventions and support for children in need.