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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika dan Rekayasa Perangkat Lunak
Pujianti Wahyuningsih
Universitas Handayani Makassar
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things Muhammad Risal; Pujianti Wahyuningsih; Suwatri Jura; Irmawaty Iskandar; Abdul Jalil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp138-149

Abstract

Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.
Arduino Mega-Based Transporter Robot for Box Handling in a Maze Arena Abdul Jalil; Suwatri Jura; A.Edeth Fuari Anatasya; Mursalim Sawawi; Pujianti Wahyuningsih
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12825

Abstract

The objective of this study was to develop a transporter robot designed to move boxes from one location to another within an Arduino Mega-based maze arena. The problems addressed by this transporter robot include autonomous navigation in the maze area, detection of boxes based on their colors, transferring boxes from one location to another, and climbing inclined surfaces. The method used to control the transporter robot was based on input from the TCS3200 color sensor and ultrasonic sensors, which were employed to detect box objects and navigate the robot. The Arduino Mega was used to process sensor data and control the robot according to the sensor inputs. A servo motor was utilized to actuate the robot arm and gripper, while DC motors were used to drive the wheels of the transporter robot. The results of this study indicate that the transporter robot was able to complete the mission of navigating the maze and transferring box objects to their designated locations, achieving a success rate of up to 70%, with an average mission completion time of 6.8 minutes, while unsuccessful mission attempts required up to 9.6 minutes.
Aquaponic greenhouse agriculture integrated with multi-modal sensors and LED-grow-light IoT-based Pujianti Wahyuningsih; Muhammad Risal; Nining Haerani; Abdul Jalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2254-2264

Abstract

This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.
Klasifikasi Penentuan Kualitas Kayu Jati Berdasarkan Citra Digital Menggunakan Algoritma K-Nearest Neighbour Kamaruddin Kamaruddin; Najirah Umar; Pujianti Wahyuningsih; Firman Sudarsono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The increase in demand for goods made from wood cannot be limited, especially the demand for table furniture, cupboards and so on. Along with development, teak wood production has shifted to superior types of teak wood because the growth period is faster, but this condition means that the quality of teak wood is not like the old type of teak wood. Difficulty in seeing the quality of wood is a problem faced by craftsmen and furniture makers. The aim of this research is to determine the quality of wood species which are divided into 3 class categories, namely class A, class B and class C. To produce a classification of wood quality, researchers use the KNN method by carrying out HSV color segmentation then analyzing the color value of each image pixel based on the tolerance value on HSV color dimensions. The results of this research were using 65 teak wood training data for each class. Testing was carried out using 27 teak wood test data with an accuracy level of 85.19%, precision reaching 85.46%, recall reaching 85.18% and F1 score reaching 85.3%.
Co-Authors A.Edeth Fuari Anatasya Abdul Jalil Firman Sudarsono Haerani, nining Irmawaty Iskandar Kamaruddin Kamaruddin MUHAMMAD RISAL Mursalim Sawawi Najirah Umar Suwatri Jura