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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Applied Engineering and Technological Science (JAETS)
Eka Sabna
Universitas Hang Tuah Pekanbaru
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Automatic Chili Plant Watering Based On Internet Of Things (IoT) Yuda Irawan; Eka Sabna; Ahmad Fauzan Azim; Refni Wahyuni; Naima Belarbi; Mbunwe Muncho Josephine
Journal of Applied Engineering and Technological Science (JAETS) Vol. 3 No. 2 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.987 KB) | DOI: 10.37385/jaets.v3i2.532

Abstract

Watering plants that are carried out by farmers on the land of thieves is still manual using human power, manual watering what is often used is to water with water through a water hose then the end of the hose is fitted with a water rotating device that is useful to rotate water so that it can reach many plants. To facilitate the watering of plants when farmers leave the city on agricultural land and know the level of soil moisture periodically, an automatic watering device based on IoT notification is made using the arduino Uno mikrocontroler. The whole tool is divided into several parts which consist of smartphone, esp, arduino uno revision three microcontroler, soil moisture,relay module, and pump. This tool works when the esp module connects to the internet, from esp then to siol moisture sensor if the level of moisture in the soil is detected below 70%, then provide information to the arduiono microcontroler to provide information on the humidity level to the LCD and the website www.pemuri.unaux.com as well as watering orders for plants. From the microcontroler then to the relay module to turn on the water pump. After the pump is running, the automation of the soil mositure sensor re-detects the soil moisture level, if it is above 70% then gives it again to the arduino microcontroler to give the command to turn off the pump. The results of the research show that esp can communicate well with arduino uno when it’s connected to the internet.
A Hybrid Approach of Factor Analysis and Decision Tree for Epilepsy Onset Prediction Using Public Datasets Eka Sabna; Des Suryani; Oktavia Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7253

Abstract

Early prediction of epilepsy onset is crucial for supporting timely clinical intervention and reducing seizure-related complications. However, the high dimensionality and complexity of Electroencephalogram (EEG) and clinical data remain major challenges for conventional predictive models. This study proposes an interpretable hybrid framework integrating Exploratory Factor Analysis (EFA) and Decision Tree classification for early epilepsy onset prediction using public datasets. EFA was employed to reduce 15 observed variables into five clinically meaningful latent factors, which were subsequently used as inputs for the Decision Tree model. . According to experimental data, the suggested framework demonstrated steady classification performance with an accuracy of 88.14% and specificity of 88.73%, while its efficacy in identifying epilepsy beginning cases for early screening is highlighted by its sensitivity of 80.77%.. The latent factor representing clinical neurological abnormalities was identified as the most influential predictor in the classification process. Compared with conventional black-box machine learning approaches, the proposed model provides transparent decision rules and clinically meaningful interpretation while maintaining reliable predictive capability. All things considered, the suggested architecture is a viable path for creating clinically interpretable prediction models that facilitate transparent and trustworthy early epilepsy screening.
Co-Authors Ahmad Fauzan Azim Des Suryani Mbunwe Muncho Josephine Naima Belarbi Oktavia Dewi Refni Wahyuni Yuda Irawan