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All Journal Jurnal Mantik Penusa SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknik Informatika UNIKA Santo Thomas JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) PITIN Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Prosiding SeNTIK STI&K INOVTEK Polbeng - Seri Informatika
Barus, Ertina Sabarita
Universitas Prima Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Real-Time Pest Monitoring System for Chili Plants Based on Internet of Things Using Image Sensors and Soil Moisture Sensors Hari Haran; Jose Given Amanro Silaen; Muhammad Fakhruddin Alrazi; Ertina Sabarita Barus; Muhammad akbar Raihansyah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8780

Abstract

Pest infestation is one of the main causes of declining productivity in chili (Capsicum annuum L.) cultivation, while conventional monitoring still relies on manual visual inspection that is subjective, time-consuming, and prone to delayed detection. This study aims to design and implement an Internet of Things (IoT) based real-time pest monitoring system that integrates an image sensor and a soil moisture sensor on chili plants. The system is built around an ESP32 microcontroller and an ESP32-CAM module that captures leaf images, which are analyzed on a server using the You Only Look Once version 8 (YOLOv8) object detection model, while a capacitive soil moisture sensor monitors the growing-media condition. The detection model was trained on a Pest Detection dataset from Roboflow Universe consisting of 38,449 images across 28 pest classes, using 40 epochs and an input size of 480 pixels. Evaluation on 1,612 test images produced a precision of 0.853, a recall of 0.735, an mAP@0.5 of 0.778, an mAP@0.5:0.95 of 0.606, and an F1-score of approximately 0.79. A dedicated eight-stage image pre-processing pipeline was applied to reduce the domain gap between the high-quality training images and the lower-quality ESP32-CAM production images, and all sensor data were transmitted to the server with an average latency below two seconds. The results show that the integration of visual detection and environmental sensing produces an accurate and responsive early-warning system that is suitable for low-cost smart-farming deployment.
Analysis of Anthracnose Disease in Curly Chilli Using Fuzzy Logic Method Esterika simangunsong; Johan Medi Situmeang; Aikel; Ertina Sabarita Barus
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/xtkp9c52

Abstract

Curly chilli (Capsicum annuum L.) is one of the horticultural products that has a high economic value and is often consumed by the people of Indonesia, both as a flavour enhancer for dishes and as a source of nutrition. However, until now, the production of chilli peppers has not been able to meet demand, one of which is caused by anthracnose disease that attacks plants through fungi of the genus Colletotrichum, potentially causing yield losses of 50 to 90%. Until now, there have not been many disease risk prediction systems that consider environmental variables adaptively. This research aims to develop an anthracnose disease risk prediction system based on the Mamdani fuzzy logic method that is able to handle the uncertainty of environmental data such as temperature, humidity, and soil pH. Data are obtained from trusted literature sources and have undergone a validation process before being used in modelling. The system was developed using MATLAB because it supports various features in the implementation of fuzzy logic. Simulation results show high consistency between manual calculations and software results, indicating that the system has a good level of accuracy and potential to be applied in agricultural management.
Forecasting Red Chilli Plant Growth using Time Series Method With Long Short-Term Memory Model Lastiur Aritonang; Brita Aryowindo; Ridho Syarif; Ertina Sabarita Barus
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/24mwkh42

Abstract

The growth of red chilli plants is a horticultural commodity whose growth is highly determined by environmental elements, as a result, it is very crucial to make predictions to help more effective agricultural planning. This study aims to examine the ability of the Long Short-Term Memory (LSTM) model in predicting the growth of red chilli plants (Capsicum annuum L.) according to 4 main parameters, namely stems, branches, leaves, and grains. The data used are red chilli plant growth data obtained from plantations located in Deli Serdang Regency, precisely in Namorambe District, namely Jatikusuma Village, over a period of 63 days and analyzed using the time collection method. The example provides high prediction accuracy for stem parameters (R² = 0.9796), branches (R² = 0.9618), and leaves (R² = 0.9489), but slightly low in fruit (R² = 0.8807) due to hyperbolic fluctuations. The consequences show the potential of LSTM in helping red chilli cultivation through better planning, green aid control, and early detection of growth anomalies. This study also demonstrates an integrative approach to four plant growth parameters using a single LSTM instance.
Co-Authors -, Amalia Aikel Aikel Amani Darma Tarigan Aritonang, Lastiur Aryowindo, Brita Atania Br Ketaren Brita Aryowindo Christian Jorena Sitepu Christin Erniati Panjaitan Darmanto Darmanto Delima Sitanggang, Delima Delmanto Saogo Denny Hasminta S. Maha Devin Viondra Sihar Matondang Dewi Sholeha Diana Alemin Barus Elviani Br Tarigan Esterika simangunsong Felix Felix Halim, Jenny Evans Hari Haran Hesalorafika Ginting Jenni Veronika Ginting Jenni Veronika Ginting Jenni Veronika Ginting Johan Medi Situmeang Jose Given Amanro Silaen Lastiur Aritonang Muhammad akbar Raihansyah Muhammad Fakhruddin Alrazi Muhammad Ikhwan Fahmi Muhammad Irwanto Niskarto Zendrato Oktrine Rohliharni Purba Oloan Sihombing, Oloan Ompusunggu, Elvis Sastra Ridho Syarif Rizki Samuel Putra F. Sianturi Sahputra, Sahputra Salim, Stanley Sembiring, Delima Chrismas simangunsong, Esterika Sinuraya, Bersama Sipayung, Ivan Hasadaon Situmeang, Johan Medi Solly Aryza Sopian Dapit Sutrisno - Syarif, Ridho Togar Timoteus Gultom Winner Nainggolan Yessica, Sally Yonata Laia