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All Journal Jurnal Masyarakat Informatika Jurnal Simetris Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Abdimasku : Jurnal Pengabdian Masyarakat Educenter: Jurnal Ilmiah Pendidikan EDUCENTER JURNAL PENDIDIKAN
Ningrum, Novita Kurnia
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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IoT-Based Prediction of Ornamental Plant Water Needs Using Sugeno Fuzzy Algorithm Dwitama, Reiza Hersa; Ningrum, Novita Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9999

Abstract

Urban plant care is increasingly important amid growing concerns about air pollution and limited time for manual maintenance. In Indonesia, air quality has deteriorated significantly, with PM2.5 pollution levels exceeding World Health Organization standards, particularly in major cities like Jakarta. Ornamental plants play a crucial role in improving air quality; however, urban residents often struggle to consistently water them. This study addresses that problem by developing an Internet of Things (IoT)-based smart irrigation system that utilizes the Sugeno fuzzy algorithm to predict the water needs of ornamental plants. The system combines a capacitive soil moisture sensor and a DHT11 temperature-humidity sensor with an ESP8266 microcontroller to monitor environmental conditions. Data is transmitted to Firebase and visualized in an Android application, which provides real-time monitoring and specific volume recommendations ranging from 10 ml to 240 ml, calibrated for medium-sized plant pots which is also based on 27 fuzzy rules derived from three input parameters: air temperature, humidity, and soil moisture. Real-world testing with the Aglaonema Snow White plant confirmed that the system functions reliably, helping users optimize water usage and support sustainable, data-driven plant care in urban environments. The system achieved an average prediction accuracy of 89.14% and a mean absolute error of 7.6% in guiding soil moisture toward a 70% target, confirming its practical effectiveness. While the system was tested on Aglaonema Snow White, the fuzzy rule base can be recalibrated for other ornamental plant species with different water needs.
Face Recognition Using MTCNN Face Detection, ResNetV1 Feature Embeddings, and SVM Classification Pratama, Ivan Putra; Ningrum, Novita Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11016

Abstract

Face recognition has become an essential component of modern security and authentication systems, yet its effectiveness is often challenged by limited datasets, class imbalance, variations in facial poses, lighting conditions, and image resolutions. This study proposes a face recognition pipeline that integrates Multi-task Cascaded Convolutional Networks (MTCNN) for face detection, Residual Network V1 (ResNetV1) for feature extraction, and Support Vector Machine (SVM) for classification. Unlike previous works that rely on large-scale datasets and end-to-end deep learning models, this study emphasizes the effectiveness of the pipeline under constrained data conditions, using 856 images across 191 classes with highly imbalanced distribution. Experimental results show that MTCNN successfully detected 97.1% of faces, while ResNetV1 produced 512-dimensional embeddings that formed well-separated clusters validated by clustering metrics (Silhouette Score = 0.578, Davies-Bouldin Index = 0.566). The SVM classifier achieved 92.9% accuracy, with macro-average precision, recall, and F1-scores of 0.89, 0.92, and 0.89 respectively, significantly outperforming a baseline k-Nearest Neighbor (k-NN) model that only reached 63.9% accuracy. These findings highlight the novelty of this study: demonstrating that a lightweight yet robust pipeline can deliver reliable recognition performance even in small, imbalanced datasets, making it suitable for real-world scenarios where large-scale training data are not available.
Co-Authors Agustina, Feri Ahmad Syahreza Ajib Susanto Astuti, Yani Parti Azkia, Naila Nur Dwitama, Reiza Hersa Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Ibnu Utomo WM Ivan Putra Pratama Karis Widyatmoko Kurniawan, Defri Kusumawati, Yupie Liya Umaroh, Liya Muhammad Naufal, Muhammad Mulyono, Ibnu Utomo Wahyu Musyaffa, Muhammad Syihabuddin Nabila, Isyeh Salma Bilqis Nareswari, Andrea Keysa Nugroho, Aulia Jilan Pramesti, Nadya Arum Qomariyah, Dania Saputra, Muhammad Alwi Nanda Sasmita, Tomy Elen Sembiring, Rinawati Septian Enggar Sukmana, Septian Enggar Setyawan, Aditya Rendy SUDARYANTO SUDARYANTO Sukamto, Titien Suhartini Sunyaruri, Wisesa Sat Syahrazy, Muhammad Anjas Syahreza, Ahmad Tangkas Surya Wibawa Taqi, Muhamad Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Wicaksono, Aldo Anggara Zahrotul Umami, Zahrotul Zaidan, Alfa Hikma