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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Global Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Best : Journal of Applied Electrical, Science and Technology Abdi Teknoyasa JGEN : Jurnal Pengabdian Kepada Masyarakat
Robi Wariyanto Abdullah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics

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Automated and Efficient Monitoring System for Organic Waste Compost Processing based on The Internet of Things (IoT) Sugiarto, Lilik; ady saputra, indrawan; Wariyanto Abdullah, Robi
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/7madhs96

Abstract

In developed countries, waste has been regarded as an important component of management systems as well as reuse practices. In contrast, developing countries, particularly Indonesia, still face various challenges in waste management. Approximately 60% of the total national waste generation originates from household waste, and about 39.98% of this amount has not been optimally managed. Processing organic waste into compost is an environmentally friendly alternative that can reduce waste volume while increasing the value of household and agricultural waste. However, conventional composting methods often encounter difficulties, especially in maintaining temperature and moisture stability, causing the decomposition process to be less optimal. Based on these issues, this study aims to design and implement an automated efficiency and monitoring system for compost processing based on the Internet of Things (IoT). The developed system utilizes an ESP32 microcontroller, a soil moisture sensor for moisture measurement, a DS18B20 sensor for compost temperature monitoring, as well as an automatically controlled water pump and a 12 V DC fan. Sensor data are transmitted in real time to the Blynk platform for remote monitoring purposes. The experimental results indicate that the system is capable of maintaining moisture levels within the ideal range of 50–60% and compost temperature within the optimal range of 30–40°C, enabling the composting process to operate more stably, efficiently, and in a controlled manner.
Klasifikasi Penyakit Acute Lymphoblastic Leukemia Menggunakan Convolutional Neural Network Efficientnet B4 Dan B7  Kusumastuti , Rajnaparamitha; Wariyanto Abdullah, Robi; Handoko
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Leukemia Limfoblastik Akut (ALL) merupakan leukemia paling umum pada anak-anak sehingga memerlukan deteksi dini berbasis analisis citra yang cepat dan akurat. Penelitian ini mengklasifikasikan citra apusan darah tepi (Peripheral Blood Smear/PBS) menggunakan Convolutional Neural Network (CNN) berarsitektur EfficientNet B4 dan EfficientNet B7. Dataset terdiri atas 5.733 citra dari 89 pasien, diproses pada ukuran input 224 × 224 piksel, kemudian dibagi menjadi data latih, validasi, dan uji dengan rasio 80:10:10, dengan jumlah 4.586 data pelatihan, 573 data validasi, dan 574 data uji. Pelatihan dilakukan dengan pendekatan transfer learning dan augmentasi data untuk meningkatkan kemampuan generalisasi model. Evaluasi dilakukan menggunakan akurasi, precision, recall, F1-score, training loss, dan validation loss. Hasil terbaik menunjukkan bahwa EfficientNet B4 pada skenario 100 epoch mencapai akurasi 99,50%, precision 98,41%, recall 98,43%, dan F1-score 99,20%. Model ini mampu memberikan performa kompetitif terhadap EfficientNet B7 dengan kebutuhan komputasi lebih rendah serta stabilitas lebih baik terhadap overfitting. Kontribusi penelitian ini terletak pada analisis trade-off antara akurasi, stabilitas validasi, risiko overfitting, dan efisiensi komputasi model sebagai dasar pemilihan arsitektur yang lebih tepat untuk deteksi dini leukemia.   Abstract Acute Lymphoblastic Leukemia (ALL) is the most common type of leukemia in children, requiring early detection through fast and accurate image-based analysis. This study classifies peripheral blood smear (PBS) images using a Convolutional Neural Network (CNN) with EfficientNet B4 and EfficientNet B7 architectures. The dataset consists of 5,733 images from 89 patients, processed at an input size of 224 × 224 pixels, and divided into training, validation, and testing sets with an 80:10:10 ratio, comprising 4,586 training images, 573 validation images, and 574 testing images. Training was conducted using transfer learning and data augmentation to improve the model’s generalization ability. Evaluation was performed using accuracy, precision, recall, F1-score, training loss, and validation loss. The best result was achieved by EfficientNet B4 in the 100-epoch scenario, with an accuracy of 99.50%, precision of 98.41%, recall of 98.43%, and F1-score of 99.20%. This model provides competitive performance compared with EfficientNet B7 while requiring lower computational resources and demonstrating better stability against overfitting. The contribution of this study lies in analyzing the trade-off between accuracy, validation stability, overfitting risk, and computational efficiency as a basis for selecting a more appropriate architecture for early leukemia detection.
Co-Authors Arif Wicaksono Septyanto Dwi Hartanti Emha Taufiq Luthfi Fendy Prasetyo Nugroho Hadis Turmudi Hanafi Hanafi Handoko Indrawan Ady Saputro Kusrini Kusumastuti , Rajnaparamitha Kusumastuti, Rajnaparamitha Lilik Sugiarto, Lilik Sugiarto Miftakhurrokhmat, Miftakhurrokhmat Moch Hari Purwidiantoro Mochammad Luthfi Rahmadi Muqorobin Muqorobin Nurhidayanto, Nurhidayanto Oktafiani, Dewi Permatasari, Hanifah Sri Wulandari Sugiarto, Lilik Tinuk Agustin Tommy Dwi Putra Widiyanto, Wahyu Wijaya Yuda Abi Bagaskara