Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JITCoS : Journal of Information Technology and Computer System Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik Dan Multi-disiplin Ilmu
Nouval Khairi
Universitas Islam Negeri Sumatera Utara
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Automatic Lightweight CNN Waste Identification for Green Campus Program Support Nouval Khairi; Yuda Apriyansyah; Haikal Habibi Siregar; Muhammad Farhan Aditya
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu Vol. 1 (2025)
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/ikosstemi.v1.31

Abstract

Effective waste management is a key indicator of success for Green Campus Programs and a major factor in the UI GreenMetric World University Rankings. However, many Indonesian universities still face difficulties due to low accuracy in manual waste sorting, particularly for inorganic and hazardous (B3) waste. This study develops a lightweight, high-accuracy automatic waste classification system using the MobileNetV2 Convolutional Neural Network (CNN) architecture. The model was trained via transfer learning on the Kaggle “Trash Classification Dataset” (Sathish, 2023) containing 19,762 images from 10 waste classes: Metal, Glass, Biological, Paper, Plastic, Cardboard, Battery, Shoes, Clothes, and Trash. To align with operational needs of the Green Campus Program, these 10 classes were mapped into three functional categories: Organic, Recyclable, and Inorganic/B3. Experimental results show that MobileNetV2 achieved 93.2% accuracy with efficient inference time (~4.8 ms per image) on a CPU. The prototype, built using Python Streamlit, outputs both predicted waste type and confidence percentage, making it practical for real-time campus waste sorting. The proposed model provides an intelligent, energy-efficient, and transparent solution to support sustainable waste management, addressing key operational challenges in inorganic (WS4) and hazardous (B3) waste (WS5) handling
Monte Carlo Simulation for Rice Yield Risk Estimation Based on Weather and Soil Quality Factors Nouval Khairi; Muhammad Farhan; Muhammad Zhilali Rahman
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.36

Abstract

This study applies Monte Carlo simulation to estimate rice yield risks in the Medan region during 2024 by incorporating key weather variables (temperature, rainfall, and humidity) and soil quality indicators (pH, water content, salinity, texture, and organic matter). Given the increasing impacts of climate change and land degradation on food security, a probabilistic approach is essential for quantifying uncertainties in crop production. Using 10,000 simulated scenarios based on historical and field-derived parameter distributions, the model estimates an average rice yield of approximately 4.2 tons per hectare with a standard deviation of 0.2 tons per hectare, indicating relatively stable production under normal conditions. However, 20% of the simulations produce yields below 3.9 tons per hectare, reflecting elevated risks of crop failure during adverse environmental situations. Sensitivity analysis identifies rainfall and soil pH as the most influential variables, where extreme deviations may reduce yields by up to 35%. These findings offer critical evidence for policymakers and farmers to develop adaptive management strategies aimed at safeguarding sustainable rice production in the region.
Co-Authors Haikal Habibi Siregar Muhammad Farhan Muhammad Farhan Aditya Muhammad Zhilali Rahman Yuda Apriyansyah