Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal PROCEEDING IC-ITECHS 2014 Civil and Environmental Science Journal (CIVENSE) Journal of Data Analytics, Information, and Computer Science (JDAICS)
Munada, Wina
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Mathematics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PERFORMANCE COMPARISON OF MACHINE LEARNING MODELS TO REDUCE MISDIAGNOSIS RATES IN PSYCHIATRIC DISORDER USING EEG DATASET Munada, Wina; Maharani, Desy Khalida; Sofiana, Anis
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 3 (2024): Juli
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i3.967

Abstract

This paper aims at comparing the suitability of three machine learning models: LightGBM, CatBoost, and Logistic Regression, to lower misdiagnosis rates for psychiatric disorders. Misdiagnosis in mental health may mean improper treatment and, hence, poor outcomes for patients. Our research aims to determine the most accurate predictive model for mental health condition diagnosis that will lead to improved clinical outcomes. We trained and tested these models on an EEG dataset with patient records that have psychiatric diagnoses labeled. For all the models, evaluation and comparison are made using key performance metrics such as Accuracy, Precision, Recall, and F1-Score. Through the use of these methods, it was shown that LightGBM performed better than CatBoost and Logistic Regression, having achieved higher accuracy and F1 scores, indicating more power to make a difference among different psychiatric disorders. These results suggest that machine learning techniques, especially LightGBM, can greatly increase diagnostic accuracy and reduce misdiagnosis in psychiatric contextual systems. Keywords Machine Learning, Psychiatric Disorder, LightGBM, CatBoost, Logistic Regression.
Intelligent Waste Segregation System Using Convolutional Neural Networks for Deep Learning Applications Rahmawati, Siti Solehah Yunita; Maharani, Desy Khalida; Munada, Wina
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1579

Abstract

Efficient waste management is essential for environmental sustainability and reducing landfill burdens. This study proposes an Intelligent Waste Segregation System leveraging Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to automate the classification of waste into recyclable and non-recyclable categories. The purpose of this research is to enhance waste sorting accuracy and efficiency using advanced deep learning techniques. The system employs VGG-16, pre-trained on a large dataset, and fine-tuned with a waste image dataset, enabling high precision in recognizing waste types. The methodology includes dataset preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, and recall. Experimental results demonstrate that the proposed system achieves a classification accuracy of 96%, surpassing existing traditional methods. The implications of this research include improving recycling processes and reducing environmental pollution through accurate waste segregation. This system has practical applications in urban waste management and recycling facilities, providing a scalable solution to global waste challenges. The findings highlight the potential of CNN-based models, particularly VGG-16, in addressing critical environmental issues. In conclusion, the proposed system offers an effective approach to automated waste segregation, paving the way for sustainable waste management practices through deep learning applications.
Spatial Performance Evaluation of Government Center: Economic Leakage Analysis Using Multiplier Effect Method in Mojokerto Regency Abbas, Mohammad Saichul; Wicaksono, Agus Dwi; Utomo, Dadang Meru; Munada, Wina
Civil and Environmental Science Journal (CIVENSE) Vol. 9 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.civense.2026.009.01.5

Abstract

Placing a government capital in the wrong location does more than cause administrative headaches. It actively breaks the local economy. This study investigates the severe spatial mismatch in Mojokerto Regency, where its government center operates as an enclave entirely within Mojokerto City. We analyzed 2018–2023 GRDP data using Location Quotient (LQ), Shift-Share Analysis (SSA), and Keynesian Multiplier models. Our findings reveal a striking irony. The Regency’s bureaucracy actually functions as a basic sector for the neighboring City while remaining non-basic for its own territory. The SSA results prove this damage. Local tertiary sectors suffer heavy competitive losses because they miss out on the daily spending of civil servants. This enclave setup triggers massive economic leakage. Money drains out through direct office procurement and indirect daily consumption, creating a textbook backwash effect. Beyond lost revenue, this mismatch forces unsustainable daily commutes that heavily spike the region's carbon footprint. We conclude that relocating the government hub into the Regency’s actual borders is an urgent economic necessity, not just a political option. Until this physical relocation happens, local planners must consider cross-border revenue-sharing to stop the immediate financial bleeding. 
Co-Authors Abbas, Mohammad Saichul Agus Dwi Wicaksono Anis Sofiana Maharani, Desy Khalida Rahmawati, Siti Solehah Yunita Utomo, Dadang Meru