Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi
Ida Nur Laela
Universitas Amikom Purwokerto
Computer Science & IT Other

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

Found 2 Documents
Search

 1 
ANALISIS KLASIFIKASI DATA KUALITAS UDARA DKI JAKARTA MENGGUNAKAN ALGORITMA C.45 Maya Astriyani; Ida Nur Laela; Dwi Puji Lestari; Laudiana Anggraeni; Tri Astuti
JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Vol. 6 No. 1 (2022): Jurnal Sistem dan Teknologi Informasi Komunikasi
Publisher : Universitas Katolik Musi Charitas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32524/jusitik.v6i1.790

Abstract

The growth and development of a city is one of the factors causing the increase in air pollution because the air quality is mixed with various components. Air pollution is a condition in which a large number of physical, biological or chemical substances in the earth's air can cause harm to the health of the human body and other living things. One of the cities with the highest level of air pollution is DKI Jakarta. The Environmental Service of the DKI Jakarta Provincial Government operates an Air Quality Monitoring Station (SPKU) to monitor air quality every day. The use of data mining methods is used to analyze the factors that cause air pollution in DKI Jakarta. This method can process ISPU parameter data into information that tells the level of air quality per day using the decision C45 or tree algorithm.
IMPROVING STUNTING CLASSIFICATION PERFORMANCE USING COMBINATION SMOTE TECHNIQUE AND ARTIFICIAL NEURAL NETWORK ALGORITHM Wiga Maulana Baihaqi; Ida Nur Laela; Darso Darso
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.4998

Abstract

Child development is at the core of the nation's future. However, there are still serious problems that hinder optimal child growth, one of which is stunting. Stunting is a condition that has become a global concern in the context of public health and development. The use of deep learning algorithms has great potential to overcome the problem of stunting classification. The ratio of stunting handling is still a problem due to imbalance data. Classification algorithms such as ANN will experience a decrease in performance when faced with unbalanced classes, this makes it difficult to take action for early diagnosis of stunting. Synthetic Minority Oversampling Technique (SMOTE) is used to balance the failure data in diagnosis. The results showed that the combination of the SMOTE oversampling technique can improve the ability of the ANN algorithm model to accurately classify stunted or minority populations. The accuracy, precision, recall, and F1-Score values of this study are 0.90, 0.85, and 0.95, respectively. The results of (MCC) obtained a value of 0.73, and (G-Mean) of 0.86 before applying SMOTE and the results after applying SMOTE MCC of 0.84 and G-Mean of 0.92. This indicates that the minority group, namely stunted toddlers, can be predicted well. The implementation of the combination of SMOTE and ANN has proven successful in classifying imbalance stunting data, so this research can be used as a reference for future research to handle unbalanced data.
Co-Authors Darso Darso Dwi Puji Lestari Laudiana Anggraeni Maya Astriyani TRI ASTUTI Wiga Maulana Baihaqi