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Jurnal Institusi
PENERAPAN TEKNIK SMOTE PADA KLASIFIKASI PENYAKIT STROKE DENGAN ALGORITMA SUPPORT VECTOR MACHINE
ZONAsi: Jurnal Sistem Informasi; Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025; 61 - 74
Stroke adalah kondisi darurat medis yang dapat menyebabkan kerusakan otak atau kematian. Deteksi dini dan klasifikasi risiko stroke sangat penting untuk pencegahan dan penanganannya. Penelitian ini menggunakan dataset sebanyak 5110 data untuk meningk...
Jurnal Institusi
Penerapan Ensemble Learning Untuk Klasifikasi Preferensi Film Pada Dataset Movielens Dengan Penanganan Kelas Tidak Seimbang
SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; Vol. 4 No. 1 (2025): Prosiding SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; 127-142
Sistem rekomendasi film semakin dibutuhkan seiring bertambahnya jumlah konten digital dan meningkatnya kebutuhan pengguna akan rekomendasi yang sesuai dengan preferensi mereka. Penelitian ini berfokus pada klasifikasi preferensi pengguna terhadap fil...
Jurnal Institusi
ANALISIS SENTIMEN ULASAN MIE GACOAN SOLO VETERAN DI GOOGLE MAPS MENGGUNAKAN ALGORITMA NAIVE BAYES
ZONAsi: Jurnal Sistem Informasi; Vol. 6 No. 3 (2024): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2024; 649 - 658
Mie Gacoan Solo Veteran is one of the fastfood restaurant branches that is very popular with various groups, so that quite a few people have left an assessment of it. This research aims to help the Mie Gacoan Solo Veteran company in its efforts to un...
Jurnal Institusi
Peningkatan Metode Support Vector Machines (SVM) pada Data Child-free Menggunakan Oversampling
SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; Vol. 2 No. 1 (2023): Prosiding SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; 19-27
Keputusan tidak untuk memiliki anak, yang dikenal sebagai child-free, semakin relevan dalam masyarakat modern. Support Vector Machines (SVM) yaitu algoritma yang terawasi digunakan dalam menganalisis keputusan seperti ini. Namun, SVM dapat menghadapi...
Jurnal Institusi
Analisis Sentimen Penipuan Asuransi Menggunakan Machine Learning
SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; Vol. 4 No. 1 (2025): Prosiding SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; 164-172
Penipuan asuransi merupakan salah satu bentuk kejahatan keuangan yang merugikan perusahaan maupun masyarakat, serta menurunkan kepercayaan publik terhadap industri asuransi. Analisis sentimen berbasis media sosial dapat memberikan gambaran mengenai o...
Jurnal Institusi
Optimization of Employee Selection Using Hybrid DSS: SAW Approach and Random Forest-Based Attrition Prediction
SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; Vol. 4 No. 1 (2025): Prosiding SEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer; 446-458
Pemilihan karyawan merupakan komponen kritis dalam manajemen sumber daya manusia yang memerlukan tingkat objektivitas dan efisiensi yang tinggi. Studi ini mengusulkan sistem pendukung keputusan (DSS) hibrida yang menggabungkan metode Simple Additive ...
Jurnal Institusi
UTILIZATION OF MACHINE LEARNING ALGORITHMS FOR CUSTOMER COMPLAINT CLASSIFICATION AT PERUMDAM
JAISEN: Journal of Advanced Information Systems and Engineering; Vol. 1 No. 1 (2025): JAISEN: Journal of Advanced Information Systems and Engineering ; 9-17
In today's digital era, managing customer complaints poses a significant challenge for public service providers, such as the Regional Drinking Water Company (PERUMDAM). With the increasing number of customers and the complexity of complaints, ma...
PubMed
Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE
PLoS One
In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced N...
PubMed
A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets
Sci Rep
Class imbalance poses a significant challenge in water quality classification, often leading to biased predictions and diminished accuracy for minority classes. This study introduces SMOTE-PCA-HDBSCAN, a novel oversampling framework that integrates t...
PubMed
Evaluating the performance of different machine learning algorithms based on SMOTE in predicting musculoskeletal disorders in elementary school students
BMC Med Res Methodol
BACKGROUND: Musculoskeletal disorders (MSDs) are a major health concern for children. Traditional assessment methods, which are based on subjective assessments, may be inaccurate. The main objective of this research is to evaluate Synthetic Minority ...
PubMed
Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms
Appl Nanosci
One of the prominent uses of Predictive Analytics is Health care for more accurate predictions based on proper analysis of cumulative datasets. Often times the datasets are quite imbalanced and sampling techniques like Synthetic Minority Oversampling...
PubMed
Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms
BMC Med Res Methodol
BACKGROUND: Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the IC...
PubMed
An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE
Sensors (Basel)
With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem ...
PubMed
Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning
Front Genet
Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanc...
PubMed
A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes
BMC Bioinformatics
PURPOSE: Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their...
PubMed
A novel method for detecting credit card fraud problems
PLoS One
Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing t...
PubMed
LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
BioData Min
BACKGROUND: Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometim...
PubMed
Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data
PLoS One
The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In th...
PubMed
SMOTE for high-dimensional class-imbalanced data
BMC Bioinformatics
BACKGROUND: Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The problem can be attenuated by ...
PubMed
Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19
Intell Based Med
Almost every dataset these days continually faces the predicament of class imbalance. It is difficult to train classifiers on these types of data as they become biased towards a set of classes, hence leading to reduction in classifier performance. Th...