1,720,982 research outputs found
Sistem basis data menggunakan Microsoft SQL server 2005 / Ema Utami, Anggit Dwi Hartanto
No full textOptimalisasi Akurasi Algoritma Naïve Bayes Dengan Metode Syntetic Minority Oversampling Technique (Smote) Pada Data Numerik
Full text linkThis research will classify numerical data, namely loan data taken from Kaggle. The data used amounted to 9578 datasets which included data classes with borrowers able to complete credit as many as 8045 records and loans that could not complete credit as many as 1533 records. From the amount of data there is an imbalance of classes so it is necessary to do balancing in order to get more accurate classification results. The purpose of this research is to improve the accuracy of the Naïve Bayes algorithm in classifying numerical data. Fraud in financial transactions is an example of a case of imbalanced data, where the number of legitimate transactions is much greater than those that are fraudulent. Optimizing accuracy in minority (fraud) classes is very important to avoid losses. The method used to improve the accuracy of the algorithm is the Synthetic Minority Oversampling Technique (SMOTE) by over sampling the minority of the dataset. In addition, it also uses the K-Fold Cross Validation method to evaluate the performance of the algorithm process used. Data preprocessing is done to clean the data from missing and invalid values and normalize the data so that all features are on the same scale and suitable for classification analysis. Based on the results of the analysis conducted, before the application of SMOTE the model's ability to recognize minority classes was 16.1%, while after the application of SMOTE the model's ability to recognize minority classes became 48.8%. besides that, before the application of SMOTE the model was able to predict the minority class correctly in 10 cases while after the application of SMOTE, the model was able to predict the minority class correctly in 102 cases. So it can be concluded that the SMOTE technique is able to improve the ability of the mode
ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA (IKN) MENGGUNAKAN METODE OVERSAMPLING SYNTHETIC MINORITY (SMOTE)
Full text linkPemindahan Ibu Kota Negara (IKN) adalah keputusan strategis yang memicu berbagai tanggapan dan reaksi dari berbagai pihak. Analisis sentimen terhadap pemindahan IKN menjadi suatu aspek penting untuk memahami pola pikir dan sikap masyarakat terhadap keputusan tersebut. Studi ini bertujuan untuk melakukan perbandingan performa algoritma klasifikasi terhadap analisis sentimen pada data teks yang berisi opini dan pendapat masyarakat terkait pemindahan IKN menggunakan metode oversampling Synthetic Minority (SMOTE) untuk mengatasi masalah ketidakseimbangan dalam data sentimen. Dua algoritma klasifikasi, yaitu Support Vector Machine (SVM) dan Random Forest, dievaluasi dalam konteks ini. Hasil penelitian menunjukkan peningkatan signifikan dalam performa kedua algoritma setelah penerapan metode SMOTE. Performa algoritma SVM meningkat dari 85% menjadi 92%, sementara algoritma Random Forest meningkat dari 84% menjadi 91%. Hasil ini menunjukkan bahwa metode SMOTE efektif dalam meningkatkan kemampuan algoritma klasifikasi untuk mengatasi ketidakseimbangan data dan menghasilkan prediksi sentimen yang lebih akurat
User Satisfaction Levels Sentiment Analysis Toward Goods Delivery Service On Twitter Using Support Vector Machine Algorithm (SVM)
No full textEnhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation
Full text linkThe development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation skills. This research aims to introduce a superior object detection method, especially to support the sustainability and effectiveness of blind navigation. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method and distance measurement technology from OpenCV. The main challenge to address involves improving object detection accuracy and performance, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking in environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects that may involve obstacles or potential threats, thus preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution to the presentation of technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact on the facilitation of their daily lives
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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