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All Journal Journal of Student Research Exploration
Lintang, Irendra
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Application of k-nearest neighbor algorithm in classification of engine performance in car companies using Rapidminer Lintang, Irendra; Lestari , Apri Dwi; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 2 No. 2: July 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i2.345

Abstract

Implementation of the k-Nearest Neighbor (k-NN) algorithm in the classification of CAR Car company engine performance using RapidMiner software. The company's engine performance is a very important aspect in the automotive industry that greatly affects operational efficiency and customer satisfaction. As an effort to monitor and improve engine performance, classification is an important key to identify machines that are feasible and require repair. The dataset used is a generated dataset from the AI Chat GPT bot whose prompts have been adapted to the research needs. The k-NN algorithm was chosen due to its ability to produce accurate predictions. The k-NN classification method utilizes training and testing data and calculates the distance between the data to determine the appropriate class. The results of this study show excellent performance in terms of accuracy, precision, and recall. The highest accuracy is 90.62% at the value of k = 2. The highest precision and recall are 100% and 93.75% at the values of k = 2, k = 4, and k = 7.
Sentiment analysis of youtube comments on the palestine-israel conflict: Performance comparison of SVM, KNN, and RFC Lintang, Irendra; Jumanto, Jumanto; Masa, Amin Padmo Azam
Journal of Student Research Exploration Vol. 3 No. 1 (2025): January 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i1.426

Abstract

The Palestine-Israel conflict, rooted in territorial and religious identity disputes in the Middle East, notably over the sanctity of Jerusalem, is impacted by various political, economic, and social factors. This study employs text-mining techniques to analyze the sentiment of YouTube comments concerning the conflict. Utilizing data collected via the YouTube API, the study preprocesses, analyzes sentiment, and classifies comments using three machine learning algorithms: K-Nearest Neighbors (K-NN), Random Forest Classifier (RFC), and Support Vector Machine (SVM). The categorization report measures are utilized to compare how well the models performed in classifying estimation as positive or negative. Outflanking all other classifiers, the Irregular Woodland Classifier (RFC) accomplishes 78curacy with accuracy rates of 0.76 for positive and 0.79 for negative assumptions. With a precision rate of 77%, SVM illustrates an inclination in favor of negative sentiments, though K-NN, with an exactness rate of 60%, shows an imbalance favoring negative over positive estimations.
Co-Authors Budi Prasetiyo, Budi Jumanto Jumanto, Jumanto Lestari , Apri Dwi Masa, Amin Padmo Azam