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All Journal Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Rahel Lina Simanjuntak
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Computer Science & IT

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Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Shopee Dengan Menggunakan Algoritma Naïve Bayes Rahel Lina Simanjuntak; Theresia Romauli Siagian; Vina Anggriani; Arnita Arnita
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 3 No. 3 (2023): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v3i3.2411

Abstract

Every year, the e-commerce industry in Indonesia grows and develops at a rapid pace. In Indonesia, many online marketplaces have sprung up, including Tokopedia, Lazada, and Shopee. People are very interested in all e-commerce companies because they are hassle-free and instant. Among the most famous is Shopee, which offers a wide range of services and also presents a ratings and reviews column. This feature allows users to express their feelings about Shopee. Based on the information gathered from previous user comments, consumers can use these ratings to identify and trust both excellent and negative recommendations of the app they want to use. Sentiment analysis results include both favorable and negative user reviews by scoring, classifying, and filtering viewpoints to help businesses or users. The author uses the naïve bayes algorithm in this research. The Naïve Bayes Classifier approach will be used in this research to perform sentiment classification. The author then uses associations between frequently discussed word terms or themes that are related to each other for the extraction and exploration process, as well as descriptive statistics. Naïve Bayes Classifier is a binary classification technique that applies Bayesian principles with a strong assumption of independence, utilizing simple statistical probabilities.
Komparasi Algoritma KNN dan SVM dalam Memprediksi Penyakit Stroke Rahel Lina Simanjuntak; Rizki Agung Ramadhan; Theresia Romauli Siagian; Vina Anggriani
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 3 No. 3 (2023): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v3i3.2474

Abstract

Stroke is a serious medical condition that affects many people around the world. The ability to predict a person's stroke risk can help in effective prevention, treatment and care. In this study, a comparison between the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms was conducted to predict stroke risk. The KNN algorithm is a method that searches for the nearest neighbors among the data points to be predicted and assigns the most common label among its neighbors. Experimental results show that both KNN and SVM can provide fairly accurate stroke predictions. However, from an operational point of view, SVM consistently performed better than KNN in terms of accuracy and precision. This research provides insight into the differences between KNN and SVM algorithms in the context of stroke prediction. The results can provide guidance for researchers and practitioners in choosing the right algorithm to predict stroke risk based on the characteristics of the available datasets.
Co-Authors Arnita Arnita Rizki Agung Ramadhan Theresia Romauli Siagian Vina Anggriani