cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jumanto
Contact Email
jumanto@mail.unnes.ac.id
Phone
+6281339762820
Journal Mail Official
josre@shmpublisher.com
Editorial Address
Jl. Karanglo No. 64 Gemah, Pedurungan, Kota Semarang
Location
Kota semarang,
Jawa tengah
INDONESIA
Journal of Student Research Exploration
Published by shm publisher
ISSN : 29641691     EISSN : 29648246     DOI : https://doi.org/10.52465/josre.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
The Journal of Student Research Exploration aim publishes articles concerning the design and implementation of computer engineering, information system, data models, process models, algorithms, and software for information systems. Subject areas include data management, data mining, machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome system papers that focus on application domains, Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2025): January 2025" : 5 Documents clear
Analyzing reading preferences based on gender and education with decision tree method Sari, Jelita Permata; Ningsih, Maylinna Rahayu
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.v3i1.341

Abstract

This study aims to analyze the suitability of book genre selection with gender and education level. A classification method using a decision tree algorithm with four different criterion parameters is used to examine reading preferences based on various demographic factors, namely Gain Index, Information Gain, Gini Index, and accuracy. Data was obtained from a dummy dataset involving 120 records with three main attributes. The results show variations in accuracy depending on the criteria selected, with the highest accuracy rate achieved being 78.57%.
Support vector machine on two-class classification problem to determine an otaku Husyen Ramadhan, Farhan; Lestari, Apri Dwi
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.v3i1.358

Abstract

Machine Learning has become a popular topic among academics and practitioners in recent years. This paper describes the use of SVM for otaku classification problem. The dataset used is a dummy dataset created with a python programme. In this research, SVM will be used as a model. The model aims to predict whether someone is an otaku or not, based on several attributes. The optimal parameters are obtained after several experiments. The parameters consist of kernel=‘poly’, C=0.1, gamma=‘auto’, degree=2, and attribute class_weight=None. The performance obtained by applying the above parameters is 100% accuracy.
Comparison of naïve bayes and support vector machine methods for jkt48 music video comment classification Abdul Aziz, Alif; Rofik, Rofik
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.v3i1.389

Abstract

The research was conducted to discuss the classification of comments on music video JKT48 "Magic Hour" in YouTube using method Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). YouTube monitors viewer emotion by adjective comments Adjectives are the descriptive powers of human communication we use to help personify how different types, i.e. different "personalities" flavors and depths reflect artistic expressions The place where interactivity meets with digital marketing signifying a shared contribution to music lore In this work, we study the comparison of The Support Vector Machine (SVM) and Naive Bayes Classifier in terms of Accuracy, Precision & Recall. This Project includes data pre-processing, collecting the data by YouTube API and build classification models which involves Support Vector Machine and Naive Bayes Classifier. SVM displayed more stable performance than NBC, showing consistent results across different data split ratios. SVM achieved its highest accuracy of 93.42% at an 80:20 ratio, with precision and recall rates reaching 92.57% and 93.42%, respectively.
Sentiment analysis of user comments on the shopeepay feature in the shopee application: Evaluation of accuracy with k-nearest neighbors (KNN) algorithm Lestari, Fitri Duwi; Prasetiyo, Budi
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.v3i1.392

Abstract

This research analyzes Shopeepay user reviews on the Shopee app using the K-Nearest Neighbor (KNN) algorithm with TF-IDF weighting and a Cosine Similarity matrix. Data was collected through web scraping 500 reviews from the Google PlayStore and labelled into positive, neutral, and negative sentiments. The process includes literature study, data collection, labelling, text preprocessing, word weighting, and sentiment classification using KNN. Results show an accuracy range of 86%-91%, with Precision, Recall, and F1-Score as evaluation metrics. The findings indicate that convenience, trust, and risk significantly affect users' interest in Shopeepay, especially during the Covid-19 pandemic. A Word Cloud was also used to visualize common terms in the reviews, providing insights for Shopee to enhance Shopeepay based on user feedback.
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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2025): July 2025 Vol. 3 No. 1 (2025): January 2025 Vol. 2 No. 2: July 2024 Vol. 2 No. 1: January 2024 Vol. 1 No. 2: July 2023 Vol. 1 No. 1: January 2023