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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 31 Documents
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Online payment fraud prediction with machine learning approach using naive bayes algorithm Rahman, Raihan Muhammad Rizki; Muslim, Much Aziz
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.v2i2.343

Abstract

The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.
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.
Classification of travel class with k-nearest neighbors algorithm using rapidminer Septiana, Dina Wachidah; Pastika, Puan Bening
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.v2i2.357

Abstract

he tourism industry in Indonesia plays an important role in the national economy. The selection of travel class according to the needs and budget of tourists is an important aspect in the tourism industry. This research aims to develop a travel class classification model using dummy datasets and the K-Nearest Neighbors (KNN) algorithm with RapidMiner software. The travel class dummy data set was obtained from the internet and modified according to research needs. The KNN algorithm was used to classify new travel classes based on previously classified dummy data. These dummy data were preprocessed and analyzed using RapidMiner software. The performance of the KNN model was evaluated using accuracy, precision, recall and F1-score. The results showed that the KNN algorithm with the values k = 1-2, k = 3-6, k = 8-10, k = 11-14 and k = 15 resulted in accuracy of 35.71%, 39.29%, 48.26%, 46.43% and 50.00%, respectively. This shows that the KNN algorithm with a value of k=15 produces the highest accuracy that can be effectively used to classify new travel classes based on dummy data.
Classification of risk of death from heart disease or cigarette influence using the k-nearest neighbors (KNN) method Fadhilah, Muhammad Syafiq; Muzayanah, Rini
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.v2i2.359

Abstract

Heart disease is one of the leading causes of death in Indonesia. In addition to coronary heart disease, smoking is the leading contributor to the death rate in Indonesia. This study aims to analyze the risk of death with the main variables of heart disease history and smoking history. This study classifies the risk of death of heart disease sufferers and smokers using the KNearest Neighbors (KNN) algorithm. The results showed that the KNN model had an accuracy of 52.38% in predicting the risk of death of smokers and heart disease patients. Confusion matrix analysis revealed that the model performed well in predicting classes 0 and 2, but had difficulty in predicting class 1. This study shows that KNN can be used to predict the risk of death of smokers and patients with heart disease with a satisfactory success rate.
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.
Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews Biyantoro, Arell Saverro; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 3 No. 2 (2025): July 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i2.396

Abstract

Dana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app.
Sentiment analysis spotify applications on google play store with naïve bayes and neural network methods Syahra, Syahra Audiyani Fitra; Pertiwi, Dwika Ananda Agustina
Journal of Student Research Exploration Vol. 3 No. 2 (2025): July 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i2.416

Abstract

Digital advancements have significantly changed the way music is accessed and enjoyed, with streaming platforms such as Spotify emerging as one of the most widely used applications worldwide. Along with this growth, user reviews on platforms like the Google Play Store have become an important source of information, offering insights into user satisfaction and areas for improvement. In this study, sentiment analysis was conducted on Spotify reviews using two classification methods, Naïve Bayes and Neural Networks. The reviews were collected, processed, and then analyzed with both approaches to evaluate their performance. The results show that Neural Networks outperformed in terms of accuracy, F1-score, and recall, while Naïve Bayes performed better in AUC, precision, and MCC. Analysis of the dataset also revealed that negative reviews dominated at 52.8%, followed by positive at 28.3%, and neutral at 19%. These findings highlight the value of sentiment analysis in understanding user perspectives and can support developers in improving application quality and user experience.

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