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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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Increased accuracy in predicting student academic performance using random forest classifier Mulyana, Aditya Fajar; Puspita, Wiyanda; Jumanto, Jumanto
Journal of Student Research Exploration Vol. 1 No. 2: July 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i2.169

Abstract

This research aims to classify the academic performance of students who are successful and who have dropped out of school with high accuracy so that these matters can be addressed quickly. Things like this need fast handling to find out what factors influence it. In addition, this research was conducted to test how good the random forest algorithm is in classifying a problem. Random forest, which includes an algorithm that is commonly used for classifying a problem. By using the random forest algorithm, the accuracy results will be better than a single decision tree. This algorithm is quite good at handling and managing large datasets. From this study it can be concluded that this method can provide good prediction accuracy with a fairly high level of accuracy, namely 89%. Utilization of this random forest can be an alternative in classifying student academic achievement. This algorithm can work well in handling large datasets. This study discusses how the use of Random Forest can work to classify students' academic performance.
Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset Reska, Nadhifa; Tsabita, Khansa
Journal of Student Research Exploration Vol. 1 No. 2: July 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i2.170

Abstract

Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
The Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm Sundari, Putri Susi; Khafidz Putra, Mahardika
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.176

Abstract

In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.
Application design for web-based car services to increase work time estimates Febriyanti, Shinta; Solehatin, Solehatin
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.231

Abstract

The aim of this research is to increase the estimated service process time by creating an online-based car service ordering application at the Sinar Jaya repair shop and introducing information about Sinar Jaya car service services to the wider public. In this information systems research, the author of this research software development method uses the waterfall model development method. By implementing a waterfall, the research stages carried out by researchers start from data analysis, system analysis, system design, implementation, and testing. Creating a website-based car service ordering application at the Sinar Jaya Workshop can help customers find out the information available at the Sinar Jaya Workshop and the car service ordering process. Before there was an application, customers had to come to the location to place an order, so it took a long time to arrive at the location. So, with the online booking application, you can save time in the service process and get a queue number online. The data processing process for ordering car services becomes more practical so that it can be processed properly by the admin.
Impact of product design and sales promotion on eiger customer loyalty Adisti, Yuniar Rahma; Lusianti, Dina; Faidah, Faridhatun
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.242

Abstract

The needs of society and developing lifestyles will result in hobbies also developing, such as the hobby of adventuring in the wild. One brand of outdoor equipment is Eiger. This research analyzes the influence of Product Design and sales Promotion on Customer Loyalty through Consumer Satisfaction. The object of this research is the Eiger product in Kudus. The data used in this research was by distributing questionnaires via online form. The instrument test in this study used a reliability test and validity test. The analytical tool in this research uses SEM-AMOS. This research shows that product design has no effect on consumer satisfaction. Sales promotions have a positive and significant effect on consumer satisfaction. Product design has a positive and significant effect on customer loyalty. Sales promotions do not affect customer loyalty. Consumer satisfaction does not affect customer loyalty. Product design and sales promotions on customer loyalty through consumer satisfaction have a weak mediating influence. Product design, sales promotions, and consumer satisfaction are important in shaping consumer perceptions of loyalty. This perception will influence customer attitudes and behavior. Therefore, companies must design good strategies so that consumers can behave and behave as expected.
Impact of sales promotion and product quality on zoya customer purchase interest Rina, Rina Amalia Putri; Lusianti, Dina; Faidah, Faridhatun
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.243

Abstract

This research analyses the influence of Sales Promotion and product Quality on Purchase Interest through Brand Awareness. This is a significant concern because product quality is an important thing that every company must strive for if it wants to compete in the market. The object of this research is Zoya Kudus. The sampling technique used purposive sampling with the rule of thumb formula to produce a sample of 120. The analysis tool in this research used SEM-AMOS. This research shows that Sales Promotion and Product Quality have a positive and significant effect on Brand Awareness. Sales Promotion and product quality have influenced purchase Intention. Sales promotion on purchasing interest through brand awareness influences partial mediation. Product quality on purchase intention through brand awareness has a mediating influence, but the influence is weak.
Customer churn prediction in the case of telecommunication company using support vector machine (SVM) method and oversampling Urrahman, Dhiya; Winanto, Raffi; Widyatama, Thierry
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.v2i2.253

Abstract

hurn is the act by which a customer withdraws from service, including service provider-initiated churn and customer-initiated churn. Churn is a big challenge for companies, especially churn-prone enterprise sectors such as telecommunications. Churn can affect both revenue and reputation if occurs for negative reasons. This study aims to predict customer churn in a telecommunication company dataset, investigating the impact of various variables and classes on churn occurrences to inform strategic decision-making for businesses. The Support Vector Machine (SVM) model is employed, and dataset imbalance is addressed through oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and random oversampling (ROS). Three SVM models are created with different training datasets (normal, SMOTE, ROS), yielding varying results. The normal dataset achieves the highest accuracy at 92%, outperforming SVM with ROS (89%) and SVM with SMOTE (87%). However, the normal dataset exhibits lower sensitivity compared to both oversampling techniques. The study identifies the cause of decreased accuracy in oversampling and low sensitivity in the normal dataset. The novelty of this research lies in testing the SVM model's ability to surpass the accuracy of previous models on the same dataset and in exploring the unique impact of oversampling in churn prediction.
Detection and prediction of rice plant diseases using convolutional neural network (CNN) method Pahlawanto, Reyhan Dzaki Sheva; Salsabila, Halimah; Pratiwi, Kusuma Ratna
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.254

Abstract

Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.
Email spam detection: a comparison of svm and naive bayes using bayesian optimization and grid search parameters Budiman, Dzaky; Zayyan, Zayyan; Mardiana, Ainun; Mahrani, Alfira Aulia
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.260

Abstract

Spam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89.
Analysis of k-means clustering algorithm in advanced country clustering using rapid miner Prabaswara, Ireneus; Pertiwi, Dwika Ananda Agustina; Jumanto, Jumanto
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.v2i2.337

Abstract

In the era of globalization, the understanding of developed countries is no longer limited to the level of per capita income alone. As part of the analysis of developed countries based on aspects of government revenue, income balance, national savings, and domestic output based on sales. This research aims to cluster and to find out how these economic indicators are interrelated and affect the status of a country as a developed country. The K-means algorithm is used to identify patterns of countries with similar economic characteristics. From the research conducted, there are 4 clusters generated based on the characteristics of developed countries.

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