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JAIA - Journal of Artificial Intelligence and Applications
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Amik Riau
ISSN : -     EISSN : 27467821     DOI : -
Core Subject : Science,
Computer Science & IT
This journal publishes research results in the form of research articles, literature studies and articles in the form of concepts and policies in the field of computers in general: Machine Learning and Deep Learrning Clustering and Classification Prediction Document Mining and Text Mining Sentiment Analysis Spatial Data Mining Multi-Agent Systems Biologically Inspired Intelligence Intelligent control systems Complex Systems and Applications Computational Intelligence Soft Computing Image and Speech Signal Processing Computer Vision Pattern Recognition Numerical Computational Knowledge Based Systems and Knowledge Networks Knowledge discovery and Database Machine Learning Neural Networks and Applications Optimization and Decision Making Rough sets and Granular Computing Self-Organizing Systems Fuzzy Logic Decision Support and Expert System Business Intelligence Intelligence System Hybrid Algorithm Social Intelligence Social Media Analytic Stochastic Systems Support Vector Statistic and Matematic Modeling Web and mobile Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 35 Documents
 1   2   3   4 
Comparison of Support Vector Machine and Random Forest Algorithms for Analyzing Online Loans on Twitter social media Hamdani; N.A, Randi; M. Khairul Anam
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1087

Abstract

Online loans represent a form of financial service wherein borrowers can apply for loans through digital platforms without the need to visit physical offices. The application, approval, and disbursement processes are conducted online, leveraging technology to facilitate financial access and transactions. However, some online lending services impose high-interest rates, resulting in a significant financial burden for borrowers. Moreover, there are instances of inappropriate debt collection practices, such as contacting the borrower's friends or family, leading to discussions and comments on social media platforms like Twitter. This research aims to analyze the patterns of comments in Indonesian society regarding online lending. The study utilizes sentiment analysis and compares machine learning algorithms to assess their accuracy. The algorithms employed in this study are Support Vector Machine (SVM) and Random Forest. The results indicate that the SVM algorithm achieves an accuracy of 93.85%, while Random Forest achieves an accuracy of 91.62%.
The Optimizing Sales Strategies to Address Excessive Stock Accumulation: A Data Mining Approach Susandri; Muhammad Arief Solihin; Hamdani; Asparizal
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1110

Abstract

The Two Pelita Weaving Business has recorded significant sales in the weaving industry, despite facing challenges in managing product stock due to the accumulation of excess stock caused by a lack of customer interest. This study employs data mining techniques, specifically the Association Rule and Apriori algorithms, to analyze sales patterns. The analysis results using Python and Orange Data Mining showed consistency in the relationship between Siku Keluang Weaving and Pucuk Rebung Weaving products, with high occurrence rates of purchase patterns (11.74% and 10%, respectively). High confidence levels with Python at 96.36% and Orange Data Mining at 99.1% indicate that customers who purchase Siku Keluang Weaving are also likely to purchase Pucuk Rebung Weaving products.
Re-Design Application Mobile “Wallet” With Method Lean UX Efendi, Yoyon; Gunadi; Muzawi, Rometdo; Utami, Nurul
JAIA - Journal of Artificial Intelligence and Applications Vol. 3 No. 2 (2023): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v3i2.1076

Abstract

Wallet is a financial product service designed to simplify the payment process. Through Wallet, users can make transactions independently without having to go to a bank branch or ATM. Apart from that, the existence of Wallet helps reduce customer queues at ATMs. To increase the number of transactions via Wallet and make service quality effective and efficient, improvements to Wallet features and design are needed. It is still incomplete and no longer attractive, so Wallet usage has decreased. Therefore, User Interface (UI) and User Experience (UX) design is needed as a basis for building a platform to create more attractive designs and features. The Lean UX method focuses on user satisfaction with the user interface created, so this method was chosen in this research to develop the user interface design. Based on analysis, implementation and evaluation, the final prototype is a combination of prototypes A and B which have been validated in terms of appearance, as well as criticism and suggestions from consumer users and technicians. Prototype A was selected for 5 features and Prototype B was selected for 5 features. On the logo page select design A with a percentage of 72.2%, on the main menu page select design A with a percentage of 80.6%, on the home page select design A with a percentage of 69.4%, on the account information page select design A with a percentage of 75%, and the selected transfer page was design B with a percentage of 55.6%. In addition, this research produces results that have a consistent user interface in terms of colors, fonts, images and layout as well as a user experience that makes it easy for users to understand the information to use the application and get it as needed.
Comparison of Support Vector Machine and Random Forest Algorithms for Analyzing Online Loans on Twitter social media Hamdani; N.A, Randi; M. Khairul Anam
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1087

Abstract

Online loans represent a form of financial service wherein borrowers can apply for loans through digital platforms without the need to visit physical offices. The application, approval, and disbursement processes are conducted online, leveraging technology to facilitate financial access and transactions. However, some online lending services impose high-interest rates, resulting in a significant financial burden for borrowers. Moreover, there are instances of inappropriate debt collection practices, such as contacting the borrower's friends or family, leading to discussions and comments on social media platforms like Twitter. This research aims to analyze the patterns of comments in Indonesian society regarding online lending. The study utilizes sentiment analysis and compares machine learning algorithms to assess their accuracy. The algorithms employed in this study are Support Vector Machine (SVM) and Random Forest. The results indicate that the SVM algorithm achieves an accuracy of 93.85%, while Random Forest achieves an accuracy of 91.62%.
The Optimizing Sales Strategies to Address Excessive Stock Accumulation: A Data Mining Approach Susandri; Muhammad Arief Solihin; Hamdani; Asparizal
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1110

Abstract

The Two Pelita Weaving Business has recorded significant sales in the weaving industry, despite facing challenges in managing product stock due to the accumulation of excess stock caused by a lack of customer interest. This study employs data mining techniques, specifically the Association Rule and Apriori algorithms, to analyze sales patterns. The analysis results using Python and Orange Data Mining showed consistency in the relationship between Siku Keluang Weaving and Pucuk Rebung Weaving products, with high occurrence rates of purchase patterns (11.74% and 10%, respectively). High confidence levels with Python at 96.36% and Orange Data Mining at 99.1% indicate that customers who purchase Siku Keluang Weaving are also likely to purchase Pucuk Rebung Weaving products.

Page 4 of 4 | Total Record : 35

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