The Model of Business Intelligence Development by Applying Cooperative Society Based Financial Technology

Al-Khowarizmi, Al-Khowarizmi; Syah, Rahmad; Elveny, Marischa

doi:10.1007/978-981-16-2377-6_13

Cited by 3 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of business and various problems as well as with big data can be solved in the field of business [11]. Various types of businesses are also a concern for the need for data mining techniques to gain new knowledge [12]. As is the case in banking which has a financial transaction process where one type of crime that often occurs is fraud in the transaction process, both in the declared type of fabrication crime [13], [14].…”

Section: Introductionmentioning

confidence: 99%

The role of Louvain-coloring clustering in the detection of fraud transactions

Mardiansyah,

Suwilo,

Nababan

et al. 2024

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Clustering is a technique in data mining capable of grouping very large amounts of data to gain new knowledge based on unsupervised learning. Clustering is capable of grouping various types of data and fields. The process that requires this technique is in the business sector, especially banking. In the transaction business process in banking, fraud is often encountered in transactions. This raises interest in clustering data fraud in transactions. An algorithm is needed in the cluster, namely Louvain’s algorithm. Louvain’s algorithm is capable of clustering in large numbers, which represent them in a graph. So, the Louvain algorithm is optimized with colored graphs to facilitate research continuity in labeling. In this study, 33,491 non-fraud data were grouped, and 241 fraud transaction data were carried out. However, Louvain’s algorithm shows that clustering increases the amount of data fraud of 90% by accurate.</span>

show abstract

Section: Introductionmentioning

confidence: 99%

The role of Louvain-coloring clustering in the detection of fraud transactions

Mardiansyah,

Suwilo,

Nababan

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

“…Business Analytics is a collection of techniques, technologies, and applications used to analyze company data and performance in order to make data-driven judgments regarding future investment strategies [12][13]. The three components of business analytics are descriptive analytics, the monitoring of key performance indicators to understand current business conditions, predictive analytics, the analysis of trend data to predict possible future outcomes, and prescriptive analytics, the use of past performance to generate recommendations on how to handle similar situations in the future [14][15][16][17]. For optimization purposes in predictive business analytics, metaheuristics are applied.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic Model for Efficient Analytical Business Prediction

Elveny,

Nasution,

Syah

2023

IJACSA

View full text Add to dashboard Cite

Accurate and efficient business analytical predictions are essential for decision making in today's competitive landscape. Involves using data analysis, statistical methods, and predictive modeling to extract insights and make decisions. Current trends focus on applying business analytics to predictions. Optimizing business analytics predictions involves increasing the accuracy and efficiency of predictive models used to forecast future trends, behavior, and outcomes in the business environment. By analyzing data and developing optimization strategies, businesses can improve their operations, reduce costs, and increase profits. The analytic business optimization method uses a hybrid PSO (Particle Swarm Optimization) and GSO (Gravitational Search Optimization) algorithm to increase the efficiency and effectiveness of the decision-making process in business. In this approach, the PSO algorithm is used to explore the search space and find the global best solution, while the GSO algorithm is used to refine the search around the global best solution. The hybrid meta-heuristic method optimizes the three components of business analytics: descriptive, predictive, and perspective. The hybrid model is designed to strike a balance between exploration and exploitation, ensuring effective search and convergence to high-quality solutions. The results show that the R2 value for each optimization parameter is close to one, indicating a more fit model. The RMSE value measures the average prediction error, with a lower error indicating that the model is performing well. MSE represents the mean of the squared difference between the predicted and optimized values. A lower error value indicates a higher level of accuracy.

show abstract