Journal of Operation System

Consumer Purchase Pattern Analysis Using FP-Growth Algorithm-Indomaret Store (Case Study: Market Basket Analysis)

2023-07-18T01:03:05+00:00

The rapid development of the business world and economic changes has led to the creation of various products that cater to customer needs. The objective of this research is to analyze consumer purchasing patterns using the FP-Growth algorithm at Indomaret stores. Increasing competition in the market has driven companies to pay more attention to consumer behavior in purchasing goods and services. The retail industry, particularly related to Alfamart and Indomaret, is one of the rapidly growing sectors in the world today. Transaction data from Indomaret stores serves as the primary data source for this research. Information is collected from purchase receipts and grouped based on the purchase date. The FP-Growth algorithm is used to identify the combinations of products that are most frequently purchased by customers. The research findings indicate that a high minimum confidence value of 0.7 or 70% is often used as a threshold to validate a rule. This means that in 70% of cases, the rule is proven to be correct. Meanwhile, the minimum support value used is 0.2 or 20% as a common threshold. A lower minimum support value allows for flexibility in capturing itemsets that may occur unexpectedly or infrequently but are still significant. This study provides insights into consumer purchasing patterns at Indomaret stores and its relevance to the FP-Growth algorithm. The implication of this research highlights the importance of understanding consumer behavior to effectively compete in a competitive market. By utilizing data analysis techniques such as the FP-Growth algorithm, companies can optimize their marketing strategies and offer products that better suit customer needs.

Application of Association Rules Market Basket Analysis to Harmoni Store Sales Data Using the FP-Growth Algorithm

2023-07-18T01:09:45+00:00

The grocery store is a type of micro-scale business, the advantage is that it is not too far from residential areas so it is easier to reach. However, in the midst of an increasingly widespread retail business upheaval, grocery stores have challenges to survive and compete. The strategy that can be carried out is to utilize sales data using the FP Growth algorithm which is an alternative algorithm for determining the data that appears most often in a data set. Using the FP Growth algorithm with a minimum support value of 0.1 and a confidence value of 0.8 it can be found that the 2 product combinations that are most often purchased together at Harmoni stores are cigarettes with toiletries and children's equipment with body & face beauty products.

Consumer purchase analysis with a market basket analysis algorithm (case study: Alfamart)

2023-07-18T01:01:24+00:00

This research applies market basket analysis with the FP-Growth algorithm to Alfamart, a retail company. The aim of this study is to identify significant patterns of co-purchases to enhance marketing strategies and customer satisfaction. The research methodology involves the collection and analysis of transactional data from Alfamart. The research findings indicate that the FP-Growth algorithm successfully identifies useful association rules for Alfamart. By understanding customer purchasing patterns, the company can improve their marketing strategies and create effective promotions. Additionally, the study tests the performance of the FP-Growth algorithm on transactional datasets, demonstrating good efficiency and accuracy in generating association rules. This research contributes to the development of market basket analysis with the FP-Growth algorithm in the context of Alfamart. The findings provide valuable insights for the company to understand customer purchasing behavior and make better marketing decisions. Using Fp-growth in value combinations A minimum of support 0.05 and a confidence rate of 0.8 can be found in six frequent combination of products purchased at Alfamart stores include beverage, snacks, cigarettes and plastic bags. It is anticipated that this research will advance the field of market basket analysis and offer benefits for business decision-making.

Application Market Basket Analysis of Circle-K Minimarket to Know Consumer Purchasing Patterns

2023-07-18T01:00:30+00:00

The increasing number of minimarkets in Indonesia, especially in Batam city, has led to intensified competition among them. To understand consumer purchasing patterns, the author employed the Market Basket Analysis method. Market Basket Analysis is an approach that examines customer shopping habits by identifying associations and correlations among various items. This study collected data directly from the Circle-K minimarket. The researcher collected and cleaned transaction sales data, and then organized it into tables using Microsoft Excel. The data was grouped into different departments, which facilitated importing it into RapidMiner software. Data processing was conducted using association rules and RapidMiner software with the FP-Growth algorithm to identify items frequently purchased together by consumers. For this research, a support value of 10% and a confidence value of 80% were used. This resulted in two association rules. The findings indicated that customers who purchased Dept 2 (snacks) always bought Dept 1 (beverages) simultaneously with a confidence level of 84.2% (0.842). Furthermore, customers who purchased Dept 2 (snacks) and Dept 7 (heavy meals) always bought Dept 1 (beverages) together with a confidence level of 100% (1.000), based on all transaction data recorded in June 2023. Hence, it can be concluded that these association rules are valid.

Utilization of Remote Sensing Data and Geographic Information Systems in Disaster Mitigation with ArcGIS Software

2023-07-27T20:58:29+00:00

The aim of this research is to map the social and physical vulnerability to landslide disasters in Tigo Lurah Sub-district using Remote Sensing and Geographic Information Systems (GIS) technology. In this study, primary data was obtained through image interpretation to understand the landforms, while secondary data was obtained from the Tigo Lurah Sub-district BPS (Central Bureau of Statistics) in 2020 and Village Administration Shapefile. Through mapping, four social vulnerability classes were identified, with Batu Bajanjang village falling under the very high vulnerability class and Garabak village in the high vulnerability class. This research provides crucial information for disaster mitigation and response planning to landslides in the area. Villages with high social vulnerability, such as Batu Bajanjang and Garabak, require specific measures to protect the communities from disaster risks. Furthermore, this study also mapped the physical vulnerability of the affected areas, with Batu Bajanjang village showing a very high level of physical vulnerability. This emphasizes the urgency of mitigation and protection efforts against landslides in the area. However, villages with low physical vulnerability have adequate facilities. Nonetheless, prevention and monitoring measures are still needed to maintain the level of physical vulnerability throughout the sub-district. The results of this research are expected to serve as a guide for local governments and relevant institutions in formulating appropriate and sustainable disaster mitigation strategies.

Optimization Optimization Of Blood Supply Using The Monte Carlo Method (Case Study Of UTD PMI Solok)

2023-08-10T04:59:27+00:00

Unpreparedness blood supply anticipates requests to affect the cessation of medical procedures, decreased quality of health and death. The difficulty of determining the optimal level of blood supply with mathematical calculations is the method of calculating probability. The Monte Carlo method is a method of calculating problems that contains elements of probability. This method works by random sampling tests based on past data. The data that will be predicted in this study are January 2023 request data using January 2022 - December 2022 and February 2023 data using February 2022 - January 2023 data. The simulation results compared to January 2023 period real demand for groups A, B, AB, and O are sequentially 79%, 96%, 100% and 92% with a total accuracy rate of 90%. The simulation results of February 2023 in groups A, B, AB, and O respectively are 80%, 81%, 83%, and 80% with a total accuracy rate of 81%.

Literature Review: The Benefits of Electronic Medical Record Implementation for Hospital Management in Indonesia

2023-07-31T15:13:19+00:00

Electronic Medical Records (EMR) represent the latest advancement in patient medical record-keeping systems in hospitals. Several countries, including Indonesia, have adopted EMR services. Based on the Regulation of the Minister of Health of the Republic of Indonesia Number 24 of 2022 concerning Medical Records, the digital technology advancement in society has resulted in the digital transformation of healthcare services, making it necessary to implement EMR electronically with a focus on data and information security and confidentiality. This study aims to analyze research related to EMR in Indonesia. The research design adopts a literature review from databases such as Google Scholar, Portal Garuda, and PubMed using Indonesian or English keywords, including EMR, Indonesia, and implementation. The search yielded 310 articles about EMR worldwide, which were further narrowed down to 100 articles by using the keyword "EMR Indonesia." Subsequently, the search was refined to 9 articles with the keywords "EMR implementation in hospitals in Indonesia." These articles focused on various aspects, such as system development, user readiness, implementation, system audits, and addressing challenges in EMR implementation in Indonesia. To achieve high-quality EMR, a phased approach is recommended, encompassing system development, utilization, and continuous improvement in EMR systems.