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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika (JUTIF)
Jiustian, Danny
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Computer Science & IT

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Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia Laurenso, Justin; Jiustian, Danny; Fernando, Felix; Suhandi, Vartin; Rochadiani, Theresia Herlina
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.4871

Abstract

In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to find out which regions in Indonesia have the potential to increase marketing based on profits (margins) to support the target market analysis process so that companies do not suffer losses and increase business success. In this study, the analysis will be carried out using vape quantity, margin, and purchasing power data in each region, which is processed using 3 algorithms: K-Means, Hierarchical, and BIRCH. The results of the clustering of the three algorithms produce two clusters. The K-means, Hierarchical, and BIRCH algorithms produce the same clusters: a potential cluster consisting of 18 cities and a non-potential cluster consisting of 45 cities. To see the performance of the model results, an evaluation was carried out using the Silhouette score, Davies Bouldin, Calinski Harabasz, and Dunn index, which obtained results of 0.765201, 0.376322, 315.949434, and 0.013554. From these results, it can be concluded that the clustering results are not too good and not too bad because the greater the Silhouette Score, Calinski Harabasz, and Dunn Index value, the better the clustering results while for Davies Bouldin the smaller the value means the better the clustering results.
PROTOTYPE OF CONTACTLESS PAYMENT SYSTEM WITH RFID AND BLOCKCHAIN TECHNOLOGY INTEGRATED WITH MOBILE APPLICATION Jiustian, Danny; Yohannis, Alfa
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2002

Abstract

The COVID-19 pandemic has led to a change in payment methods, with a shift towards cashless payments to avoid germs and viruses. Contactless payments, especially those using RFID technology, have become popular due to their convenience and no need to enter security codes. However, there is a risk of data manipulation in transaction records, leading to decreased trust and transparency. To solve this problem, this research develops an RFID contactless payment system connected with blockchain technology as the main goal of the research. Blockchain is known for its security and transparency, making it suitable for minimizing data manipulation that often occurs. This research will use the Sepolia Test Network of the Ethereum base network for development in terms of blockchain to serve as a security layer in this research. The Waterfall method will be used for application development, focusing on structured and linear stages such as requirements analysis, system design, implementation, testing, and maintenance. The application development process has shown positive results, with successful black box testing and the ability to track and validate transactions stored in the database and blockchain. This validation process is critical to ensure the integrity of transactions and detect any data manipulation.
Co-Authors Fernando, Felix Laurenso, Justin Rochadiani, Theresia Herlina Suhandi, Vartin Yohannis, Alfa