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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Innovation Information Technology and Application (JINITA)
Mawengkang , Herman
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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MODEL PREDIKSI PROFIL PELANGGAN BERDASARKAN KLASIFIKASI MELALUI PENDEKATAN SUPPORT VECTOR MACHINE Kiro, Ogiana; Mawengkang , Herman; Zamzami, Elviawaty Muisa
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11608

Abstract

Nowadays the market is characterized globally, products and services are almost identical and there are many suppliers. The most important aspect in classifying data in data mining is classification. Classification techniques have been widely used in many problems in research. The purpose of this research is to build a model that can predict behavior based on the information of each customer. This research was conducted by making a Prediction Model of Customer Profile Based on Classification Through the Support Vector Machine Approach which aims to obtain a package prediction accuracy value that is suitable for WO (Wedding Organizer) customers in classifying based on the profile of prospective customers. In the optimization results on the SVM model kernel function, the linear and polynomial kernels get the same accuracy value on the training data of 99.29% and the testing data of 94.92%. The lowest accuracy value was obtained in the RBF kernel function of 97.16% on training data and 96.61% on testing data. the best precision class value in the data testing was obtained in the basic package at 100%. The total value of the appropriate prediction on the training data was obtained by 56 samples from a total of 59 samples, and 3 samples that did not match the prediction with an accuracy of 94.92% on the data testing
Evaluating ERD Models and RAID-Based Storage for Query Performance Optimization in Relational Databases Lubis, Juanda Hakim; Handayani, Sri; Mawengkang , Herman; Yuliska
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2707

Abstract

The amount of data stored in magnetic disks (e.g., floppy disks) increases by 100% each year for each department in a company, necessitating efforts to maintain an optimal database system. Designing a database is the initial step in creating a system with optimal performance. However, database design alone is not sufficient to enhance performance. One approach to improving data transaction speed is by optimizing query processing. This research evaluates different relational database models using varying amounts of data. Query costs are analyzed using the Cost-Based Optimizer method and access time measurements. The results of this study provide insights for database administrators in designing relational database models effectively and selecting appropriate query structures to optimize database performance. The findings indicate that: (1) database design can be optimized by separating entities based on specialized usage, and (2) factors such as record count, attribute size, query type, use of unique or primary keys, order-by clauses, index sequences, and SQL function usage significantly impact query cost and overall performance.
Co-Authors Elviawaty Muisa Zamzami Handayani, Sri Juanda Hakim Lubis Kiro, Ogiana Yuliska