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All Journal Building of Informatics, Technology and Science Journal of Software Engineering and Information System (SEIS)
Yaasin, Muhammad
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder Pahlevi, M. Riza; Rasywir, Errissya; Pratama, Yovi; Istoningtyas, Marrylinteri; Fachruddin, Fachruddin; Yaasin, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.4864

Abstract

The False Positive (FP) interpretation shows a negative prediction result and is a type 1 error answer with an incorrect positive prediction result. Based on this, we try to reduce type 1 errors to increase the accuracy value of the classification results. A low FP rate is critical for the use of Computer Aided Detection (CAD) systems. In this research proposal, to reduce FP, we use a Random Forest (RF) evaluation result design which will be reinterpreted by the Auto Encoder (AE) algorithm. The RF algorithm was chosen because it is a type of ensemble learning that can optimize accuracy in parallel. RF was chosen because it performs bagging on all Decision Tree (DT) outputs used. To suppress TP reduction more strongly, we use the Auto Encoder (AE) algorithm to reprocess the class bagging results from RF into input in the AE layer. AE uses reconstruction errors, which in this case is Job Placement classification. From the test results, it was found that combining the use of a random forest using C4.5 as a decision tree with an Autoencoder can increase accuracy in the Job Placement Classification task by a difference of 0.004652 better than without combining it with an autoencoder. Apart from that, in testing using a combination of RF and AE, fewer False Positive (FP) values ​​were produced, namely 11 items in the Cross Validation-5 (CV-5) Test, then 13 items in the Cross Validation-10 (CV-10) test and in testing split training data of 60%, the FP was only 12. This value is less than the false positives produced by testing without Autoencoder, namely 12 items on CV-5, 15 items on CV-10, and 13 on split training data
PENERAPAN E-COMMERCE DENGAN METODE CRM BERBASIS WEBSITE PADA TOKO BATAVIA COLLECTION Mulyadi, Iriene Putri; Yaasin, Muhammad; Rahman, Fadil Aulia; Rifaldi, Dianda; Ramadhan, Fauzan Purma; Saputra, Vanji
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.9736

Abstract

Website-based e-commerce is a crucial strategy for increasing transaction efficiency and fostering stronger relationships with customers, especially in the fashion sector. Among the fashion brands, Batavia Collection uses digital systems with Customer Relationship Management (CRM) integration to make it easier for customers to access product information, conduct online transactions, and communicate wirelessly with customers. The waterfall method is used to build the system, covering the phases of requirement analysis, system design, implementation, and pengujian. CRM integration is useful for managing customer data, supporting sales activities, and expanding personal services. According to the study's findings, operational efficiency has increased, customer satisfaction, and the potential for more extensive market expansion. This system also aids in more systematic promotion and pelaporan processes. The implementation of CRM-based e-commerce can increase Batavia Collection's sales and service quality.
Co-Authors Dianda Rifaldi Errissya Rasywir Fachruddin Fachruddin Istoningtyas, Marrylinteri Mulyadi, Iriene Putri Pahlevi, M. Riza Rahman, Fadil Aulia Ramadhan, Fauzan purma Saputra, Vanji Yovi Pratama