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Arif Rachmat
Institut Teknologi Bandung
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Survei Penerapan Model Machine Learning Dalam Bidang Keamanan Informasi Arif Rachmat
Jurnal Sistem Cerdas Vol. 2 No. 1 (2019): Artificial Intelligence for Smart Society
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (797.229 KB) | DOI: 10.37396/jsc.v2i1.20

Abstract

This paper provides a survey that discusses the spread used of machine learning models and algorithm for problems in information security. The breadth of the various types of techniques and methods by machine learning on this survey also figured by given examples of each model in the application for problems related to information security. The results of the study can be concluded that the use of machine learning in information security has spread widely in its use. Some methods are published in standard ways, with expectations this paper will give the insight to develop better models of machine learning applications in information security.
Predictive Analytic Klasifikasi Penentuan Tarif Sewa Bus Arif Rachmat; Nuqson Masykur Huda; Sri Anita
Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (999.602 KB) | DOI: 10.37396/jsc.v2i2.29

Abstract

Currently, the bus rental business has become the choice of consumers in traveling, because of the decision of flexibility and better availability. The government regulates that consumer and business owner agreements determine bus rental rates without routes. In this study intends to do clustering from the history of raw data that already exists before. Data is obtained from companies in the form of spreadsheet files originating from non-information systems. The raw data is combined and normalized, to eliminate the noise data and the data is not abnormal. The clustering results using the K-Means algorithm and Louvain clustering produce several tariff groups that can be used as a reference for determining fare. In this paper also concludes about unbalanced data, which can cause data clustering errors.
Co-Authors Nuqson Masykur Huda Sri Anita