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All Journal Jurnal Informatika JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Teknologi dan Sistem Komputer Indonesian Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal GERVASI: Jurnal Pengabdian kepada Masyarakat INSIST (International Series on Interdisciplinary Research) Jurnal Informatika Global Jurnal Teknologi Terpadu bit-Tech Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat Teknosains : Jurnal Sains,Teknologi dan Informatika Journal of Computer Networks, Architecture and High Performance Computing Idealis : Indonesia Journal Information System Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Indonesian Community Journal Jurnal Teknologi Sistem Informasi Jurnal INFOTEL SISFOTENIKA
Rendra Gustriansyah
Universitas Indo Global Mandiri, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JUITA : Jurnal Informatika

 1 
Performance Evaluation of Contract Employees Using the Best-Worst and Simple Additive Weighting Methods Rendra Gustriansyah; Juhaini Alie; Nazori Suhandi
JUITA : Jurnal Informatika JUITA Vol. 9 No. 2, November 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1506.27 KB) | DOI: 10.30595/juita.v9i2.11989

Abstract

All companies need qualified employees to ensure the continuity and progress of the company. Therefore, some companies are very selective in evaluating employee performance based on the criteria set by the company. However, because of the many criteria used in the assessment, a large number of contract employees to be evaluated, and the evaluation that must be proportional, the Human Resources department has difficulty evaluating the performance of contract employees. Therefore, this study aimed to develop a system for evaluating the performance of contract employees by integrating the Best-Worst (BW) and Simple Additive Weight (SAW) methods. The BW method is used to determine the weight of each criterion related to the performance appraisal of contract employees, and the SAW method is used to evaluate the performance of contract employees. The results showed that the system developed can provide a more proportional evaluation. So, this study contributes as a recommendation system for HR managers in determining eligible contract employees to have their work contracts extended based on criteria determined by the company.
DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine Ahmad Sanmorino; Rendra Gustriansyah; Juhaini Alie
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (861.248 KB) | DOI: 10.30595/juita.v10i2.14939

Abstract

In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.
Co-Authors Abel Destria Ade Fathurahman Adhe Suryani Agung Prakesakwa Ahmad Sanmorino Ahmad Sanmorino Aminuyati Andri Agus Agus S Arief Ramadhan Cahyani, Septa Dana Indra Sensuse Darius Antoni Destria, Abel DEWI SARTIKA Dewi Sartika Dhamayanti Dhamayanti Dhamayanti, Dhamayanti Ditho Hersilava Dwi Asa Verano Ermatita - Febrianty Fery Antony Fery Antony Filian Falanda Hartini Hartini Hartini Hartini Hastha Sunardi Haversyalapa, Ditho Isabella, Isabella Juhaini Alie M. Ricky Hikmawan Marshanda Amalia Megat Norulazmi Megat Mohamed Noor Mitha Sofiana Muhammad Farhan Nazori Suhandi Nazori Suhandi Nazori Suhandi Nining Ariati Pratama, M Duta Purbasari, Nadira Putri Purnamasari, Evi Reza Iman Pribadi Rudi Heriansyah Ryan Ramanda Putra Sari, Nursella Sartika, Dewi Shinta Puspasari Shinta Puspasari Shinta Puspasari Suryati Suryati Suryati Suryati Terttiaavini, Terttiaavini Verano, Dwi Asa Via Kris Wilza Nadia Wiyanto, Ari Zulkifli Zulkifli