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All Journal CommIT (Communication & Information Technology) TEKNOINFO JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining FIRM: Journal of Management Studies JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems Jurnal Sosial dan Teknologi IT For Society : Journal of Information Technology Ranah Research : Journal of Multidisciplinary Research and Development Jurnal Manajemen Sistem Informasi
Rusdianto Roestam
Badan Pengkajian dan Penerapan Teknologi (BPPT)
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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Online Monitoring and Analysis of Lube Oil Degradation for Gas Turbine Engine using Recurrent Neural Network (RNN) Nugroho, Febrianto; Roestam, Rusdianto
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1108

Abstract

Lubrication is one of the important aspects of the engine that will impact the overall performance of the gas turbine engine. Degradation of oil is usually known by offline analysis that use oil sample to check some properties and contaminant. The offline analysis will take a longer time, as needed to collect the sample, send it to the laboratory, analyze the sample and create the report. The purpose of this research is to analyze oil parameters in real-time so can predict oil degradation. Sensors and transducers installed on the lube oil system can read some parameters of the oil then transmit easily to the server. The method that will use in this paper is Recurrent Neural Network (RNN) with multi-step Long Short Term Memory (LSTM). The result of this paper will predict oil degradation on the future operation of gas turbine engine.
Combining Random Forest with Firefly Algorithm to Improve Darknet Traffic Detection Timothy Lim, Vincent; Roestam, Rusdianto
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 2 (2026): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i2.2023

Abstract

Darknet traffic detection system, a cyber-crime activity detection system that detects the use of Tor and VPNs, is one way to reduce the occurrence of darknet cyber-crimes. Current existing detection tools such as machine learning models have shown its capability in detecting darknet network traffics. However, it still faces some limitations in its performance due to suboptimal hyperparameters. One of the existing classification models used for darknet traffic detection, such as Random Forest demonstrated great performance in detecting darknet activities. This research utilizes the Firefly Algorithm (FA), a prominent swarm intelligence method, to fine-tune hyperparameters and enhance the detection capabilities of the Random Forest (RF) model. The proposed RF-FA (Random Forest – Firefly Algorithm) approach is evaluated against the standard Random Forest model. Tests performed on the CIC-Darknet2020 dataset reveal that the Firefly Algorithm improves the RF model's performance in all key metrics. The optimized RF-FA model attains an accuracy, precision, recall, and F1-score of 98.73%, surpassing the baseline RF model, which achieves 98.62% in accuracy, precision, and recall, along with an F1-score of 98.61%.
Co-Authors Afrina, Afrina Alfaqih, Muhammad Subhan Andika, Inno Reza Andres, M Anto Satriyo Nugroho, Anto Satriyo Aryanti, Evy Aryanti Asmail, Asmail Bawono, Bimo Adi christian christian Christina Christina ChungKiat, Ong Ediansa, Oka Fahmi, Hasanul Ferianasari, Inneke Winda Gulo, Trisnawati Hariyanto, Dian Tri Heri Suryadi Hernando, Irham Hi Rauf, Siti Nuraini Indahsari, Ayu Nur Irawan, Dodi Kurniawan, Alhadi Qori Lianzhai, Duan Lila Setiyani Linli, Peng Maulana, Ikmal Montolalu, Ivan Adrian Muliadi, Jemie Najuantah, Putra Nasrullah Armi Nasution, Wahyudi Nori Sahrun, Nori Nugroho, Febrianto Pamungkas, Lukas Sangka Purwoko, Reza Yuridian Putra, Sumpena Adi Rahayaan, Manuela Rima Melati ROBY SETIAWAN Rosalina Rosalina Ruhdiat, Rudi Santriono, Refki Saputra, Silvan Sarjon Defit Siregar, Rizky Maulana Supriyadi, Muhamad Rodhi Suwarto Suwarto Suyanti Suyanti Timothy Lim, Vincent Tjong Wan Sen Triadi, Albet Wahyu, R. B. Wahyu, Raden Bagus Wan Sen, Tjong Wan Sen Yunarti, Welaspen Yusnita, Lita