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All Journal International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA JPT : JURNAL PROTEKSI TANAMAN (JOURNAL OF PLANT PROTECTION) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Gunawan, Rahmad
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Imbalanced data handling in multiclass distributed denial of service attack detection using deep learning Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Hasanatul Fu’adah Amran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26005

Abstract

In data analysis, imbalanced datasets are a frequent issue, where classes in a dataset have an uneven distribution, which can lead to poor performance in machine learning (ML) and predictive modeling. In this study, we analyze distributed denial of service (DDoS) attacks at the application layer. Three primary strategies are studied in this study to address the issue of data imbalance in multiclass techniques: random oversampling (ROS), random undersampling (RUS), and the use of class weights. A model using a deep learning (DL) technique has been proposed in this paper to be trained and tested for DDoS attack detection. Based on the results obtained and presented in this paper, it is observed that RUS outperforms class-weight and ROS in multiclass settings in terms of resolving imbalanced data when implemented with the deep learning-based DDoS attack detection model.
Recent systematic review on student performance prediction using backpropagation algorithms Edi Ismanto; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Januar Al Amien; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.21963

Abstract

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years
Intrusion detection system for imbalance ratio class using weighted XGBoost classifier Januar Al Amien; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Edi Ismanto; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24735

Abstract

The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances.
Deep learning approach to DDoS attack with imbalanced data at the application layer Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Januar Al Amien; Edi Ismanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24857

Abstract

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
Efektivitas Sosialisasi Narkotika, Psikotropika, dan Zat Adiktif (NAPZA) dalam Meningkatkan Pengetahuan dan Kesadaran Siswa SMP Negeri 05 Sungai Apit Anugerah Putra, Bayu; Soni, Soni; Gunawan, Rahmad; Fatma, Yulia; Firdaus, Rahmad; Taufiq, Reny Medikawati; Handayani, Fitri; Mukhtar, Harun; Mualfah, Desti; Azim, Fauzan; Aprilya, Regiesta Lintang; Ramadhan, Rafi Fakhri; Abadi, Abdi Nauli
Jurnal Pengabdian UntukMu NegeRI Vol. 10 No. 2 (2026): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v10i2.11302

Abstract

The abuse of narcotics, psychotropic drugs, and addictive substances (NAPZA) is a major problem that threatens the future of adolescents, especially junior high school students. Many students do not fully understand the dangers of NAPZA, making them vulnerable to the influence of their surroundings. Therefore, this study was conducted to determine the extent to which NAPZA awareness activities have succeeded in increasing the knowledge and awareness of students at SMP Negeri 05 Sungai Apit regarding the dangers of drug abuse. This study used a qualitative descriptive method with a field study approach. Data were obtained through material presentations and questions given to students after the socialization was conducted. The results showed an increase in students' knowledge about the types of NAPZA, their negative effects, and ways to prevent their abuse. Students were also better able to recognize the factors that could trigger abuse and showed a refusal to try NAPZA. The conclusion of this study states that NAPZA socialization is effective in increasing knowledge and forming a preventive attitude among students at SMP Negeri 05 Sungai Apit. Therefore, activities such as this need to be carried out regularly and continuously to create a healthy, safe, and NAPZA-free school environment.
MACHINE LEARNING UNTUK PREDIKSI SUHU: SEBUAH TINJAUAN Adityo, Tri Novian; Mukhtar, Harun; Firdaus, Rahmad; Taufiq, Reny Medikawati; Gunawan, Rahmad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.12177

Abstract

Global climate change has increased the demand for accurate temperature prediction to support decision-making in sectors such as agriculture, disaster mitigation, and energy management. Machine Learning (ML) and Deep Learning (DL) approaches have been widely applied to model the non-linear and dynamic characteristics of temperature data. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol. From 125 identified articles, 45 studies published between 2021 and 2025 were selected for detailed analysis. The results indicate that Long Short-Term Memory (LSTM) is the most frequently used algorithm, both as a standalone model and within hybrid architectures. Most studies employ multivariate datasets sourced from BMKG, ERA5 Reanalysis, satellite imagery, and the Internet of Things (IoT). Data preprocessing techniques, particularly norssmalization and time-series construction, play a crucial role in improving model stability. However, challenges remain, including hyperparameter sensitivity, complex weather data characteristics, and geographical variability. Future research opportunities include adaptive model development, multi-source data integration, and comprehensive comparative studies among algorithms.
Co-Authors . Reflinaldon Abadi, Abdi Nauli Abdurachman, Muhammad Andhika Ade Pratama Adityo, Tri Novian Alfaridzi, M Ilmi Alfian, Haris Amin Hariyanto Aminuyati Andi Nur Insani ANDRIANSYAH Apri Yanto Aprilya, Regiesta Lintang Arfa, Laura Zevira Avicenna, Achyar Zein Baidarus Bayu Anugerah Putra Damayanti, Risma Danang Mulyadipa Suratno Desti Mualfah Edi Ismanto Edi Rian Kartiko Erik Suanda Handika Evans Fuad Fadilla, Niken Rahma Fathurrahman, Raihan Fatma, Yulia Fauzan Azim Fauzan Salim Febby Apri Wenando Filamori, Refly Fauzan Fitri Handayani Fitri, Nurkhairi Hadhrami Ab Ghani Hadhrami Ab Ghani Harmawan, Muhamad Rizki Harun Mukhtar Hasanatul Fu’adah Amran Hayami, Regiolina Ilham Pratama, Muhammad Illahi, Kevanda Sondani Issandra, Febri Januar Al Amien Jasmin, Muhammad Iqbal Maysa Putri, Yulia Mirano, Muhammad Fitter Mualfah, Desti Nadira, Besti Zahratul Naufal Nazifah, Hayatun Nofrial . Nugroho, Altaric Nurul Izrin Md Saleh Nurul Izrin Md Saleh Nurulaqilla Khamis Pradipa, Raditya Pratiwi, Husnatul Fadillah Putra, Yogi Ernanda Rahmad Al Rian Rahmad Firdaus Rahman Septiadi Rahmania, Marsha Nailah Rais, Muhammad_Akmal Ramadhan, Rafi Fakhri Ramadhan, Syahrudin Ramadhoni Razkia, Binta Riani, Della Ayunda Rudi Ardiansyah Soni Soni Soni, Soni Sri Wahyuni Sugiyadi, Riski Syahril Tania, Manzilah Ditiara Taufiq, Reny Medikawati Ulva Elviani Vania, Azra Gusti Vitriani Wahyudhy, Adhe Indra Wesley, Royman Wibowo, Angga Yudha Wide Mulyana Widyaningrum, Amelia Ismania Sita Yaherwandi Yanti, Elis Yordan, Gibril Yulia Fatma Yuliskania, Aisyara Zaskiv S, Marshal Khairana Zilham, Adib