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All Journal International Journal of Artificial Intelligence Research Technology and Informatics Insight Journal
Anggit Gusti Nugraheni
Universitas Putra Bangsa
Computer Science & IT Electrical & Electronics Engineering

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Security Mitigation of the Open Journal System (OJS) Against Online Gambling Content Hijacking Using the ISSAF Framework Sarjimin Sarjimin; Anggit Gusti Nugraheni
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1546

Abstract

The urgency of this research is to identify the causes, develop mitigation methods, and enhance the security of OJS websites, as many are infiltrated or hijacked for online gambling or other harmful content. Securing OJS websites is never easy because attacks are increasingly diverse and innovative every day. OJS system security is essential to protect the information contained therein and protect the services provided by scientific journal publishers. The ISSAF framework, which uses a simulation approach similar to a real server, can serve as a basis for identifying OJS Website vulnerabilities in Webmin for a system administrator. The results of the identification in this study indicate that the leading cause of OJS web server attacks originates from outside the simulation environment, specifically the internet network via ports 80/443. Vulnerability Session Hijacking with Cookies receives a CVSS vulnerability score of 9.1. A vulnerability in the web server configuration folder structure, traceable by crawler tools, receives a CVSS vulnerability score of 5.3. Repeated login attempts to the OJS system are not banned, and blocking the Attacker's IP receives a CVSS vulnerability score of 6.5. A file with the .php extension was successfully uploaded; it may be a backdoor file with a CVSS vulnerability score of 5.3. Although the OJS PKP changed/forced the file to .txt, the malicious file could be exploited in the future by unauthorized users. The novelty of this research lies in a server simulation that mimics a real server and the ISSAF framework for assessing the security of the Webmin web-based system administration tool on OJS websites.
Klasifikasi Curah Hujan Harian Menggunakan Convolutional Neural Network (CNN) 1D Laili Meifa Ayuningtias; Muhammad Nizar Asagaf; Novi Ari Wardani; Anggit Gusti Nugraheni
Technology and Informatics Insight Journal Vol. 5 No. 2 (2026): TIIJ
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/cnskqe82

Abstract

Daily rainfall is an important climatic element for agriculture, water resource management, and hydrometeorological disaster mitigation. The temporal variability of rainfall makes its classification a challenging task. This study aims to develop a daily rainfall classification model using a one-dimensional Convolutional Neural Network (1D CNN) based on rainfall (RR) data from the Climatology Station of D.I. Yogyakarta for the period from July 1, 2025, to June 30, 2026, comprising 364 observations. The classification uses two classes, namely No Rain (RR = 0 mm) and Rain (RR > 0 mm), with a 30-day sliding window. The 1D CNN architecture consists of two convolutional blocks, batch normalization, ReLU, max pooling, global average pooling, a dense layer, L2 regularization, and dropout. Evaluation on 67 test samples yielded an accuracy of 74.63%, balanced accuracy of 66.84%, macro F1-score of 0.671, and ROC-AUC of 0.777. However, five-fold TimeSeriesSplit resulted in a balanced accuracy of 50.00%. These results indicate that the model performance is not yet stable due to the limited one-year dataset and the use of a single predictor variable.
Penerapan Long Short-Term Memory (LSTM) untuk Prediksi Konsentrasi PM2.5 di Kota Semarang Anom Pangestu; Putri Tripangesti; Nur Azizah; Anggit Gusti Nugraheni
Technology and Informatics Insight Journal Vol. 5 No. 2 (2026): TIIJ
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/hp1w8083

Abstract

Air pollution is an environmental problem with important implications for public health. Particulate Matter 2.5 (PM2.5) is a critical air-quality parameter because its fine particles can penetrate deeply into the respiratory system. This study develops a Long Short-Term Memory (LSTM) model to predict daily PM2.5 concentrations in Semarang City using historical observations. The research stages include preprocessing, Min-Max normalization, sequence generation with a 30-step time window, chronological 80:20 data splitting, LSTM training, and evaluation using MAE, RMSE, and MAPE. The model consists of two LSTM layers with 64 and 32 units, dropout of 0.2, the Adam optimizer, and MSE loss. The testing results produced an MAE of 5.7560, an RMSE of 7.4613, and a MAPE of 16.73%. These results indicate that LSTM can capture temporal patterns in PM2.5 data, although further evaluation using benchmark models and meteorological variables is needed.
Co-Authors Anom Pangestu Laili Meifa Ayuningtias Muhammad Nizar Asagaf Novi Ari Wardani Nur Azizah Putri Tripangesti Sarjimin, Sarjimin