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All Journal The IJICS (International Journal of Informatics and Computer Science) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Mochamad Sanwasih
Politeknik Digital Boash Indonesia, Bogor
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Multi-Criteria Decision Making Using the WASPAS Method in Webcam Selection Decision Support Systems Arisantoso Arisantoso; Mochammad Hasymi Somaida; Mochamad Sanwasih; Mohammad Imam Shalahudin
The IJICS (International Journal of Informatics and Computer Science) Vol 7, No 1 (2023): March 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v7i1.6001

Abstract

To carry out all virtual or online activities, you need hardware that can support it, one of which is a webcam. Many webcam products issued by various electronics companies are compatible for laptops and computers. However, to make a webcam selection the user must know one by one the specifications of each webcam. This of course takes a long time to determine the right webcam. This study aims to implement the Multi-Criteria Decision Making (MCDM) approach with Aggregated Sum Product Assessment (WASPAS) on a webcam selection decision support system, in order to get the best, right and fast alternative. The WASPAS method can determine the best alternative through prioritization that is relevant to the weighting used. Based on the case studies conducted, the WASPAS method was able to determine the best webcam with the best alternative results, namely NYK Nemesis A96 with a value of 0.7053, followed by Aukey PC-LM7 with a value of 0.6826, JETE W2 with a value of 0.6799, Logitech C922 with a value of 0.6499 and Ausdom AF660 with a value 0.6271. Because the findings are identical to manual calculations, the created system generates legitimate WASPAS method calculations. Based on the tests carried out using the black-box testing approach, it shows that all the functions in the system can run as they should
Deteksi Intrusi Jaringan Berbasis Machine Learning Menggunakan Model Boosting dengan Session-Level Feature Representation Mochamad Sanwasih; Fajar Septian; Ristasari Dwi Septiana
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8775

Abstract

The increasing complexity of network security threats demands intrusion detection systems that are both contextual and adaptive. Conventional signature-based Intrusion Detection Systems (IDS) suffer from limitations in detecting emerging and previously unseen attack patterns, making machine learning–based approaches a more flexible alternative. However, fragmented packet-level feature representations still limit the ability of models to capture network behavior comprehensively. This study aims to evaluate the performance of boosting models, namely XGBoost and LightGBM, using the publicly available Cybersecurity Intrusion Detection Dataset from Kaggle, which represents network activity at the session level. The proposed approach develops a session-level feature representation based on aggregated and ratio-based features to capture network behavior characteristics more comprehensively. Experimental results demonstrate that the implementation of session-level feature representation yields consistent improvements across multiple evaluation metrics. Accuracy increased from 0.8779 to 0.8847, while the F1-score improved from 0.8452 to 0.8525 for XGBoost and from 0.8455 to 0.8523 for LightGBM. Furthermore, ROC-AUC increased from 0.8789 to 0.8844 for XGBoost and from 0.8793 to 0.8859 for LightGBM. Although the improvement in accuracy is relatively moderate, the gains in F1-score and ROC-AUC indicate enhanced discriminative capability and a better balance between precision and recall. The main contribution of this study lies in the integration of session-level feature engineering with boosting models within a systematic evaluation framework, emphasizing the critical role of behavioral feature representation in improving intrusion detection performance.
Co-Authors Arisantoso Arisantoso Fajar Septian Mochammad Hasymi Somaida Mohammad Imam Shalahudin Ristasari Dwi Septiana