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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) TRIDARMA: Pengabdian Kepada Masyarakat (PkM)
Christopher Michael Lauw
Universitas Bumigora
Arts Humanities Computer Science & IT Engineering Social Sciences

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Penerapan E-Commerce dalam memasarkan produk UMKM Fitri Mutiara Lombok Husain; Sirojul Hadi; Muhammad Haris Nasri; Tomi Tri Sujaka; Lilik Widyawati; Bambang Krismono Triwijoyo; Christopher Michael Lauw
TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Vol. 5 No. 2 (2022): Nopember: Pengabdian Kepada Masyarakat (PkM)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/abdimas.v5i2.3350

Abstract

Perkembangan teknolgi infromasi saat ini yang serba digitalisasi mendorong UMKM Fitri Mutiara Lombok dalam memasarkan produk UMKM nya secara online. Menurunnya penjualan secara langsung yang salah satu disebabkan terjadinya pandemic covid 19 mendorong pengabdi dalam membantu UMKM untuk memasarkan secara online. Kegiatan ini dilakukan bertujuan untuk membantu UMKM Fitri Lombok dalam memasarkan penjualan mutiaranya secara online atau e-Comers agar dapat meningkatkan penjualan. PKM ini menghasilkan sebuah aplikasi pengjuan online atau disebut juga dengan e-comers. Dengan e-comers yang telah di bangun dapat membantu UMKM Fitri Mutiara Lombok dalam meningkatkan produktifitas dalam hal penjualan yang biasanya dilakukan secara ofline tetapi juga bias secara online
K-Nearest Neighbor Performance Optimization for Multiclass Imbalance of Intrusion Detection Data Using SMOTE and Distance Variation-Based Parameter Tuning Hairani Hairani; Christopher Michael Lauw; Sri Farida Utami; Afrig Aminuddin; Abu Tholib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7489

Abstract

The increasing use of computer networks and internet-based services has made cybersecurity threats more complex. Intrusion Detection Systems (IDS) play a crucial role in identifying network attacks; however, conventional signature- or rule-based approaches are limited in handling novel attacks and dynamically changing attack patterns. Therefore, machine learning approaches are applied to enhance the adaptive capabilities of IDS. Nevertheless, the use of machine learning in IDS still faces a major challenge: data imbalance, where normal traffic significantly outweighs attack traffic. This condition biases models toward the majority class, leading to suboptimal detection of minority attacks. Based on this issue, this study aims to improve the performance of the K-Nearest Neighbor (KNN) method in network attack detection by applying the Synthetic Minority Over-sampling Technique (SMOTE) and parameter tuning. The study employs KNN with parameter tuning and SMOTE to address multiclass data imbalance in network attack detection. Parameter tuning is conducted to determine the optimal value of k and distance functions, including Euclidean, Manhattan, and Cosine Similarity. The results show that KNN with k = 3 and Manhattan distance on SMOTE-balanced data achieves the highest accuracy of 96.51%, outperforming Euclidean and Cosine Similarity distances. These findings conclude that applying SMOTE and appropriately selecting k and distance metrics significantly improve KNN performance in network attack detection and increase overall detection accuracy.
Co-Authors Abu Tholib Afrig Aminuddin Hairani Hairani Husain Husain Lilik Widyawati Muhammad Haris Nasri Sirojul Hadi Sri Farida Utami Tomi Tri Sujaka Triwijoyo, Bambang Krismono