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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Bryant Tinambunan
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Application of K-Means Clustering: Bot Activity and Sybill Attack Detection on the Solana Blockchain Bryant Tinambunan; Hafizam Mufti; Ahmad Zulfan; Guez Rade
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2235

Abstract

With the development of Blockchain technology, for example, the Solana Blockchain has generated enormous amounts of data and possesses the 5Vs of Big Data: volume, velocity, value, veracity, and variety. This has brought challenges, for example, in distinguishing transactions carried out by humans from automated bots that often carry out market manipulation or Sybil attacks. Therefore, this research aims to detect bot activity on the Solana network by applying data mining techniques, namely the K-Means Clustering algorithm. From the large transaction data that will be extracted only a portion from the public Solana dataset in BigQuery, it will then be processed through a preprocessing stage to normalize the data and simplify complex data into simpler variables before being grouped. Because the extracted data is in the form of unlabeled data groups (unsupervised data), the Clustering Method is used because of its ability to recognize data groups based on behavioral or characteristic similarities without requiring initial data labels (unsupervised learning). The main variables used for the grouping process include transaction frequency, inter-arrival time (inter-transaction), and the number of unique program interactions. The results of this analysis are expected to map transaction accounts into several clusters based on their transaction patterns, allowing for the classification of bots and humans. This research is expected to demonstrate that Big Data infrastructure such as Google Cloud, using data mining techniques (Clustering), can be used to maintain the security and integrity of the blockchain ecosystem.
RANCANG BANGUN SISTEM DETEKSI TOXIC DAN SPAM BERBASIS FINITE AUTOMATA DENGAN ALGORITMA AHO-CORASICK Alya Namira; Zulfahmi Indra; Adinda Soleha; Bryant Tinambunan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9864

Abstract

Perkembangan media sosial meningkatkan risiko penyebaran konten toxic dan spam yang dapat menurunkan kualitas interaksi pengguna. Penelitian ini bertujuan merancang dan mengimplementasikan sistem deteksi konten toxic dan spam berbahasa Indonesia berbasis Finite Automata menggunakan algoritma Aho-Corasick. Dataset yang digunakan terdiri dari dataset_kata_kasar_idn sebanyak 3.111 data dan dataset_spam_idn sebanyak 2.636 data. Dari kedua dataset tersebut diekstrak 89 kata toxic dan 83 kata/frasa spam yang digunakan sebagai kamus keyword sistem. Sebelum proses deteksi, teks melalui tahap preprocessing berupa case folding dan normalisasi karakter berulang. Selanjutnya, keyword dimasukkan ke dalam struktur trie dan dibangun failure function untuk membentuk automata Aho-Corasick. Pengujian dilakukan terhadap 48 kalimat uji yang terdiri atas kategori Toxic, Spam, Normal, dan Toxic+Spam. Hasil penelitian menunjukkan bahwa sistem mampu mencapai akurasi sebesar 91,7% dengan nilai precision rata-rata 0,925, recall 0,939, dan F1-score 0,931. Hasil tersebut menunjukkan bahwa pendekatan Finite Automata dengan algoritma Aho-Corasick efektif digunakan untuk mendeteksi konten toxic dan spam secara cepat pada teks berbahasa Indonesia.
Co-Authors Adinda Soleha Ahmad Zulfan Alya Namira Guez Rade Hafizam Mufti Zulfahmi Indra, Zulfahmi