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All Journal Annual Research Seminar Indonesian Journal of Artificial Intelligence and Data Mining
Ahmad Heryanto
Sriwijaya University
Computer Science & IT

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Visualisasi Serangan Brute Force Menggunakan Metode K-Means dan Naive Bayes Sari Sandra; Deris Stiawan; Ahmad Heryanto
Annual Research Seminar (ARS) Vol 2, No 1 (2016)
Publisher : Annual Research Seminar (ARS)

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Abstract

Penelitian ini menyajikan visualisasi dalam bidang two dimensional (2D) untuk mengkategorikan paket ISCX dan DARPA dataset. Paket data akan dibedakan dalam dua kategori yaitu paket data attack dan paket data normal berdasarkan pattern serangan brute force. Serangan brute force melakukan penyerangan pada beberapa layanan protokol seperti secure shell (SSH) dan telecommunication network (Telnet). Pada ISCX dataset serangan brute force terjadi pada layanan SSH , sedangkan DARPA dataset terjadi pada layanan TELNET.Metode K-Means dan metode Naïve Bayes diimplementasikan pada penelitian ini untuk mendapatkan hasil pengkategorian yang efektif Hasil akhir dari penelitian menunjukkan metode yang digunakan mendapatkan hasil yang baik dalam hal accuracy dengan mengurangi false alarm yang terjadi.
Optimized Indonesia Language Preprocessing Framework for Gojek Reviews Sentiment Analysis Using Linear Support Vector Machine Jahda Rusti Putri; Hendra Wijaya; Ali Ibrahim; Ahmad Heryanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study analyzes sentiment in Gojek application user reviews using Natural Language Processing (NLP) and machine learning techniques to classify sentiments into positive, negative, and neutral categories. A dataset of 8,091 Bahasa Indonesia reviews from Kaggle (Gojek version 4.8) was processed, yielding 5,685 valid instances after cleaning, with sentiment distribution of neutral (52.7%), positive (26.8%), and negative (20.3%). An optimized Indonesian preprocessing pipeline was developed, incorporating text cleaning, slang normalization using a curated 1,247-word mapping dictionary, tokenization, stemming via Sastrawi, and stopword removal. Feature extraction employed TF-IDF Vectorizer (max_features=10,000; n-gram=(1,2)). Four algorithms, Naïve Bayes, Linear SVM, Logistic Regression, and Random Forest (tuned) were evaluated using stratified 80:20 split. Linear SVM and Random Forest achieved the highest accuracy at 93% (weighted F1-score: 93%), followed by Logistic Regression (92%) and Naïve Bayes (68%). Ablation study confirmed that slang normalization contributed the greatest performance gain (4.8%). Keyword-based aspect extraction on negative reviews identified three priority improvement areas: customer service responsiveness (38%), pricing transparency (32%), and application stability (24%).
Co-Authors Ali Ibrahim Deris Stiawan Hendra Wijaya Jahda Rusti Putri Sari Sandra