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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Terpadu Tematik : Jurnal Teknologi Informasi Komunikasi JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga)
Martin Clinton Tosima Manullang, Martin Clinton Tosima
Program Studi Sistem Komputer, Fakultas Teknik, Universitas Diponegoro
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparative Analysis of CNN, Transformers, and Traditional ML for Classifying Online Gambling Spam Comments in Indonesian Manullang, Martin Clinton Tosima; Rakhman, Arkham Zahri; Tantriawan, Hartanto; Setiawan, Andika
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9468

Abstract

The rise of user-generated content on social media and live-streaming platforms has intensified the spread of spam, particularly online gambling (Judi Online) promotions, which remain prevalent in Indonesian comment sections. This study investigates the effectiveness of various machine learning (ML) and deep learning (DL) approaches in classifying such spam content in Bahasa Indonesia. We compare five models: Support Vector Machine (SVM), Random Forest (RF), a CNN-based model, IndoBERT, and a custom lightweight transformer model named Wordformer. While IndoBERT achieves the highest performance across all metrics, it comes with high computational demands. Wordformer, in contrast, delivers a strong balance between accuracy and efficiency, outperforming traditional models while being significantly more lightweight than IndoBERT. Wordformer achieved 0.9975 accuracy and macro F1-score, surpassing SVM (0.9578) and Random Forest (0.9729), while maintaining a significantly smaller model size and fewer multiply-add operations. An extensive ablation study further explores the architectural and training design choices that influence Wordformer’s performance. The findings suggest that lightweight transformer models can offer practical, scalable solutions for spam detection in low-resource language settings without the need for large pretrained backbones.
From Speech to Summary: A Pipeline-Based Evaluation of Whisper and Transformer Models for Indonesian Dialogue Summarization Manullang, Martin Clinton Tosima; Yulita, Winda; Kartagama, Fathan Andi; Putra, A. Edwin Krisandika
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11826

Abstract

The rapid increase in online meetings has produced massive amounts of undocumented spoken content, creating a practical need for automatic summarization. For Indonesian, this task is hindered by a dual-faceted resource scarcity and a lack of foundational benchmarks for pipeline components. This paper addresses this gap by creating a new synthetic conversational dataset for Indonesian and conducting two systematic, discrete benchmarks to identify the optimal components for an end-to-end pipeline. First, we evaluated six Whisper ASR model variants (from tiny to turbo) and found a clear, non-obvious winner: the turbo (distil-large-v2) model was not only the most accurate (7.97% WER) but also one of the fastest (1.25s inference), breaking the expected cost-accuracy trade-off. Second, we benchmarked 13 zero-shot summarization models on gold-standard transcripts, which revealed a critical divergence between lexical and semantic performance. Indonesian-specific models excelled at lexical overlap (ROUGE-1: 17.09 for cahya/t5-base...), while the multilingual google/long-t5-tglobal-base model was the clear semantic winner (BERTScore F1: 67.09).
Co-Authors Amrulloh, Iqbal Andika Setiawan, Andika Aprini, Istighfariza Hartanto Tantriawan I Wayan Wiprayoga Wisesa Kartagama, Fathan Andi Novaldi, Fahri Putra, A. Edwin Krisandika Rakhman, Arkham Zahri Resfita, Nova Yulita, Winda