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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKNOLOGIA Jurnal Linguistik Komputasional Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Data Science and Its Applications Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Dike Jurnal INFOTEL CSRID
Kemas Muslim Lhaksmana
Universitas Telkom, Indonesia
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Comparative Analysis of Random Forest and Convolutional Neural Network (CNN) Algorithms for Pneumonia Detection in Chest X-ray Images: Accuracy, Interpretability, and Computational Efficiency Zaena, Siffa; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7840

Abstract

Pneumonia is a lung infection that can be detected through chest X-ray images. Manual diagnosis requires radiological expertise and time, thus an accurate automated method is needed. This study aims to compare the performance of two image classification algorithms, Convolutional Neural Network (CNN) and Random Forest (RF), in detecting pneumonia. The dataset used was obtained from Kaggle, consisting of 5,863 X-ray images categorized into three classes: bacterial pneumonia, viral pneumonia, and normal. Preprocessing steps include image resizing, normalization, and data augmentation. The CNN model was built using multiple convolutional and pooling layers, while RF utilized numerical features derived from histograms and texture. The CNN model demonstrated superior performance, achieving 92.4% accuracy, 93.1% precision, 91.6% recall, and 92.3% F1-score, compared to 82.7%, 80.3%, 85.1%, and 82.6% for Random Forest, respectively. Although CNN offers better accuracy, RF excels in interpretability. In conclusion, CNN is more effective for image-based pneumonia classification, yet RF remains relevant in applications requiring transparent decision-making. Potential biases, such as class imbalance and limited demographic representation in the dataset, could influence model performance and generalizability across different patient populations.
Comparison of Convolutional Neural Network and Support Vector Machine for Student Question Classification in ChatGPT-based Learning Tools Jordan, Brilliant; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7841

Abstract

Artificial Intelligence (AI) has revolutionized educational tools by enabling systems that proactively understand and respond to student needs. ChatGPT, a widely used generative model for education in Indonesia. However, it struggles to classify student questions accurately due to ambiguous phrasing, overlapping sentence structures, and difficulty recognizing intent, which limits its effectiveness as a learning assistant. This study compares the performance of Convolutional Neural Networks (CNN), which extract locally important features from word sequences with Support Vector Machines (SVM) in classifying student questions known for handling high-dimensional data and efficiently finding the optimal hyperplane for text classification. A dataset of 2,797 Indonesian ChatGPT interactions (71% clear vs. 29% unclear) was preprocessed through case folding, stop-word removal, stemming, and tokenisation, followed by data augmentation based on synonyms, which was applied to the minority class to balance the dataset. The models were tuned through grid or random search with prediction testing of the best model using 5-fold cross-validation comparisons across three data splits (70:30, 80:20, and 90:10). Results showed that CNN achieved balanced accuracy, precision, recall, and F1-score of 0.90 on the 90:10 split, outperforming SVM, which plateaued at 0.85 accuracy and dropped to 0.76 in F1-score. The embedded filters of the CNN found generality from lexical variation through the process of augmentation, while the TF-IDF sparse vectors in the SVM failed to maintain this level of semantics. These findings underscore that CNN is more adaptive to diverse data and better suited for integration into ChatGPT-based educational tools, particularly in supporting reliable classification and personalised AI feedback in student learning contexts.
Classification of English Language Anxiety Using Support Vector Machine on Twitter User Marozi, Ericho; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8015

Abstract

This study aims to classify expressions of language anxiety in English as a foreign language, as reflected in user-generated texts on Twitter. The research applies machine learning approaches Support Vector Machine (SVM) and Convolutional Neural Network (CNN) to perform automatic classification of anxiety levels. The dataset was collected through Twitter crawling, filtered for relevance, and annotated manually using a three-point scale (low, medium, high) based on psychological indicators such as fear of speaking, avoidance, and self-perceived inability. Preprocessing included text normalization, tokenization, stopword removal, and feature extraction using TF-IDF with unigram to trigram representations. Model training was conducted on a balanced dataset, and performance was evaluated through cross-validation and tuning of key hyperparameters. SVM achieved the highest accuracy of 98.40%, showing strong stability across various test conditions. CNN initially performed competitively but experienced a slight performance drop after tuning, suggesting its sensitivity to parameter settings and data volume. The findings demonstrate that SVM is more robust and suitable for limited-data environments, making it a practical tool for classifying psychological traits like language anxiety in digital communication. This research offers insight into the potential of machine learning in psychological and linguistic analysis, especially through social media platforms.
Klasifikasi Sentimen pada Dataset Ulasan Film menggunakan Machine Learning dan OpenAI Text Embedding Abdurrahman, Azzam; Bijaksana, Moch. Arif; Lhaksmana, Kemas Muslim
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Analisis sentimen pada ulasan film menjadi semakin penting seiring dengan meningkatnya volume data tekstual. Performa model machine learning untuk tugas ini sangat bergantung pada kualitas representasi teks yang digunakan. Penelitian ini bertujuan untuk mengevaluasi efektivitas model embedding teks kontekstual dari OpenAI, Text-embedding-3-large, untuk klasifikasi sentimen pada dataset Movie Reviews. Metodologi penelitian mencakup dua pendekatan klasifikasi: supervised learning menggunakan Support Vector Machine dan Logistic Regression, serta klasifikasi zero-shot. Performa Text-embedding-3-large dibandingkan secara langsung dengan model embedding statis Word2Vec pada dataset yang telah dibersihkan dan dataset asli. Hasil penelitian menunjukkan bahwa Text-embedding-3-large secara signifikan mengungguli Word2Vec, dengan peningkatan F1-score dari 78.01% menjadi 93.20%. Konfigurasi terbaik dicapai oleh kombinasi Support Vector Machine dengan hyperparameter default pada dataset yang tidak dibersihkan, yang mengindikasikan kemampuan model memanfaatkan informasi kontekstual dari tanda baca. Selain itu, pendekatan zero-shot menunjukkan kinerja yang cukup baik dengan F1-score 86.29%, yang membuktikan kapabilitas generalisasi model tanpa memerlukan data latih berlabel. Kata kunci : klasifikasi sentimen, ulasan film, machine learning, openai, embedding teks, zero-shoT
Co-Authors Abdurrahman, Azzam Achmad Salim Aiman Adelia, Dila Adhyaksa Diffa Maulana Aditya Eka Wibowo Aditya Gifhari Soenarya Adiwijaya Aghi Wardani Agni Octavia Agus Kusnayat Ahmad Syafiq Abiyyu Ahmad, Alif Faidhil Al Faraby, Said Alberi Meidharma Fadli Hulu Amalia Elma Sari Andiani, Annisa Dwi Angraini, Nadya Arda Anisa Herdiani Annisa Miranda Arini Rohmawati Athallah, Muhammad Rafi Aura Sukma Andini Bayu Muhammad Iqbal Bonar Panjaitan Brata Mas Pintoko Chandra Jaya Riadi Chlaudiah Julinar Soplero Lelywiary Choirulfikri, Muhammad Rizqi Damayanti, Lisyana Dana Sulitstyo Kusumo Danang Triantoro Murdiansyah David Winalda Delva, Dwina Sarah Deni Saepudin Denny Darlis Dewantara, Muhammad Pascal Dida Diah Damayanti Didit Adytia dina juni restina Dino Caesaron Donni Richasdy Donny Rhomanzah Dzidny, Dimitri Irfan Edgarsa Bramandyo Widyarto Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fathih Adawi Ahmad Ferdian Yulianto Fhira Nhita Ghina Annisa Shabrina Guido Tamara Haga Simada Ginting Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Ika Puspita Dewi Intan Khairunnisa Fitriani Iqmal Lendra Faisal Amien Irgi Aditya Rachman Isabella Vichita Kacaribu Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Lukito Agung Waskito Luqman Bramantyo Rahmadi Luthfi, Muhammad Faris M. Mahfi Nurandi Karsana Mahendra Dwifebri Mahendra Dwifebri Purbolaksono Mahendra, Muhammad Hafizh Marendra Septianta Marozi, Ericho Mehdi Mursalat Ismail Meira Reynita Putri Mira Rahayu Moch Arif Bijaksana Mohamad Reza Syahziar Muhammad Abdurrohman Al Fatih Muhammad Adzhar Amrullah Muhammad Arif Kurniawan Muhammad Ilham Maulana Muhammad Rifqi Fauzi Ramdhani Muhammad Yuslan Abu Bakar Muhammad Zaid Dzulfikar muhammad zaky ramadhan Muhammad Zidny Naf'an Murman Dwi Praseti Musyafa’noer Sandi Pratama Nanda Yonda Hutama Naufal Furqan Hardifa Naufal Hilmiaji Naufal Rasyad Nibras Syihabil Haq Octaryo Sakti Yudha Prakasa Okky Zoellanda A. Tane Pamungkas, Danit Hafiz Praja, Yudhistira Imam Purwita, Naila Iffah Putri, Arla Sifhana Putrisia, Denada R. Fajrika hadnis Putra Rafi Hafizhni Anggia Rafisa Arif Irfan Rahadian, Muhammad Rafi Rastim Rastim Rayhan, Muhammad Aditya Resky Nadia Rizki Luthfan Azhari Rizki Nurhaliza Harahap Rizky Ahmad Saputra Rizky, Fariz Muhammad Salman Farisi Setya Hadi Seno Adi Putra Seto Sumargo Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Tri Widarmanti Try Moloharto Try Moloharto Vitalis Emanuel Setiawan Wardhani, Fitri Herinda Widi Astuti Widi Astuti Youga Pratama Yuliant Sibaroni Yusuf Nugroho Doyo Yekti Zaena, Siffa Zaenal Abidin ZK Abdurahman Baizal Zulkarnaen, Imran