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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 LOGIC: Jurnal Penelitian Informatika
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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Journal : Building of Informatics, Technology and Science

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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.
Prediction of Student Work Readiness using Artificial Neural Network and Decision Tree Andini, Bilqiis Shahieza; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7316

Abstract

The readiness of students for the workforce is a critical metric in evaluating the quality of higher education. Forecasting students' work readiness before their entrance into the industry calls for a data-driven approach since they often lack the necessary skills and experience until graduation. Using two machine learning techniques—Artificial Neural Network (ANN) and Decision Tree (DT)—this research aims to create a classification model to predict students' employment readiness. Among the several aspects the data covers are academic knowledge, professional attitude, soft skills, hard skills, and socio-economic background. Data preparation, data cleansing, feature selection, model training, and performance evaluation make up the study approach. The ANN model comprised four hidden layers, while the DT was refined with RandomizedSearchCV. The test results showed that DT had an accuracy of 90.80%, and ANN had 90.69%, indicating that both performed very well and can be selected based on what the user needs most. This research contributes a predictive model for educational institutions to assess students' employment preparedness in a more objective and systematic way.
Sentiment Analysis of Digitalization of Small and Medium Enterprise on Social Media X Using SVM and KNN Methods Haidar, Muhammad Dzakiyuddin; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6723

Abstract

The rapid digitalization of Small and Medium Enterprises (SMEs) has led to significant shifts in business operations, especially in their adaptation to digital platforms. Public perception towards this digital transformation is crucial to understand, as it reflects the success and acceptance of these efforts. This research conducts sentiment analysis on social media platform X to classify public opinions regarding the digitalization of SMEs. The analysis employs two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The study compares the performance of both models under baseline and hyperparameter-tuned conditions. The results show that the SVM model consistently outperforms KNN in terms of accuracy, precision, recall, and F1-score. The highest accuracy achieved by the SVM model is 81.97% after hyperparameter tuning with a sigmoid kernel. Meanwhile, the best KNN model records an accuracy of 81.31% using Manhattan distance with 11 neighbors. This study demonstrates that SVM provides better stability and performance in sentiment classification related to SME digitalization. The findings are expected to help policymakers better understand public sentiment and formulate more effective strategies for supporting SME digital transformation.
Co-Authors Abdurrahman, Azzam Abiyyu, Ahmad Syafiq Achmad Salim Aiman Adelia, Dila Adhyaksa Diffa Maulana Aditya Eka Wibowo Aditya Gifhari Soenarya Adiwijaya Aghi Wardani Agni Octavia Agus Kusnayat Ahmad Y, Rafly Ahmad Y Ahmad, Alif Faidhil Ahmad, Fathih Adawi Al Faraby, Said Alberi Meidharma Fadli Hulu Amalia Elma Sari Amien, Iqmal Lendra Faisal Andiani, Annisa Dwi Andini, Bilqiis Shahieza 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 Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Guido Tamara Hadi, Salman Farisi Setya Haga Simada Ginting Haidar, Muhammad Dzakiyuddin Harahap, Rizki Nurhaliza Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Ika Puspita Dewi Intan Khairunnisa Fitriani Irgi Aditya Rachman Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kacaribu, Isabella Vichita Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Lukito Agung Waskito Luqman Bramantyo Rahmadi Luthfi, Muhammad Faris M. Mahfi Nurandi Karsana Mahendra Dwifebri Purbolaksono Mahendra, Muhammad Hafizh Marendra Septianta Marozi, Ericho Mehdi Mursalat Ismail Mira Rahayu Moch Arif Bijaksana Mohamad Reza Syahziar Muhammad Adzhar Amrullah Muhammad Arif Kurniawan 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 Putri, Meira Reynita Putrisia, Denada R. Fajrika hadnis Putra Rafi Hafizhni Anggia Rahadian, Muhammad Rafi Ramdhani, Muhammad Rifqi Fauzi Rastim Rastim Rayhan, Muhammad Aditya Resky Nadia Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky Aria Mu’allim Rizky, Fariz Muhammad Seno Adi Putra Seto Sumargo Shabrina, Ghina Annisa 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