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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 Jurnal Abdimas Kartika Wijayakusuma Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Dike Jurnal INFOTEL CSRID LOGIC: Jurnal Penelitian Informatika IJoICT (International Journal on Information and Communication Technology)
Kemas Muslim Lhaksmana
Telkom University
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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Symptom-Based Classification of Migraine Severity Using Random Forest and LassoNet Syehan Fariz Gustomo; Kemas Muslim Lhaksmana
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9900

Abstract

This study aims to develop and compare classification models for predicting symptom-based migraine intensity using a public dataset from Kaggle. The research was conducted on multiclass data with an imbalanced class distribution. Therefore, target creation and result interpretation must be carefully designed so that the resulting evaluation remains consistent with the characteristics of the data used. In this study, the Intensity variable was recoded into three operational classes: low, moderate, and high. The features used included symptoms, characteristics of migraine episodes, and `symptom_count`, which represents the number of symptoms in each sample. The two models compared were Random Forest and LassoNet, both of which were tested using Stratified 5-Fold Cross-Validation. Model performance was assessed using the Macro F1-score as the primary metric, Balanced Accuracy as the main supplementary metric, and Accuracy as a complementary metric. The test results showed that Random Forest performed better, with a Macro F1-score of 0.6366, Balanced Accuracy of 0.6181, and Accuracy of 0.6225. Meanwhile, LassoNet achieved a Macro F1-score of 0.2474, a Balanced Accuracy of 0.3333, and an Accuracy of 0.5900. These results indicate that symptom patterns in the dataset can still be utilized to distinguish migraine intensity within a computational classification framework, although the separation between closely related classes is not yet fully robust.
Detecting Language Anxiety in Indonesian Students: Deep Learning and Traditional Classification Methods for English Learning Anxiety Kemas Muslim Lhaksmana; Muhammad Sya’bani Falif; Iis Kurnia Nurhayati; Muhammad Yudhi Rezaldi; Esa Prakasa; Rickman Roedavan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7109

Abstract

Mastery of the English language represents a fundamental determinant of professional achievement, particularly for individuals seeking to develop their careers and participate in international contexts. However, when learning a foreign language such as English, Indonesian students may experience language anxiety that cause them to hesitate to practicing English in the class, both in orally and in writing. For English teachers, it is crucial to identify students experiencing language anxiety early on, so that they may provide appropriate teaching strategies and interventions from the first class meeting. To address this issue, this study compares machine learning methods to provide a solution for early detection of students experiencing language anxiety. Furthermore, these methods are classification models, including LSTM, GRU, decision tree, naïve Bayes, logistic regression, and SVM. The implementation of each of these models is combined with different text representation techniques, such as Word2Vec, BERT, FastText, Glove, and TF-IDF. The advantage of our model is that despite the imbalance, limited, and smaller than baseline dataset size, this research finds that GRU with focal loss achieves the highest F1 score of 0.89. This result outperforms our baseline and thus suggests that this method is effective in detecting students who experience language anxiety.
Hadith Text Classification Based on Topic Using Convolutional Neural Network (CNN) and TF-IDF Muhammad Rafi Athallah; Kemas Muslim Lhaksmana
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.20354

Abstract

Convolutional Neural Networks (CNN) will develop a hadith classification system to categorize texts based on specific topics or categories. This study compares two text representation techniques, namely Term Frequency- Inverse Document Frequency (TF-IDF) and Word2Vec, concerning the application of stemming and without stemming in the process. This study utilizes Category ID 0-5. About 2,845 data have been processed as required for testing. The data was divided into two parts, with a proportion of 80:20 for training and testing. Next, several models were evaluated, namely Word2Vec with stemming, TFIDFCNN without stemming, and TFIDFCNN with stemming. Accuracy, precision, recall, and F1 score metrics were used to assess the performance. The results show that the TFIDFCNN model without stemming performs best with 85% accuracy in topic-based text classification. This is due to the stability and efficiency of the model in processing data.
Klasifikasi Sentimen pada Dataset Ulasan Film menggunakan Machine Learning dan OpenAI Text Embedding Azzam Abdurrahman; Moch. Arif Bijaksana; Kemas Muslim Lhaksmana
LOGIC: Jurnal Penelitian Informatika Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v4i1.10992

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.
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 Arif Irfan, Rafisa Arini Rohmawati Aura Sukma Andini Azzam Abdurrahman Bayu Erfianto Bayu Muhammad Iqbal Bonar Panjaitan Bramandyo Widyarto, Edgarsa 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 Dwifebri, Mahendra Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Esa Prakasa Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Ghina Annisa Shabrina Guido Tamara Hadi, Salman Farisi Setya Hafizh Putra Ardhana Haga Simada Ginting Haidar, Muhammad Dzakiyuddin Harahap, Rizki Nurhaliza Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Iis Kurnia Nurhayati Ika Puspita Dewi Ilham Maulana, Muhammad Intan Khairunnisa Fitriani Irgi Aditya Rachman Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kacaribu, Isabella Vichita Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Kemas Saleh Rahmat Wiharja 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 Aqil Ghazali Anhein Muhammad Arif Bijaksana Muhammad Arif Kurniawan Muhammad Rafi Athallah Muhammad Sya’bani Falif Muhammad Yudhi Rezaldi 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 Nungki Selviandro Nur Ghaniaviyanto Ramadhan 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 Razaka, Akmal Sidki Resky Nadia Rickman Roedavan Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky Aria Mu’allim Rizky, Fariz Muhammad Seno Adi Putra Seto Sumargo Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Syehan Fariz Gustomo 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