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All Journal Prosiding SNATIF Infotech Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL ILMIAH MATEMATIKA DAN TERAPAN Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Journal La Multiapp Jurnal Locus Penelitian dan Pengabdian Enrichment: Journal of Multidisciplinary Research and Development Jurnal Informatika Polinema (JIP) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Gunawan Abdillah, Gunawan
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Mathematics Social Sciences

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Journal : JURIKOM (Jurnal Riset Komputer)

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Implementasi Metode Recurrent Neural Network Untuk Prediksi Kejang Pada Penderita Epilepsi Berdasarkan Data Electroenephalogram Febiyane, Raisya; Chrisnanto, Yulison Harry; Abdillah, Gunawan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8656

Abstract

Epilepsy is a chronic neurological disorder that causes patients to experience recurrent seizures. Seizures are one of the main symptoms of epilepsy, requiring medical treatment and close monitoring. A major challenge in epilepsy management is the difficulty in predicting when seizures will occur. Electroencephalogram (EEG) can detect seizures as it contains physiological information about brain neural activity. This study aims to predict seizures using a Recurrent Neural Network (RNN) method based on EEG data. Deep Learning is a branch of Machine Learning that uses artificial neural networks to solve problems involving large datasets. The data used in this research is the Epileptic Seizure Recognition dataset obtained from Kaggle. It consists of patient ID attributes, 178 numerical attributes representing EEG signals, and a label y indicating conditions during the recording, including eyes open, eyes closed, healthy brain, tumor location, and seizure activity. The deep learning model tested is a Recurrent Neural Network (RNN) designed to learn patterns in the data. Performance evaluation was conducted using metrics including accuracy, precision, recall, and F1-Score. Based on the application of the RNN method and testing using EEG data, the best condition was achieved with a three-layer Long Short-Term Memory architecture and optimal training parameters, resulting in a seizure prediction accuracy of 98.6%. This result demonstrates that the model is capable of effectively and efficiently predicting the likelihood of seizure occurrences.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Implementasi Metode Recurrent Neural Network Untuk Prediksi Kejang Pada Penderita Epilepsi Berdasarkan Data Electroenephalogram Febiyane, Raisya; Chrisnanto, Yulison Harry; Abdillah, Gunawan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8656

Abstract

Epilepsy is a chronic neurological disorder that causes patients to experience recurrent seizures. Seizures are one of the main symptoms of epilepsy, requiring medical treatment and close monitoring. A major challenge in epilepsy management is the difficulty in predicting when seizures will occur. Electroencephalogram (EEG) can detect seizures as it contains physiological information about brain neural activity. This study aims to predict seizures using a Recurrent Neural Network (RNN) method based on EEG data. Deep Learning is a branch of Machine Learning that uses artificial neural networks to solve problems involving large datasets. The data used in this research is the Epileptic Seizure Recognition dataset obtained from Kaggle. It consists of patient ID attributes, 178 numerical attributes representing EEG signals, and a label y indicating conditions during the recording, including eyes open, eyes closed, healthy brain, tumor location, and seizure activity. The deep learning model tested is a Recurrent Neural Network (RNN) designed to learn patterns in the data. Performance evaluation was conducted using metrics including accuracy, precision, recall, and F1-Score. Based on the application of the RNN method and testing using EEG data, the best condition was achieved with a three-layer Long Short-Term Memory architecture and optimal training parameters, resulting in a seizure prediction accuracy of 98.6%. This result demonstrates that the model is capable of effectively and efficiently predicting the likelihood of seizure occurrences.
Co-Authors Ade Kania Ningsih, Ade Kania Andri Taufiq, Andri Anggoro, Sigit Ashaury, Herdi Atamimi, Fadel Muhamad Hafid Azzahra, Cynthia Nur Chrisnanto, Yulison Harry Christanto, Yulison Herry Darmawan, Raja Dewi Purnamasari Edvin Ramadhan Eriyadi, Maulidina Norick Fadilah, Rifal Faiza Renaldi, Faiza Febiyane, Raisya Firdaus, Muhammad Fauzi Ghozali, Mohamad Aditya Muttaqin Hadiana, Asep Id Haikal, Ahmed Hasbia, Muhamad Komarudin, Agus Komarudin, Agus Kurniawan, Muhammad Randy Maulana, Akbar Dena Rahayu, Ania Ramadhani, Hary Rizqi Ridwan Ilyas Riyadi, Saiful Faris Rusmana, Hendri Diana Santoso, Enrico Budi Wahyudiputra, Helmy Fauzan Wina Witanti Yulison Herry Chrisnanto Zizilia, Regitha