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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Terpadu Engineering, Mathematics and Computer Science Journal (EMACS)
Nabiilah, Ghinaa Zain
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform Prasetyo, Simeon Yuda; Nabiilah, Ghinaa Zain
Jurnal Teknologi Terpadu Vol 9 No 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.605

Abstract

Brain tumors are abnormal tissue growths characterized by excessive cell growth in certain brain parts. One of the reliable techniques currently available to identify brain tumors is using Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are monitored and examined for tumor detection by a specialist. Developing more effective and efficient tools to help medical professionals identify brain tumors is urgent as the number of people suffering from brain tumors soars, and the death rate will reach 18,600 in 2021. In previous research, machine learning-based models demonstrated the ability to detect brain tumors with a classification accuracy of 92%, and this result is reliable. We computationally tested several hyperparameters using publicly available MRI datasets to obtain the most reliable binary classification accuracy in MRI brain images. A high level of model accuracy is achieved by testing various existing machine-learning model architectures and inserting a feature map extracted from the Discrete Cosine Transform (DCT). Classification of MRI images achieved the highest accuracy on test data at 93% using the Support Vector Machine (SVM) model.
Indonesian multilabel classification using IndoBERT embedding and MBERT classification Nabiilah, Ghinaa Zain; Alam, Islam Nur; Purwanto, Eko Setyo; Hidayat, Muhammad Fadlan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1071-1078

Abstract

The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thus, a model is needed to classify comments, especially toxic ones, in Indonesian. Transformer-based model development and natural language processing approaches can be applied to create classification models. Some previous research related to the classification of toxic comments has been done, but the classification results of the model still require exploration to get optimal results. So, this research uses the proposed model by using different pre-trained models at the embedding and classification stages, in the embedding stage using Indonesia bidirectional encoder representations from transformers (IndoBERT), and classification using multilingual bidirectional encoder representations from transformers (MBERT). The proposed model provides optimal results with an F1 value of 0.9032.
Parking System Application Using a Greedy Algorithm Approach Saputri, Hanis Amalia; Syaputra, William; Charles, Charles; Irawan, Andreas Dwi; Nabiilah, Ghinaa Zain
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12575

Abstract

Indonesia has recently witnessed a significant increase in the number of automobiles, reaching an estimated 17.2 million units by the end of 2022, according to the Central Statistics Agency (BPS). Extensive ownership and usage of vehicles in public parking areas, including campuses, have created a high demand for parking spaces. However, challenges still exist within the parking system, such as longer search times for available parking spaces and the lack of technological regulation, leading to uncertainty. Our research focuses on addressing these issues by employing a priority-based greedy algorithm for the nearest lift, prioritizing convenience and speed. We utilize an SQL database to store parking data, leveraging its comprehensive features for efficient processing. The result of this research is a website where customers can input their license plate numbers, processed by our algorithm to generate parking tickets, granting access to designated parking areas. The algorithm works by providing parking slot locations from even-numbered floors first; when all even-numbered floors are filled, it will then allocate parking slots on odd numbered floors. The implementation of the greedy algorithm and SQL database has proven to be efficient in the context of the nearest lift in the Binus parking lot, handling a manageable amount of data and prioritizing data processing speed over achieving the optimal solution in all scenarios
Effectiveness Analysis of RoBERTa and DistilBERT in Emotion Classification Task on Social Media Text Data Nabiilah, Ghinaa Zain
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12618

Abstract

The development of social media provides various benefits in various ways, especially in the dissemination of information and communication. Through social media, users can express their opinions, or even their feelings. In this regard, sometimes users also convey information or opinions according to the user's feelings or emotions. This triggers the impact of aggressive online behavior, including cyberbullying, which triggers unhealthy debates on social media. The development of deep learning models has also been developed in several ways, especially emotion classification. In addition to using deep learning models, the development of classification tasks has also been carried out using transformer architectures, such as BERT. The development of the BERT model continues to be carried out, so this study will analyze and explore the application of BERT model development, such as RoBERTa and DistilBERT. The optimal result of this study is with an accuracy value of 92.69% using the RoBERTa model.
Co-Authors Alam, Islam Nur Charles Charles Hidayat, Muhammad Fadlan Irawan, Andreas Dwi Prasetyo, Simeon Yuda Purwanto, Eko Setyo Saputri, Hanis Amalia Syaputra, William