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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika INOVTEK Polbeng - Seri Informatika Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Abu Salam
Universitas Dian Nuswantoro
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Journal : Building of Informatics, Technology and Science

 1 
Implementasi Model Convolutional Neural Network (CNN) pada Aplikasi Deteksi Kanker Kulit Menggunakan Expo React Native Yonismara, Arvie Arvearie; Salam, Abu
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5351

Abstract

Skin is the outermost organ of the human body, serving to protect the internal parts from threats such as sunlight exposure. Excessive exposure to sunlight can potentially cause skin cancer. Over the past decade, the number of skin cancer cases in Indonesia has increased. The most common method for detecting skin cancer is biopsy, which is quite expensive and time-consuming. Considering this issue, a skin cancer detection application using Deep Learning technology is needed to identify skin cancer at an early stage. Therefore, this research aims to develop a skin cancer detection application using Expo React Native and implement a CNN deep learning model to classify seven classes of skin lesions based on the HAM10000 dataset. The performance evaluation of the CNN model used shows a high performance score, with an average overall score of 0.98. Given this performance, the model is feasible and ready to be implemented in a mobile application. This study demonstrates that the skin cancer detection application using Expo React Native is capable of implementing the deep learning model and can be used to detect skin cancer. Based on the results of the application testing using the black box testing method, perfect results were obtained with 100% success precentage. From the four parts of the application, namely select image, open camera, predict image, and delete image that were tested, all four parts demonstrated that the functionality and features of the skin cancer detection application work well
Optimalisasi Model BioBERT untuk Pengenalan Entitas pada Teks Medis dengan Conditional Random Fields (CRF) Nafanda, Cynthia Dwi; Salam, Abu
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.v6i4.7042

Abstract

This research evaluates the performance of various models in the Named Entity Recognition (NER) task for medical entities, focusing on imbalanced datasets. Six BioBERT model configurations were tested, incorporating optimization techniques such as Class Weight, Conditional Random Fields (CRF), and Hyperparameter Tuning. The evaluation was conducted using Precision, Recall, and F1-Score metrics, which are particularly relevant in the context of NER, especially for addressing class imbalance in the data. The dataset used is BC5CDR, which targets chemical and disease entities in unstructured medical texts from PubMed. The data was divided into three parts: a training dataset for model training, a validation dataset for model tuning, and a test dataset for performance evaluation. The dataset was split evenly to ensure unbiased model testing, leading to more accurate results that can serve as a reference for developing more efficient medical NER systems. The evaluation results indicate that BioBERT + CRF is the model with an F1-Score that reflects an optimal balance between Precision (ranked 3rd, 0.6067 for B-Chemical, 0.5594 for B-Disease, 0.4600 for I-Disease, and 0.5083 for I-Chemical) and Recall (ranked 3rd, 0.5580 for B-Chemical, 0.4491 for B-Disease, 0.5718 for I-Disease, and 0.3840 for I-Chemical) compared to other models. This model proved to be more accurate in detecting medical entities without compromising prediction precision. The model's stability is also enhanced by a smaller gap between Precision and Recall, making it the best choice for NER in medical texts. The application of early stopping techniques effectively prevented overfitting, ensuring the model learned optimally without losing generalization. With better balance in recognizing medical entities from unstructured texts, this model presents the most effective approach for NER systems in the medical domain.
Optimalisasi Model SciBERT dengan Attention-BiLSTM-CRF untuk Pengenalan Entitas Penyakit dalam Teks Biomedis Pamungkas, Tahta Arya; Salam, Abu
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.v7i1.7263

Abstract

This research aims to improve the performance of medical entity recognition in biomedical text by modifying the SciBERT model with Attention-BiLSTM-CRF. Although SciBERT, based on the BERT architecture and trained on biomedical text data, has proven effective in entity recognition, it still has limitations in handling complex medical entities, especially nested entities. As a solution, this research integrates Attention, BiLSTM, and CRF components into the SciBERT model to enhance entity recognition accuracy. Experimental results show that the SciBERT + Attention-BiLSTM-CRF model outperforms the SciBERT model across all key evaluation metrics. Precision improved by 1.7% (from 0.8221 to 0.8364), Recall increased by 2.9% (from 0.8537 to 0.8768), and F1-Score increased by 2.1% (from 0.8372 to 0.8554). These improvements demonstrate that this modification significantly enhances the model's ability to recognize more complex medical entities in biomedical text. The addition of Attention and BiLSTM enriches contextual understanding, while CRF ensures consistency across entity labels. These results indicate that this approach could significantly contribute to automated systems in processing medical data.
Pengembangan Chatbot Kesehatan Mental Berbasis Web Menggunakan Model Long Short-Term Memory (LSTM) Ardin, Akbar Ilham; Salam, Abu
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.v7i1.7282

Abstract

Mental health issues such as stress, anxiety, and academic burnout are increasingly prevalent among university students. However, many students remain reluctant or unable to access counseling services due to time limitations, social stigma, and a lack of available professionals. This study aims to develop CuraBot, a web-based chatbot designed to provide preliminary emotional support and mental health education in an instant, anonymous, and easily accessible manner for students. The system was developed using the Long Short-Term Memory (LSTM) algorithm, which is proven to be effective in understanding contextual text-based conversations. The dataset used consists of 1,624 conversational entries across 77 intent classes, adapted and localized from an open-source corpus to reflect the linguistic style and needs of Indonesian students. The development process involved several stages, including data preprocessing (lemmatization, tokenization, stopword removal, and padding), model training using TensorFlow, and deployment into a Flask-based web application. The model was evaluated using a separate test set of 244 entries, resulting in an accuracy of 89.9%, precision of 90.4%, recall of 89.1%, and an F1-score of 89.8%. These results indicate that the model can classify user intent with high accuracy. This research contributes to the development of a contextual, practical, and AI-based digital solution that supports early access to psychological services within university environments.
Co-Authors Adhitya Nugraha Alan Ma’ruf, Farda Alpiana, Vika Alzami, Farrikh Anisatawalanita Ukhifahdhina Ardin, Akbar Ilham Ardytha Luthfiarta Arifianti, Fidela Putri Arifin, Muhammad Farhan Astuti, Yani Parti Candra Irawan Catur Supriyanto Catur Supriyanto Damaswara, Silvester Aditya Debrina Luna Arghata Mangkawa Diana Aqmala Dimas Pratama, Yohanes Diyan Adiatma Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erwin Yudi Hidayat Erwin Yudi Hidayat Fahmi Amiq Farda Alan Ma'ruf Ferry Bintang Nugroho Haresta, Alif Agsakli Ifan Rizqa Ika Novita Dewi Iskandar, Deo Andrianto Juli Ratnawati Junta Zeniarja Kahingide, Hastyantoko Dwiki Khafiizh Hastuti Kurniawan, Defri Kurniawan, Wira Adi Kusmiyati Kusmiyati L. Budi Handoko Lesmarna, Salsabila Putri Maulana, Fadhli Faqih Megantara, Rama Aria Moh. Sholik Muhammad Jamhari Mukti, David Ramantya Muljono Muljono Mulyanto, Edy Muzaki, Rizki Nuzul Nabila, Talitha Safa Nafanda, Cynthia Dwi Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Pamungkas, Tahta Arya Paramita, Cinantya Pawidya, Novandra Putra Prinantyo, Gilang Djati Putra, Aditya Herdiansyah Ramadhan Rakhmat Sani Ramadhan, Irfan Surya Restu Agung Pamuji Ricardus Anggi Pramunendar Riyan Ardiansyah Sidiq, Syaiful Rizal Sindhu Rakasiwi Utomo, Danang Wahyu Verdian Putra Wicaksana Wibowo, Isro' Rizky Yonismara, Arvie Arvearie