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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 : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5 Kahingide, Hastyantoko Dwiki; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7771

Abstract

Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers optimal responsiveness, providing adaptive ease of use, especially in medical image processing. Evaluation of the model shows impressive results, with a mean Average Precision (mAP) of 0.987 for the 'kidney tumor' label. Detection on public data demonstrated high performance with accuracy, precision, recall, and F1-Score of 98.56%, 98.66%, 99.66%, and 99.16%, respectively. This study also utilized clinical data comprising 62 CT-Scan images. Evaluation of the clinical data revealed that YOLOv5 produced an accurate detection model with accuracy, precision, recall, and F1-Score of 95.16%, 96.72%, 98.33%, and 97.52%, respectively. The research shows that both public and clinical data models can accurately detect kidney tumors based on CT-Scan images. The deployment process using the Flask web-based platform allows direct interaction with users through an intuitive interface, enabling users to upload their CT-Scan images and quickly obtain detection results. These test results provide evidence that object detection using YOLOv5 achieves high accuracy in detecting both public and clinical datasets.
Classification of Brain Tumors by Using a Hybrid CNN-SVM Model Nabila, Talitha Safa; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8277

Abstract

Brain tumors are diseases that involve the growth of brain cells, causing abnormalities in the brain region. An MRI scan is a useful tool for tumor detection. Researchers can process the obtained image data to conduct research capable of detecting brain tumor disease. Classifying brain tumors facilitates effort, planning, and accurate diagnosis, enabling the formulation and evaluation of treatment options for a patient with a brain tumor. The research was conducted to classify whether or not there was a tumor in the brain by using a combination of algorithms, namely CNN, to extract features from image data and then use SVM as a classification. CNN is a popular algorithm that deals very effectively with the complexity and variation of image data, whereas SVM is an algorithm for classification that maximizes margins and generalizations to produce accurate classifications. The project's goal is to create a hybrid model that can classify two labels based on image preprocessing processes, feature extraction, and brain tumor image data classification. In this study, the results of the CNN-SVM hybrid were able to obtain the highest score with Adam optimization and learning rate 0.001, accuracy of 98.92%, precision 98.92%, recall 98.92%, and f1-score 98.92%.
SciBERT Optimisation for Named Entity Recognition on NCBI Disease Corpus with Hyperparameter Tuning Salam, Abu; Sidiq, Syaiful Rizal
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9283

Abstract

Named Entity Recognition (NER) in the biomedical domain faces complex challenges due to the variety of medical terms and their context of use. Transformer-based models, such as SciBERT, have proven to be effective in natural language processing (NLP) tasks in scientific domains. However, the performance of these models is highly dependent on proper hyperparameter selection. Therefore, the aim of this study is to analyse the impact of hyperparameter tuning on the performance of SciBERT in NER tasks on the NCBI Disease Corpus dataset. The methods used in this study include training the baseline SciBERT model without tuning, followed by hyperparameter optimisation using grid search, random search, and bayesian optimisation methods. Model evaluation is done with precision, recall, and F1-score metrics. The experimental results showed that of the three methods grid search and random search produced the best performance with a precision, recall and F1-score of 0.82, improving from the baseline which only achieved a precision and recall of 0.72 and F1-score of 0.68. This study confirms that proper hyperparameter tuning can improve model accuracy and efficiency in medical entity extraction tasks. These results contribute to the development of optimisation methods in biomedical text processing, particularly in improving the effectiveness of the SciBERT Transformer model for NER.
The Development of a Deployment System Architecture for a Flask-Based Chatbot Using an LSTM NLP Model for Customer Service Question & Answer Mukti, David Ramantya; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9305

Abstract

In the past two decades, the rapid growth of e-commerce has significantly transformed global business practices. E-commerce has not only revolutionized the retail industry but also positively impacted businesses and consumer experiences. The ease of online shopping enables users to select products at more competitive prices. Amidst these changes, human-computer interactions have increasingly evolved toward natural conversations through Natural Language Processing (NLP). This study aims to develop a chatbot utilizing Long Short-Term Memory (LSTM) technology as a medium for e-commerce customer service. The dataset used for chatbot development is in JSON format and consists of 580 entries spanning 38 categories or classes. Data processing involves several preprocessing stages, including case folding, lemmatization, tokenization, and padding. The model is developed using a bidirectional LSTM and GRU architecture, followed by regularization techniques to enhance performance. Evaluation results show the model achieves 90% training accuracy and 63% validation accuracy with an F1-score of 62%. While there are indications of overfitting, the observed differences are not statistically significant, indicating the model remains capable of providing reliable responses. Additionally, the model is integrated into a Flask-based web application with an interactive interface to facilitate user access. This study demonstrates that LSTM is effective in addressing vanishing gradient problems.
A Comparative Performance of SMOTE, ADASYN and Random Oversampling in Machine Learning Models on Prostate Cancer Dataset Putra, Aditya Herdiansyah; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9308

Abstract

Class imbalance in medical datasets, including prostate cancer, can affect the performance of machine learning models in detecting minority cases. This study compares three oversampling techniques - SMOTE, ADASYN, and Random Oversampling - to address data imbalance in prostate cancer classification. These techniques are applied to Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), which are evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. In improving the reliability of the evaluation, K-Fold Cross Validation was used to reduce the risk of overfitting and ensure stable results. The findings show that oversampling techniques improve model performance compared to the baseline. Random Oversampling has the best performance for Random Forest with accuracy 0.85, recall 0.888, precision 0.873, F1-score 0.879, and ROC-AUC 0.838. SMOTE produced the highest Decision Tree performance with accuracy 0.80, recall 0.838, precision 0.843, F1-score 0.839, and ROC-AUC 0.788. ADASYN provided the most improvement for LightGBM, achieving accuracy 0.89, recall 0.919, precision 0.913, F1-score 0.913, and ROC-AUC 0.879. These results confirm that the oversampling method improves prostate cancer classification performance by tailoring the resampling technique to the model characteristics.
Comparison of Data Normalization Techniques on KNN Classification Performance for Pima Indians Diabetes Dataset Dimas Pratama, Yohanes; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9353

Abstract

This study analyzes the comparison of data normalization techniques in the K-Nearest Neighbors (KNN) model for diabetes classification using the Pima Indians Diabetes dataset. The three normalization techniques evaluated are Min-Max Scaling, Z-Score Scaling, and Decimal Scaling. After preprocessing, such as handling missing values and removing duplicates, as well as feature selection using the Random Forest method, the features removed include SkinThickness, Insulin, Pregnancies, and BloodPressure. The evaluation was carried out using accuracy, precision, recall, F1-Score, specificity, and ROC AUC metrics. The results show that Min-Max Scaling provides a significant improvement in all metrics, with the highest accuracy of 0.8117 and ROC AUC of 0.8050. Z-Score Scaling provides good results, but not as good as Min-Max Scaling. Decimal Scaling shows the lowest performance. Statistical tests using Paired T-Test show significant differences between Min-Max Scaling and without normalization on all metrics (P-Value <0.05), while Z-Score Scaling and Decimal Scaling are only significant on some metrics, with P-Values of 0.08363 and 0.43839 respectively for accuracy and ROC AUC. Overall, Min-Max Scaling proved to be the best normalization method for improving KNN performance in diabetes classification.
Enchancing Enhancing Medical Named Entity Recognition with Ensemble Voting of BERT-Based Models on BC5CDR Maulana, Fadhli Faqih; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9549

Abstract

The rapid development in biotechnology and medical research has resulted in a large amount of scientific literature containing critical information about various medical entities. However, the primary challenge in managing this data is the vast volume of unstructured text, which requires Natural Language Processing (NLP) techniques for automatic information extraction. One of the main applications in NLP is Named Entity Recognition (NER), which aims to identify important entities in the text, such as disease names, drugs, and proteins. This study aims to enhance the performance of medical Named Entity Recognition (NER) by applying ensemble Voting to three BERT-based models: BioBERT, TinyBERT, and ClinicalBERT. The results show that the ensemble voting technique provides the best performance in medical entity extraction, with improvements in precision (0.9494), recall (0.9483), and F1-score (0.9488) compared to individual models, especially when handling less common medical entities. This approach is expected to contribute to the development of automated systems for analyzing and searching information in medical literature.
Enhancing Liver Cirrhosis Staging Accuracy using Optuna-Optimized TabNet Arifin, Muhammad Farhan; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11011

Abstract

Liver cirrhosis is a progressive chronic disease whose early detection poses a clinical challenge, making accurate severity staging crucial for patient management. This research proposes and evaluates a TabNet deep learning model, specifically designed for tabular data, to address this challenge. In the initial evaluation, a baseline TabNet model with its default configuration achieved a baseline accuracy of 65.11% on a public clinical dataset. To enhance performance, hyperparameter optimization using Optuna was implemented, which successfully increased the accuracy significantly to 70.37%, with precision, recall, and F1-score metrics each reaching 70%. The model's discriminative ability was also validated as reliable in multiclass classification through AUC metric evaluation. In addition to accuracy improvements, the model's interpretability was validated through the identification of key predictive features such as Prothrombin and Hepatomegaly, which align with clinical indicators. This study demonstrates that Optuna-optimized TabNet is an effective and interpretable approach, possessing significant potential for integration into clinical decision support systems to support a more precise diagnosis of liver cirrhosis.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
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&#039;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