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All Journal Media Ilmu Keolahragaan Indonesia Journal of Physical Education Health and Sport Sistemasi: Jurnal Sistem Informasi Scholaria: Jurnal Pendidikan dan Kebudayaan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Triadik Gelanggang Olahraga: Jurnal Pendidikan Jasmani dan Olahraga (JPJO) JURNAL PENDIDIKAN TAMBUSAI JUARA : Jurnal Olahraga JURIKOM (Jurnal Riset Komputer) Jurnal Bidan Komunitas ACTIVE: Journal of Physical Education, Sport, Health and Recreation Journal of Physical Education and Sports Mobile and Forensics Respati Madani : Indonesian Journal of Civil Society Journal of Research and Educational Research Evaluation Community Empowerment Indonesian Journal of Sport Management Indonesian Journal for Physical Education and Sport Prosiding Seminar Nasional Pascasarjana Proceeding of International Conference on Science, Education, and Technology Journal Of Artificial Intelligence And Software Engineering
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Anemia Classification with Clinical Feature Engineering and SHAP Interpretation Amalia, Ikhlasul; Rumini, Rumini
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.v9i5.10912

Abstract

Anemia is a global health issue that has a significant impact on quality of life and productivity. Early and accurate detection is essential to prevent more serious complications. This study aims to develop an anemia classification model based on machine learning technology using the XGBoost algorithm, as well as compare its performance with Logistic Regression and Random Forest methods. The dataset used in this study was obtained from the Kaggle platform, consisting of 1,421 samples and six clinical attributes, namely Gender, Hemoglobin (HGB), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), Mean Corpuscular Volume (MCV), Result. During the feature engineering process, the derived feature of the hemoglobin-to-MCV ratio (Hb/MCV) was added, which is medically relevant in distinguishing types of anemia. Evaluation results showed that XGBoost and Random Forest achieved an accuracy rate and F1-Score of 100%, while Logistic Regression achieved a rate of 98.9%. XGBoost was selected as the primary model due to its efficient computational capabilities and support for interpretation using SHAP (SHapley Additive exPlanations). SHAP visualization revealed that the Hb/MCV ratio and hemoglobin were the most influential features in classification. This model has the potential to be used as a decision support system for automated anemia screening and can be further integrated into clinical systems.
Pap Smear Image Classification for Cervical Cancer Prediction with Transfer Learning on ResNet101 Architecture Dewi, Sila Cahya; Rumini, Rumini
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.v9i5.10343

Abstract

Early detection of cervical cancer remains a pivotal strategy to improve clinical outcomes and mitigate mortality associated with this disease. This study introduces a robust deep learning framework employing the ResNet101 architecture to facilitate the automated classification of cervical cell images derived from Pap smear examinations. By leveraging transfer learning, the pre-trained ResNet101 model was fine-tuned to extract salient morphological features critical for distinguishing among diverse cervical cell categories. A comprehensive dataset of labeled Pap smear images, systematically expanded through augmentation techniques, was utilized to enhance model generalizability. The proposed approach achieved a remarkable classification accuracy of 99.6%, highlighting its effectiveness in reliably differentiating between normal and abnormal cellular structures. These findings substantiate the promise of deep residual networks coupled with transfer learning as a powerful tool in advancing computer-aided diagnostic systems, thereby reinforcing early screening initiatives for cervical cancer.
Comparative Study of Logistic Regression, Random Forest, and XGBoost for Bank Loan Approval Classification Putra, Hamdika; Rumini, Rumini
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.v9i5.10862

Abstract

Bank loan approval plays a vital role in ensuring financial institutions can minimize credit risk while supporting economic growth. Default prediction is a crucial aspect of banking credit risk management. This study compares three machine learning algorithms Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) to classify bank loan approvals using a combination of application, previous application, and bureau datasets. The workflow includes data merging, cleaning, missing value imputation, handling unknown values, feature engineering (such as converting day-based variables into years, calculating total submitted documents, income-to-annuity ratio, and employment-to-income ratio), encoding (label and one-hot), scaling (min-max normalization), feature selection based on correlation analysis, handling class imbalance with SMOTE, as well as modeling and evaluation using Accuracy, Precision, Recall, F1-score, and AUC. The results show that Logistic Regression yields the highest AUC of 0.741498, outperforming Random Forest (0.713758) and XGBoost (0.715944). From a business perspective, implementing the best model reduced the Loss Given Default (LGD) by 39.77 %, from $1,705,098,055.50 to $1,026,944,185.50. This finding confirms that simpler models remain competitive on imbalanced datasets when supported by appropriate preprocessing and balancing strategies.
MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods Jiven, Firza Findia; Rumini, Rumini
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.v9i6.10363

Abstract

Brain tumor classification using MRI images plays a crucial role in modern medical diagnostics, offering fast and accurate support for disease detection. This study proposes a classification approach that combines feature extraction using EfficientNet B0 with conventional machine learning algorithms. MRI brain images are preprocessed and resized to match EfficientNet B0 input dimensions. Feature vectors are extracted and subsequently processed using PCA for dimensionality reduction and SMOTE for class balancing. The resulting data are classified using various machine learning algorithms including Support Vector Machine, XGBoost, LightGBM, and others. Experimental results show that Support Vector Machine achieved the highest accuracy of 96%, followed by XGBoost and LightGBM at 94%. The combination of EfficientNet B0 feature extraction and lightweight classifiers proved to be effective, matching the performance of more complex deep learning models. This study does not focus on measuring computational cost directly, but rather demonstrates that combining EfficientNetB0 feature extraction with machine learning algorithms can achieve performance comparable to deep learning approaches. This highlights that lightweight models remain competitive in terms of accuracy without requiring highly complex architectures. Future work can explore this method on other medical imaging datasets and enhance model interpretability for clinical adoption.
Hypertension Risk Prediction Using Stacking Ensemble of CatBoost, XGBoost, and LightGBM: A Machine Learning Approach Alfath, Abisakha Saif; Wardhana, Ajie Kusuma; Rumini, Rumini
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.v9i6.10370

Abstract

Hypertension is a leading cause of cardiovascular diseases, chronic kidney failure, and strokes, affecting millions worldwide. Early detection and accurate risk prediction are crucial for effective management and prevention. This study aims to evaluate and compare the performance of different algorithms for predicting hypertension risk using a stacking ensemble approach. The model combines three gradient boosting algorithms XGBoost, LightGBM, and CatBoost as base learners, with Logistic Regression as the meta learner. The dataset, sourced from Kaggle, contains 4,240 instances with demographic and clinical attributes relevant to hypertension. The preprocessing steps included imputing missing values using the median, removing residual null entries, and addressing class imbalance through the SMOTE algorithm. Data were divided into 80% for training and 20% for testing. The evaluation showed that the stacking ensemble model achieved an overall accuracy of 92,65%, with precision, recall, and F1-scores consistently reaching 0.92 for both classes. The confusion matrix revealed minimal misclassification, indicating the model’s strong ability to differentiate between low and high risk individuals. These results emphasize that the primary goal of this research is to identify which algorithm provides the best performance for hypertension risk prediction. By evaluating and comparing different models, this study offers insights into choosing the most effective algorithm for clinical decision-making and early detection strategies.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Rusli Abidarin Rosidi Adzalika, Ayu Reza Agung Wahyudi Ahmad Alwi Nurudin Ahmad Alwi Nurudin Aisyah, Hanif Akhirmaini, Zelda Alam, Bima Tangguh Alfath, Abisakha Saif Ali Mustofa Alirman Sarpan Amalia, Ikhlasul Ansori, Ikhsan Anwar Anwar Aprilandri, Gusti Aprilia, Nurazmi Aribowo, Dhany Suhartantyo Ariyadi, Jamal Imam Asmoro, Jimy Purbo Bambang Priyono Billy Castyana, Billy bubunrum@gmail.com, Irda Dewi, Sila Cahya Dina Maulina Donny Firmansyah Dwinanda Saputra, Ahmad Faris Eunike Raffy Rustiana Fakhruddin Fakhruddin Farah Nur Oktavia Febriyanto, Dimas Bayu Gema, Abdul Rachim Gilang, Meillenio Agung Gumono Gumono, Gumono Handoko, Lovi Harry Pramono, Harry Harsono, Tri Nur Heny Setyawati Hermahayu Hidayah, Ayu Nur Ilman Zuda Septiawan Imawati, Veni Indriyanti Indriyanti, Indriyanti Irawan, Fredi Januarto, Khoerul Rohman Jiven, Firza Findia Kasmudi, Udin Khotibul Umam Kusuma, Donny Wira Yudha Kusuma, Prita Widia Lesmana, Dian Lukmanul Hakim Luthfi, Auliya Ma'muroh, Ma'muroh Mardanto, Langgeng Asmoro Budi Mugi Hartono Nasuka Nasuka, Nasuka Norhikmah Norhikmah Oktia Woro Kasmini Handayani Pambudi, Satrio Rilo Parista, Vivi Septiana Patria, Lalu Demung Pradana, Sulistyo Pradana, Toufan Wahyu Pramandhika, Reddy Prasepty, Winda Pratama, Rifqy Rayhan Andi Riga Pratiwi, Adinda Resta Putri pratiwi, Debby Purnomo, Galih Puspita Sari, Ika Endah Putra, Hamdika Rachman, Imaniar Ranu Baskora Aji Putra Ria Lumintuarso Rizki SP, Tri Agustin Wulandari Roas Irsyada, Roas roni, Ahmad Sya'roni S.Pd. M Kes I Ketut Sudiana . Said Junaidi Saputra, Irawan Hadi Saputro, Irfan Toni Satria Armanjaya Seran, Yohanes Arka Maria Setya Rahayu Shodikin, Shodikin Siti Baitul Mukarromah Siti Hajar SOEGIYANTO Soegiyanto Soegiyanto, Soegiyanto Sudarmawan, Sudarmawan Sugiharto - Sugiharto Sugiharto Sugiharto Sugiharto Sukestiyarno, Y.L Sulaiman Sulaiman Suratman Suratman Tandiyo Rahayu Taufiq Hidayah Taufiq Hidayah Tommy Soenyoto Tri Rustiadi, Tri Tri Susanto Ucu Muhammad Afif Wahyuli, Wahyuli Wardhana, Ajie Kusuma Wibowo, Ristianto Adi Wicaksono, Restu Aji Wicaksono, Wisnu Widy Astuti, Widy Winara Winara Winasis, Probo Wiwit Kurniawan, Wiwit Wiyono, Indra Zahirma, Mutia Zola, Nicko