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All Journal SMARTek Foristek CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL ILMIAH MATEMATIKA DAN TERAPAN JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Jurnal Informatika Global ScientiCO : Computer Science and Informatics Journal Sains, Aplikasi, Komputasi dan Teknologi Informasi Applied Engineering and Technology
Amriana Amriana
Universitas Tadulako
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Performance Comparison of Multilayer Perceptron (MLP) and Random Forest for Early Detection of Cardiovascular Disease Setiawan, Dita Widayanti; Lapatta, Nouval Trezandy; Amriana, Amriana; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa Ar.
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.v9i6.10826

Abstract

Cardiovascular disease is a disorder of the heart and blood vessels that can lead to heart attacks, strokes, and heart failure, so early detection is essential. This study compares Multilayer Perceptron (MLP) and Random Forest for risk classification in a Kaggle dataset containing 70,000 samples with balanced targets. Pre-processing included age conversion, outlier cleaning, standardization, and feature selection based on feature importance. Both models were optimized using RandomizedSearchCV and evaluated using accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, and k-fold cross-validation. The results show that the accuracy of MLP is 73.90% and Random Forest is 74.23% with an AUC of 0.80 for both. Random Forest is more stable across all folds and performs better on the negative class, while MLP is slightly more sensitive to the positive class. Independent t-test and Mann-Whitney U tests show p>0.05, indicating that the difference in performance is not significant. The most influential features were diastolic blood pressure, age, cholesterol, and systolic blood pressure. The non-clinical Streamlit prototype demonstrated the model's potential for education and initial decision support.
Implementation of Collaborative Filtering in the Salted Fish Recommendation Process Rizky, Moh Taufiq; Rinianty, Rinianty; Nugraha, Deny Wiria; Amriana, Amriana; Lapatta, Nouval Trezandy
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.v9i6.11576

Abstract

The development of e-commerce in the current era has been so rapid that buying and selling transactions are carried out online through various media, including websites and applications. With so many products available in the application, users often feel confused when choosing the product they want to buy, so it takes a long time to choose a product to avoid regret after purchasing it. In this study, a web-based recommendation system was created for the process of recommending salted fish with the aim of making it easier for customers to choose the type of salted fish. The Collaborative Filtering method was used, employing Pearson Correlation as a tool to calculate the similarity value between users, then using Weighted Sum to calculate the prediction value. Collaborative Filtering often experiences the cold start problem, where the system has difficulty providing recommendations to users who do not yet have a transaction history. Therefore, the author proposes a popularity-based strategy as a measure to overcome this problem. Based on testing, the author obtained results of MAE = 0.63 and RMSE = 0.81 based on train-test split results with a data distribution of 80:20, 80% of the dataset for training and 20% of the dataset for testing with an accuracy of 70-80%, indicating that this system works well. This system has been tested using the Blackbox method.
Co-Authors Albrecht Yordanus Erwin Dodu Allo, Yerry Anita Ahmad Kasim Ardiansyah, R Asri Arif Chairunnisa Ar. Lamasitudju Deny Wiria Nugraha Firmansyah Firmansyah Maghfirat Maghfirat Masykur, M Meidji, Farah Nabila Nouval Trezandy Lapatta Pebri Ramadhan Mas Rahma Tanti Rieska Setiawaty Rinianty, Rinianty Rizky, Moh Taufiq Setiawan, Dita Widayanti Wirdayanti Yuri Yudhaswana Joefrie