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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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Implementation of simple additive weighting (SAW) in determining nutrition in toddlers Amriana, Amriana; Ardiansyah, R; Wirdayanti, Wirdayanti; Masykur, M
Applied Engineering and Technology Vol 2, No 1 (2023): April 2023
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (337.252 KB) | DOI: 10.31763/aet.v2i1.685

Abstract

The stunting or malnutrition in toddlers are the main problems facing society today[1]. Also, the fact that these young children experience lack of adequate nourishment in Indonesia is quite indisputable. Handling nutritional problems is closely related to the strategy of a nation to create healthy, intelligent and productive human resources. Efforts to improve quality human resources begin with how to manage children's growth as part of a family with good nutrition and care For this reason, this study takes part in supporting the alleviation of cases of malnutrition by using the Simple Additive Weighting (SAW) Method which is used to find the optimal alternative from a number of alternative criteria. The basic concept of SAW is to find the weighted sum of the performance ratings for each alternative across all attributes[2]. The SAW method requires a decision matrix normalization process (X) to a scale that can be compared with all alternative ratings[3]. The SAW method recognizes 2 attributes, namely the benefit criteria and the cost criteria. The method used was Simple Additive Weighting (SAW) which consists of 2 criteria, namely Height (TB) and Weight (BW) according to age for toddlers. The calculation accuracy of this application is 90% of the tested data
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.
Comparison of VGG16 and ResNet50 Performance in Rice Leaf Disease Classification Valda Laura Uswary; Amriana Amriana
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13628

Abstract

Paddy (Oryza sativa L.) is a strategic staple food commodity in Indonesia, yet its production is frequently disrupted by various plant diseases that cause significant yield losses each year. Conventional visual disease identification is inefficient and prone to error, necessitating the adoption of more reliable, automated diagnostic technology. This study compares the performance of two pre-trained convolutional neural network architectures, VGG16 and ResNet50, in classifying rice leaf images into four categories: Brown Spot, Leaf Blast, Healthy Rice Leaf, and Rice Hispa. Both models were fine-tuned using transfer learning under an identical experimental configuration, including the same data split, optimizer, learning-rate schedule, and augmentation pipeline, to ensure a controlled architectural comparison. Model evaluation was conducted using accuracy, precision, recall, F1-score, the Matthews Correlation Coefficient (MCC), confusion matrix analysis, training convergence behaviour, inference-time computational efficiency, and Grad-CAM interpretability visualization. Experimental results show that ResNet50 achieved a test accuracy of 99% and an MCC of 0.9874, outperforming VGG16, which achieved a test accuracy of 95% and an MCC of 0.9306. Confusion matrix analysis revealed that ResNet50's errors were concentrated almost exclusively within the visually similar brown spot–leaf blast class pair, whereas VGG16 exhibited additional confusion between the healthy rice leaf and rice hispa classes. ResNet50 also demonstrated faster and more stable training convergence, more spatially coherent Grad-CAM activation patterns, and substantially higher throughput under batched inference (356.27 vs. 207.63 images/second at batch size 32), while VGG16 retained a marginal latency advantage under single-image inference. These findings indicate that, under the configuration examined in this study, ResNet50 is the more suitable architecture for rice leaf disease classification, offering an advantageous combination of accuracy, interpretability, and computational efficiency for potential deployment in agricultural monitoring systems.
Co-Authors Albrecht Yordanus Erwin Dodu Allo, Yerry Anita Ahmad Kasim Ardiansyah, R Asri Arif Deny Wiria Nugraha Firmansyah Firmansyah Lamasitudju, Chairunnisa 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 Valda Laura Uswary Wirdayanti Yuri Yudhaswana Joefrie