Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal Sensi: Strategic of Education in Information System
Maulana, Reffy
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Application of Artificial Neural Network Algorithm with Principal Component Analysis for Diagnosis of Breast Cancer Tumors Almunawar, Muhammad Irfan; Maulana, Reffy; Sumbogo, Rifqi Putrawan
Journal Sensi: Strategic of Education in Information System Vol 10 No 2 (2024): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v10i2.3474

Abstract

Cancer is a health disorder where abnormal cells proliferate uncontrollably and is the second leading cause of death worldwide. Breast cancer, in particular, is prevalent among women in Indonesia. This study aims to diagnose breast cancer, identifying whether it is malignant or benign, using Artificial Neural Network (ANN) algorithms to enhance the accuracy of tumor diagnosis. The fundamental principle is to develop a neural network capable of processing information efficiently without relying on Python packages such as scikit-learn. The ANN operates through forward propagation and backward propagation to optimally predict outcomes and update weights. The dataset used is from the UCI Machine Learning Repository, consisting of 569 samples and 30 features. This dataset is divided into a training set (80%) and a cross-validation set (20%). The ANN model comprises one input layer, two hidden layers, and one output layer, utilizing tanh activation functions for the hidden layers and a sigmoid activation function for the output layer. Training results indicated an accuracy of 95.6% on the training set and 93.2% on the cross-validation set. This demonstrates that the model performs well in detecting breast cancer, with a low error rate and strong generalization capability. This study successfully developed an effective and reliable ANN model for breast cancer detection with high accuracy, supporting clinical breast cancer diagnosis.
Diabetes Classification Algorithm Optimization Using Particle Swarm Optimization on Naïve Bayes, C4.5 and Random Forest Maulana, Reffy; Eliyani, Eliyani
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2431

Abstract

The global rise in diabetes prevalence presents a significant public health concern, emphasizing the need for accurate and efficient early detection systems. This study investigates the performance of three classification algorithms—Naïve Bayes, C4.5, and Random Forest—for predicting diabetes and explores the impact of hyperparameter tuning via Particle Swarm Optimization (PSO) on model performance. The research employs the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset from the Centers for Disease Control and Prevention (CDC), which includes a wide range of health-related and demographic variables from adult respondents across the United States. Each algorithm was tested under two conditions: with default parameters and after optimization using PSO. Experimental results demonstrate that the Random Forest algorithm, even without optimization, yielded the highest accuracy at 95.15%, whereas Naïve Bayes showed the weakest performance. However, applying PSO significantly improved the performance of initially suboptimal models, particularly Naïve Bayes and C4.5. Specifically, Naïve Bayes accuracy increased from 80.80% to 82.24% (a 1.44% increase), and C4.5 accuracy increased from 91.22% to 91.31% (a 0.09% increase). In contrast, the effect of optimization on Random Forest was minimal, showing a slight decrease in accuracy to 94.37%, indicating the model’s robustness in its default configuration. These findings underscore the importance of algorithm selection and tailored optimization strategies in enhancing the accuracy of diabetes classification systems.
Co-Authors Almunawar, Muhammad Irfan Eliyani Eliyani Sumbogo, Rifqi Putrawan