Article Per Year (5 Year)
p-Index From 2020 - 2025
3.267
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Nasional Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Responsif : Riset Sains dan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Erfian Junianto
Universitas Adhirajasa Reswara Sanjaya
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Neuroscience Physics Transportation

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Breast cancer identification using machine learning and hyperparameter optimization Arifin, Toni; Prasetyo Agung, Ignatius Wiseto; Junianto, Erfian; Rachman, Rizal; Wibowo, Ilham Rachmat; Agustin, Dari Dianata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1620-1630

Abstract

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Stock’s selection and trend prediction using technical analysis and artificial neural network Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian; Rabbani, Muhammad Ihsan; Mayangsari, Ariefa Diah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp151-163

Abstract

Stock trading offers potential profits when traders buy low and sell high. To maximize profits, accurate analysis is essential for selecting the right stocks, timing purchases, and selling at peak prices. The authors propose a new method for selecting potential stocks that are highly likely to rise in price. The method has two stages. First, technical analysis, using moving averages and stochastic oscillators, filters stocks with downward trends, anticipating a reversal and subsequent rise. Second, for selected stocks, future price trends are predicted using artificial neural networks, specifically long short-term memory (LSTM) with adaptive moment estimation (Adam) optimizer. The second step ensures that only stocks with increasing prices will be chosen for trading. This study analyzes five hundred Fortune 500 stocks over three different periods, with 250 days of data each. Simulations conducted in Python showed that technical analysis could filter 5 to 6 candidate stocks. Subsequently, the LSTM model predicted that only 4 of these stocks would have an upward trend. Validation shows that trend predictions are correct, resulting in an average profit of 5.51% within 10 working days. This profit outperforms the profits generated by other existing methods.
Klasifikasi Emosi pada Teks Berbahasa Inggris Menggunakan Pendekatan Ensemble Bagging Erfian Junianto; Mila Puspitasari; Salman Ilyas Zakaria; Toni Arifin; Ignatius Wiseto Prasetyo Agung
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 4: November 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i4.14440

Abstract

This study highlights the importance of emotion classification in English text, particularly in human interaction on social media, which often involves unstructured data. Emotions play a crucial role in communication; a better understanding of these emotions can aid in analyzing user behavior. The main objective of this research is to enhance accuracy, recall, precision, and F1-score in emotion classification by applying an ensemble bagging approach, combining the naïve Bayes, logistic regression, and k-nearest neighbor (KNN) algorithms. The methodology used included data collection from various sources, followed by data cleaning and analysis using text mining and machine learning techniques. The collected data were then analyzed to detect emotions such as anger, happiness, sadness, surprise, shame, disgust, and fear. Performance evaluation was conducted by comparing the results of the ensemble bagging method with individual algorithms to measure its effectiveness. The findings reveal that the logistic regression method achieved the highest accuracy at 98.76%, followed by naïve Bayes and KNN. This ensemble method overcame the limitations of each individual algorithm, enhancing overall classification stability and reliability. These findings provide valuable insights into text-based emotion analysis techniques and demonstrate the potential of ensemble methods to improve classification accuracy. Future research directions can explore additional ensemble techniques and optimize model complexity for improved performance in emotion analysis across broader datasets.
Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Co-Authors Abdul Rahman Wahid Rapsanjani Ade Mubarok Agung Baitul Hikmah Agustin, Dari Dianata Ali Akbar Rismayadi Ali Akbar Rismayadi Alif Muhamad Nisar Arif Rifan Hidayat, Arif Rifan Dwiza Riana Fathin Sophosta Tocas Hari Heryadi Iedam Fardian Anshori, Iedam Fardian Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Irwan Tanu Kusnadi Kusnadi, Weli Mayangsari, Ariefa Diah Mila Puspitasari Muhamad Iqbal Maulana Muhammad Yoga Pratama Muhammad Zaid Zuhdi Nadya Paramitha Novia Nur Fatonah Prasetyo Agung, Ignatius Wiseto Rabbani, Muhammad Ihsan Ripandi, Rudi Rizal Rachman, Rizal Rizki Tri Prasetio Salman Ilyas Zakaria Salman Topiq Sari Susanti Syarif Hidayatulloh Tessa Putri Mallini Toni Arifin Toni Arifin Wibowo, Ilham Rachmat Yogi Aditya Pratama Yudi Ramdhani Yusa Primaesha