Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Asian Journal Science and Engineering
Sifaunnufus Ms, Fi Imanur
Unknown Affiliation
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Physics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Format Methods on Storage Media (Hard Disk) for Optimization Data Storage Capacity Saukani, Imam; Nuraini, Eko; Nurhadi, Slamet; Sumarno, Agus Sukoco Heru; Saptawati, Rina Tri Turani; Prasetyo, Prasetyo; Sifaunnufus Ms, Fi Imanur
Asian Journal Science and Engineering Vol. 2 No. 2 (2023): Asian Journal Science and Engineering
Publisher : CV. Creative Tugu Pena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51278/ajse.v2i2.1018

Abstract

This research is to determine how much storage capacity in the File Allocation Table 16 (FAT16), File Allocation Table 32 (FAT 32) and New Technology File System (NTFS), the use of the hard drive is currently the of the maximum capacity will not be able to use when not using the appropriate partition, because it can affect the amount of storage capacity available after the hard disk in the partition. This type of research is reviewed based on its purpose of use, so the research to be conducted is applied research because the products of this research can be used by all computer users. Ultimately, the findings from this study will contribute to a comprehensive understanding of how file system selection and partitioning can influence the actual storage capacity of hard drives, thus informing best practices for maximizing available storage space. Keywords: New Technology File System, File Allocation Table, Storage Media
Deteksi Stres Berbasis Electroencephalography (EEG) menggunakan Metode Random Forest Sifaunnufus Ms, Fi Imanur; Bachtiar, Fitra Abdurrachman; Prasetio, Barlian Henryranu
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 8 No 13 (2024): Publikasi Khusus Tahun 2024
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Jurnal ini akan dipublikasikan pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM Sifaunnufus Ms, Fi Imanur; Bachtiar, Fitra Abdurrachman; Prasetio, Barlian Henryranu
Jurnal Neutrino:Jurnal Fisika dan Aplikasinya Vol 17, No 1 (2024): October
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/neu.v17i1.28471

Abstract

Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. As for the contribution of this research, a reliable method for stress identification has been suggested, using a random forest model to categorize stress levels from EEG signals. Data were filtered using a bandpass filter, Independent Component Analysis, and more so using the Z-score to remove outliers and poor signals. Data that has been cleaned from noise and outliers will go through a feature extraction process using Power Spectral Density (PSD). The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. Thus, the model obtained an accuracy level of 0.8571, thereby approving the tool’s efficiency in distinguishing between different degrees of stress. The computational efficiency of the model, with a classification time of 0.2762 seconds, demonstrates its feasibility for practical applications. Based on these findings, it can be concluded that the Random Forest algorithm can be used to integrate wearable technology and for offering suggestions and timely interventions for better mental health.
Predicting Heart Disease with Enhanced Genetic Algorithms: The Role of Latin Hypercube Sampling and Hamming Distance-Based Diversity Sifaunnufus Ms, Fi Imanur; Amila Fadhila Rahmaniati; Maulida Khairunisa Argaputri; Yonathan Fanuel Mulyadi; Lailil Muflikhah
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102946

Abstract

Heart disease remains a predominant cause of mortality globally and in Indonesia, impacting both older and younger demographics due to pervasive unhealthy lifestyles, heredity, and lack of awareness of heart disease. This study addresses the critical problem of necessitating early detection methods to mitigate severe complications and fatalities associated with heart disease. With that, it is crucial to develop a robust and highly accurate prediction model for heart disease by integrating Artificial Neural networks (ANN) with Genetic Algorithms (GA). The model starts by constructing an ANN model utilizing the Keras Framework for streamlined training, followed by hyperparameter optimization through GA. As a result, this research found that the integrated ANN and GA model attains superior predictive accuracy, with the optimal configuration achieving an accuracy of 85.33%, precision of 92.55%,  recall of 81.32%, and f1-score of 86.57% via Latin Hypercube Sampling (LHS). These show that the combination of ANN and GA can significantly increase prediction accuracy and model efficiency, as a solution for more effective heart disease identification at an early stage.
Co-Authors Amila Fadhila Rahmaniati Barlian Henryranu Prasetio Fitra Abdurrachman Bachtiar Lailil Muflikhah Maulida Khairunisa Argaputri Nuraini, Eko Nurhadi, Slamet Prasetyo Prasetyo Saptawati, Rina Tri Turani Sumarno, Agus Sukoco Heru Yonathan Fanuel Mulyadi