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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Asian Journal Science and Engineering Journal of Evrimata: Engineering and Physics
Sifaunnufus Ms, Fi Imanur
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Physics

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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

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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.
The Buck-boost converter in photovoltaics for battery chargers saukani, imam; Nuraini, Eko; Sukoco Heru Sumarno, Agus; Tri Turani Saptawati, Rina; Islahunufus, Imanur; Sifaunnufus Ms, Fi Imanur
Journal of Evrímata: Engineering and Physics Vol. 02 No. 01, 2024
Publisher : PT. ELSHAD TECHNOLOGY INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70822/journalofevrmata.vi.26

Abstract

Alternative energy is energy that is widely developed by scientists nowadays, especially in terms of electricity. Currently, the alternative energies that are widely developed are wind power, hydropower, geothermal energy, and solar power. Solar power is a promising energy source in Indonesia. The utilization of solar energy requires photovoltaics to convert solar energy into electricity, while for charging a12V/7Ah battery, a buck-boost converter is used. However, the use of the buck-boost converter still has some shortcomings, such as reversed voltage polarity. To address this issue, it is proposed to use a2-switch buck-boost converter. With the2-switch buck-boost converter, it is capable of charging the battery with an initial voltage of19V, which is then reduced to14.25V to charge the battery with an initial voltage of10.08V to11.16V within60 minutes, maintaining the same polarity as the initial input and with a maximum current from the photovoltaic of3.48A. In this thesis, Atmega16 is used to control the2-switch buck-boost converter and a50Wp photovoltaic with a maximum voltage of 21V and a current of 3.48A..
Co-Authors Barlian Henryranu Prasetio Fitra Abdurrachman Bachtiar Islahunufus, Imanur Nuraini, Eko Nurhadi, Slamet Prasetyo Prasetyo Saptawati, Rina Tri Turani Sukoco Heru Sumarno, Agus Sumarno, Agus Sukoco Heru Tri Turani Saptawati, Rina