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All Journal JOIV : International Journal on Informatics Visualization Jurnal Computer Science and Information Technology (CoSciTech)
Mandahadi Kusuma, Mandahadi
Universitas Gadjah Mada
Computer Science & IT

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Pengujian sistem keamanan wireless router pada ekosistem rumah cerdas berbasis NIST sp800-115 Kusuma, Mandahadi; Hariyadi, Dedy; Kurniawan, Hendarto; Muttaqin, Filda Fikri Faizal
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6315

Abstract

It is important to safeguard personal information captured by the device being used against theft or unauthorized use, which makes network design that is more resistant from hacking attempts. This includes personal information within the ecosystem of smart homes. A house that should be the most comfortable place should not be disturbed because of the wrong configuration of the smart home ecosystem. Several previous studies have discussed security configuration gaps in network infrastructure, but have not focused on the smart home ecosystem from the user's internal side. Therefore, in this research, a 2.4 GHz wireless network configuration testing model is proposed in a smart home ecosystem using an application developed specifically for checking misconfiguration security gaps, which is expected which is expected to expand research on network infrastructure security gaps based on the NIST 800-115 framework. The application's testing technique can reveal details about a wireless network ecosystem's safe and risky conditions. The results of these findings will provide recommendations to wireless network owners to take action to reconfigure their wireless networks to make them more secure.
Indonesian Word Sound Recognition Using Convolutional Neural Network Method Kusuma, Mandahadi; Aunilbarr, Fayyadh
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2679

Abstract

Access to education, particularly in a university environment, is essential for deaf and hard-of-hearing students as more of them pursue higher education. At UIN Sunan Kalijaga the current challenges are a limited number of sign language interpreters and translating technical terminology in lectures. Many methods are available for speech recognition, but research on how well this method performs in Indonesian has not been published, especially in education-level recognizers. This experimental study aims to investigate if Indonesian words can be recognized through Convolutional Neural Networks (CNN) and to find out the Data Ratio for Training, Validation, and Testing set to get the best performance. The study used a dataset of 4 Indonesian words with the total voice sample, each with 50 voice samples from young adults aged 19-23. Audio data is preprocessed into spectrograms, inputs to the CNN model using TensorFlow. The CNN Model had a 90% accuracy with a 60:20:20 ratio between training, validation, and test data. The other ratios (70:15:15 and 80:10:10) provided accuracy ranges of between 80% to 90%. This study shows that CNNs are the best for Indonesian word recognition and that the data ratio of 60:20:20 is optimal. This result has valuable benefits, such as using voice-to-text over lectures to enhance the ease of learning and education in Indonesia. Further studies should be conducted using different neural network approaches; the denoise approach is also necessary to increase accuracy.
Co-Authors Aunilbarr, Fayyadh Dedy Hariyadi Kurniawan, Hendarto Muttaqin, Filda Fikri Faizal