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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JoMMiT : Jurnal Multi Media dan IT SAINTEK
Usman Sudibyo
Universitas Dian Nuswantoro
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Kriptografi Audio MP3 Menggunakan RSA dan Transposisi Kolom Cinantya Paramita; Usman Sudibyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (242.672 KB) | DOI: 10.29207/resti.v5i3.2996

Abstract

Mp3 is one form of audio file extension that is widely used today. With a variety of uses in a variety of mp3 systems become one of the audio extensions that are commonly found in technology systems of the Internet of Things era. However, with the many uses of the .mp3 file extension, there is a new problem, namely the security of the data itself. From these problems, the author aims to examine the security of the mp3 file by designing cryptographic science-based applications. The cryptographic algorithm used in the application is a combination of the asymmetric RSA 2048 algorithm and symmetric columnic transpositions. RSA 2048 algorithm was chosen because it has a key length in accordance with NIST standards in securing data. By combining the two algorithms, the application system will have the ability to manage mp3 files and encrypt mp3 files with the results of data that cannot be played like mp3 files in general. This application system will be developed by prototype method which is the best method in developing a system with trial and error in algorithm development.
Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Muhamad Akrom; Usman Sudibyo; Achmad Wahid Kurniawan; Noor Ageng Setiyanto; Ayu Pertiwi; Aprilyani Nur Safitri; Novianto Hidayat; Harun Al Azies; Wise Herawati
JoMMiT Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Abstract

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Klasifikasi Curah Hujan di Kota Semarang Menggunakan Machine Learning Carissa Devina Usman; Usman Sudibyo
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/SAINTEK0101.0105

Abstract

The erratic distribution of rainfall greatly affects people's daily activities, especially in the Semarang City. Therefore, it is necessary to predict rainfall in Semarang City. Correct prediction of rainfall can improve community preparedness in dealing with various natural disasters caused by rain. Machine learning algorithms and data mining have been widely used in research for rainfall data in various regions. The main purpose of this study is to obtain predictions of rainfall in the city of Semarang using machine learning algorithms and to find out the best algorithm for classifying. The dataset used was obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) which is the daily rainfall data in Semarang City. From the dataset, three machine learning algorithms will be classified, namely Logistic Regression, Random Forest, and Gradient Boosting. To measure the performance of the machine learning algorithm, the classification accuracy of each algorithm is measured. From the research results, the performance of the Gradient Boosting algorithm is better than other algorithms, with an accuracy value of 71.6%. Keywords: Machine Learning, Logistic Regression, Random Forest, Gradient Boosting, Rainfall Prediction
Co-Authors Achmad Wahid Kurniawan Aprilyani Nur Safitri Ayu Pertiwi Carissa Devina Usman Cinantya Paramita Harun Al Azies Muhamad Akrom Noor Ageng Setiyanto, Noor Ageng Novianto Hidayat Wise Herawati