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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Information Management For Educators And Professionals (IMBI) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik Sebatik METIK JURNAL Building of Informatics, Technology and Science Just TI (Jurnal Sains Terapan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Informatika Wicida eProceedings of Engineering J-Icon : Jurnal Komputer dan Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Sasambo: Jurnal Abdimas (Journal of Community Service)
Pitrasacha Adytia
Sistem Informasi, STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Modeling The Prediction of Hard Drive Capacity Usage on Server Computers Based on Linear Regression Wahyuni Wahyuni; Pitrasacha Adytia; Siti Namira Rizqi Astin; Kelik Sussolaikah; Fadly Kasim
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28851

Abstract

Bank of XYZ has a server computer that is used to run several information technology application services such as ATMs and others. Because the server computer uses a hard drive, the full hard drive can cause problems with the service not operating properly. Full hard drives occur without being noticed. So that this makes the computer server problematic, resulting in customer dissatisfaction and decreased customer loyalty to Bank XYZ. To solve the problem at XYZ Bank, one of the machine learning algorithms can be used to predict hard drive capacity. The method used to predict hard drive storage or usage. The machine learning algorithm used is Multiple Linear Regression. The results of this study show that the linear regression model successfully predicts the use of hard drive capacity on server computers with a sufficient level of accuracy.But it is still not optimal because only a few servers can be predicted. For further research, may consider using the LSTM (Long Short-Term Memory) algorithm. LSTM is an algorithm that is well-suited for sequence prediction problems, including time series forecasting.
Coffee Type Classification Using Backpropagation Artificial Neural Network Pitrasacha Adytia; Wahyuni Wahyuni; Dimas Asmaramany; Kelik Sussolaikah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28853

Abstract

Coffee has several types including robusta coffee, arabica coffee and luwak coffee. Each coffee has certain characteristics of color, texture, aroma and also the quality of the taste. Coffee counterfeiting is also common. This coffee counterfeiting usually uses materials such as corn, wheat, soybeans, husks, sticks and robusta coffee beans. So that a model is needed to be able to classify the type of coffee. This research uses artificial neural network machine learning algorithms to identify and classify coffee. Quality training and testing data is needed in this method because it will affect the final results. Initial data is collected via e-nose, with this equipment data on changes in electrical voltage will be obtained from 4 sensors, namely MQ-2, MQ-3, MQ-7 and MQ-135. These 4 features will be used in the classification process. With 900 sets of training data, the test results show that the neural network is able to provide correct classification 99% of the 3 sets of testing data. The results of training and testing show that the neural network formed can identify and distinguish coffee types with good results.
Co-Authors Adha Trisna Lidya Alvin Hernanda Ekaputra Andriawan Arriyanti, Eka Aulia Abdul Rahman Didi Kuswandi Dimas Asmaramany Ekawati Yulsilviana Ekawati, Hanifah Fadly Kasim Fariz Mirza Ahmad Hanifah Ekawati Heny Pratiwi Heny Pratiwi I Nengah Riandika Ika Nurmila Avinda Ita Arfyanti Kelik Sussolaikah Kelik Sussolaikah Lisda Aulia Rahmi Muhammad Ahsanu Qaulan Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Nurhuda, Asep Nursobah, Nursobah Rahmat Hidayat Reza Andrea Rizky Zakariyya Rasyad Salmon Salmon Siti Namira Rizqi Astin Solly Aryza Sugih Niansyah Supriady Supriady Tommy Bustomi Ujang Sumarwan WAHYUNI Wahyuni - Wahyuni wahyuni wahyuni Yudha Satria Yuli Adam Prasetyo Yunita Yunita Yunita Yunita