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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Komputer JISA (Jurnal Informatika dan Sains)
Wiranto Herry Utomo
President University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisis perbandingan Cloud DOCUMENT pada EYeOS dan Google docs Utomo, Wiranto Herry; Pormes, Rivort
Jurnal Sistem Komputer Vol 5, No 1 (2015)
Publisher : Jurnal Sistem Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsk.v5i1.77

Abstract

Penelitian ini bertujuan menganalisis perbandingan antara eyeOS dengan Google Docs sebagai alternatif yang lebih baik yang harus digunakan untuk membantu pengguna dalam masalah pendokumntasian.  Dokumen berupa dokumen pengolah kata, spreadsheet, dan presentation. Perbandingan antara eyeOS dengan Google Docs ini nantinya akan diketahui manakah yang paling efektif serta efisien yang dibutuhkan untuk keperluan dokumentasi dan dokumen dapat dibagikan ke para user lainnya.
Student performance prediction using simple additive weighting method Harco Leslie Hendric Spits Warnars; Arif Fahrudin; Wiranto Herry Utomo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp630-637

Abstract

In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.
Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification Novia Andini; Wiranto Herry Utomo
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.911

Abstract

The yield of an agricultural process is very important and influential, where the harvest is used as a support for human life both as food and a source of income. Many factors can influence the success of agriculture, such as the climate that is going on around in the surrounding area. The wrong prediction in determining the future climate will cause crop failure due to incompatibility with the type of plant. In this era, many technologies have been able to predict climate, one of which is technology machine learning that has many types and techniques, which machine learning technology has been widely used in predicting many things. This study aims to predict the climate in an area which is intended to determine crop yields based on the Koppen classification, and also the prediction based on several parameters such as temperature, humidity, duration of sun exposure and rainfall. And the results of this study is have a loss of 0.006 and with the MAPE value as an indicator of the percentage error and as an indicator for determining the accuracy of the prediction results, which is 3.29%, which means that it is included in the very accurate category in predicting climate to estimate agricultural yields.
Co-Authors Arif Fahrudin Novia Andini Pormes, Rivort Warnars, Harco Leslie Hendric Spits