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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT) JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Fanani, Nurul Zainal
Mechatronics Engineering Technology Study Program, Department Of Engineering, Politeknik Negeri Jember, Jember, Jawa Timur 68121, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Penentuan Kemampuan Motorik Halus Anak dari Proses Menulis Hanacaraka Menggunakan Random Forest Nurul Zainal Fanani; Adri Gabriel Sooai; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1328.116 KB) | DOI: 10.22146/jnteti.v9i2.153

Abstract

The children's Fine Motor Skill Assessment (FMS) at the beginning of school age is essential to get information about children's school readiness. The process of measuring FMS has been carried out by observing children, both directly and from the results of sketches or children's writing. This observation process is very dependent on the observer's perception. This study aims to determine the children's FMS using Javanese script. This research develops a new method for determining children's FMS from the process of writing the Javanese script. The system was recording data directly when the child is writing the Javanese script. Retrieval of data recording from the writing process involved 14 students in 1st grade and 2nd grade from three elementary schools in Jember district. The process of recording data from each student produces a large enough raw data. Therefore, this study uses random forest classification method,because this method can carry out the classification process on large amounts of data by combining several decision trees. Other classification methods, including naïve Bayes and k-NN, were used as a comparison. The experiment results show that the random forest classification method is the bestwith an accuracy of 98.7%.
Co-Authors Adri Gabriel Sooai Ahmad Rofi'i Atyanta Nika Rumaksari Faisal Lutfi Afriansyah Felix V even D.C Fendik Eko Purnomo Khamid Khamid Mauridhi Hery Purnomo Mauridhi Hery Purnomo Puspa Grahito Adhi Surya Sumpeno