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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan
E Elmayati
Universitas Bina Insan, Lubuk Linggau, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Sistem Pengenalan Wajah Dengan Metode Template Matching Untuk Absensi Kelas Berbasis Web Tresa Citra Alvonita; E Elmayati; Budi Santoso
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.185

Abstract

The problems that are often encountered in collecting attendance data at schools, especially in Indonesia, are still done manually and are very inefficient and ineffective. In Selangit State High School students who still take attendance manually through recording in books. This can lead to inaccuracies and fraud during attendance because books are prone to damage, loss, or being left behind, so that significant data can be lost. Therefore, the use of facial recognition technology which is included in the field of image processing can be used to strengthen the attendance system. Making an attendance application with face recognition using the Template Matching method can help facilitate attendance data collection, prevent inaccuracies and fraud, reduce the risk of data corruption, make it easier to make attendance reports from students, and make attendance activities more efficient. The results showed that the face recognition program using the Template Matching method was used for student attendance at school to make it more effective and efficient.
Penerapan Algoritma Random Forest Dalam Klasifikasi Penjurusan Di SMA Negeri Tugumulyo Haiza Marlina; E Elmayati; Antoni Zulius; Harma Oktafia Lingga Wijaya
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.188

Abstract

The problem in this study is the difficulty in determining the majors at Tugumulyo State High School, for that a classification model is needed that can determine the right majors in student majors at Tugumulyo State High School. This research uses data collection methods, by observing and recording directly at the research site (observation), asking questions directly to the source (interview), and documentation by reading literature guidelines. The results showed that the majors classification model using the Random Forest algorithm. It can be concluded that the model built using the R programming language gets an accuracy value of 0.9314 and is included in the good classification category.
Co-Authors Antoni Zulius Budi Santoso Haiza Marlina Lingga Wijaya, Harma Oktafia Tresa Citra Alvonita