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All Journal RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology
Bimastari Aviani, Tri Hasanah
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Computer Science & IT

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Penerapan Algortima Support Vector Machine (SVM) Untuk Prediksi Tingkat Kelulusan Siswa SMA Wulandari, Cindi; Bimastari Aviani, Tri Hasanah; Rian Saputra
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 4 (2024): RESOLUSI March 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i4.1753

Abstract

Graduation is the desire of every student to be able to complete their studies, and to achieve graduation, students must complete stages such as taking 6 semesters of learning with a school exam score for each subject above 70, and this is a rule in the school. In this study, researchers used student data for the 2022/2023 school year, which researchers took in senior high school number one Lubuklinggau. The method used by the researchers is data mining. Data mining is a term used to describe knowledge discovery in databases. The algorithm the researchers use to predict graduation is the Support Vector Machine (SVM) algorithm because it is able to predict good graduation. In predicting graduation, the accuracy value is 98.81% for XIIth grade students, 96.49% for XIth grade students, and 98.25% for Xth grade students.
Optimalisasi Model Jaringan Syaraf Untuk Pengenalan CAPTCHA dengan Metode LeNet-5 Rocky Putra A; Kurniawan, Rudi; Bimastari Aviani, Tri Hasanah
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.476

Abstract

In general, CAPTCHA is an image containing distorted letters or numbers. This test involves users typing the results of guessing letters or numbers in the distorted image as security before users can enter or access a website they want to go to. In this paper, a simulation of an automatic reader system for CAPTCHA has been created where the letters and numbers in the image are carried out in several stages. The initial stage involves training using the EMNIST dataset to train the model to recognize general letter and digit characters before recognition in the CAPTCHA image. Furthermore, the process of recognizing letters and numbers in different CAPTCHA images is carried out to read the text contained in the CAPTCHA. The Convolutional Neural Network (CNN) model of the LeNet-5 method is used as a method for reading distorted letters and numbers in CAPTCHA with a high level of accuracy, achieving 88.56%.
Co-Authors rian saputra Rocky Putra A Rudi Kurniawan Wulandari, Cindi