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All Journal Indonesian Journal of Electronics and Instrumentation Systems Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Informatik : Jurnal Ilmu Komputer SEINASI-KESI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ABDIKOM

Santoni, Mayanda Mega

Fakultas Ilmu Komputer, Universitas Pembangunan Nasional Veteran Jakarta

Author-ID : 979193

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Pengaruh Oversampling pada Klasifikasi Hipertensi dengan Algoritma Naïve Bayes, Decision Tree, dan Artificial Neural Network (ANN) Nurul Chamidah; Mayanda Mega Santoni; Nurhafifah Matondang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records. Oversampling in this study is applied to hypertension dataset where hypertensive class has a small number of records when compared to the number of records for non-hypertensive classes. This study aims to evaluate the effect of oversampling on the classification of hypertension dataset consisting of hypertensive and non-hypertensive classes by utilizing the Naïve Bayes, Decision Tree, and Artificial Neural Network (ANN) as well as finding the best model of the three algorithms. Evaluation of the use of oversampling on hypertension dataset is done by processing the data by imputing missing values, oversampling, and transforming data into the same range, then using the Naïve Bayes, Decision Tree, and ANN to build classification models. By dividing 80% of data as training data to build models and 20% as validation data for testing models, we had an increase in classification performance in the form of accuracy, precision, and recall of the oversampled data when compared without oversampling. The best performance in this study resulted in the highest accuracy using ANN with 0.91, precision 0.86 and recall 0.99.

Penerapan Convolutional Neural Networks untuk Mesin Penerjemah Bahasa Daerah Minangkabau Berbasis Gambar Mayanda Mega Santoni; Nurul Chamidah; Desta Sandya Prasvita; Helena Nurramdhani Irmanda; Ria Astriratma; Reza Amarta Prayoga
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

One of efforts by the Indonesian people to defend the country is to preserve and to maintain the regional languages. The current era of modernity makes the regional language image become old-fashioned, so that most them are no longer spoken. If it is ignored, then there will be a cultural identity crisis that causes regional languages to be vulnerable to extinction. Technological developments can be used as a way to preserve regional languages. Digital image-based artificial intelligence technology using machine learning methods such as machine translation can be used to answer the problems. This research will use Deep Learning method, namely Convolutional Neural Networks (CNN). Data of this research were 1300 alphabetic images, 5000 text images and 200 vocabularies of Minangkabau regional language. Alphabetic image data is used for the formation of the CNN classification model. This model is used for text image recognition, the results of which will be translated into regional languages. The accuracy of the CNN model is 98.97%, while the accuracy for text image recognition (OCR) is 50.72%. This low accuracy is due to the failure of segmentation on the letters i and j. However, the translation accuracy increases after the implementation of the Leveinstan Distance algorithm which can correct text classification errors, with an accuracy value of 75.78%. Therefore, this research has succeeded in implementing the Convolutional Neural Networks (CNN) method in identifying text in text images and the Leveinstan Distance method in translating Indonesian text into regional language texts.

Co-Authors Aditya Surya Wijaya Andreas Silalahi Anita Muliawati Bayu Permana Sukma Cindy Chairunnisa Deo Haganta Depari Desta Sandya Prasvita Desta Sandya Prasvita Desta Sandya Prasvita Desta Sandya Prasvita Hafizd, Maulana Helena Nurramdhani Irmanda Helena Nurramdhani Irmanda Helena Nurramdhani Irmanda Iin Ernawati Ilham Ramadhan Irmanda, Helena Nurramdhani Irmanda, Helena Nurramdhani Jayanta Jayanta Krisanto Abilowo Matondang, Nurhafifah Muhammad Nuradli Hasbi Gumay Novi Trihastuti Nurhafifah Matondang Nurul Chamidah Nurul Chamidah Prajamukti, Reino Qahtan Said Reza Amarta Prayoga Reza Amarta Prayoga Ria Astriratma Ria Astriratma Ria Astriratma Rio Wirawan Said, Qahtan Silalahi, Donni S Widiastiwi, Yuni Yulistiani, Risma Yulnelly Yulnelly Yulnelly Yulnelly Yuni Widiastiwi

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)