Article Per Year (5 Year)
p-Index From 2021 - 2026
1.475
P-Index
This Author published in this journals
All Journal JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Educational Management and Innovation (IJEMI) Abdimas: Papua Journal Of Community Service Journal of Informatics and Computer Science (JINACS)
Rochmawati, Naim
Unknown Affiliation
Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search

 1 
Implementasi Ensemble Learning Adaboost Pada Algoritma Klasifikasi Decision Tree dan SVM Untuk Klasifikasi SMS Berbahasa Indonesia Rosyidi, M Ibnu Umar; Rochmawati, Naim
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n1.p7-13

Abstract

Perkembangan teknologi di berbagai sektor yang sangat cepat selama satu dekade ini, membuat kita semakin dimudahkan dalam melakukan aktivitas sehari-hari. Kebutuhan dalam berkomunikasi terus mengalami kemajuan dari yang awalnya menggunakan surat dengan jangkauan terbatas dan lama, hingga sekarang menggunakan layanan provider SMS (Short Message Service). Layanan SMS sangat digemari karena harganya yang murah dan dapat memilih untuk menjawab nanti jika tidak ada waktu pada saat tersebut. Bertumbuhnya pengguna SMS dibersamai dengan orang-orang yang tidak bertanggung jawab memanfaatkan situasi untuk keuntungan sendiri dengan melakukan tindak penipuan melalui SMS. Untuk mencegah tersebut, diperlukan sistem yang dapat memilah SMS agar tidak semua masuk ke pengguna. Salah satu cara yang dapat digunakan adalah membuat model machine learning yang dapat mengenali dan mengelompokkan SMS apakah SMS yang masuk tersebut adalah SPAM atau tidak. Algoritma SVM dan decision tree adalah dua algoritma klasifikasi yang mudah digunakan untuk memecahkan masalah klasifikasi, pada penelitian ini juga digunakan teknik ensemble adaboost yang dapat meningkatkan nilai akurasi dari base algoritma yang digunakan. Data yang digunakan adalah dataset SMS dari penelitian Rahmi dan Wibisono[1]. Algoritma SVM memilki nilai akurasi tertinggi dengan nilai 0.96 atau 96%, ditemukan juga bahwa algoritma SVM + Adaboost hanya membuahkan hasil yang baik saat diterapkan data jenis unigram dan bigram tanpa tf-idf dan unigram tanpa tf-idf. Algoritma SVM + Adaboost tidak cocok menggunakan tf—idf karena dapat menyebabkan penurunan nilai akurasi.
Teknik Bagging Pada Algoritma Klasifikasi Decision Tree dan SVM Untuk Klasifikasi SMS Berbahasa Indonesia Rosyidi, M Ibnu Umar; Rochmawati, Naim
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 02 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n02.p265-271

Abstract

Analisis Sentimen Masyarakat terhadap Fenomena Childfree Menggunakan Metode Long Short Term Memory dan Bidirectional Encoder Representations from Transformers di Twitter Farida, Zaemita Wahidatul; Rochmawati, Naim
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 03 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n03.p369-376

Abstract

Prediksi Siswa Putus Sekolah Menggunakan Algoritma Decision Tree C4.5 Fatma, Yofi Lailatul; Rochmawati, Naim
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 04 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n04.p486-493

Abstract

Pengembangan Aplikasi Virtual Reality Pemandu Senam Lansia Berbasis Android Untuk Motivasi Olahraga Lansia Ayuningtyas, Rizka; Rochmawati, Naim
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 04 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n04.p568-583

Abstract

Pengembangan Aplikasi Virtual Reality Pemandu Senam Lansia Berbasis Android Untuk Motivasi Olahraga Lansia Ayuningtyas, Rizka; Rochmawati, Naim
Journal of Informatics and Computer Science (JINACS) Vol. 6 No. 01 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v6n01.p39-54

Abstract

Exploring the tree algorithms to generate the optimal detection system of students' stress levels Yamasari, Yuni; Qoiriah, Anita; Rochmawati, Naim; Prapanca, Aditya; Prihanto, Agus; Suartana, I Made; Ahmad, Tohari
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp548-558

Abstract

The significant changes in the world of education after the coronavirus disease 2019 (COVID-19) pandemic have increased students' anxiety levels. This anxiety can trigger stress which can interfere with students' academic performance. Therefore, this condition is a critical problem that needs to be addressed immediately. However, researchers have not previously conducted much research to detect post-COVID stress levels. Apart from that, the existence of a system capable of carrying out this detection is still lacking. Therefore, this research focuses on building a system for detecting student stress levels. First, an exploration of the tree algorithm was carried out to find the most optimal method for recognizing student stress levels. Then a detection system is built using this optimal method. The research results show that the tree ID3 (Iterative Dichotomiser 3) algorithm achieves the highest accuracy value of 95% compared to other tree algorithms with the scenario of dividing training data into test data of 80%:20%. Moreover, this telegram bot-based detection system works well in recognizing three categories of stress, namely: light-, moderate-, and heavy stress based on black-box testing techniques.
The Influence of Student Stress Levels and Student’s Mobile Technology Acceptance Levels on Student’s Academic Performance Buditjahjanto, I.G.P. Asto; Rochmawati, Naim; Diptya Widayaka, Parama; Arwin Dermawan, Dodik; Indah Trisanti, Rosalia; Wildan Habibi, Mohammad
International Journal of Educational Management and Innovation Vol. 5 No. 3 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijemi.v5i3.9169

Abstract

One way to measure education quality is to evaluate students' learning outcomes. Therefore, this study aims to determine the effect of student stress levels and mobile technology acceptance levels on their learning outcomes. This research method used two methods, namely the Chi-Square method and the correlation method. The Chi-Square method tests the following hypotheses: (a) is there a significant effect between student stress levels and their learning outcomes? (b) is there a significant effect between students' mobile technology acceptance level and learning outcomes? Meanwhile, the correlation method used to test the hypothesis is as follows: (c) is there a significant correlation between the stress level of students and their learning outcomes? Moreover, (d) Is there a significant correlation between students' mobile technology acceptance level and learning outcomes? The Chi-Square results for testing hypothesis (a) show that there is a significant influence between the stress level of students and student academic performance. The Chi-Square results for testing hypothesis (b) show no significant effect between the level of mobile technology acceptance and student academic performance. The correlation result for testing hypothesis (c) shows a significant negative correlation between student stress levels and academic performance. The correlation result for testing hypothesis (d) shows a significant positive correlation between the mobile technology acceptance level and students' academic performance
Pelatihan Pemanfaatan Internet untuk Menunjang Kreativitas Guru dalam Penyampaian Materi secara Daring Yamasari, Yuni; Qoiriah, Anita; Yustanti, Wiyli; Rochmawati, Naim; Nurhidayat, Andi Iwan; Kurniawan, Ari
Abdimas: Papua Journal of Community Service Vol. 6 No. 1 (2024): Januari
Publisher : Lembaga Pengembangan dan Pengabdian Masyarakat Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/pjcs.v6i1.2749

Abstract

   
Co-Authors Aditya Prapanca Agus Prihanto Andi Iwan Nurhidayat ANITA QOIRIAH ARI KURNIAWAN Arwin Dermawan, Dodik Ayuningtyas, Rizka Farida, Zaemita Wahidatul Fatma, Yofi Lailatul I Gusti Putu Asto Buditjahjanto Indah Trisanti, Rosalia Parama Diptya Widayaka Rosalina, Siti Rosyidi, M Ibnu Umar Suartana, I Made Tohari Ahmad Wildan Habibi, Mohammad Wiyli Yustanti YUNI YAMASARI