Article Per Year (5 Year)
p-Index From 2021 - 2026
4.379
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Jupiter Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics ICON-CSE Annual Research Seminar PROCEEDING IC-ITECHS 2014 Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Linguistik Komputasional Sriwijaya Journal of Informatics and Applications Jurnal Generic JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pendidikan IPA Indonesia
Novi Yusliani
Department Informatics Engineering, Faculty Of Computer Science, Sriwijaya University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Physics Social Sciences Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : PROCEEDING IC-ITECHS 2014

 1 
Questions Classification Software based on Bloom’s Cognitive Levels using Naive Bayes Classifier Method Novi Yusliani, M. Fachrurrozi, Lidya Irfiyani Silaban,
PROCEEDING IC-ITECHS 2014 PROCEEDING IC-ITECHS 2014
Publisher : PROCEEDING IC-ITECHS 2014

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Questions Classification is one way to know how the student understanding some lessons.Those questions can be collected and classified based on cognitive Bloom level. Bloom Cognitive Level organized question in groups that represents contents of those questions. Words contained in every question have certain meaning and can be used as base to determine category of question. This study aims to classify amounts of questions based on cognitive Bloom level with Naive Bayes Classifier method. According to Blooms taxonomy of cognitive level divided into six levels of the Knowledge (C1), Comprehension (C2), Application (C3), Analysis (C4), Synthesis (C5), and Evaluation (C6). In this study, questions classified into 3 classes based on cognitive Bloom level, Knowledge (C1), Comprehension (C2), Application (C3). The amount of collective data used for training process is 240 questions. Result of this study generates accuracy of 75 % from 60 question samples tested. Susceptibility often occured because of same vocabularies from each categories, thus cause mistakes in classification.
Questions Classification Software based on Bloom’s Cognitive Levels using Naive Bayes Classifier Method Muhammad Fachrurrozi; Lidya Irfiyani Silaban; Novi Yusliani
IC-ITECHS Vol 1 (2014): Prosiding IC-ITECHS 2014
Publisher : IC-ITECHS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1452.813 KB)

Abstract

Questions Classification is one way to know how the student understanding some lessons. Those questions can be collected and classified based on cognitive Bloom level. Bloom Cognitive Level organized question in groups that represents contents of those questions. Words contained in every question have certain meaning and can be used as base to determine category of question. This study aims to classify amounts of questions based on cognitive Bloom level with Naive Bayes Classifier method. According to Bloom's taxonomy of cognitive level divided into six levels of the Knowledge (C1), Comprehension (C2), Application (C3), Analysis (C4), Synthesis (C5), and Evaluation (C6). In this study, questions classified into 3 classes based on cognitive Bloom level, Knowledge (C1), Comprehension (C2), Application (C3). The amount of collective data used for training process is 240 questions. Result of this study generates accuracy of 75 % from 60 question samples tested. Susceptibility often occured because of same vocabularies from each categories, thus cause mistakes in classification.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Ahmad Fali Oklilas Aini Nabilah Al Fatih, Zaky Alvi Syahrini Alvi Syahrini Utami Angelia, Nadya Anna Dwi Marjusalinah Annisa Darmawahyuni Ari Firdaus Ari Firdaus Ari Wedhasmara Ari Widodo Ariska, Meli Armansyah, Risky Armenia Yuhafiz Aruda, Syechky Al Qodrin Aspirani Utari Astero Nandito Ayu Purwarianti Az Zahra, Lutfiah Betharia Sri Fitrianti Danny Matthew Saputra Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Desty Rodiah Desty Roodiah Dhiya Fairuz Diah Kartika Sari Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Fadel Muhammad, Fadel Firdaus Firdaus Fitria Khoirunnisa Ghita Athalina Gilbert Christopher Jambak, Muhammad Ihsan Kanda Januar Miraswan Kartika, Diah Lidya Irfiyani Silaban M Fachrurrozi M. Fachrurrozi . Mastura Diana Marieska Melly Ariska Milka, Ikbal Adrian Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Naufal Rachmatullah Muhammad Omar Braddley Muhammad Raihan Habibullah Muhammad Rizqi Assabil Muharromi Maya Agustin Nur Hamidah Nurul Izzah Oktadini, Nabila Rizky Osvari Arsalan Plakasa, Gerald Rahma Haniffia Rahmannisa, Amanda Rahmat Fadli Isnanto Raisha Fatiya Reyhan Navind Shaquille Ridho Putra Sufa Rifka Widyastuti Rizki Kurniati Rizki Ramadandi Rusdi Efendi Saputra, Danny Mathew Saputra, Danny Matthew Sari, Tri Kurnia septi ana Siti Nurmaini Syechky Al Qodrin syechky al qodrin aruda Tiara Dewangga Tristi Dwi Rizki Wenty Octaviani Winda Kurnia Sari Yenny Anwar Yesi Novaria Kunang YUNITA Yunita Yunita Yunita Yunita Yunita Yunita