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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Teknika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Faktor Exacta JMM (Jurnal Masyarakat Mandiri) Jurnal Teknoinfo JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Informasi dan Komputer Dinasti International Journal of Education Management and Social Science Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal Infortech Jurnal Generic Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat Journal of Research in Social Science and Humanities International Journal of Advanced Science and Computer Applications TECHSI - Jurnal Teknik Informatika Jurnal INFOTEL Journal of Digital Literacy and Volunteering Journal of Digital Literacy and Volunteering Journal of Digital Market and Digital Currency
M Said Hasibuan
Institute Informatika Dan Bisnis Darmajaya
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Journal : Jurnal INFOTEL

 1 
Detection of learning styles with prior knowledge data using the SVM, K-NN and Naïve Bayes algorithms Muhammad Said Hasibuan; RZ Abdul Aziz
JURNAL INFOTEL Vol 14 No 3 (2022): August 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i3.788

Abstract

The two types of automatic learning style detection approaches are data driven (DD) and literature based (LB). Both methods of automatic learning style detection have advantages over traditional learning style detection methods because they use external data sources, such as forums, quizzes and views of teaching materials, that are more accurate than the questionnaires used in traditional styles of detection. The results of automatic detection, on the other hand, do not always reflect learning styles. This paper presents a learning style recognition method that uses data from the learner’s internal source, namely prior knowledge, to overcome these challenges. Prior knowledge is proposed because it is based on the learner’s knowledge or skills, which better reflect the learner’s characteristics, rather than on the learner’s behaviour, which tends to be dynamic. By using past knowledge, this paper presents a method for detecting automatic learning patterns. The learning style detection framework is unique in that it consists of three stages: prior knowledge question development, prior knowledge measurement and learning style detection using the Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbour (K-NN) classification methods. The accuracy of learning style detection using prior knowledge data was higher than detection results using behavioural data or hybrid data (prior knowledge + behaviour) in this study
Analysis of Lampung Provincial Social Service website using PIECES framework Muhammad Said Hasibuan; Andry Ferianto
JURNAL INFOTEL Vol 15 No 1 (2023): February 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i1.870

Abstract

The official website of the Social Office Lampung Provincial contains news content, routine activities, and information on public services. The website has been changed from an internal point of view but does not yet know how satisfaction is based on user perceptions. As a result, users no longer want to visit the Social Office website, which is one of the problems, so it is necessary to build a measurement model for the classification of website user problems using the Framework Pieces. Previously, the reliability and validity test of the questionnaire was taken from 338 samples of respondents, then entered the data analysis stage by assessing user characteristics based on the Likert scale and class intervals. Framework Pieces succeed to classify the problems of 338 samples in the range of good and poor. For the Performance domain with a score of 3,62 (Good), the Informations or Data domain with a score of 2,57 (Poor), Economics domain with a score of 3,35 (Moderate), Control or Security domain with a score of 3,61 (Good), Efficiency domain with a score of 3,36 (Moderate), finally for Service domain with a score of 2,60 (Poor).
Co-Authors - Nurfiana A Adven Tonny A Feriyanto Abdi Darmawan Abror , Muhamad Achmad Aldi Sakoni Adam Japal Adi Wijaya Admi Syarif Afdal Wahyu Prayuda Agus, Isnandar Al Hafiz, Dzaki Ali Nasution Andry Ferianto anggalia wibasuri Anuar Sanusi Anuar Sanusi Arbi Gunawan ARDIANSYAH ARDIANSYAH Ari Rohmawati Arie Setya Putra Arman Suryadi Karim, Arman Suryadi Aziz, RZ. Abdul Bagus Yuda Pratama Baruna Wisnu Wardana Baskoro Baskoro Chaniago, Firdaus Dani Apriansyah Danil, Sapni Delli Maria Denny Prastiawan Destiawan Destiawan Dewi, Deshinta Arrova Dian Saputra Dika Tondo W Diki Andita Kusuma Doni Andrianto Dworo, Dworo Effendi, M. Junius Eko Budi Wicaksono Eko Zulkaryanto Elis Malana Fauziah Zahra Ramadhani Febriana, Annisa Arsya Fely Dany Prasetya Fernando, Rhino Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Tri Wahyudi Fitria Fransiska, Devi Gismara, Rio Gumelar, Muhamad Agung Guntur Tiara Wahyu Hidayah handoyo widi nugroho Hardiansyah, Deni Henderi . Hendri Purnomo Herawadi S, Novi Hermansyah, Idi Hermanto, Muhammad Haris Herwanto, Riko Indriani, Della Niken Ismail, Abdul Azim Bin Isnaini Bastari Iwan Tri Bowo Khristina Henny R Kumala, Dian Agustin Arta Kurnia Muludi Kurniawan Kurniawan, Tri Basuki Kusuma, Rizfan Radya Widyan Aditya Laila, Siti Nur M. Arif Prayoga M. Arif Rifai M. Royan Fauzi Mahfut Maizary, Ary Marzuki Marzuki Melda Agarina Melda Agharina Muhammad Fahmi Hafidz Mukhas Munif Ahsani Munaa Munaa Munaa, Munaa Nathan Nurdadyansyah Nathan, Yogeswaran Netty Sefriyanti Nosiel Nosiel Novi Herawadi Sudibyo Novi Herawadi Sudibyo, Novi Herawadi Novita Sari Nurdiyanto, Heri Nurhayati Nurhayati Nurpambudi, Ramadhan Nuryana, Sapta Adi Oktaviani, Hafina Onno W Purbo Periasamy, Jeyarani Pratama, Bagus Yuda Pratama, Tomy Adi Prayoga, M. Arif Prayuda, Afdal Wahyu Prilian Ayu Winarni Purbo, Onno W Putra, M. Natsir Hendy Tri R Rizal Isnanto R, Khristina Henny Rahmadi, Lendy Rahmalia Syahputri Rahmalia Syahputri Rahmawati, Lilla Rakhman, M. Nugrahadi Ramadhani, Fauziah Zahra Rangga Firdaus Ratih Pratiwi Ratna Nurhaya Renita Dwi Astuti Ridho Kurniawan RIDHO KURNIAWAN, RIDHO Rizky Yulizar Rahman Romadhoni, Nuzul Rahmat Rosandi, Triowali Rosandy, Triowali Ruki Rizal Sakoni, Achmad Aldi Sapni Danil Saputra, Agus Savitri, Ratna Selfiyana, Reva Setiawan, Anton Setiyono . Sigit Andriyanto Singagerda, Faurani Santi Singh, Harprith Kaur Rajinder Siti Khodijah Situmorang, Klaudia SB SRI RAHAYU Sri Ratna Sulistiyanti Suci Mutiara Sutedi Sutedi Suwandi Tetra Praja Utama Tri Winarti Triloka, Joko Wahyu Bintono Wardiasa, Komang Wasilah Wibaselppa, Anggawidia Widi Nugroho, Handoyo Winda Rika Lestari Y Verawati Y. Suhendro Yeh, Ming-Lang Yeni Purnamasari, Yeni Yogi Maulana Yoni Hisbullah Yudha, Efrian Prama Yusuf, Suhendro Zainal A. Hasibuan Zakaria, Mohd Zaki