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All Journal TEKNIK INFORMATIKA Jurnal Simetris Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) PROtek : Jurnal Ilmiah Teknik Elektro JOIN (Jurnal Online Informatika) Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Mantik Penusa JMM (Jurnal Masyarakat Mandiri) Krea-TIF: Jurnal Teknik Informatika JURTEKSI Jurnal Riset Informatika Jurnal Kesmas dan Gizi (JKG) Jurnal Mantik Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Abdi Dosen : Jurnal Pengabdian Pada Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Jurnal Pendidikan Guru (JPG) ETNIK : Jurnal Ekonomi dan Teknik
Novita Br Ginting
Universitas Ibn Khaldun Bogor
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Machine Learning for the Model Prediction of Final Semester Assessment (FSA) using the Multiple Linear Regression Method Rachmawati, Fitria; Jaenudin, Jejen; Ginting, Novita Br; Laksono, Panji
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.28652

Abstract

Corona virus (COVID-19) is the reason behind the collapse of the National Assembly. The first is the Final Semester Assessment (FSA) , which is a component of the student's graduation. The aforementioned evaluation process is a crucial consideration for the teacher since it uses several intricate surveys and mark components. A prediction model is employed to assist teachers in providing suitable results for student learning. The method that is used is called the multiple linear regression. This multiple linear regression algorithm yields an accuracy level of approximately 92%. The analysis results using the method are used as a guide to understanding student’s index. This index is a rating that appears based on the Minimum Credit Count (MCC). Therefore, the goal of this study is to determine students' understanding of the FSA prediction value, which will be taken into consideration through the results of the MCC weights in the form of a range in the form of "Grade." Additionally, the research aims to determine the accuracy of the results from the model obtained using multiple linear regression algorithms in predicting students' FSA.
IMPLEMENTATION OF 360-DEGREE FEEDBACK AND SAW FOR DECISION SUPPORT SYSTEM OF ACHIEVING TEACHER'S RECOMMENDATION Novita Br Ginting; Zulkarnaen Noor Syarif; Mamay Maesaroh; Jejen Jaenudin; Dahlia Widhyaesteoty; Muhamad Alfian Yusuf; Leny Tritanto Ningrum
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i4.209

Abstract

Education requires quality teacher resources in the current era of industry 4.0 and society 5.0. Teachers act as educators, teachers, mentors, directors, trainers, assessors, and evaluators. Students must have critical thinking competencies, creativity and innovation, interpersonal and communication skills, teamwork and collaboration, and self-confidence. At SMK Yasbam, the selection process for outstanding teachers is carried out every year. The problem faced is that the selection process is still assessed, selected, and determined by the school principal only, so there is still a process that is deemed not transparent, accountable, and fair. To make the assessment process fairer, try using the 360-degree feedback method, a multi-source assessment, and then weighting the performance value using the Simple Additive Weighting method to obtain recommendations for outstanding teachers. Respondents consisted of principals, fellow teachers, students, and themselves (the assessed teachers). Furthermore, combining these two methods is applied in a decision support system to make the assessment process and selection of outstanding teachers more objective.
Co-Authors A. Rahmat Rosyadi Ahmad Hafizullah Ritonga Andi Robiyanto Annisa, Sarah Chairul Ardiyantoko Ardiyantoko Asep Syaepudin Dahlia Widhyaesteoty Dahlia Widhyaesteoty Dahlia Widhyaestoeti Dani Ramdani, Dani Deni Arsandi Dewi Primasari Dewi, Kalih Puspita Didik Setiyadi Eko Hadi Purwanto EKO WAHYUDI Endin Mujahidin Fahrezi, Muhamad Rizky Fathih Miftahul Asror Fithri Muliawati Fitria Rachmawati Frieyadie Frieyadie Gibhta Fitri Laxmi Gibtha Fitri Laxmi Gunawan, Iwon Handy Wiguna Putra Hartono, Rudi Haya Najma Husniyyah Hersanto Fajri Ilham Maulana Indriyawati, Indriyawati Jaenuddin, Jejen Jejen Jaenuddin Jejen Jaenudin Laksono, Panji Lely Masnita Leny Tritanto Ningrum Leny Tritanto Ningrum Leny Tritanto Ningrum Mamay Maesaroh Mamay Maesaroh Maulana, Diki Muhamad Alfian Yusuf Muhamad Alfian Yusup Ningrum, Leny Tritanto Noor Syarif, Zulkarnaen Rizky Tri Mardiansyah Ryan Abdul Gofur Safaruddin H. Al Ikhsan Sarah Chairul Annisa SITI NURJANAH Slamet Riyadi Slamet Riyadi Suratun Suratun Suratun Suratun Tohir Solehudin Virgiawan, Rivaldi Wisnu Indra Giri Wulandari, Berlina Yashintya Nelawati Yolanda Agusti Astri Yuggo Afrianto Yusup Maolani Ziyad Rais Zulkarnaen Noor Syarif Zulkarnaen Noor Syarif Zulkarnaen Noor Syarif Zulkifly Fadly Pratama