Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Al-Ta'lim Insearch: Information System Research Journal Knowbase : International Journal of Knowledge in Database The Indonesian Journal of Computer Science
Lizar, Yaslinda
Unknown Affiliation
Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Pengambilan Keputusan Pemilihan Guru Terbaik Menggunakan Metode Analytical Hierachy Process (AHP) Lizar, Yaslinda; Adli, Imam
Insearch: Information System Research Journal Vol 3, No 02 (2023): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v3i02.6831

Abstract

Penting bagi sekolah untuk menyediakan guru terbaik untuk menungkatkan kinerja sekolah. Selama ini proses pemilihan guru terbaik diseleksi berdasarkan rasa suka tidak suka saja, kurangnya laporan penilaian guru juga mengurangi motivasi dalam meningkatkan kinerja. Tujuan penelitian ini untuk membangun sebuah DSS (Decision Support System) untuk pemilihan guru terbaik, yang akan menghasilkan penilaian yang lebih objektif dan meningkatkan kualitas pengajaran di sekolah. Metode Analytical Hierarchy Process (AHP) akan digunakan dalam penelitian ini dengan beberapa kriteria tambahan dalam menentukan bobot, seperti orientasi pelayanan, integritas, komitmen, kedisiplinan, dan kerjasama, untuk menentukan bobot penilaian. Hasil penelitian menghasilkan sebuah sistem DSS untuk pemilihan guru terbaik, untuk meminimalisir kesalahan penilain dalam metode yang digunakan. Hasil dari metode pemilihan ini dapat juga menjadi motivasi dan bahan pembelajaran untuk para guru agar dapat bisa meningkatkan kualitas kinerja sehingga bisa memenuhi kategori guru terbaik
Data Mining Analysis to Predict Student Skills Using Naïve Bayes Method Lizar, Yaslinda; Firrizqi, Alya Sahira; Guci, Asriwan; Sunadi, Joko
Knowbase : International Journal of Knowledge in Database Vol. 3 No. 2 (2023): December 2023
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v3i2.7481

Abstract

The possession of specific skills by students not only has a positive impact on the students themselves but also on the Study Program within a Faculty and the University as a whole. However, Study Programs sometimes face difficulties in determining the skills of numerous students even after they have completed 7 semesters of study. Therefore, a method to extract available data in order to determine student skills quickly and accurately is essential. This research aims to apply a data mining method to predict student skills in the Information Systems Study Program at UIN Imam Bonjol Padang. The study focuses solely on predicting student skills in the fields of data processing and programming. The method employed in this data mining analysis is the Naïve Bayes method. Data will be collected from student course grades related to data processing and programming. The data will be processed using an application and subsequently tested using a Confusion Matrix. The research results indicate that predicting the determination of student skills in the Information Systems Study Program at UIN Imam Bonjol can be achieved using the Naïve Bayes algorithm, which yielded a Naïve Bayes model accuracy of 93%, precision of 81%, and recall of 81%. The obtained model can be implemented in the form of an application to determine decision-making strategies for students.
Data Mining Analysis to Predict Student Skills Using Naïve Bayes Method Lizar, Yaslinda; Firrizqi, Alya Sahira; Guci, Asriwan; Sunadi, Joko
Knowbase : International Journal of Knowledge in Database Vol. 3 No. 2 (2023): December 2023
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v3i2.7481

Abstract

The possession of specific skills by students not only has a positive impact on the students themselves but also on the Study Program within a Faculty and the University as a whole. However, Study Programs sometimes face difficulties in determining the skills of numerous students even after they have completed 7 semesters of study. Therefore, a method to extract available data in order to determine student skills quickly and accurately is essential. This research aims to apply a data mining method to predict student skills in the Information Systems Study Program at UIN Imam Bonjol Padang. The study focuses solely on predicting student skills in the fields of data processing and programming. The method employed in this data mining analysis is the Naïve Bayes method. Data will be collected from student course grades related to data processing and programming. The data will be processed using an application and subsequently tested using a Confusion Matrix. The research results indicate that predicting the determination of student skills in the Information Systems Study Program at UIN Imam Bonjol can be achieved using the Naïve Bayes algorithm, which yielded a Naïve Bayes model accuracy of 93%, precision of 81%, and recall of 81%. The obtained model can be implemented in the form of an application to determine decision-making strategies for students.
Artificial Intelligence–Based Information Systems to Support Educational Decision-Making Lizar, Yaslinda; Guci, Aswirman; Sunadi, Joko
AT-TA'LIM Vol 32, No 3 (2025)
Publisher : Institut Agama Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jt.v32i3.904

Abstract

This study explores the potential of artificial intelligence–based information systems in supporting educational processes within higher education institutions in Indonesia. The rapid adoption of digital platforms in academic administration and learning management has increased the need for intelligent systems that can enhance efficiency, transparency, and data-driven decision-making. This research aims to examine how artificial intelligence–based information systems are utilized in educational contexts, particularly in relation to curriculum implementation, academic management, and institutional readiness. A qualitative research design was employed using semi-structured interviews with academic stakeholders, complemented by document analysis. The findings indicate that artificial intelligence–based information systems contribute positively to improving administrative efficiency, supporting systematic curriculum evaluation, and facilitating evidence-based academic decision-making. However, challenges related to system transparency, interpretability, and user readiness were also identified as critical factors influencing system effectiveness. These findings highlight that the successful integration of artificial intelligence in education is not solely determined by technological capability but also by organizational support and human factors. This study contributes to the interdisciplinary integration of information systems and educational research by providing empirical insights into the role of artificial intelligence in higher education. The results offer practical implications for institutions seeking to adopt responsible and effective artificial intelligence–based information systems.
Tren Global Penelitian Tentang Digital Twin: Analisis Bibliometrik Lizar, Yaslinda; Mal Novizam, Defa; Butar-Butar, Mhd Sufiananda; Guci, Asriwan
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3513

Abstract

Penelitian ini bertujuan untuk menganalisis perkembangan publikasi ilmiah bidang digital twin. Metode penelitian adalah kajian bibliometrik terhadap 16.504 artikel jurnal internasional terindeks Scopus periode 2014-2023. Hasil menunjukkan terjadi peningkatan publikasi yang signifikan dalam satu dekade terakhir, didominasi oleh Tiongkok, Jerman, Amerika Serikat, Inggris dan Italia. Sebagian besar publikasi adalah conference papers dan articles di bidang Engineering dan Computer Science. Berdasarkan analisis kata kunci, tema utama meliputi smart systems, machine learning, cloud computing, augmented reality, automation, dan big data. Kesimpulannya, antusiasme peneliti terhadap digital twin tercermin dari lonjakan publikasi global. Hal ini diperkirakan mendorong perluasan riset digital twin ke berbagai disiplin di masa depan.
Co-Authors Adli, Imam Butar-Butar, Mhd Sufiananda Firrizqi, Alya Sahira Guci, Asriwan Guci, Aswirman Mal Novizam, Defa Sunadi, Joko