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All Journal Al-Ta'lim Insearch: Information System Research Journal Knowbase : International Journal of Knowledge in Database
Sunadi, Joko
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences Other

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Analisis Bibliometrik Terhadap Penelitian Business Process Reengineering dan Manajemen Menggunakan Vosviewer Sunadi, Joko; Malnovizam, Defa
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.v3i02.6832

Abstract

Penelitian ini membahas tentang Analisis Bibliometrik Terhadap Penelitian Business Process Reengineering Dan Manajemen Pada Google Scholar Menggunakan VOSViewer. Penelitian ini bertujuan untuk untuk mengetahui jumlah publikasi jurnal ilmiah pada interval waktu 2013 – 2023. Peneliti yang paling aktif dalam riset tentang Business Process Reengineering dan Manajemen serta pemetaan riset tentang Business Process Reengineering dan Manajemen. Metode penelitian pada penelitian ini adalah Tinjauan Pustaka menggunakan pendekatan bibliometric. Data yang digunakan pada penelitian adalah publikasi ilmiah yang terindeks google scholar berjumlah 257 publikasi dengan kata kunci pencarian "Business Process Reengineering" dan "Manajemen". Hasil penelitian ini menunjukkan dari tahun 2013 hingga 2023 mengalami peningkatan yang fluktuatif. Puncak jumlah terbanyak publikasi terjadi pada tahun 2022 sebanyak 52 artikel sedangkan publikasi terendah pada tahun 2019 sebanyak 7 artikel. Terdapat 5 peneliti yang aktif dalam pempublikasi jurnal ilmiah tentang Business Process Reengineering dan Manajemen adalah Wildan Suharso, Sugeng Santoso, Ilyas Nuryasin, Farrikh Al Zami dan Dian Nuswantoro. Dimana Wildan Suharso dan Ilyas Nuryasin memiliki korelasi yang erat. Densitas penelitian yang masih rendah memberikan peluang bagi peneliti lain untuk menciptakan novelty pada penelitian ini melalui publikasi online
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.
Co-Authors Firrizqi, Alya Sahira Guci, Asriwan Guci, Aswirman Lizar, Yaslinda Malnovizam, Defa