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All Journal International Conference on Engineering and Technology Development (ICETD) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Ilmiah Sinus bit-Tech Jurnal Informatika Ekonomi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal Automation Computer Information System (JACIS) Bulletin of Information Technology (BIT) International Journal Software Engineering and Computer Science (IJSECS) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Journal of Practical Computer Science (JPCS) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri Universal Raharja Community (URNITY Journal) Jurnal Lentera Pengabdian Jurnal Informatika Ekonomi Bisnis Riwayat: Educational Journal of History and Humanities International Journal of Applied Research and Sustainable Sciences (IJARSS) International Journal of Sustainable Applied Sciences (IJSAS) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK International Journal of Integrated Science and Technology EduBase: Journal of Basic Education
Muhamad Fatchan
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning Danny, Muhtajuddin; Fatchan, Muhamad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1259

Abstract

The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven.
Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning Danny, Muhtajuddin; Fatchan, Muhamad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1259

Abstract

The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven.
Ethno-Edutainment Electronic Module (EMEE) to Strengthen Local Cultural Character in Elementary School Students Titin Sunaryati; Muhamad Fatchan; Muhamad Sudharsono; Pipin Angela
EduBase : Journal of Basic Education Vol. 6 No. 1 (2025): EduBase : Journal of Basic Education
Publisher : LJPI UI Bunga Bangsa Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47453/edubase.v6i1.3186

Abstract

The erosion of local cultural values due to globalization poses a significant challenge to the development of character in Indonesian students, who are expected to uphold tolerance, ethics, and mutual respect. Objective: This study aims to develop an Android-based ethno-edutainment electronic module to strengthen local cultural character among elementary school students. Novelty: The module integrates ethno-edutainment and local cultural values into a digital learning platform, offering an innovative approach to character education. Methods: The module was developed using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) within a Research and Development (R&D) framework. The product was validated by media, content, and language experts, and tested on 55 students at Pondok Bambu 06 Duren Sawit State Elementary School, East Jakarta. Results: The validation results showed that the module was categorized as "very good" across all aspects. The practicality test revealed positive student responses, and the effectiveness test demonstrated a significant improvement in learning outcomes, with an average pre-test score of 70.54 and a post-test score of 87.45. Conclusion: The module is effective in supporting character education, with interactive features and local cultural content that enhance student engagement and strengthen their national identity.
Co-Authors . Ermanto . Suratman Abdul Halim Anshor Abdul Hasyim Abizar Ar Rifa’i Rifa’i Agus Suwarno, Agus Aguswin, Ahmad Ahmad Turmudi Zy al fiyan Andri Firmansyah Andrian Andrian Anisa Rahmawati Annisa Maulana Majid Aprila Hardi, Resty Apriyandi M Ariza, Rini Asep Hidayat Asep Suprianto Ayu Fitriyani Aziz, Faruq B.M.A.S. Anaconda Bangkara Bagoes Ramadhan Bagus Dwi Saputro Butsianto, Sufajar Clarita, Anggita Risqi Nur Dahyoung Yenuargo Dendy K. Pramudito Doni, Muhamad Edora Edora Edora Edy Widodo Edy Widodo Edy Widodo Elkin Rilvani Endah Yaodah Kodratilah Fadhillah, Faizah Via Febro Herdyanto Fitriani Galang Rintang Widya Pratama Hadiansyah, Zikri Halim Anshor, Abdul Hari Sugeng Hendra Lesmana Hidayat, Chaerul Indra Permana, Indra Irfan Afriantoro Irsyad Syhruddin Jamroni, A. Reza Baehaqa Jamroni Linda Marlinda Listanto, Firgiawan Marayasa, I Gde Bayu Priyambada Moch. Nauval Faris Muzaki Muhamad Ekhsan Muhamad Sudharsono Muhammad Farhan Alfarizi Muhtajuddin Danny Najwa Sabilla, Nurul Nanang Tedi Kurniadi Nasution, Annio Indah Lestari Naufal Muyassar Naya, Candra Ngudi Wiyatno, Tri Nuraeniah, Iin Nurhadi Surojudin Nurhaliza, Zahra Nur’aeni Nur’aeni Oktavianto, Rainal Zulian Pengestu, Rayendra Pipin Angela Purwanto Purwanto Putri Nabila Amir Qori yumansyah Qori Retno Purwani Setyaningrum Reza Maulana, Muhammad Rika Anugrahaini, Savariana Rindiani Tri Lestari Rozikin, Zaenur Sifa Fauziah Sri Indriyani Sugiarto, Jumat Azzam SUPRAPTO suratman Surya Bintarti Surya Bintarti Suryadi Tedi, Nanang Tiani Ayu Lestari TITIN SUNARYATI Tri Ngudi Wiyatno Turmudi Zy, Ahmad Valentin*, M Ryan Bagus Wahyu Hadi Kristanto Wahyu Hadikristanto Wahyu Indrarti Widi Winjani Widiyawati , Widiyawati Yumansyah, Qori Yupita Fitria Riyanti