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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka Jurnal Abdimas Maduma LOGIC : Jurnal Ilmu Komputer dan Pendidikan OKTAL : Jurnal Ilmu Komputer dan Sains BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika
Yuda Samudra
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Pengenalan Teknologi Artificial Intelligence Untuk Guru Dalam Peningkatan Pembelajaran Di SMA Alia Islamic School Meidy Fajar Wahyu; Yuda Samudra; Lely Panca Andriyanto
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 12 : Januari (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

One aspect that supports teaching and learning activities in the 21st century is mastery of aspects of digital technology for the use of cyberspace. This is also explained in Law of the Republic of Indonesia Number 14 of 2005 concerning Teachers and Lecturers that a teacher must have four competencies, namely pedagogical, personality, social and professional competencies. With this law, it is hoped that it will provide an opportunity for teachers to increase their professionalism through training held in scientific forums. However, there are several obstacles that teachers still face in improving their professionalism, one of which is mastery of technology. Artificial Intelligence (AI) is an application and a series of instructions related to programming where the results will look like from a human perspective. Apart from that, the reach of this technology has been used widely, including in the education sector. The implementation of AI in the education sector has had many positive impacts. Some of the positive impacts of applying AI in the education sector are increasing speaking, listening, reading and writing skills as well as the development of fun learning methods. This activity aims to explain the development of the Introduction of Artificial Intelligence Technology for Teachers in Improving Learning and its impact on modern society. We will look at the role of Artificial Intelligence in creating vibrant social networks, the emergence of leading social networking sites, and the shift from traditional media to social media. In addition, we will also discuss the concept of Artificial Intelligence and the potential it has in changing the way we search for and manage information. The introduction of Artificial Intelligence Technology for Teachers in Improving Learning at SMA ALIA Islamic School Tangerang, aims to enable teachers to be aware of developments in Artificial Intelligence technology, especially in today's digital world. The Community Service Team coordinates with the principal and deputy principal to hold activities for students at ALIA Islamic School.
COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION Sartika Lina Mulani Sitio; Darmawati; Yuda Samudra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7579

Abstract

Student graduation prediction is an important aspect in supporting academic decision-making in higher education. However, conventional evaluation approaches have not been able to identify the risk of early graduation delays. This study aims to compare the performance of two ensemble learning approaches, namely Bagging using Random Forest and Boosting using XGBoost, in predicting student graduation. The study used  the Predict Students' Dropout and Academic Success dataset  consisting of 4,424 student data. Both models were trained on the same data and evaluated using the Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results of the experiment showed that both models had almost equal accuracy, i.e. 82.6% for Random Forest and 82.5% for XGBoost. However, XGBoost showed better performance on Recall (0.878) and F1-Score (0.834), which indicated a higher ability to detect students who actually graduated. Based on these results, this study concludes that XGBoost is more effective than Random Forest in the context of predicting student graduation and is more suitable to be applied to  the Academic Early Warning System in universities
Co-Authors Ade Putra Prima Suhendri Amin Hidayat Amin Hidayat Andriyanto, Lely Panca Badriah NurSakinah Darmawati Fadilah, Muhammad Farhan Fajriansyah, Satria Nur Fizram, Muhammad Herwis Gultom Hizkia Bayu Wijaya Lely Panca Andriyanto Lely Panca Andriyanto Meidy Fajar Wahyu Meidy Fajar Wahyu Nanang Nanang Nanang nanang nanang Nardiono Nurfajriansyah, Dandy NURHALIMAH Oke Hariansyah Pramadhan, Harsya Rafif Rahmat Hidayat Safa, Alaudin Sartika Lina Mulani Suhendri, Ade Putra Prima Suryaningrat Wijaya, Muhammad Subaktiar Wiwin Winarti Zurnan Alfian