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All Journal Jurnal Teknologi Pendidikan JURNAL PENGABDIAN KEPADA MASYARAKAT Sinkron : Jurnal dan Penelitian Teknik Informatika JCRS (Journal of Community Research and Service) Jurnal Mantik BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan INFOKUM Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Seminar Nasional Pengabdian Kepada Masyarakat QISTINA: Jurnal Multidisiplin Indonesia Journal of Vocational Education and Information Technology (JVEIT) Jurnal Penelitian Pendidikan Indonesia The Indonesian Journal of Computer Science
Putri, Tansa Trisna Astono
Universitas Negeri Medan
Religion Arts Humanities 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 Mechanical Engineering Nursing Social Sciences Other

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Academic Performance Prediction of PTIK Students through Machine Learning Models at Universitas Negeri Medan Tansa Trisna Astono Putri; Reni Rahmadani; Rosma Siregar; Hanapi Hasan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29570

Abstract

This study addressed the need for an effective approach to predicting student academic performance in higher education using data-driven methods. The study aimed to implement machine learning models to predict the academic performance of students in the Information and Communication Technology Education Study Program at Universitas Negeri Medan. A quantitative predictive design was employed using a dataset of 40 student records. Five classification models were tested, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The results showed that all models produced strong predictive performance. Decision Tree achieved the highest accuracy at 93.1%, Logistic Regression produced the highest precision at 95.9% and the highest F1-score at 93.2%, while Support Vector Machine obtained the highest recall at 93.2%. These findings indicated that machine learning was feasible for predicting student academic performance in the study program. The study concluded that Logistic Regression provided the most balanced overall performance and had strong potential to support early academic intervention and data-based academic decision making in higher education.
OPTIMALISASI PRODUKSI ARANG RAMAH LINGKUNGAN DENGAN SMART SYSTEM BERBASIS AI DAN IOT DI DESA PUNGGULAN, KABUPATEN ASAHAN Tansa Trisna Astono Putri; Bayu Angga Wijaya; Hanapi Hasan
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 31 No. 4 (2025): OKTOBER-DESEMBER
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v31i4.69316

Abstract

Produksi arang tradisional di Desa Punggulan, Kabupaten Asahan, kerap menghadapi tantangan pada aspek kualitas dan dampak lingkungan akibat proses pembakaran yang belum optimal dan kurang terkontrol. Kegiatan pengabdian masyarakat ini bertujuan mengimplementasikan smart system berbasis Artificial Intelligence (AI) dan Internet of Things (IoT) untuk memprediksi serta mengendalikan kualitas arang secara real-time guna mendukung produksi yang ramah lingkungan. Metode yang digunakan meliputi pelatihan dan pendampingan masyarakat dalam pemanfaatan perangkat sensor IoT untuk pemantauan suhu dan kelembapan pada tungku pembakaran, serta penggunaan algoritma AI untuk analisis prediksi kualitas hasil arang. Hasil kegiatan menunjukkan adanya peningkatan pemahaman masyarakat terhadap teknologi digital dan kemampuan mengoperasikan smart system, yang berdampak pada peningkatan efisiensi proses produksi dan penurunan emisi limbah pembakaran. Kesimpulannya, penerapan smart system berbasis AI dan IoT terbukti efektif dalam mendukung optimalisasi produksi arang ramah lingkungan dan memberdayakan masyarakat menuju industri berbasis teknologi di pedesaan.
Implementation Of Learning Based On Creative Thinking Skills Using Augmented Reality To Improve Academic Achievement Of Students In Computer Assembly Courses Reni Rahmadani; Tansa Trisna Astono Putri; Muhammad Dominique Mendoza; Olnes Hutajulu; Elsa Sabrina
Journal of Vocational Education and Information Technology (JVEIT) Vol. 7 No. 1 (2026): Jurnal JVEIT : Vol 7 No 1 2026
Publisher : Lembaga Pengembangan dan Inovasi Universitas Dharmas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56667/jveit.v7i1.2135

Abstract

This study aims to evaluate the effectiveness of implementing Creative Thinking Skills (CTS)-based learning using Augmented Reality (AR) media to improve students’ academic achievement in the Computer Assembly course. The research employed a quantitative approach with a quasi-experimental method and a one-group pretest–posttest design. The subjects consisted of 30 undergraduate students of the Information and Computer Technology Education Study Program at Universitas Negeri Medan. The instruments used included a learning outcomes test, a CTS assessment rubric, and a student satisfaction questionnaire toward AR media. The learning outcomes were analyzed using the Normalized Gain (N-Gain) test, while the CTS scores were examined using a paired t-test, and the satisfaction data were analyzed descriptively. The findings showed that the use of AR significantly improved students’ learning outcomes with an average N-Gain value of 0.72 (high category). The students’ CTS scores also increased across all indicators—fluency, flexibility, originality, and elaboration—achieving an overall category of “highly creative.” Furthermore, the average satisfaction score of 4.26 (out of 5) indicated that students were very satisfied with AR-based learning. These results demonstrate that integrating AR media with CTS-based learning effectively enhances both academic performance and creative thinking abilities. Therefore, this model is recommended for use in other technology and vocational courses to promote innovation and deeper conceptual understanding.
Co-Authors Abdullah, MT, Dr. Rijal Adilla Ulfhi Adwiyah, Rabiatul Al Farishi, M Farid Ambiyar, Ambiyar Ami, Hutri Asmar Yulastri Azmi Rizky Lubis Baharuddin . Baharuddin Baharuddin Bakti Dwi Waluyo Barus, Tirta Yasa Agung Bayu Angga Wijaya Calista, Sandha Dwiki Muda Yulanto Elsa Sabrina Fadhilah Fadhilah Fahmi Rizal Fali, Rifki Fattah, M Faturrahman, M Nadhif Ganefri Ginting, Ariyantika Br Gorat, Loveyanni Marito Benedikta Hanapi Hasan Hanapi Hasan Hasan Maksum Hesti Fibriasari Hesti Fibriasari Jauharah, Jauharah Khofifah Qalbun Salima Lubis, Khodijah May Nuri Manurung, Ricardo Mardhiyah, Hanifah Marta, Rizkayeni Maulana, Bagoes Maulidiyah, Ratna Mendoza, Muhammad Dominique Mhd. Dominique Mendoza Muhammad Fadhiel Alie Nazwa, Safira Nizwardi Jalinus Nurilam Harianja Nurrahma, Suci Olnes Hutajulu Olnes Y. Hutajulu Perdana, Nugraha Aditama Putra Rahmadany, Savitri Rahmadhani, Alfi Rahmi, Alya Ramadani, Nadila Reni Rahmadani Ressy Dwitias Sari Rosma Siregar Saragi, Frans Jhonatan Sarumaha, Matius Irvan Savitri Rahmadany Siman Sinaga, Novi Novani Sitorus, Andika Syahputra, Fahmy Tanzila, Laili Tarigan, Febrinata Silvinna Boru Tarigan, Gabriel Frandika Tarigan, Ray Rivandi Tengku Ratna Soraya Uyuni Widiastuti Waskito Wulansari, Rizky Ema Zai, Frans Pratamarifai Doya