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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 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 Nursing Social Sciences Other

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Utilization chatbot for Indonesian tourism: a post-pandemic solution of information accessibility Hesti Fibriasari; Bakti Dwi Waluyo; Baharuddin Baharuddin; Tansa Trisna Astono Putri; Savitri Rahmadany
JPPI (Jurnal Penelitian Pendidikan Indonesia) Vol. 10 No. 3 (2024): JPPI (Jurnal Penelitian Pendidikan Indonesia)
Publisher : Indonesian Institute for Counseling, Education and Theraphy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/020243791

Abstract

Indonesia is a renowned tourist destination worldwide. However, the COVID-19 pandemic has resulted in a decrease in both foreign and domestic tourist visits. Therefore, this research aims to develop a Telegram-based ChatBot application to increase post-pandemic tourist visits. The ChatBot is designed to make it easier for tourists to obtain information related to transportation and accommodation at tourist attractions. The ChatBot is built using the Knuth-Morris-Pratt (KMP) algorithm and web scraping method. The ChatBot's response to the keyword matching is then presented to tourists. To measure travelers' perceptions of ChatBot, the System Usability Scale (SUS) was used. SUS is a questionnaire consisting of 10 questions that were answered by 20 anonymous users. Based on the calculations, the average SUS score is 71, which indicates that the developed ChatBot is in the good category and suitable for use. Using this ChatBot will make it easier for tourists to obtain tourism information in Indonesia. With its ability to retrieve relevant information related to transportation and accommodation at tourist attractions, the ChatBot can serve as a useful tool to increase post-pandemic tourist visits.
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.
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 Fadhilah Fadhilah Fahmi Rizal Fali, Rifki Fattah, M Faturrahman, M Nadhif Ganefri Ginting, Ariyantika Br Gorat, Loveyanni Marito Benedikta 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 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