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All Journal Jurnal Sistem dan Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Syntax Idea Journal of Applied Data Sciences Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Informatics Management and Information Technology Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Sistem Informasi Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Smart Techno (Smart Technology, Informatic and Technopreneurship)
I Nyoman Yudi Anggara Wijaya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Engineering Social Sciences

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IMPLEMENTASI SISTEM MONITORING MENGGUNAKAN ZABBIX DAN NOTIFIKASI REALTIME TELEGRAM UNTUK JARINGAN INTERNET BSI (BALI SMART ISLAND) DI KABUPATEN JEMBRANA Sugiarta, I Kadek; I Gede Juliana Eka Putra; I Nyoman Yudi Anggara Wijaya
Jurnal Sistem Informasi (JUSIN) Vol 6 No 2 (2025): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v6i2.3233

Abstract

With the advancement of information and communication technology, society has become increasingly dependent on access to information through the internet. However, this growing reliance has also led to a rise in the frequency of network disruptions, which can cause delays in information dissemination and interfere with institutional operations. PLN ICON PLUS, as an Internet Service Provider (ISP), faces the challenge of monitoring 123 routers used by the customers of the Jembrana Regency Communication and Information Office (Diskominfo). This research highlights the importance of implementing a network monitoring system using Zabbix, which can provide real-time notifications through Telegram to detect disruptions and minimize downtime.Currently, PLN ICON PLUS does not yet have an effective monitoring system, resulting in delays in addressing network issues. By adopting a more advanced monitoring solution, network performance is expected to improve, enabling technical teams to respond quickly and efficiently to disruptions, thereby enhancing service quality for Diskominfo Jembrana customers. The final results of the monitoring system show that it can operate properly and consistently send real-time notifications to Telegram whenever disruptions occur on devices or network services. This proves that the system can improve the responsiveness of the technical team in handling issues and minimizing downtime. Thus, the monitoring system is considered effective in supporting proactive and efficient network management.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Rancang Bangun Aplikasi Berbasis Android Klasifikasi Penyu dengan Menggunakan Metode Extreme Programming I Made Agus Priatna Putra Arnata; I Gede Juliana Eka Putra; I Nyoman Yudi Anggara Wijaya
JATISI Vol 13 No 1 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i1.15539

Abstract

The turtle population in Indonesia continues to decline every year and is categorized as an endangered species. Lack of public knowledge about protected turtle species is one of the factors hampering conservation efforts. This study aims to design and build an Android-based application that can classify three types of turtles: the Green Turtle (Chelonia mydas), the Hawksbill Turtle (Eretmochelys imbricata), and the Olive Ridley Turtle (Lepidochelys olivacea). Software development uses the Extreme Programming (XP) method, consisting of planning, design, coding, and testing stages. The application is built with the Flutter framework and integrates a Convolutional Neural Network (CNN)-based machine learning model to classify images. Users can input images through the camera or device gallery. Blackbox testing results show that all application functionality, including navigation, image capture, and the classification process, runs as expected. This application is expected to be an educational medium to increase public awareness in turtle conservation efforts. Keywords— Classification, Turtle, Flutter, Android, Extreme Programming
Co-Authors Ade Suci Laksana Anak Agung Gede Adi Mega Putra Astawa, Ni Luh Putu Ning Septyarini Putri barkah, affu Gede Martha I Gede Adrian Edy Pratama I Gede G Sastrawan I Gede Juliana Eka Putra I Gede Nyoman T.A. Panji Wijaksana I Gede Putu Krisna Juliharta I Gusti Agung Ngurah Indra Adnyana I Gusti Arya Mahasastrawan I Gusti Ngurah Mahendra Putra I Komang Yoga Gangga Putra I Made Agus Priatna Putra Arnata I Made Suka Danawan, I Made Suka Danawan I Putu Agus Darma Putra I Putu Kusuma Negara I Putu Satwika Ida Bagus Gede Baskara Komang Tri Werthi Made Adi Paramartha Putra Nengah Widya Utami Ni Made Satvika Iswari Ni Putu Widya Yuniari Nur Budi Nugraha Nyoman Ayu Nila Dewi Nyoman Ayu Nila Dewi Pradiva Widya Anggara Putu Trisna Hady Permana S Sudiatmika, Ida Bagus Kresna Sugiarta, I Kadek