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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Jurnal Ilmiah Mahasiswa Kelautan Perikanan Unsyiah Jurnal Ilmiah Mahasiswa Ekonomi Pembangunan Jurnal Ekonomi dan Kebijakan Publik Indonesia Mulia International Journal in Science and Technical Motor Bakar : Jurnal Teknik Mesin Ekonomi Dan Pembangunan Ikhtisar: Jurnal Pengetahuan Islam Panrita: Jurnal Bahasa dan Sastra Daerah serta Pembelajarannya Prosiding Seminar Nasional Official Statistics Bhinneka: Jurnal Bintang Pendidikan dan Bahasa Jurnal Manajemen dan Pendidikan Agama Islam Al-Mudharabah The Indonesian Journal of Computer Science Desentralisasi: Jurnal Hukum, Kebijakan Publik, Dan Pemerintahan Al-battar: Jurnal Pamungkas Hukum
Muhammad Abrar
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Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : The Indonesian Journal of Computer Science

 1 
Implementation of Deep Learning Using YOLOv7 and Telegram Notifications for Preventing Illegal Fishing in the Waters of Batam Muhammad Abrar; Deosa Putra Caniago
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3472

Abstract

Batam Island is one of Indonesia's outermost islands that directly borders neighboring countries. The implementation of YOLOv7 to detect ships in the waters of Batam is capable of identifying ship objects, with test results after 100 training epochs producing a precision value of 1.00 and a confidence value of 0.882, indicating a high level of confidence in the detection results of the YOLOv7 model. The F1 score of 0.99 at a confidence level of 0.729 shows that this model achieves a high level of accuracy in object detection. Based on the evaluation results using a confusion matrix, it indicates high accuracy for each class in the YOLOv7 model: Ferry 93%, Indonesian Fishing Boat 85%, Malaysian Fishing Boat 89%, Thai Fishing Boat 91%, Vietnamese Fishing Boat 82%, Speedboat 94%, and Tanker 83%. The testing results of the website application integrated with YOLOv7 and Telegram bot produce a website that can detect objects and send notifications, thus expected to prevent illegal fishing.
Sistem Monitoring Kecepatan Angin Dan Suhu Udara Berbasis Notifikasi Telegram Muhammad Abrar; Deosa Putra Caniago; Rifa’atul Mahmudah Burhan; Refli Noviardi
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4305

Abstract

The unpredictable weather in Batam City impacts the use of sea transportation, which is frequently used by residents for inter-island crossings. There is a need for a real-time monitoring system for wind speed and air temperature, along with notifications when wind speeds become potentially dangerous for sea transportation users. In this research, an Internet of Things (IoT)-based monitoring system for wind speed and air temperature was implemented using the Thingspeak platform for real-time monitoring. The test results demonstrated that the monitoring system could successfully store wind speed and air temperature data in the Thingspeak database. Additionally, Thingspeak was able to display graphs of wind speed and air temperature. The notification tests via Telegram showed that when the wind speed reached 1.5-2.9 m/s, an alert warning was sent, and when the wind speed exceeded 3 m/s, a danger warning was issued.
Co-Authors Agustinus Suwodjo Al Munawir Al-Munawir Al-Munawir Anhar Anhar Aos Kuswadi Arifin Abdullah Aulil Amri Azmi, Alray Habib Caniago, Deosa Putra Cut N. Defira Cut Zakia Rizki Darma Setiawan Putra Desi Salsabilla Atanaya Elfia Sukma Erniza Felayati Fatari Fitriyani Fitriyani Hamidah Hamidah Hany Khairunnisa Kobat Irham Iskandar Iwan Hasri Latifah Qorimahtul Fajri Maila Lidinia Mayong Mayong Mohd Rizki Maulana Muhammad Herizal Ihza Muhammad Ilhamsyah Siregar Muslim Nesi Alia Putri Norliza Jamaluddin Noviardi, Refli Queen Shinta Dewi Rafi'i Rafi'i Rauzatul Jannah Rifa’atul Mahmudah Burhan Riki Chandra Putra Riska Nurmalinda Rustam Effendi Safrirullah Safrirullah Sufirmansyah Sufirmansyah Syahrul R, Syahrul Taufiq Carnegie Dawood Yusnita Rahayu Zulfan Adi Putra