Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Artificial Intelligence and Data Mining JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Manurung, Jeremia
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Automatic Classifier of Road Condition and Early Warning System for Potholes Manurung, Jeremia; As, Mansur; Nasution, Hamidah; Al Idrus, Said Iskandar; Saputra S, Kana
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31866

Abstract

Damaged roads can have a negative impact on road users and can fatally cause accidents. One sign of a damaged road is the presence of holes in the road. This research aims to develop an Android application that can display the location of potholes and provide early warning to driver in Simalungun Regency - North Sumatra. This research implements the Convolutional Neural Network (CNN) algorithm using the transfer learning techniques on the pre-trained MobileNetV3 model for automatic classification of road conditions. The dataset used in the research consisted of 22.538 images which were divided into two classes, namely pothole and normal. This research uses dataset with a ratio of 60:20:20, 70:20:10 and 80:10:10. MobileNetV3 large variant with a dataset ratio of 60:20:20 shows the best value with an F1-Score of 0,9035. The model was further converted to Tensorflow Lite with an F1-Score of 0.8985. This research succeeded in implementing the trained and evaluated model along with early warning of potholes via audiovisual in Android application. Application functionality testing that is carried out using black box testing, showing that the application can run well.
Deteksi Kemacetan dengan Deep Learning YOLOv4 dan Euclidean Distance Tracker pada Jalan Raya di Kota Medan Manurung, Jeremia; Azizi, Nur; Anastasya, Disty; Valentino, Nicholas; Sanjaya, Aditia; Saputra, Kana
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 8 No. 1 (2023): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v8i1.220

Abstract

Kemacetan lalu lintas di Kota Medan menyebabkan waktu yang hilang sebesar 35,6 menit per hari untuk sepeda motor dan 48,5 menit per hari untuk mobil. Total biaya kemacetan di Kota Medan mencapai Rp. 22.535.355.867/tahun.  Dengan adanya pendeteksian kemacetan secara realtime maka diharapkan dapat mengurangi kemacetan lalu lintas apabila diintegrasikan dengan sistem pengatur lalu lintas. Penelitian ini menerapkan metode Deep Learning YOLO versi 4 Euclidean Distance Tracker. YOLOv4 digunakan untuk mendeteksi objek seperti mobil, motor, bus, dan becak. Euclidean Distance Tracker digunakan untuk melacak perpindahan objek yang telah dideteksi oleh YOLOv4. Adapun data yang digunakan adalah data lalu lintas berupa video dari CCTV yang disediakan oleh Pemerintah Kota Medan, Sumatera Utara. Dari hasil penelitian ini dapat diambil kesimpulan YOLOv4 dapat digunakan untuk mendeteksi kendaraan yang memiliki jarak kendaraan yang cukup antara kendaraan yang satu dengan kendaraan yang lainnya (Akurasi 61,3%). Dengan mengintegrasikan Euclidean Distance Tracker, pendeteksi kemacetan memiliki hasil akurasi maksimum (Akurasi 100%) pada sample frame yang diuji.
Co-Authors anastasya, disty Azizi, Nur Kana Saputra S MANSUR AS Nasution, Hamidah . Said . Iskandar Sanjaya, Aditia Valentino, Nicholas