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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Agrotropika Jurnal Basement : Jurnal Teknik Sipil JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Manurung, Jeremia
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering

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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.
SELEKSI INDIVIDU PADA POPULASI KACANG BOGOR (Vigna subterranea L. Verdc.) ASAL SUKABUMI DAN SUMEDANG DI LAHAN ULTISOL JAMBI Manurung, Jeremia; Nusifera, Sosiawan; Buhaira; Theodora; Rif'atunidaudina, Ria
JURNAL AGROTROPIKA Vol. 25 No. 1 (2026): Jurnal Agrotropika Vol 25 No 1, Mei 2026
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/ja.v25i1.11787

Abstract

Bogor bean (Vigna subterranea (L.) Verdc.) are plants that have many advantages. Bogor beans are easy to cultivate because they are tolerant to water and nutrient limitations. Bogor beans have good nutritional content, namely high carbohydrate and protein and relatively low fat, so they can be used as an alternative food source. In addition, Bogor beans can also be used as an alternative crop for dry land cultivation. This study aims to select superior individuals from the Bogor bean populations originating from Sukabumi and Sumedang and to determine the closeness of the relationship between two variables in the Bogor bean populations originating from Sukabumi and Sumedang. This study used an experimental method without a specific environmental design and was designed by dividing the planting plots into two large plots differentiated by population origin. The characteristics observed included the number of leaves, canopy width, plant height, fresh pod weight, dry pod weight, number of pods per plant, number of filled pods, number of empty pods, fresh pod weight, pod length and width, seed length and width, and weight of 100 seeds. This study was conducted at the Teaching and Research Farm of the Faculty of Agriculture, University of Jambi, Unja Mendalo Campus, Jambi Luar Kota District, Muaro Jambi Regency from September 2024 to March 2025. The data obtained were analyzed using the coefficient of variation, mean value, t-test, differential selection, and correlation. The results show that of the 10 superior individuals selected from the original population in Sukabumi, they include A1, A2, A11, A31, A37, A46, A52, A57, A81, A82, while in the Sumedang population they included B6, B21, B35, B39, B44, B55, B56, B64, B68, B77. The Sukabumi population, when compared to the Sumedang population, has superior characteristics based on the variables of fresh pod weight, dry pod weight, number of pods per plant, and number of mature pods. Meanwhile, the Sumedang population, when compared to the Sukabumi population, has superior characteristics based on the variables of fresh pod weight, pod length, seed length, and weight of 100 seeds.
Identifikasi Failure mode Pada Pekerjaan Struktur Bangunan Gedung SDN Kalibata Kota Palangka Raya Manurung, Jeremia; Kristiana, Wita
Jurnal Basement : Jurnal Teknik Sipil Vol. 4 No. 1 (2026): Jurnal Basement : Jurnal Teknik Sipil
Publisher : Program Studi Teknik Sipil Fakultas Teknik Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36873/basement.v4i1.25278

Abstract

Structural work on multi-storey buildings, such as foundations, sloofs, columns, beams, and floor slabs, carries a high level of technical risk and has the potential to cause failure in aspects of OHS, quality, technical, cost, and time. However, previous studies generally only focus on one risk dimension without classifying risks multidimensionally and do not describe the risks per structural work item specifically. This study aims to identify potential failure modes in all structural work items of the 2-storey Kalibata Elementary School building construction project in Palangka Raya City. The method used is descriptive qualitative through three stages of identification, namely literature study, field observation, and interviews, which were then validated by two competent respondents from the contractor and the supervising consultant. The results showed that of the 83 potential failure modes identified in 15 structural work items, 74 were deemed relevant and 9 were irrelevant to the actual conditions of the project. The identified failure modes were dominated by the OHS risk category, followed by aspects of quality, technical, and cost and time. This study also found a number of new failure modes that have not been documented in previous literature, which reflect the specific characteristics of educational building projects in Central Kalimantan.
Co-Authors anastasya, disty Azizi, Nur Buhaira Kana Saputra S MANSUR AS Nasution, Hamidah . Ria Rif’atunidaudina Said . Iskandar Sanjaya, Aditia Sosiawan Nusifera Theodora Valentino, Nicholas Wita Kristiana