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All Journal FROPIL (Forum Profesional Teknik Sipil) Pena Teknik : Jurnal Ilmiah Ilmu-ilmu Teknik Journal Malige (Media Arsitektur Lintas Generasi) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Bandar: Journal of Civil Engineering Community Empowerment Jurnal UNITEK Jurnal Momen : Teknik Sipil Suryakancana Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Teknik Sipil
Amir, Andi Ahdan
Universitas Nahdlatul Ulama Sulawesi Tenggara
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Transportation Other

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Analisis Karakteristik dan Tingkat Hambatan Samping Ruas Jalan D.I. Panjaitan Kota Kendari Amir, Andi Ahdan; Arsyad, Muhammad; Sukman, Sukman; Diansar, Diansar
FROPIL (Forum Profesional Teknik Sipil) Vol 11 No 2 (2023): FROPIL (Forum Profesional Teknik Sipil)
Publisher : Jurusan Teknik Sipil Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Progress inland transportation has not escaped the emergence of new problems from environmental aspects, accidents, and congestion. Traffic congestion occurs due to the imbalance between the growth of vehicles with improved infrastructure, rapid urbanization, low of discipline of road users, and side friction. Land use, type of road, and size of the city result in the characteristics and level of side friction. From these things, research on the analysis of characteristics and level of side friction on the D.I. Panjaitan Road section of Kendari City became interesting. The purpose of this study is to identify the characteristics of side friction and determine the level of side friction. From research and analysis, it was found that side friction that occurs at three observation points is generally caused by the type of activity of stopped vehicles, parking on street, and vehicles come into and exit from the parking area and coming into or leaving the neighborhood. Based on side friction frequency data at three observation points showed the lowest Side Friction Frequency (SFF) of 33.5 and 39.4 events at point T3, then 67.6 and 64.4 at point T2, and the highest at point T3 70.5 and 59.9 events. The data shows that the side friction class on the road is categorized is very low. The low side friction at the observation location is caused by the observation time carried out during the decline in community activities due to the implementation of health protocols due to the increase in Covid-19 cases
Pengaruh Karakteristik Agregat Terhadap Kuat Tekan Beton Mutu Tinggi Amir, Andi Ahdan; Parung, Herman; Djamaluddin, Rudy
FROPIL (Forum Profesional Teknik Sipil) Vol 11 No 1 (2023): FROPIL (Forum Profesional Teknik Sipil)
Publisher : Jurusan Teknik Sipil Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/fropil.v11i1.4100

Abstract

The aggregate's quality, geology, geography, climatic conditions, and the technique used to manufacture aggregate all have an effect on the quality of high-strength concrete. In this study, the characteristics of the fine and coarse aggregate from the three sources the Jeneberang, Maros, and Pangkajene rivers—were examined, as well as their effects on the compressive strength of high-strength concrete. The research was conducted in stages, which included the testing of coarse and fine aggregate characteristics, composition of concrete, creat of concrete specimens, compressive strength test, and analysis of aggregate characteristics effect on the compressive strength of high-strength concrete. The results showed that that the fine aggregate characteristics of the Jeneberang River as a whole are better than those of the Maros River and Pangkajene River. While the coarse aggregate properties of the main Lekopancing River are greater than the coarse aggregate characteristics of the Pangkajene River and Jeneberang River. The compressive strength varies based on the fine and coarse aggregates used. Results of the linear regression study of the correlation of aggregate features to fine aggregate compressive strength showed a considerable influence contribution; Organic content 99.6%, water content 86.7%, specific gravity 98.2%, volume weight 98%, absorption 98.8%, and fine modulus 90.3%. Sludge content has an approximate 7% impact. Similarly, the coarse aggregate of parameters showed effect: wear 99.8%, water content 99.8%, specific gravity 99.2%, volume weight 92.6%, and absorption 76.2%, with exception of sludge content, which is only 33.2% 33.2%, and fine modulus 29.8%.
Optimization of Traffic Performance Analysis in Kendari City with Deep Learning Amir, Andi Ahdan; Sukman, Sukman; Lihara, Astri Delviana; Muhammad Nabil , Muhammad Nabil; Duwi Nurmayanti , Duwi Nurmayanti
PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik VOLUME 11 NUMBER 1 MARCH 2026
Publisher : Faculty of Engineering, Andi Djemma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51557/3fxt8a88

Abstract

Urban traffic congestion in developing cities like Kendari requires rapid and accurate monitoring solutions. This research aims to design and implement an integrated traffic performance analysis system utilizing the YOLOv8 deep learning architecture. The system, named KILALIN, automates vehicle detection, classification, and tracking to calculate road capacity and saturation levels based on the PKJI 2023 standards. A comprehensive dataset of 1,606 annotated images was utilized, partitioned into training (57%), validation (29%), and testing (13.7%) subsets. The developed YOLOv8s model achieved high performance with a mean Average Precision (mAP@0.5) of 0.948, precision of 0.941, and recall of 0.935 across all vehicle classes. Functional validation through black-box testing confirmed the system's ability to process real-time video inputs under various conditions. Comparative results with manual surveys showed a 96% counting accuracy, indicating the system's robustness in quantified traffic flow estimation. Furthermore, the integration of automatic Passenger Car Equivalent (EMP) conversion allows for an immediate determination of the Degree of Saturation (DS) and Level of Service (LoS). These findings indicate that the YOLO-based traffic performance analysis system provides a reliable and efficient framework for urban traffic management, effectively replacing conventional manual survey methods while maintaining high technical standards.
Sistem Analisis Kinerja Lalu Lintas Berbasis Deep Learning dengan Arsitektur You Only Look Once (YOLO) Amir, Andi Ahdan; Sukman; Lihara, Astri Delviana; Nabil, Muhammad
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1503

Abstract

Tingginya tingkat kemacetan lalu lintas di perkotaan memerlukan solusi pemantauan dan analisis yang cepat dan akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem analisis kinerja lalu lintas otomatis yang mengintegrasikan model deteksi objek YOLOv8 dengan parameter standar Pedoman Kapasitas Jalan Indonesia (PKJI) 2023. Berbeda dengan penelitian sebelumnya yang umumnya berfokus pada kuantifikasi kendaraan, sistem ini secara otomatis mengonversi data deteksi visual menjadi metrik kinerja teknis. Hasil pengujian menunjukkan model YOLOv8s mencapai nilai mAP@0.5 rata-rata 0.948. Implementasi sistem pada dashboard interaktif menunjukkan akurasi perhitungan jumlah kendaraan sebesar 96% dibandingkan data manual, yang kemudian diolah menjadi indikator Derajat Kejenuhan (Degree of Saturation) dan Tingkat Pelayanan (Level of Service). Temuan ini membuktikan bahwa integrasi deep learning dengan regulasi rekayasa transportasi nasional dapat meningkatkan efisiensi pemantauan infrastruktur jalan secara signifikan.
Sistem Inventarisasi Kerusakan Perkerasan Jalan Deep Learning Dengan Arsitektur Convolutional Neural Network (CNN) YOLOv8 Mahmud; Yuandi, Intan Anuggrah; Amir, Andi Ahdan; Sulaiman, Muh Asep; Humera, Alya Putri
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1511

Abstract

Proses survei dan inventarisasi kerusakan jalan konvensional saat ini memerlukan biaya tinggi, durasi pengerjaan yang lama, dan risiko keselamatan bagi petugas. Penelitian ini bertujuan untuk merancang sistem inventarisasi kerusakan perkerasan jalan otomatis menggunakan arsitektur Deep Learning YOLOv8 yang diintegrasikan dengan kerangka penilaian Pavement Condition Index (PCI). Metodologi penelitian meliputi akuisisi dataset gabungan Road Damage Dataset (RDD2022) dan data lokal (2.272 objek), pra-pemrosesan melalui augmentasi mosaik, serta pelatihan model menggunakan teknik transfer learning. Secara fungsional, sistem yang dikembangkan terbukti berjalan sesuai rancangan pada menu deteksi citra dan video. Hasil evaluasi menunjukkan akurasi deteksi moderat dengan nilai rata-rata mAP50 sebesar 0,547. Performa tertinggi dicapai pada kategori retak buaya (alligator crack) (AP 0,709), sedangkan retak melintang (transverse crack) menjadi kelas dengan kinerja terlemah (AP 0,454). Kelemahan utama yang teridentifikasi adalah rendahnya akurasi lokalisasi bounding box (mAP50-95 0,254) dan tingginya tingkat misklasifikasi objek sebagai latar belakang (background). Sebagai rekomendasi, pengembangan selanjutnya perlu difokuskan pada peningkatan Recall melalui diversifikasi data dan integrasi koordinat GPS absolut guna mendukung manajemen aset jalan yang lebih presisi.
Co-Authors Abdul Fattaah Mustafah Ahmad Guntur Arman Hidayat Diansar, Diansar Djamaluddin, Rudy Duwi Nurmayanti , Duwi Nurmayanti Fachry Abda El Rahman Fadli Masri Fitriani Rahmawati Gunawansyah Gunawansyah, Gunawansyah Hasrudin Hasrudin Humera, Alya Putri Intan Anuggrah Yuandi Lihara, Astri Delviana Made Suda Mahmud Mahmud Muhammad Irwan Syahib Muhammad Nabil Muhammad Nabil , Muhammad Nabil Muhammad Taufik Toib Koten Parung, Herman Rufi'i Sakur, Sakur Sukman Sukman Sukman Sukman, Sukman Sulaiman, Muh Asep Syarif Rangga Saputra Yuandi, Intan Anuggrah