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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Jurnal Teknik Sipil Cerucuk. Prodi S1 T. Sipil.
Puguh Budi Prakoso
Department Of Civil Engineering, Faculty Of Engineering, Lambung Mangkurat University
Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Vehicle detection using background subtraction and clustering algorithms Puguh Budi Prakoso; Yuslena Sari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.10144

Abstract

Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
Application of neural network method for road crack detection Yuslena Sari; Puguh Budi Prakoso; Andreyan Rizky Baskara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14825

Abstract

The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.
ANALYSIS OF DELAY ENTRY AND PLANNING OF PARKING GATES QMALL BANJARBARU Ali Charoenplien; Puguh Budi Prakoso
CERUCUK Vol 5, No 2 (2021): CERUCUK VOL. 5 NO. 2 FEBRUARY 2021
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/crc.v5i2.4317

Abstract

Qmall Banjarbaru is located at Jalan Ahmad Yani KM. 37. The existence of Qmall Banjarbaru caused the impact of increased traffic density and decreased speed in the surrounding road network. With the increasing movements that occur from Qmall Banjarbaru, it will potentially be the cause of congestion between vehicles that will enter the Qmall Banjarbaru with vehicles moving straight on Jalan Ahmad Yani KM. 37. The purpose of this research is to know the influence of delay entrance parking Qmall Banjarbaru against the performance of Jalan Ahmad Yani KM. 37.This research conducted a field survey that aims to find volume data on the road, the time of parking door service, the number of vehicles that enter the parking, the time delay the parking door, and the length of the delay that occurs on the parking door. From the results of data analysis using the Calculation of field survey (realistic) data obtained the distance of the parking door previously 16.5 meters to be redated to 25 meters and the parking door that originally had two doors of parking service made into three doors parking service. This change was made to delay enter parking Qmall Banjarbaru does not reach Jalan Ahmad Yani Km. 37.
ANALISIS PENGGUNAAN BAHAN LOKAL SEBAGAI LAPIS PONDASI AGREGAT SEMEN (CTB) KELAS B UNTUK PERKERASAN JALAN Lestari, Utami Sylvia; Yasruddin; Prakoso, Puguh Budi; Markawie; Rahman, Fauzi; Rahmah, Meuthia Rezqa Hidayaty
Jurnal Teknik Sipil Vol 9 No 1 (2025): Jurnal Gradasi Teknik Sipil - Juni 2025
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/gradasi.v9i1.14585

Abstract

Lapis Pondasi Agregat Semen (CTB) Kelas B adalah lapis pondasi bawah perkerasan jalan raya yang terletak di antara tanah dasar dan lapis pondasi. Lapis pondasi bawah memiliki fungsi sebagai pendukung lapisan perkerasan serta menyebarkan beban yang terjadi akibat roda kendaraan. Penelitian ini bertujuan untuk menganalisis kekuatan tekan Lapis Pondasi Agregat Semen (CTB) Kelas B menggunakan bahan lokal sebagai alternatif material untuk perkerasan jalan raya. Penelitian ini mengeksplorasi tiga jenis pasir lokal dari Kalimantan, yakni Pasir Barito, Pasir Palangka Raya, dan Pasir Liang Anggang, yang masing-masing memiliki gradasi berbeda yaitu agak halus, agak kasar, dan kasar. Metodologi penelitian mencakup pengujian karakteristik fisik pasir dan uji tekan CTB dengan berbagai proporsi campuran agregat halus dan kasar, untuk menentukan komposisi yang menghasilkan kekuatan optimal. Pengujian yang dilakukan yaitu uji kuat tekan dengan ketentuan nilai kuat tekan yang dihasilkan berkisar diantara 35 kg/cm2 – 45 kg/cm2.  Hasil penelitian menunjukkan bahwa gradasi kasar memberikan kekuatan tekan tertinggi, dengan Pasir Liang Anggang mencapai nilai kuat tekan optimal pada komposisi agregat halus sebesar 45%. Kekuatan tekan CTB yang menggunakan pasir bergradasi kasar ini memenuhi spesifikasi CTB Kelas B dan Kelas A, menjadikannya material yang ekonomis dan efektif untuk konstruksi jalan. Temuan ini mengindikasikan bahwa pasir lokal bergradasi kasar memiliki daya ikat yang kuat dalam campuran semen, sehingga dapat meningkatkan stabilitas dan durabilitas pondasi jalan.
Automatic identification of herbal medicines using deep learning on leaf images Anita Ahmad Kasim; Lukman Nadjamudiin; Muhammad Bakri; Chairunnisa Ar Lamasitudju; Puguh Budi Prakoso; Anindita Septiarini; Bima Prihasto
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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

Abstract

Indonesia has a high diversity of medicinal plants that are widely used in traditional healthcare practices. Identification of medicinal plants is commonly based on leaf morphology; however, similarities in leaf shape, texture, and color often cause misidentification, particularly among non-experts. This limitation highlights the need for an automated and reliable identification approach. The primary objective of this study is to develop and evaluate a deep learning–based system for the automatic identification of medicinal plants using leaf images, with a specific focus on comparing the performance and efficiency of MobileNetV2 and ResNet50V2 architectures. The research design adopts an experimental approach using an internally collected dataset of medicinal plant leaf images representing multiple plant classes. The dataset is divided into training and testing sets to evaluate model generalization. The methodology involves image preprocessing steps, including resizing, normalization, and data augmentation, followed by the application of transfer learning using MobileNetV2 and ResNet50V2 as feature extractors. Both models are trained under the same experimental settings and evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The main outcomes and results indicate that both deep learning models achieve high classification performance. MobileNetV2 achieves an accuracy of 98.77%, precision of 98.84%, recall of 98.77%, and F1-score of 98.77%, while ResNet50V2 achieves an accuracy of 97.53%, precision of 97.87%, recall of 97.53%, and F1-score of 97.58%. The results demonstrate that MobileNetV2 provides slightly superior performance with lower computational complexity. In conclusion, lightweight deep learning architectures such as MobileNetV2 are effective and efficient for medicinal plant leaf identification and are suitable for implementation in mobile or resource-constrained environments.
Co-Authors Ali Charoenplien Andreyan Rizky Baskara Anindita Septiarini, Anindita Anita Ahmad Kasim Bima Prihasto Fauzi Rahman Lamasitudju, Chairunnisa Lestari, Utami Sylvia Lukman Nadjamudiin Markawie Markawie Muhammad Bakri Rahmah, Meuthia Rezqa Hidayaty Yasruddin Yasruddin Yuslena Sari, Yuslena