Anan Nugroho
Program Studi Pendidikan Geografi, Universitas Negeri Semarang

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Kajian Kapasitas Masyarakat Berbasis Aset Penghidupan Terhadap Bencana Kekeringan Trida Ridho Fariz; Fajar Adie Nugraha; Gede Aswin Yoga Putra; Ananto Aryo Nugroho; Dyah Ratna Salima; Lestarina Estifani Pradiny; Ahmad Faesal Mubarizi
LaGeografia Vol 21, No 1 (2022): Oktober
Publisher : UNIVERSITAS NEGERI MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1029.293 KB) | DOI: 10.35580/lageografia.v21i1.37174

Abstract

AbstractWindurojo Village in Kesesi District, Pekalongan Regency is one of the areas with the most susceptible to drought. Therefore, this study aims to assess community capacity against drought in Windurojo Village at the household level and to analyze the relationship between variables. This study used a livelihood asset approach with data collection focused on Serang Hamlet, which is the area with the worst drought. The results of the study stated that the livelihood asset with the highest scale in Serang Hamlet was human capital. The results of the crosstab analysis also show that human capital is related to other capital, but the Spearman correlation results show that the highest relationship is found in financial and physical capital. The scoring results show that the capacity of the community against drought in Serang Hamlet is mostly medium class capacity, only 5 families are of high class capacity.  AbstrakDesa Windurojo di Kecamatan Kesesi, Kabupaten Pekalongan merupakan salah satu daerah yang paling rawan terhadap kekeringan. Oleh karena itu, penelitian ini bertujuan untuk menilai kapasitas masyarakat terhadap kekeringan di Desa Windurojo pada tingkat rumah tangga dan menganalisis hubungan antar variabelnya. Penelitian ini menggunakan pendekatan aset penghidupan dengan pendataan terfokus pada Dusun Serang yang merupakan daerah dengan kekeringan terparah. Hasil penelitian menyebutkan bahwa aset penghidupan dengan skala tertinggi di Dusun Serang adalah modal manusia. Hasil analisis crosstab juga menunjukkan bahwa modal manusia berhubungan dengan modal lainnya, namun hasil korelasi Spearman menunjukkan hubungan tertinggi terdapat pada modal fisik dan finansial. Hasil skoring menunjukkan bahwa daya tampung masyarakat terhadap kekeringan di Dusun Serang sebagian besar kelas menengah dan hanya 5 kepala keluarga yang kelas tinggi.
Web-based Application for Cancerous Object Segmentation in Ultrasound Images Using Active Contour Method Dwi Oktaviyanti; Anan Nugroho; Hari Wibawanto; Subiyanto
Jurnal Sistem Informasi Vol. 19 No. 2 (2023): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jsi.v19i2.1280

Abstract

Segmentation, or the process of separating clinical objects from surrounding tissue in medical images, is an important step in the Computer-Aided Diagnosis (CAD) system. The CAD system is developed to assist radiologists in diagnosing cancer malignancy, which in this research is found in ultrasound (US) medical imaging. The manual segmentation process, which cannot be accessed remotely, is a limitation of the CAD system because cancer objects are screened frequently, continuously, and at all times. Therefore, this research aims to build a user-friendly web application called COSION (Cancerous Object Segmentation) that provides easy access for radiologists to segment cancer objects in US images by adopting an active contour method called HERBAC (Hybrid Edge & Region-Based Active Contour). The waterfall method was used to develop the web application with Django as the web framework. The successfully built web application is named Cosion. Cosion was tested on 114 radiology breast and thyroid US images. Functional, portability, efficiency, reliability, expert validation, and usability testing concluded that Cosion runs well and is suitable for use with a functionality value of 0.9375, an average GTmetrix score of 96.43±0.66%, 100% stress testing percentage, 77.5% expert validation, and 75.8% usability. These quantitative performances indicate that the COSION web application is suitable for implementation in the CAD system for US medical imaging.
Detection of Motorcycle Tire Endurance based on Tire Load Index using CNN Birawa Kaca Buana Gora; Nugroho Tegar Maulana; Muhamad Novan Aulia Zam Zami; Anan Nugroho; Alfa Faridh Suni
DoubleClick: Journal of Computer and Information Technology Vol 7, No 1 (2023): Optimalisasi Teknologi Informasi
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/doubleclick.v7i1.12995

Abstract

With increasingly rapid technological developments, the production of motorized vehicles will increase with the use of robotic power in production. The increasing number of motorized vehicles in big cities does not escape the rise of traffic accidents that occur. One aspect of accidents that we usually underestimate is the resistance of our vehicle tires to support the load on the vehicle. Therefore, we need a system to detect the resistance of a tire in supporting the load on the vehicle. For this reason, this study was conducted to detect the durability of motorcycle tires based on tire load index using a convolutional neural network. A 70% result was found in classifying tire resistance based on tire load index.
Peningkatan Kompetensi Siswa dalam Pengelolaan Proyek dengan Pemanfaatan Software Bantu Smartsheet di SMK Pembangunan Mranggen Demak Agus Suryanto; Anan Nugroho; Uswatun Hasanah; Najma Aulia
Journal on Education Vol 6 No 4 (2024): Journal on Education: Volume 6 Nomor 4 Mei-Agustus 2024
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v6i4.6069

Abstract

Kebutuhan yang sangat utama didalam memasuki era digitalisasi adalah mempersiapkan lulusan siswa membekali diri memiliki kemampuan skill digital diantaranya penguasaan lulusan SMK dalam mengelola suatu Proyek di lapangan nanti, Siswa dituntut memahami dan memiliki kemampuan pengelolaan proyek dengan berbantuan Software pengeloloaan suatu Proyek Pekerjaan. Permasalahan di lapangan akan ditemui oleh lulusan SMK, jika dalam melakukan pengelolaan Proyek masih melakukan mekansisme tanpa memanfaatkan Software bantu dalam melaksanakan Proyek tersebut. Software bantu yang dapat digunakan dalam pengelolaan suatu Proyek adalah Smartsheet dalam merencankan proyek dan mengevaluasi Proyek Kegiatan ini bertujuan, : 1) Siswa mengenal penggunaan aplikasi komputer dalam hal ini Smartsheet sebagai software bantu dalam pengelolaan suatu Proyek, 2) Siswa mampu menggunakan software Smartsheet secara mudah dan tepat untuk pengelolaan pekerjaan berbasis Proyek, 3) Siswa mampu menggunakan Software bantu Smartsheet sebagai alat bantu untuk memecahkan masalah dalam mengambil keputusan untuk perencanaan proyek pekerjaan. Metode pelaksanaan kegiatan Pengabdian berupa adalah Memberikan pelatihan dan peningkatan kemampuan siswa SMK Pembangunan Mranggen Demak dalam penggunakan aplikasi. Metode yang akan dilaksanakan dalam kegiatan ini adalah metode Workshop praktikum di labortatorium Komputer SMK Pembangunan Mranggen Demak. Luaran Kegiatan Pengabdian ini berupa Publikasi di Jurnal Nasional ber ISSN dan pemberitaan di Media Cetak atau Elektronik.
PERFORMANCE OF THE YOLOV5 ALGORITHM TO DETECT HUMANS IN THE REAR EXCAVATOR AREA Hanna Naili Syifa'; Anan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5576

Abstract

Work involving excavators carries a high risk of accidents that can result in fatalities, making Occupational Safety and Health (OSH) critically important. Most excavator accidents are caused by blind spots at the rear, where the operator's limited field of view increases the risk of hitting nearby objects or workers. Despite safety features such as reverse alarms and rear cameras, these technologies only display real-time video without automatically detecting workers, thus still posing a significant risk. This study aims to develop a human detection system for the rear area of excavators using the YOLOv5 algorithm based on image processing. The system's main features include real-time human detection, distance estimation, and audible warnings if a human is detected within a high-risk distance. The system was tested using three video recordings depicting human objects behind the excavator in different scenarios. Despite the limited number of video samples, the human objects provided sufficient complexity to evaluate the system's effectiveness. The test results showed an average accuracy of 80.5% and an F1-score of 87.78%. These findings indicate that the YOLOv5-based detection system performs well in various video conditions and shows potential effectiveness in real operational situations. Consequently, this system is expected to reduce the risk of work accidents with excavators caused by rear blind spots and improve on-site worker safety. This research contributes to the field of occupational safety by integrating image processing algorithms into the development of heavy equipment safety technology, thereby enhancing worker protection.
COMPARATIVE STUDY OF YOLO VERSIONS FOR DETECTING VACANT CAR PARKING SPACES Muhammad Fathurrahman; Anan Nugroho; Ahmad Zein Al Wafi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6236

Abstract

The increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of  98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency.