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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Difusi Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Nurjannah Syakrani
Politeknik Negeri Bandung
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Social Sciences

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Early Stopping Effectiveness for YOLOv4 Afif Rana Muhammad; Hamzah Prasetio Utomo; Priyanto Hidayatullah; Nurjannah Syakrani
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.11-20

Abstract

Background: YOLOv4 is one of the fastest algorithms for object detection. Its methods, i.e., bag of freebies and bag of specials, can prevent overfitting, but this can be combined with early stopping as it could also prevent overfitting. Objective: This study aims to identify the effectiveness of early stopping in preventing overfitting in the YOLOv4 training process. Methods: Four datasets were grouped based on the training data size and object class, These datasets were tested in the experiment, which was carried out using three patience hyperparameters: 2, 3, and 5. To assess the consistency, it was repeated eight times. Results: The experimental results show that early stopping is triggered more frequently in training with data below 2,000 images. Of the three patience hyperparameters used, patience 2 and 3 were able to halve the training duration without sacrificing accuracy. Patience 5 rarely triggers early stopping. There is no pattern of correlation between the number of object classes and early stopping. Conclusion: Early stopping is useful only in training with data below 2,000 images. Patience with a value of 2 or 3 are recommended. Keywords: Early Stopping, Overfitting, Training data, YOLOv4
APLIKASI AUDIT MUTU INTERNAL ONLINE STUDI KASUS SPM POLITEKNIK NEGERI BANDUNG Ade Chandra Nugraha; Nurjannah Syakrani
Jurnal Difusi Vol 1 No 2 (2018): Jurnal Difusi
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M) Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.902 KB) | DOI: 10.35313/difusi.v1i2.1299

Abstract

Salah satu program dari unit Sistem Penjaminan Mutu (SPM) di Politeknik Negeri Bandung yang tercantum dalam kalender akademik tahunan adalah Audit Mutu Internal (AMI). Dalam tiga tahun terakhir, program ini  telah dilaksanakan secara online menggunakan aplikasi bernama PAMIOL. Penyimpanan data hasil Audit dalam PAMIOL menggunakan link data yang disimpan dalam format Excel maupun Word. Penggunaan dan evaluasi terhadap PAMIOL sebagai media AMI menunjukkan perlunya perbaikan serta disain ulang aplikasi. Berdasarkan analisis PAMIOL dan studi borang program studi (BORANG 3A) BAN PT sebagai salah satu instrumen audit,  dibuat rancangan dan implementasi media audit mutu internal online baru bernama AMIOnline untuk perguruan tinggi atau instansi umum. AMIOnline memiliki kemampuan entry pertanyaan audit yang bersifat dinamis, mencakup definisi model pertanyaan, tipe data jawaban, juga formulasi penilaiannya yang dihitung secara otomotis oleh aplikasi ataupun dari entri skor oleh auditor, menghasilkan laporan asesmen,  serta pilihan data pendukung untuk laporan AMI. Aplikasi ini juga memiliki fitur merekam data master auditor, auditee dan fitur pemetaan penugasan Auditor dengan pengaturan  waktu aktif audit oleh admin. Pengembangan aplikasi AMIOnline ini berdasarkan  metodologi pengembangan perangkat lunak waterfall dan menggunakan basis data relasional. Aplikasi AMIOnline sedang dipersiapkan untuk mendukung AMI SPM Polban mulai tahun 2017 dengan kemungkinan modifikasi serta fitur pengembangan template  pertanyaan audit lainnya.  Kata Kunci:  PAMIOL, AMI, model pertanyaan, formulasi penilaian,  AMIOnline.
Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya Nurjannah Syakrani; Yudi Widhiyasana; Abid Arinu Efendi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 1: Februari 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1482.159 KB)

Abstract

Liver is one of the most important organs in the human body. One of the dangerous diseases of the liver is tumor. In the CT scan image, the tumor has different texture, color, shape, and position, according to patient's condition. In this study, a tumor detection was carried out by tree stages: firstly some steps of preprocessing, such as filtering, edge detection, and erotion; secondly, finding the liver among organs in abdomen using segmentation and checking the liver position in the right abdomen; and thirdly performing the tumor detection in the liver using graph cut and push relabel algorithm. Usually, segmentation using graph cut needs two interactive inputs, namely sample of object area and sample of background area. In this paper, the interactive inputs on graph cut were replaced by deviation standard calculation. Testing using three sets of CT image and the ground truth produces average of the dice similarity coefficient (DSC), volumetric overlap error (VOE), and absolute volume difference (AVD) parameters of 78.15%, 25.72%, 19.30%, respectively. Furthermore, volume of liver tumor is approximated by utilizing area of tumor in each slice of CT image, then displayed in 3D view.
Pengembangan SiPaDI: Platform Marketplace Penyedia Jasa Terlatih Binaan Disnakertrans Kabupaten Karawang Maisevli Harika; Asri Maspupah; Djoko Cahyo Utomo Lieharyani; Muhammad Rizqi Sholahuddin; Nurjannah Syakrani; Sofy Fitriani
Jurnal Difusi Vol. 9 No. 1 (2026): Jurnal Difusi
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M) Politeknik Negeri Bandung

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Abstract

Transformasi digital pada sektor layanan jasa diperlukan untuk meningkatkan akses promosi tenaga kerja terampil dan mempermudah masyarakat memperoleh layanan yang terpercaya. Namun, alumni Balai Latihan Kerja (BLK) binaan Disnakertrans Kabupaten Karawang masih menghadapi keterbatasan dalam memasarkan kompetensi secara digital. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan mengembangkan SiPaDI (Sistem Pasar Digital Alumni) sebagai platform marketplace jasa berbasis web yang menghubungkan alumni BLK dengan masyarakat pengguna jasa. Metode pelaksanaan menggunakan pendekatan participatory iterative development melalui tahapan analisis kebutuhan, perancangan sistem, implementasi aplikasi, pengujian, pelatihan, dan evaluasi bersama mitra. Sistem dikembangkan menggunakan Next.js, React, TypeScript, dan MariaDB. Hasil kegiatan menunjukkan bahwa SiPaDI berhasil mendukung pengelolaan layanan jasa secara digital melalui fitur registrasi pengguna, verifikasi penyedia jasa, pemesanan layanan, dan ulasan pelanggan. Selain menghasilkan platform digital, kegiatan ini juga meningkatkan kesiapan mitra dalam mengelola layanan tenaga kerja terampil berbasis teknologi informasi secara mandiri dan berkelanjutan
YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Priyanto Hidayatullah; Nurjannah Syakrani; Muhammad Rizqi Sholahuddin; Trisna Gelar; Refdinal Tubagus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6598

Abstract

In the domain of deep learning-driven computer vision, YOLO is revolutionary. However, not all YOLO models are accompanied by academic articles and architectural diagrams. It complicates the comprehension of the model's operation. Moreover, the existing review papers fail to examine each model comprehensively. This work aims to provide a thorough comparative analysis of the architectures from YOLOv8 to YOLO11, allowing readers to swiftly understand the operational mechanisms and differences among the models. We analyzed the architecture of each YOLO version by reviewing relevant scholarly articles, official documentation, and examining the source code. In particular, we discovered that YOLOv8 through YOLO11 differ in novelty while sharing similarities in the anchor-free and Non-Maximum Suppression (NMS) aspects, except YOLOv10 (NMS-free). Each also has drawbacks, such as differing levels of complexity in the way features are connected (v8), architectural structure and training (v9), training methods or dual assignments (v10), inference, and code implementation (v11). While each version improves architecture, some blocks remain unchanged. This study helps readers understand different YOLO version architectures and inspires how to improve their performance. It also provides readers with a comprehensive architecture diagram and detailed descriptions of each block, serving as a reference for both academic and practical applications. In terms of performance, a benchmark using the Roboflow 100 dataset reveals that YOLOv9 achieves superior accuracy; however, it is eight times slower owing to its NMS mechanism. YOLOv10 is the fastest but least accurate, whereas YOLOv8 and YOLO11 provide a balanced compromise between speed and accuracy.
Co-Authors Abid Arinu Efendi Ade Chandra Nugraha Afif Rana Muhammad Asri Maspupah Djoko Cahyo Utomo Lieharyani Gelar, Trisna Hamzah Prasetio Utomo Maisevli Harika Muhammad Rizqi Sholahuddin Priyanto Hidayatullah Priyanto Hidayatullah, Priyanto Refdinal Tubagus Sofy Fitriani Yudi Widhiyasana