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Arie Vatresia
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INDONESIA
Rekursif: Jurnal Informatika
Published by Universitas Bengkulu
ISSN : 23030755     EISSN : 27770427     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Rekursif adalah jurnal ilmiah yang diterbitkan oleh Program Studi Informatika, Fakultas Teknik, Universitas Bengkulu. Rekursif menerima artikel ilmiah dengan topik; Informatika, Sistem Informasi, dan Teknologi Informasi dari peneliti, dosen, guru, dan mahasiswa. Rekursif diterbitakan secara berkala setiap bulan Maret dan November berdasarkan hasil peer-reviewed. ISSN 2303-0755
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 216 Documents
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Analisis Komparatif Metode Peningkatan Kontras Citra Bawah Air Menggunakan HE, AHE, dan CLAHE Ernawati, Ernawati; Oktoeberza, Widhia KZ; Andreswari, Desi; Purnama Sari, Julia; Erlansari, Aan; Farady Coastera, Funny; Dwi Jayanto, Paksi
Rekursif: Jurnal Informatika Vol 13 No 1 (2025): Volume 13 Nomor 1 Maret 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i1.42151

Abstract

significant challenge in the field of digital image processing due to poor lighting conditions and uneven intensity distribution. This study aims to compare three contrast enhancement techniques Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to underwater imagery. The evaluation was conducted using quantitative metrics including entropy, contrast (RMS), and Structural Similarity Index (SSIM) to assess the improvement in image detail, intensity distribution, and structural similarity to the original image. Experimental results indicate that AHE achieves the highest entropy values, reflecting a significant enhancement of local information. HE provides the highest contrast values but tends to compromise the structural integrity of the image. CLAHE demonstrates the most balanced performance, producing the highest SSIM scores while maintaining stable enhancements in both contrast and detail. Based on these findings, CLAHE is recommended as the most effective contrast enhancement technique for underwater images, as it improves visual quality while preserving the original image structure. Key words : Underwater image enhancement; Contrast enhancement; CLAHE; HE; AHE.
Implementasi Standar ISO 15489 Dalam Perancangan SOP Kearsipan di Dinas Dukcapil Kabupaten Seluma Nabila, Nisreina; Purwandari, Endina Putri; Ramadani, Niska
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.42418

Abstract

The digital era encourages the transformation of archive management, demanding a standardized system. Based on Indonesian Law No. 43 of 2009, archives are classified into active and inactive. At the Dukcapil Office of Seluma Regency, the management of inactive archives is not yet supported by written guidelines, which poses a risk to information security and access. This research designs Standard Operating Procedures (SOP) based on ISO 15489 and integrates them with a digital archiving system. Qualitative methods were used to explore the process and its impact. The results show that the security score (62) and accessibility score (63) are still relatively low. The designed SOP serves as an operational reference and the foundation for the development of the digital archive information system, which has proven to enhance efficiency and compliance with regulations. This research makes an important contribution to archival policy and suggests evaluating the implementation of SOPs as well as developing a digital system prototype in future studies.
Penerapan Metode Multi Attribute Utility Theory (MAUT) Untuk Menentukan Prioritas Penerima Bantuan Bencana Alam (Studi Kasus: BPBD Bengkulu Tengah) Wahyudi, Rahmat Fikri; Andreswari, Desi; Purnama Sari, Julia
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43289

Abstract

Indonesia, as an equatorial archipelago located between the Asian and Australian continents, faces high risks of natural disasters, particularly floods and landslides. These disasters cause various adverse impacts, such as infrastructure damage, psychological trauma, and social and economic losses for victims. The Regional Disaster Management Agency (BPBD), as the primary institution for disaster response, must provide effective services for community recovery, thus requiring a fast and accurate system. Therefore, this research aims to develop a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method to assist BPBD in determining priority recipients of disaster aid. The advantage of the MAUT method lies in its ability to process multi-criteria decisions, consider stakeholder preferences, and produce quantitative and transparent outputs. The system was built using PHP and designed with Unified Modeling Language (UML). Testing was conducted on 16 alternative datasets, producing a priority ranking based on the highest scores. Accuracy tests showed an 87.5% success rate, while black-box testing achieved 100%. The highest preference score (0.92083) proves MAUT's accuracy in decision-making.
Implementasi YOLOv11 Untuk Deteksi Penyakit Tanaman Padi Berdasarkan Citra Daun Alifyandra Akbar, Farrel; Sari, Julia Purnama; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43876

Abstract

Rice (Oryza sativa) is a strategic commodity for food security in Indonesia, yet it is highly vulnerable to diseases such as bacterial leaf blight (blight), blast, and tungro, which can significantly reduce productivity. Early detection of these diseases through manual observation by farmers is often inaccurate and slow. This study aims to implement the YOLOv11 algorithm, a deep learning-based approach, to detect rice plant diseases from leaf images with high accuracy. The research method follows the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, encompassing business understanding, data collection, data preparation, modeling, and evaluation. The dataset consists of 500 rice leaf images classified into three disease categories. The data was processed through augmentation and resizing to balance class distribution and standardize image dimensions. The YOLOv11 model was trained with parameters set at 100 epochs, an image size of 224x224 pixels, and a batch size of 32. Evaluation results demonstrate that the model achieved 95% accuracy, with average precision and recall exceeding 95%. The confusion matrix revealed excellent classification performance, particularly for tungro disease (100% accuracy). The model also proved efficient in prediction, with an inference time of 8.2 milliseconds per image. In conclusion, this research confirms the effectiveness of YOLOv11 for rice disease detection based on leaf images. Recommendations for future development include expanding dataset diversity, integrating the model into mobile applications, and conducting field tests to validate real-world performance. Keywords: YOLOv11, rice disease detection, deep learning, leaf image, computer vision.
Deteksi Hama Whiteflies (Aleyrodidae) Pada Tanaman Cucurbitaceae Menggunakan YOLOv11 Rizky Ananda, Naufal; Ernawati, Ernawati; Putri Purwandari , Endina
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43914

Abstract

Whitefly (Aleyrodidae) pests pose a significant threat to the productivity of Cucurbitaceae crops in Indonesia, leading to substantial harvest losses and the transmission of plant viruses. Because traditional manual detection methods are often slow and inefficient, there is a clear need for a technological solution for early identification. This study details the development and evaluation of an object detection model using the YOLOv11 architecture. The methodology involved four primary stages: preparing a dataset of 1,940 images from public repositories, preprocessing the data through annotation and augmentation (including blur, brightness, and noise), training the model, and conducting a thorough performance evaluation. The resulting model was deployed into a web-based application for real-time detection. The evaluation demonstrated the model's excellent performance, achieving a mean Average Precision at a 0.5 IoU threshold (mAP@50) of 85.6% and an mAP@50-95 of 81.2%. Furthermore, it achieved a precision of 83.1%, a recall of 89.0%, and an F1-Score of 86.0%, proving its capacity to consistently and accurately detect these small-sized pests. This research successfully delivers an effective and accessible early detection system, making a practical contribution to precision agriculture and supporting food security in Indonesia through the application of deep learning.
Pengembangan Sistem Deteksi Dini Mahasiswa Berisiko Menggunakan Machine Learning Berbasis Data Learning Management System: Studi Kasus: rumahilmu.org Syahputra, Wahyu; Purwandari, Endina Putri; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43948

Abstract

Abstract: This research aims to develop an early detection system for at-risk students using machine learning based on data from the Learning Management System (LMS) rumahilmu.org. The system was designed for the Information Systems Study Programs at the University of Bengkulu, analyzing data from 459 student enrollments across five courses. A total of 37–76 features were extracted from LMS activities to predict students likely to score below the 30th percentile at three strategic time points (25%, 50%, and 75% of the semester). This study implemented a per-class optimization approach, testing 11 algorithms to find the best model for each course. The results showed that no single algorithm was universally superior; the most effective models varied for each course, with Gaussian Process, Logistic Regression, and Voting Classifier being the most frequently chosen. However, evaluation on the test data revealed significant challenges: despite high cross-validation scores (F1-score > 0.80), overfitting and performance degradation occurred. The most critical finding was the model's low capability in detecting the 'At-Risk' minority class, with the Recall (At-Risk) metric reaching 0.00 in 8 out of 15 scenarios. The best detection performance was achieved in the Statistics & Probability course with a Recall of 0.50. The implemented system, featuring a 3-tier architecture (FastAPI and React), provides an interactive dashboard, but its predictive effectiveness for early detection is limited by small and imbalanced datasets.

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