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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 217 Documents
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Sistem Informasi Jurnal Mengajar Studi Kasus SMA Negeri 3 Kota Bengkulu Zarah Juaita, Aisyah Amelia; Saputri, Dian Ardiyanti; Oktoeberza, Widhia KZ
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.v13i1.39609

Abstract

SMA Negeri 3 Kota Bengkulu masih melakukan pencatatan kegiatan pembelajaran secara manual sehingga kurang efisien dalam pelaksanaan dan dokumentasi kegiatan belajar mengajar. Untuk meningkatkan pengelolaan jurnal mengajar di SMA Negeri 3 Kota Bengkulu, maka dikembangkan Sistem Informasi Jurnal Mengajar berbasis website agar dapat mempermudah dalam proses pengelolaan data pembelajaran dan mengoptimalkan pencatatan jurnal mengajar. Dengan menggunakan framework Laravel dan metode waterfall sebagai metode pengembangan sistem, diperoleh sebuah sistem informasi dengan pengujian blackbox dengan tingkat keberhasilan yang tinggi. Sistem informasi yang telah diuji menggunakan metode blackbox menunjukkan bahwa setiap fitur-fitur yang disediakan mampu mempermudah proses pencatatan kegiatan pembelajaran di SMA Negeri 3 Kota Bengkulu, sehingga evaluasi dan pelaporan hasil kegiatan pembelajaran yang dihasilkan lebih akurat. Kata Kunci: Sistem Informasi, Jurnal Mengajar, Waterfall, Laravel, Data Pembelajaran.
Sistem Manajemen Reservasi Ruangan di Gedung Pusat Kegiatan Mahasiswa (PKM) Universitas Bengkulu Hijrayanti, Hikmah; Butar Butar, Federika; Oktoeberza, Widhia KZ
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.v13i1.39624

Abstract

The Bengkulu University Student Activities Center (PKM) requires an efficient room reservation management system to overcome manual processes that are prone to errors, delays and lack of transparency. This project aims to design and develop a web-based Room Reservation Management System using the Laravel framework, which allows students to see real-time room availability and make online reservations. This system also makes it easy for admins to manage room and reservation data systematically and avoid schedule conflicts. With the Waterfall development methodology, system design is carried out using Unified Modeling Language (UML) and testing using the Black Box Testing method. This system is expected to increase the efficiency of room management, provide information transparency, and minimize manual process obstacles, thereby supporting the effectiveness of student activities. Implementation of this system contributes to the modernization of campus administration services and optimization of PKM facilities.
Identifikasi Pola Polimorfisme Pada Punggung Kumbang Coccinellidae Menggunakan Metode Yolo Untuk Membedakan Jenis Kumbang Predator dan Hama Nurmalasari, Eva; Susanto, Agus; Zarkani, Agustin
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.v13i1.40182

Abstract

Identifikasi pola polimorfisme pada punggung kumbang Coccinellidae penting untuk membedakan predator dan hama dalam pertanian. Namun, perbedaan visual sering kali sulit dikenali. Penelitian ini mengusulkan metode deep learning menggunakan YOLOv5 untuk identifikasi otomatis. Data kumbang Coccinellidae dikumpulkan, melalui preprocessing dan augmentasi, menghasilkan 1.821 gambar. Model YOLO dilatih dengan parameter optimal hingga epoch 729 dan patience 300. Evaluasi menggunakan metrik Mean Average Precision (mAP) menunjukkan kinerja tinggi, dengan mAP@0,5 sebesar 0,966 dan mAP@0,75 sebesar 0,962. Selain itu, aplikasi berbasis Android dikembangkan untuk implementasi model ini dan diuji dengan hasil kepuasan pengguna sebesar 80. Hasil penelitian menunjukkan bahwa pendekatan ini efektif dalam membedakan kumbang predator dan hama secara akurat serta berpotensi membantu pengendalian hama di bidang pertanian. Kata Kunci: Identifikasi, Coccinellidae, Polimorfisme, YOLOv5.
Rancang Bangun Sistem Presensi Pegawai Berbasis IP Publik Statik Untuk Meningkatkan Efisiensi dan Akurasi Pencatatan Kehadiran Ganjar Prasetyo, Karno
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.v13i1.41006

Abstract

This research aims to create an employee attendance system based on static Public IP from internet service providers. Limitations of conventional attendance systems often include long lines and dependence on specific technologies, such as cards and fingerprint readers. Employees can take attendance flexibly using static public IP technology by utilizing a computer or mobile phone connected to the company's Wi-Fi network. The system development method used is a waterfall, made using the UML visualization model and the PHP programming language with the CodeIgniter framework and MySQL as a database. The test results show that the developed static public IP-based attendance system can increase the efficiency of the attendance process faster and reduce attendance recording errors significantly.
Rancang Bangun Arsitektur Microservices untuk Massive Open Online Courses
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.v13i1.39997

Abstract

The advancement of information technology has revolutionized learning, enabling remote teaching with flexible schedules. MOOCs, a new learning model, offer accessible education anytime, anywhere, with no participant limits. In developing MOOCs, microservices architecture can be applied, as shown in the Vocasia.id case study, which includes services like authentication, catalog, course, email, enrollment, finance, instructor, order, and payment using business capability decomposition. Docker is used for containerization to ensure application portability, while Kubernetes handles orchestration for efficient deployment and scaling. Benchmarking identifies optimal configurations, with maximum CPU usage reaching 28 cores and memory usage at 13 GiB. To ensure system stability, a horizontal pod autoscaler is configured with a 60% target for CPU and memory usage, supporting a minimum of 2 pods and a maximum of 5 pods.
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.

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