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All Journal Jurnal Masyarakat Informatika Rekursif: Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Communications in Science and Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Medika Teknika : Jurnal Teknik Elektromedik Indonesia
Oktoeberza, Widhia KZ
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Rekursif: Jurnal Informatika

 1 
Sistem Informasi Pelaksanaan Zakat di Masjid Al-Khair Kota Bengkulu Muthi, Marsa Hulwa Indri; Luthfi, Irfan; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 11 No 2 (2023): Volume 11 Nomor 2 November 2023
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v11i2.32346

Abstract

Zakat, as a fundamental pillar in Islam, plays a crucial role in maintaining the social and economic well-being of the Muslim community. Despite the transformation of societal behavior in the era of digitalization, the utilization of technology in the execution of zakat remains limited. In Masjid Al-Khair, Bengkulu City, challenges related to financial information and low awareness of charitable contributions pose issues. This research proposes a solution in the form of a mosque information system. This system is designed to facilitate the community in contributing to zakat and enhance the financial transparency of the mosque, bridging the gap between needs and limitations in the digital era. It is hoped that the implementation of this website system will boost community participation, provide easily accessible information, and promote transparency and efficiency in zakat activities in the digital era.
Sistem Informasi Presensi di SMA Negeri 1 Kepahiang Menggunakan Quick Response Code Aliana, Mareta; Ikhsan, Arif Rahmat; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 11 No 2 (2023): Volume 11 Nomor 2 November 2023
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v11i2.32347

Abstract

SMA Negeri 1 Kepahiang is one of the public high schools located in Kepahiang Regency, Bengkulu Province. The school currently utilizes a manual attendance system, which poses several drawbacks, including the risk of inaccurate data entry and the time-consuming process of cross-referencing individual student/teacher attendance records to identify those who are absent due to illness, leave, or other reasons. The implementation of QR Code technology in the student attendance system is expected to address these challenges and facilitate the recording of attendance data, enabling a faster and more accurate recapitulation process. A system capable of addressing these issues is deemed necessary. The researcher conducted the design of a database, developed User Interface (UI) / User Experience (UX) designs, implemented the system, and concluded with a testing phase. The introduction of this information system is anticipated to streamline and expedite the student attendance process. Keywords: Information System, Attendance, QR Code, Public High School.
Rancang Bangun Sistem E-Raport di MAN 2 Kota Bengkulu Berbasis Website Gita Fitria, Nabila; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 12 No 1 (2024): Volume 12 Nomor 1 Maret 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i1.33690

Abstract

MAN 2 Bengkulu City still collects and processes daily student assessment data manually. The teacher records the test scores on paper and then processes them in Excel. This manual process results in less efficient assessments due to potential errors and data loss. To overcome this problem, it is necessary to have a website-based e-report system at MAN 2 Bengkulu. This system is intended to benefit teachers in processing grades effectively and efficiently until the final output is obtained in the form of a report. The system is designed using the PHP programming language, Laravel framework, and MySQL as the database. System development follows the waterfall method and uses data collection methods to be used as research material. Furthermore, the system was tested using black box testing and a percentage of 100% was obtained, which indicated that the system was very effective to use
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.
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 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.
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.
Co-Authors Aan Erlansari Afdhal Kurniawan Mainil Aliana, Mareta Alifyandra Akbar, Farrel Anggarah, Rengga Butar Butar, Federika Dartiko, Fedryanto Desi Andreswari Dwi Jayanto, Paksi Endina Putri Purwandari Ernawati Ernawati Funny Farady Coastera Gita Fitria, Nabila Hanung Adi Nugroho Hijrayanti, Hikmah Hotama, Christianus Frederick Huda, Widya Nurul Igi Ardiyanto Ikhsan, Arif Rahmat Indah Soesanti Julia Purnama Sari Julia Purnama Sari Listyalina, Latifah Luthfi, Irfan Manurung, Wahyu Ozorah Muthi, Marsa Hulwa Indri Pradana, Refki Jorgi Rahman, Muhammad Farchan Al Rusdi Efendi Saputri, Dian Ardiyanti Sari, Renti Epana Sunu Wibirama Syahputra, Wahyu Vasiguhamiaz, Azvadennys Yulyanti, Vesi Zarah Juaita, Aisyah Amelia