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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains JRIIN :Jurnal Riset Informatika dan Inovasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Chairina Fachrunnida
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Astronomy Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Merangkul Teknologi AI Untuk Memperkuat Pendidikan di Pesantren Rhoudhotul Qurro Al - Akhyar Fajar Alvi Rizki; Damars Alfi Syahri; Raden Muhammad Alfajar Samsoe; Fitrah Nanda Ar Ridho Edi; Chairina Fachrunnida; Siti Badriah; Iffan Maulana Syahidan; Parhan; Andre Surya Anidika; Alfiero Kusuma
Jurnal Riset Informatika dan Inovasi Vol 2 No 10 (2025): JRIIN: Jurnal Riset Informatika dan Inovasi
Publisher : shofanah Media Berkah

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Abstract

Pesantren, sebagai lembaga pendidikan Islam tradisional di Indonesia, menghadapi tantangan besar dalam mengadopsi teknologi modern, terutama di era Revolusi Industri 4.0 dan Society 5.0. Artificial Intelligence (AI) menawarkan berbagai peluang untuk meningkatkan kualitas pendidikan pesantren, seperti pembelajaran adaptif, asistensi dalam kajian kitab, serta manajemen pendidikan. Namun, implementasi AI juga menghadirkan tantangan, seperti keterbatasan infrastruktur, kesenjangan pengetahuan, dan resistensi terhadap perubahan. Artikel ini mengkaji potensi penerapan AI di Pesantren Rhoudhotul Qurro Al-Akhyar, strategi integrasi teknologi, serta langkah-langkah konkrit untuk memulai proses transformasi digital tanpa mengorbankan nilai-nilai tradisional pesantren.
Klasifikasi Penyakit Bercak Daun Pada Tanaman Gandum Menggunakan Metode Convolutional Neural Network Raihan Salman Al Parisy; Damars Alfi Syahri; Reyvalqy; Chairina Fachrunnida
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Wheat leaf diseases such as yellow rust and powdery mildew are very harmful to wheat yields worldwide. It is important to detect these diseases as early as possible so that losses can be minimized. In this work, we have used lightweight convolutional neural networks (CNNs) and Transformer-based methods to detect wheat leaf diseases under complex environmental conditions. In the first study, we tried several lightweight CNN models, such as MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2. These models were trained using different learning methods and achieved the highest accuracy of 98.65% using MnasNet and a fine-tuned learning rate. The second study focused on detecting yellow rust with UNET Segmentation and Swin Transformer classification methods. They achieved 95.8% accuracy in the field without manual intervention. These studies created a complete pipeline, including finding and delimiting wheat leaves from a complex background. They used YOLOv8 to quickly find leaves, then performed Segmentation and classification. The results showed that the combination of Segmentation, lightweight CNN, and Transformer techniques can handle leaf disease detection in nature with different backgrounds. This system has high accuracy and good efficiency for use in the field. This method can help the development of smart agricultural applications by accelerating and facilitating automatic detection of wheat leaf diseases. Using technologies such as Convolutional neural networks, Transformers, and Segmentation to overcome complex backgrounds.
Perancangan Sistem Informasi Absensi Berbasis Website Menggunakan Model Pengembangan Waterfall Gilang Perdana; Damars Alfi Syahri; Chairina Fachrunnida; Wasis Haryono
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.948

Abstract

Employee PT TRAFINDO PRIMA PERKASA is a company engaged in the medium voltage electrical equipment industry. The employee attendance system currently in place at PT TRAFINDO PRIMA This is still done manually, starting from recording employee entry times, recording employee exit times to making employee attendance reports. Therefore, during the recording process, there is a possibility of errors, or the resulting report is less precise and slow in finding the required data. The research method applied is the waterfall method. This employee attendance information system is web-based. With this application, the attendance information system will be better than the manual system, so that it can operate more effectively and efficiently, and this system will be more supportive and improved when compared to the previous system. In addition, the best solution is to use a computerized system that can solve various problems in this company, to support activities within the company.
Co-Authors Alfiero Kusuma Andre Surya Anidika Damars Alfi Syahri Fajar Alvi Rizki Fitrah Nanda Ar Ridho Edi Gilang Perdana Haryono, Wasis Iffan Maulana Syahidan Parhan Raden Muhammad Alfajar Samsoe Raihan Salman Al Parisy Reyvalqy Siti Badriah