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All Journal Annual Research Seminar Titian Ilmu: Jurnal Ilmiah Multi Sciences JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Ilmu Komputer Jurnal Pengembangan Sistem Informasi dan Informatika Computing and Education Technology Journal MDP Student Conference Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer Jurnal Indonesia Mengabdi Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Indra Irawan
Politeknik Manufaktur Negeri Bangka Belitung
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences Other

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Implementasi Convolutional Neural Network (CNN) dalam Diagnosa Penyakit Daun Padi Berdasarkan Citra Digital Irawan, Indra; Wathan, M.Hizbul; Swengky, Better; Ramadani, Ardi
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2756

Abstract

This study investigates the implementation of Convolutional Neural Network (CNN) in classifying rice leaf diseases based on digital images. The model classifies three types of diseases: Bacterial Leaf Blight, Rice Blast, and Rice Tungro Virus. A dataset of 240 images was obtained from Kaggle, with 80 images per class. Four training scenarios were applied using 25, 50, 75, and 100 epochs. Preprocessing steps included resizing all images to 150x150 pixels and normalizing pixel values. Evaluation results show that classification accuracy increases with the number of training epochs. The best model was achieved at 100 epochs, yielding a validation accuracy of 91.67% and testing accuracy of 92%. These results demonstrate that CNN is effective in diagnosing rice leaf diseases and can support early detection efforts to strengthen national food security.
Klasifikasi Mata Katarak dan Mata Normal Menggunakan Algoritma Dasar Convolutional Neural Network (CNN) Swengky, Better; Wathan, M Hizbul; Irawan, Indra; Aulia, Rosaura
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2758

Abstract

Eye diseases encompass a wide range of conditions, from mild visual impairments to complete blindness, with cataracts being one of the leading causes. Despite advances in medical imaging, automated classification of cataract versus normal eye images remains a challenging task. This study proposes a classification method using a Convolutional Neural Network (CNN) to distinguish between cataract-affected eyes and normal eyes accurately. The approach involves collecting and preprocessing a labeled dataset, extracting features such as color and vein patterns (including average RGB values), and training the CNN model with optimized parameters. Experimental results demonstrate that the proposed model achieves a high classification accuracy of 95.1%. These findings indicate that CNN-based image classification is a promising tool for supporting automated cataract detection and early diagnosis
Penerapan YOLOv11 untuk Penghitungan Otomatis Jumping Jack pada Video Latihan Fisik Wisesa, Bradika Almandin; Putri, Vivin Mahat; Faristasari, Evvin; Duli, Sirlus Andreanto Jasman; Irawan, Indra; Agustin, Silvia
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2795

Abstract

The Jumping Jack Counter is an image processing-based application developed to automatically count the number of jumping jack movements in exercise videos. This study aims to implement the YOLOv11 model to detect and count jumping jack movements by analyzing body posture. YOLOv11 is utilized to identify body positions categorized into two main classes: "open" (arms and legs spread apart) and "closed" (arms and legs together). The dataset consists of 15,000 video frames collected from various exercise videos, with research stages including data collection, data labeling, preprocessing, model training, and testing. The results demonstrate that YOLOv11 achieves a 92% accuracy rate in counting jumping jack movements. These findings are expected to assist coaches and users in monitoring physical exercise in real-time, thereby enhancing training effectiveness. The majority of movement detections (78%) were for the open position, followed by the closed position (20%), with 2% detection errors attributed to lighting variations or camera angles. [1].
Pengembangan Aplikasi Penjualan Sembako di Toko Sundari Berbasis Desktop Tukirah, Pamuji Muhamad Jakak; Irawan, Irawan; Uli Riski, Uli Riski; Rahman, Miftakhul
Jurnal Nasional Ilmu Komputer Vol. 5 No. 1 (2024): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v5i1.1674

Abstract

The grocery trade sector is one of the vital sectors that provide basic needs for society. Grocery stores play a crucial role in maintaining the availability and accessibility of staple goods for consumers. However, in increasingly tight competition and evolving market demands, grocery stores must update and enhance their operational systems to remain competitive in the dynamic market.  In this context, the use of information technology, particularly in the form of desktop-based sales applications, has become an effective solution for improving operational efficiency and customer experience. Such applications enable grocery stores to manage inventory, record sales transactions, and analyze data more efficiently, which in turn can help improve productivity and customer satisfaction.  One of the grocery stores focused on in this research is Toko Sundari, which has long been an integral part of the local community. Toko Sundari faces challenges in efficiently managing grocery sales and enhancing customer experience. Therefore, developing a desktop-based grocery sales application at Toko Sundari can benefit the store significantly.  The main objective of this research is to create a desktop-based grocery sales application that can assist Toko Sundari in improving operational efficiency, inventory accuracy, and customer experience. Additionally, this research aims to evaluate the implementation of the application at Toko Sundari and analyze its impact on store performance and customer satisfaction. This research utilizes Extreme Programming (XP) methodology as a model for designing the system
Pelatihan Membuat Aplikasi Tanpa Coding Bagi Siswa SMK Yapensu Sungailiat Bradika Almandin Wisesa; Vivin Mahat Putri; Indra Irawan; M. Syafrizal Zain; Putri Armilia Prayesy
Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat Vol. 3 No. 2 (2025): Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/dharma.v3i2.2154

Abstract

Kegiatan pengabdian masyarakat berbentuk pelatihan pembuatan aplikasi mobile berbasis no-code sukses digelar untuk siswa SMK Yapensu Sungailiat dengan memanfaatkan Glide Apps—platform yang dipilih berkat kemudahan penggunaan dan fitur gratisnya yang cukup untuk aplikasi sederhana tanpa perlu coding. Prosesnya meliputi persiapan administratif, survei pra-pelatihan melalui kuesioner, penyuluhan disertai demonstrasi dan praktik langsung, hingga evaluasi pasca-pelatihan; peserta berhasil mengembangkan Aplikasi Siswa Yapensu menggunakan Glide Tables dengan antarmuka mirip Excel. Evaluasi menunjukkan rata-rata 80 % responden memberikan penilaian “sangat baik” dan “sangat sesuai”, menandakan respons positif serta kemampuan peserta menyelesaikan seluruh tahap pengembangan aplikasi secara mandiri.
Pengembangan Sistem Pengelolaan Permintaan Olah Data Dengan Notifikasi Otomatis Di Badan Pusat Statistik Provinsi Kepulauan Bangka Belitung Ramadani, Ardi; Rindri, Yang Agita; Irawan, Indra
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 7 No. 1 (2026): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v7i1.3241

Abstract

This research aims to develop a web-based data request management system with automatic notifications at the BPS Bangka Belitung Province to overcome manual system constraints that cause response delays and difficulties in status monitoring. The method used is Rapid Application Development (RAD), comprising four stages: requirements planning, system design using UML, implementation using Laravel 11 and MySQL, and deployment. The system involves four actors: Admin, PST Officer, Data Processor, and Customer, with an integrated workflow from submission to output delivery, accompanied by automatic email notifications. User Acceptance Testing results from 13 respondents indicated an excellent acceptance rate of 90.6%, demonstrating that the system can improve service efficiency and the transparency of data request processes. The system successfully addresses documentation challenges, facilitates internal coordination between officers, and enables customers to track request status independently.
RiceGuard: A Lightweight PSO-Optimized MobileNetV2 Framework for Stable Rice Leaf Disease Classification M. Hizbul Wathan; Better Swengky; Indra Irawan; Fatir Atthariq Zami
Computing and Education Technology Journal Vol 6, No 1 (2026): APRIL
Publisher : Pendidikan Komputer FKIP Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/cetj.v6i1.18663

Abstract

Rice leaf diseases such as blast, brown spot, bacterial blight, and tungro pose a serious threat to agricultural productivity, with potential yield losses exceeding 50% under epidemic conditions. Therefore, rapid and accurate early detection is essential to support sustainable food security and precision agriculture. This study proposes a rice leaf disease classification system based on the MobileNetV2 architecture optimized using Particle Swarm Optimization (PSO). A dataset consisting of four rice leaf disease classes, collected from public repositories and sources representing real field conditions, is used to evaluate the proposed approach. Transfer learning is employed to improve training efficiency, while PSO is applied to optimize key hyperparameters to enhance model stability and convergence.Experimental results show that MobileNetV2 optimized with PSO consistently achieves superior classification performance and improved training stability compared to the standard MobileNetV2 baseline. The baseline MobileNetV2 achieves 92% accuracy, with the highest F1-score of 0.99 on the tungro class and the lowest performance of 0.88 on the blast class. In contrast, MobileNetV2–PSO demonstrates a significant improvement, reaching 99% accuracy, with F1-scores of 0.98 for bacterial blight, 0.98 for blast, 0.99 for brown spot, and 1.00 for tungro. The largest improvement occurs in the blast class, with a 10-point increase in F1-score, indicating that PSO optimization provides greater sensitivity to complex disease patterns that were previously difficult to classify.These findings indicate that the proposed framework provides an accurate and lightweight solution with strong potential for deployment on mobile and resource-constrained platforms to support intelligent rice disease diagnosis.
Co-Authors Ainun Ummi Sholikhah Anisatur Rofiqah, Siti Aulia, Rosaura Better Swengky Bradika Almandin Wisesa Dian Palupi Rini Dwi Andriani Dwi Andriani, Dwi Effendi Effendi Effendi Effendi Evvin Faristasari Fajar, Yahya Ibnu Fatir Atthariq Zami Febyani, Yanita Iqrom, Redho Aidil M Riski Qisthiano M. Hizbul Wathan Muhammad Syahril Muzakki, Haris Pamuji M. Jakak Pamuji Muhamad Jakak Prayogi, M. Bagus Putri Armilia Prayesy Rahman, Miftakhul Ramadani, Ardi Silvia Agustin Sirlus Andreanto Jasman Duli Uli Riski, Uli Riski Vivin Mahat Putri Wardianto, Wardianto Yang Agita Rindri Zain, M Syafrizal