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All Journal Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER) Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa JCOSIS (Journal Computer Science and Information Systems)
Ibnu Aqil
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Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Mathematics Social Sciences

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ADVANCES IN COMPUTER TECHNOLOGY: FROM MECHANICS TO ARTIFICIAL INTELLIGENCE Muhammad Ridho Ardiansyah; Ibnu Aqil; Irwansyah
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 2 No. 5 (2024): MAY
Publisher : Adisam Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of computer technology has undergone a significant evolution from the outset as a mechanical tool for calculating and storing data into systems capable of performing tasks with artificial intelligence. Digitization of data and processes has driven efficiency in various aspects of life, while the development of artificial intelligence enables machines to learn, adapt, and perform tasks that normally require human intelligence. The research method in this research is the study of literature by finding the theory in accordance with the context of penalty. The results of this research reveal that the development of computer technology has driven major innovations in a variety of fields, including industrial automation, development of autonomous cars, and the application of artificial intelligence in health. However, while these developments bring positive benefits such as improved efficiency and ease of access to information, there are also new challenges such as concerns about privacy and the impact of artificial intelligence on everyday life. The development of computer technology continues, and in the future we will probably see more innovations that will affect the way we live and work. It is therefore important to ensure that these developments are managed wisely, considering their impact on society and finding the right balance between technological progress and the general interest.
Pendeteksian Penyakit Daun Padi Menggunakan Model ResNet50 dengan Optimasi Hyperparameter Ibnu Aqil
JCOSIS (Journal Computer Science and Information Systems) Vol. 2 No. 2 (2025): Oktober
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v2i2.257

Abstract

Padi (Oryza sativa) merupakan komoditas pangan utama di Indonesia yang sangat rentan terhadap serangan penyakit daun, seperti brown spot, blast, dan bacterial leaf blight. Deteksi penyakit secara manual masih memiliki keterbatasan dari segi waktu, akurasi, dan membutuhkan keahlian khusus. Penelitian ini bertujuan mengembangkan model pendeteksian penyakit daun padi berbasis deep learning menggunakan arsitektur ResNet50 dengan optimasi hyperparameter untuk meningkatkan performa model. Dataset citra daun padi dikumpulkan dari sumber terbuka, kemudian dilakukan preprocessing berupa resize, normalisasi, dan augmentasi. ResNet50 digunakan dengan metode transfer learning, sementara hyperparameter seperti learning rate, batch size, jumlah epoch, dan dropout rate dioptimasi menggunakan Bayesian Optimization. Hasil penelitian menunjukkan bahwa optimasi hyperparameter mampu meningkatkan akurasi model dari 89,7% menjadi 94,5%. Evaluasi menggunakan data uji menghasilkan nilai akurasi rata-rata sebesar 94,2%, presisi 94%, recall 94%, dan F1-score 94%. Dengan demikian, ResNet50 yang dioptimasi terbukti efektif untuk mendeteksi penyakit daun padi secara otomatis. Model ini berpotensi diimplementasikan dalam aplikasi berbasis mobile atau web untuk membantu petani dalam identifikasi penyakit secara cepat, tepat, dan efisien, sehingga dapat mendukung sistem pertanian cerdas (smart farming).
Implementation of QRIS: A Case Study of SMEs in Indonesia Mohammad Mirza Munthaha; Ibnu Aqil; Syaikhu Ramadhan Maulana; Andi Sri Wahyuni; Arinal Muna
Dinasti International Journal of Economics, Finance & Accounting Vol. 5 No. 3 (2024): Dinasti International Journal of Economics, Finance & Accounting (July - August
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijefa.v5i3.2780

Abstract

After COVID-19, one of the most widely used payment methods for online purchases is the Indonesian Standard Quick Response Code (QRIS), particularly since Bank Indonesia has accepted it. Utilizing the Innovation Resistance Theory (IRT) approach, which includes two primary components—psychological and functional—this study aimed to uncover impediments to the adoption of QRIS through the use of usage, value, risk, tradition, and image barriers. SMEs in the food and beverage industry that have implemented QRIS serve as the research’s unit of analysis. Ten Majalengka-based food and beverage SMEs participated in in-depth interviews as part of this case study-based qualitative research technique. The study’s findings demonstrate that QRIS has benefits that outweight its minor drawbacks for both SMEs and consumers. Large-scale QRIS socialization, optimization, and equitable use are also under the purview of the government.
Penerapan Algoritma CNN Untuk Mendeteksi Tulisan Tangan Angka Romawi dengan Augmentasi Data Mochammad Toyib; Tegar Decky Kurniawan Pratama; Ibnu Aqil
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 2 No. 3 (2024): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v2i3.69

Abstract

This research aims to develop and apply a Convolutional Neural Network (CNN) algorithm to detect handwritten Roman numerals. Handwriting recognition is a classic challenge in the fields of image processing and machine learning, especially for less common characters such as Roman numerals. In this research, we use data augmentation techniques to increase the diversity and number of datasets used in model training, which is expected to increase model accuracy and generalization. The dataset used consists of 1,120 images for testing and 280 images for validation, each of which is divided into 14 classes of Roman numerals I, II, III, IV, V, VI, VII, VIII, IX, X, L, C, D , and M. Image data was created directly using simple software, namely Paint version 6.3. This research uses the Python programming language and Google Colab as a computing platform. Model training was carried out for 300 epochs and showed significant accuracy in the 150th to 300th iteration. The results at the 300th epoch show an accuracy of 0.9607 and a loss of 0.1162. The implementation of this algorithm shows significant potential in practical applications, such as in the fields of education and historical documentation. The conclusion of this research is that data augmentation is an effective technique to improve the performance of CNN models in detecting handwritten Roman numerals.
Co-Authors Andi Sri Wahyuni Arinal Muna IRWANSYAH Mochammad Toyib Mohammad Mirza Munthaha Syaikhu Ramadhan Maulana Tegar Decky Kurniawan Pratama