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All Journal JARES (Journal of Academic Research and Sciences) JITU : Journal Informatic Technology And Communication JAMI: Jurnal Ahli Muda Indonesia
Sandi Widya Permana
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Social Sciences

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Rancang Bangun Sistem Marketplace Toko Karya Sembako Dengan Stak Teknologi MERN Wijaya, Gilbert; Sandi Widya Permana; Nanda Nalendra Bagaskara; Indyah Hartami Santi
JITU Vol 9 No 1 (2025)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v9i1.1813

Abstract

Usaha mikro kecil menengah atau UMKM merupakan jenis skala usaha yang dijalankan perorangan atau kelompok, detail yang dapat dilihat secara umum adalah usahanya cenderung kecil dan dijalankan oleh seorang amatir dalam bidang usaha. Contoh implementasi usaha UMKM adalah toko kelontong atau sembako. Umkm umumnya memiliki modal yang kecil pada rentang 50 hingga 100 juta Rupiah. UMKM cenderung memiliki kendala seperti sulitnya mencari pembeli karena lokasi toko yang kurang strategis sehingga sulit melakukan perputaran modal kembali. Saat ini teknologi berkembang pesat seperti marketplace pada website yang dapat diakses internet. Namun banyak marketplace cenderung memiliki fitur kompleks sehingga sulit digunakan oleh user yang kurang memahami teknologi, untuk mengatasi hal ini solusinya dengan merancang marketplace mandiri sesuai kebutuhan dengan fitur lain yang bersifat opsional seperti fitur sales yang dapat membantu penjual untuk mencari pembeli secara langsung dilapangan. Stak  teknologi MERN(MySql, Express Js, React Js,Node Js) diharapkan menjadi solusi untuk membangun sistem saat ini sehingga dapat menyelesaikan kendala yang dialami oleh pemilik usaha
SiPuTiH: Model Convolutional Neural Network untuk Sistem Pengenalan Tulisan Tangan Hijaiyah Saiful Nur Budiman; Sri Lestanti; Sandi Widya Permana
JAMI: Jurnal Ahli Muda Indonesia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v6i2.390

Abstract

This research presents the development of SiPuTiH (Handwritten Hijaiyah Character Recognition System) using the Convolutional Neural Network (CNN) algorithm to address the challenges of handwriting variability in Arabic scripts. The methodology includes dataset acquisition and preprocessing, CNN architecture design, model training, and performance evaluation. The dataset consists of 1,680 handwritten images representing 30 Hijaiyah characters, divided into 80% training and 20% testing data. The proposed CNN architecture employs four convolutional and pooling layers with a total of 6.8 million trainable parameters. Experimental results show that SiPuTiH achieved a 99.7% accuracy rate in recognizing Hijaiyah characters, with only one misclassification between ‘ta’ (ت) and ‘tsa’ (ث) due to morphological similarity. The trained model was implemented in an interactive Streamlit-based application that includes learning modules, quizzes, and real-time handwriting prediction. SiPuTiH demonstrates high reliability not only as a handwriting recognition system but also as an engaging educational platform for learning Arabic letters. This study confirms the effectiveness of CNNs in handling the morphological complexity of Hijaiyah characters and contributes to the development of intelligent educational tools. Future work may explore larger datasets, transfer learning architectures, and contextual (word-level) recognition to enhance system scalability and performance.
A Convolutional Neural Network Model for the Handwritten Hijaiyah Recognition System (SiPuTiH) with Domain-Specific Data Augmentation Sri Lestanti; Sandi Widya Permana; Nur Budiman, Saiful
JARES (Journal of Academic Research and Sciences) Vol 10 No 2 (2025): September 2025
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/jares.v10i2.5430

Abstract

This paper presents SiPuTiH, a Convolutional Neural Network (CNN)-based approach for handwritten Hijaiyah character recognition that addresses performance degradation caused by morphological variations in handwriting. The study employs a dataset of 1,680 handwritten images representing 30 Hijaiyah characters, where domain-specific data augmentation is applied solely during the training phase. The augmentation strategy incorporates controlled geometric and stroke-based transformations, including rotation, scaling, shear, slant variation, and stroke thickness adjustment, to model realistic handwriting diversity. The proposed CNN architecture consists of multiple convolutional layers with ReLU activation, max-pooling operations, and a softmax classifier. Experimental results show that the proposed method achieves an accuracy of 99.70%, with weighted precision and F1-score of 99.85% and 99.77%, respectively. Furthermore, the use of domain-specific data augmentation effectively reduces misclassification among visually similar characters, such as ta and tsa, demonstrating improved robustness and generalization capability.
Co-Authors Indyah Hartami Santi Nanda Nalendra Bagaskara Nur Budiman, Saiful Saiful Nur Budiman Sri Lestanti Wijaya, Gilbert