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All Journal Smatika Jurnal : STIKI Informatika Jurnal
Eva Nurlatifah
UIN Sunan Gunung Djati Bandung, Indonesia
Computer Science & IT

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Implementasi YOLOv8 Sebagai Pendeteksi Nominal Uang Rupiah Kertas Berbasis Android Arif Muhamad Iqbal; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1545

Abstract

The number of visually impaired individuals in Indonesia reaches 1.5%, or around 4 million people, who often face difficulties in recognizing the denominations of rupiah banknotes. Although Bank Indonesia has added distinguishing features to the banknotes, this method is less effective due to limitations in understanding or the physical condition of the currency. Object detection technology, such as YOLOv8, offers a solution thanks to its advantages in accuracy and speed. This research employs the CRISP-DM approach, which includes six stages: business understanding to understand the needs of visually impaired individuals, data understanding to study the characteristics of the 2022 rupiah banknote dataset, data preparation to prepare 5,435 images of 8 currency denominations (1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 75,000, and 100,000), modeling by training the YOLOv8n model, evaluation to assess model performance using a confusion matrix, and deployment on an Android application capable of real-time currency denomination detection through the camera. The evaluation results show an accuracy of 0.98, a precision of 0.988, a recall of 0.993, an average mAP50 score of 0.994, and an mAP50-95 score of 0.955, indicating that this model is quite effective in helping visually impaired individuals recognize the denominations of rupiah banknotes.
Klasifikasi Ras Kucing Dengan Pendekatan Convolutional Neural Networks Menggunakan Arsitektur Inception V4 Adryan Putra Pratama; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1547

Abstract

Classifying cat breeds based on images presents challenges due to subtle differences in appearance among breeds and environmental influences. This study developed an automated classification system utilizing the Inception V4 architecture with a CRISP-DM approach, encompassing business understanding, data preparation, modeling, evaluation, and deployment. The dataset used was derived from the Oxford IIIT Pet Dataset, covering 12 popular cat breeds, and underwent cleaning, augmentation, normalization, and partitioning into training (80%) and validation (20%) datasets. The model was trained over 25 epochs, achieving a highest validation accuracy of 93.31% with average precision, recall, and f1-score of 93%. The system was implemented as a Flask-based web application, enabling real-time classification through image uploads. While overall performance was strong, certain breeds such as Bengal exhibited potential for further improvement.  The findings demonstrate the model's significant potential to support pet health diagnosis and breed conservation efforts. This study contributes substantially to the development of image-based classification technology, with recommendations for performance enhancements through GAN-based data augmentation and testing on more diverse datasets to improve generalizability.
Classification of Rice Leaf Diseases Using CNN-SVM Hybrid Model Agus Tri Adiana; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1548

Abstract

Produksi padi di Indonesia menghadapi tantangan serius akibat berkurangnya luas lahan pertanian dan serangan penyakit seperti Bacterial Leaf Blight, Blast, dan Brown Spot, yang dapat menurunkan hasil panen hingga 80% dan mengancam ketahanan pangan nasional. Penyakit tersebut tidak hanya merusak stabilitas produksi tetapi juga menyebabkan kerugian yang signifikan bagi petani. Identifikasi dini penting untuk mencegah kerugian, namun keterbatasan pengetahuan petani sering menyebabkan kesalahan diagnosis dan penanganan. Untuk mengatasi masalah ini, penelitian ini mengusulkan pengembangan model klasifikasi penyakit daun padi berbasis hybrid Convolutional Neural Network (CNN) dan Support Vector Machine (SVM), yang dirancang menggunakan metode CRISP-DM (Cross-Industry Standard Process for Data Mining) dari tahap business understanding hingga evaluation. Dengan dataset berisi 11.790 gambar daun padi dari sembilan kelas penyakit.CNN menggunakan arsitektur VGG-16 yang dipakai untuk ekstraksi fitur, sedangkan SVM menangani klasifikasi multi-kelas dengan metode one-vs-rest. Hasil evaluasi menunjukkan akurasi model sebesar 95%, dengan precision, recall, dan F1-score yang tinggi di sebagian besar kelas penyakit. Hasil tersebut menunjukkan potensi yang signifikan dan diharapkan dapat membantu petani untuk melakukan deteksi dini penyakit pada padi.
Co-Authors Adryan Putra Pratama Agus Tri Adiana Arif Muhamad Iqbal Jumadi Jumadi