Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Azis, Putri Alysia
Unknown Affiliation
Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Identifying Rice Plant Damage Due to Pest Attacks Using Convolutional Neural Networks Tenriola, Andi; Azis, Putri Alysia; Kaswar, Andi Baso; Adiba, Fhatiah; Andayani, Dyah Darma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6125

Abstract

Rice (Oryza Sativa) is an important crop for meeting global food needs; however, one of the main challenges in its cultivation is the attack of stem borer pests, which can cause significant damage. This study aims to identify the damage caused by these pest attacks using Convolutional Neural Networks (CNN) methods. We developed and trained several CNN architectures, including the proposed architecture, MobileNet, and EfficientNetB0, to detect pest attacks on rice. The dataset used consists of 700 images per class taken directly from the field, where the images depict rice plants that have been peeled or opened to inspect for the presence of pests, specifically stem borer pests. To enhance the quality and diversity of the dataset, we applied a rigorous selection process, ensuring that only high-quality images were used. Additionally, augmentation techniques such as rotation were employed to expand the dataset to 2000 images per class. Labeling was carried out carefully to ensure that each image accurately reflected the condition of the pest attack. The results of the study indicate that the proposed CNN model can identify damage with high accuracy, thereby contributing to efforts to increase rice production through early detection of pest attacks using computer vision technology.
Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern Azis, Putri Alysia; Andi, Tenriola; Surianto, Dewi Fatmarani; Budiarti, Nur Azizah Eka; Risal, Andi Akram Nur; Zulhajji, Zulhajji
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6323

Abstract

Schizophrenia is a chronic brain disorder characterized by symptoms such as delusions, hallucinations, and disorganized speech, posing significant challenges for accurate diagnosis. This research investigates an innovative Natural Language Processing (NLP) framework for classifying the speech patterns of schizophrenia patients using Word2Vec, with the aim of determining whether there are significant differences between the two features. The dataset comprises speech transcriptions from 121 schizophrenia patients and 121 non-schizophrenia participants collected through structured interviews. This study compares two Word2Vec architectures, Continuous Bag-of-Words (CBOW) and Skip-Gram (SG), to determine their effectiveness in classifying schizophrenia speech patterns. The results indicate that the SG architecture, with hyperparameter tuning, produces more detailed word representations, particularly for low-frequency words. This approach yields more accurate classification results, achieving an F1-score of 93.81%. These results emphasize the effectiveness of the framework in handling structured and abstract linguistic patterns. By utilizing the advantages of both static and contextual embedding, this approach offers significant potential for clinical applications, providing a reliable tool for improving schizophrenia diagnosis through automated speech analysis.
Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.
Co-Authors Adiba, Fhatiah Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Andi, Tenriola Arifky, Reza Dewi Fatmarani Surianto R, Muh Raflyawan Tenriajeng, Andi Afrah Tenriola, Andi Zulhajji, Zulhajji