cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dede Kurniadi
Contact Email
dede.kurniadi@itg.ac.id
Phone
+6287880007464
Journal Mail Official
jistics@aptika.org
Editorial Address
Green Garden Residence C-87, Kabupaten Garut, Provinsi Jawa Barat, Indonesia, 44151
Location
Kab. garut,
Jawa barat
INDONESIA
Journal of Intelligent Systems Technology and Informatics
Published by Aliansi Peneliti Informatika
ISSN : -     EISSN : 3109757X     DOI : https://doi.org/10.64878/jistics
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
The Journal of Intelligent Systems Technology and Informatics (JISTICS) is an international peer-reviewed open-access journal that publishes high-quality research in the fields of Artificial Intelligence, Intelligent Systems, Information Technology, Computer Science, and Informatics. JISTICS aims to foster global scientific exchange by providing a platform for researchers, practitioners, and academics to disseminate original findings, critical reviews, and innovative applications. The journal is published three times a year (March, July, November) and may also publish special issues on emerging topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025" : 5 Documents clear
Fruit Image Classification Using CNN With EfficientNet Architecture for Visual Education Nashrulloh, Muhammad Hallaj; Subarkah, Adie
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.9

Abstract

Advancements in artificial intelligence and computer vision have significantly influenced education, particularly by enhancing visual-based learning for young learners. One promising application is fruit image classification, which helps students recognize and differentiate fruits through visual cues. Traditional methods often struggle with varied backgrounds and lighting conditions, making deep learning models more suitable. This study aims to develop an efficient fruit classification system using the EfficientNetB0 architecture within a convolutional neural network (CNN) framework. This study evaluates the model's effectiveness as a visual learning tool in educational contexts while ensuring computational efficiency. The dataset, sourced from Kaggle, consists of eight fruit categories: apples, bananas, kiwis, lemons, passion fruits, peaches, pineapples, and raspberries. It was split into training and validation sets with an 80:20 ratio using stratified random sampling to ensure balanced class representation during evaluation. Preprocessing steps included resizing images to 224×224 pixels, normalization with EfficientNet preprocessing, and data augmentation techniques to improve generalization. A custom classification head was added, and the EfficientNetB0 base was frozen. Training employed the Adam optimizer, categorical cross-entropy loss, early stopping, and class weighting across 30 epochs. The model achieved a validation accuracy of 99%, with near-perfect precision, recall, and F1-score across all classes. The confusion matrix showed minimal misclassification, indicating strong generalization even among visually similar fruits. In conclusion, the EfficientNetB0-based model demonstrates high accuracy, balance, and computational efficiency. It is ideal for integrating interactive visual learning tools to enhance concept recognition in educational settings, particularly among early learners.
Sentiment Analysis of Indonesian-Language Plantix Application Reviews for Plant Disease Diagnosis Using Naive Bayes Methods Rahmaliyadi, Virzza; Maridjan, Maula Muhammad
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.12

Abstract

The Plantix app is one of the digital solutions widely utilized by farmers to diagnose plant diseases through image recognition technology and support from the user community. The large amount of Indonesian-language customer feedback on Google's application can be a valuable source of information for assessing the effectiveness and user satisfaction of this application. This study uses naive Bayes algorithms to classify sentiments based on the Plantix application's customer feedback. The dataset was obtained by implementing web scraping techniques with the Google Play scraper library, resulting in more than 354 reviews. Data preprocessing stages include case folding, text cleaning, tokenization, stemming using the Sastrawi library, and text transformation into numerical form using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Sentiment labels are determined based on user star ratings, which are divided into three categories: positive, neutral, and negative. The Multinomial Naive Bayes algorithm performs the classification process and is assessed through the K-fold Cross Validation technique (K=10). The assessment results show that the model achieves the highest accuracy of 75.10% and F1-score of 72.35% with the shuffle sampling method, which falls into the category of fairly good classification. This study demonstrates that naive Bayes methodology is effectively used in sentiment analysis of text-based agricultural application reviews in Bahasa Indonesia.
Development of MyCare AI: A Dual-AI Mental Health Chatbot for Personalized Emotional Support Arief, Zaenal Syamsyul; Hamzah, Muhamad; Azham, Moch Nazham Ismul
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.34

Abstract

Access to mental health services remains a critical challenge in Indonesia, primarily due to societal stigma and limited availability of professional support. In response to this issue, this study introduces MyCare AI. This web-based mental health chatbot platform combines a Bi-LSTM-based emotion classification model with a generative conversational model provided by Google Vertex AI. This Dual-AI architecture enables the system to detect user emotions from Indonesian text inputs and deliver real-time, contextually appropriate, and empathetic responses. The emotion classification model is trained on a balanced English-language dataset representing four key emotional states: sadness, suicidal ideation, fear, and anger. The system employs a translation mechanism to convert Indonesian input into English before classification and then uses the detected emotion to condition the response generation process dynamically. The model achieved a classification accuracy of 95%, outperforming comparable models based on BERT-SVM and conventional LSTM architecture. This platform is intended for individuals who require immediate, anonymous, and continuous emotional support, including users in underserved or remote communities. MyCare AI represents a scalable and practical solution for digital emotional assistance and lays the groundwork for future integration with professional mental health services and native-language support frameworks. Key limitations include the system's reliance on real-time translation and an English-based dataset, highlighting the need for future development of culturally specific models.
From Local Features to Global Context: Comparing CNN and Transformer for Sundanese Script Classification Agustiansyah, Yoga; Fauzi, Dhika Restu
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.38

Abstract

The digital preservation of historical writing systems like Aksara Sunda is critical for cultural heritage, yet automated recognition is hindered by high character similarity and handwriting variability. This study systematically compares two dominant deep learning paradigms, Convolutional Neural Networks (CNNs) and Transformers, to evaluate the crucial trade-off between model accuracy and real-world robustness. Using a transfer learning approach, we trained five models (ResNet50, MobileNetV2, EfficientNetB0, ViT, and DeiT) on a balanced 30-class dataset of Sundanese script. Performance was assessed on a standard in-distribution test set and a challenging, independently collected Out-of-Distribution (OOD) dataset designed to simulate varied real-world conditions. The results reveal a significant performance inversion. While EfficientNetB0 achieved the highest accuracy of 96.9% on in-distribution data, its performance plummeted on the OOD set. Conversely, ResNet50, despite being lower in in-distribution accuracy, proved to be the most robust model, achieving the highest accuracy of 92.5% on the OOD data. This study concludes that for practical applications requiring reliable performance, the generalization capability demonstrated by ResNet50 is more valuable than the specialized accuracy of EfficientNetB0, offering a crucial insight for developing robust digital preservation tools for historical scripts.
User Sentiment Analysis X Towards Makan Bergizi Gratis Program Using Automatic Labeling Technique with Deepseek AI Julianto, Indri Tri; Nurpajar, Dini Siti
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.43

Abstract

Public perception of national nutrition initiatives is instrumental in shaping inclusive and data-driven policy development. In Indonesia, the "Makan Bergizi Gratis" (MBG) program introduced by President Prabowo has drawn significant attention, particularly on the X platform (formerly Twitter). This research topic was selected due to its national urgency and political significance, as the MBG program emerged as a key agenda during the 2024–2025 political transition. Therefore, examining public sentiment is essential to assess policy acceptance and identify areas for improvement. This study analyzes user sentiment toward the MBG policy using an automatic labeling approach supported by DeepSeek AI and the VADER Lexicon, followed by sentiment classification through the K-Nearest Neighbor (KNN) algorithm. The research involved five main stages: collecting 1,704 tweets from X between January 2024 and March 2025, preprocessing the text, conducting automatic sentiment labeling, applying TF-IDF for vectorization, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), and classifying sentiments using KNN. The results indicate that without SMOTE, the VADER model achieved higher accuracy (93.49%) but lower Cohen's Kappa (0.16), while DeepSeek AI yielded lower accuracy (73.67%) but slightly higher Kappa (0.17). After SMOTE was applied, accuracy declined (VADER to 77.25%, DeepSeek AI to 64.72%), but Kappa scores improved significantly (VADER to 0.65, DeepSeek AI to 0.47), indicating more balanced and consistent sentiment predictions across classes. In conclusion, integrating automatic labeling, SMOTE, and KNN provides a reliable and scalable framework for analyzing large-scale sentiment on social media platforms, particularly in contexts with imbalanced opinion distributions.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025 Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025 Vol 1 No 1 (2025): JISTICS Vol. 1 No. 1 March 2025