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Yahya Auliya Abdillah
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Jalan Timur, SMA Nurul Jadid, Dusun Krajan, RT 015 / RW 007, No. 89, Desa Karanganyar, Kecamatan Paiton, Kabupaten Probolinggo, Jawa Timur, Indonesia.
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Journal of Artificial Intelligence
Published by CV Narasi Khatulistiwa Indonesia
ISSN : 3163852X     EISSN : 30899710     DOI : https://doi.org/10.67490/kjarti.v3i1
Core Subject :
Journal of Artificial Intelligence is a Journal of Published by The CV Narasi Khatulistiwa Indonesia, Probolinggo, East Java, Indonesia. It publishes biannually on June and December (twice a year). Khatulistiwa : Journal of Artificial Intelligence covers a wide range of topics in the field of Artificial Intelligence (AI). Its focus includes the development of algorithms, machine learning techniques, and AI applications for problem-solving across various sectors. The journal also highlights innovations in big data processing, natural language processing, and knowledge-based intelligent systems. Additionally, it emphasizes ethical considerations and the social impact of AI to promote responsible technological advancements. Journal of Artificial Intelligence menerbitkan karya tulis orisinal di bidang informatika dan teknologi komputasi, yang mencakup, tetapi tidak terbatas pada, bidang *Artificial Intelligence, Computer Vision, Software Engineering, Natural Language Processing, Data Science, Soft Computing,* dan *Big Data Infrastructure*.
Arjuna Subject : -
Articles 21 Documents
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Hybrid Method Optimization For Classifying Heart Disease Using Knn And Pca Algorithms Based On Web Streamlite Khofiyatul; Moh Ainol Yaqin; Cahyuni Novia
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.295

Abstract

Heart disease is one of the leading causes of death worldwide and often goes undetected early. This necessitates a decision support system capable of facilitating rapid and accurate diagnosis. This study aims to develop a heart disease classification system by combining two methods: K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA), in a web-based application using Streamlit. PCA is used to reduce data dimensionality and eliminate less relevant features to improve classification efficiency and performance. Meanwhile, the KNN algorithm is used to determine the class (heart disease or not) based on the proximity of the new data to the labeled data. This study used the heart.csv dataset and was tested using several methods, including accuracy, classification reports, and confusion matrices. The test results showed that the hybrid PCA and KNN model was capable of providing relatively high accuracy and informative visualizations. The best accuracy rate achieved in this study reached 90%, demonstrating the model's effectiveness in classifying data. Using the Streamlit interface, this system is easily accessible and usable by users without requiring special installation. The conclusion of this study is that the combination of PCA and KNN is effective in classifying heart disease efficiently and accurately.
Web-Based Classification Optimization of Grape Leaf Diseases Using Transfer Learning in CNN to Improve Model Accuracy and Efficiency Zainal Arifin; Diana Indri Rukmana; Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.323

Abstract

Grapes are a high-value crop, but their yield is easily reduced due to leaf disease. This study focuses on developing a web-based grape leaf disease classification system that works automatically and in real time. The approach used is Convolutional Neural Network (CNN) with transfer learning using the EfficientNetB0 architecture. The research data consists of 8,000 grape leaf images divided into four classes (healthy, black rot, black spot, and leaf spot) with a composition of 80% for training, 10% for validation, and 10% for testing. The initial CNN model achieved an accuracy of 97%, which then increased to 99% after optimization using EfficientNetB0 with fine-tuning. The implementation of the system through Flask showed fast and accurate prediction results, proving that transfer learning plays an important role in improving classification performance.
Classification of Rice Leaf Disease Images Using Convolutional Neural Network (CNN) Algorithm Meirike Diana Eka Lestari
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.325

Abstract

Rice (Oryza sativa) is a major commodity that plays an important role in Indonesia's food security. However, rice productivity often decreases due to leaf diseases, such as neck blast, leaf blight, and rice leafhopper. Manual disease identification still has limitations, as it requires a long time, depends on farmers' expertise, and may lead to misclassification. To address these issues, this study develops a rice leaf disease classification system using a Convolutional Neural Network (CNN) algorithm. The dataset used was from Kaggle, consisting of a total of 3,631 images divided into three disease classes. The data was split with a ratio of 80% for training, 10% for validation, and 10% for testing. The pre-processing steps included resizing, augmentation, and image normalization. The CNN architecture was custom-built with several convolutional, pooling, flatten, and dense layers. The training results showed that the model could achieve a training accuracy of 97.80% and a validation accuracy of 97.42%. The model was then implemented into a web application based on Flask, allowing users to upload images of rice leaves and obtain classification results quickly, accurately, and in real-time. Based on the research results, CNN has been proven effective in classifying rice leaf diseases with a high level of accuracy. This system is expected to help farmers detect diseases early, reduce the risk of crop failure, and support the implementation of smart farming in Indonesia.
Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods Ahmad Halimi; Eka Herliana Agustini; Nur Diyana Kholidah
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.326

Abstract

This research, titled "Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods," defines poisonous ornamental plants as those containing toxic substances that can cause pain, allergies, or even death. These plants come in many different varieties, each with its own unique appeal. Many laypeople still find it difficult to distinguish poisonous from non-poisonous ornamental plants, especially in household environments that pose a risk to children and pets. Therefore, vigilance is needed in recognizing these plants. This study aims to develop a classification system for poisonous and non-poisonous ornamental plants using CNN methods with and without ResNet architecture. Furthermore, this study also aims to implement the trained model into a web application using Flask, so users can easily upload images of ornamental plants and obtain information about their potential toxicity in real-time. This research method uses deep learning techniques, specifically Convolutional Neural Network (CNN) with ResNet-50 and regular Convolutional Neural Network (CNN) or without ResNet with data divided into (70% training, 15% validation, and 15% testing). The test results for ResNet showed an accuracy of 98.25%, while the test results for regular CNN reached an accuracy of 87.47%. These accuracy results indicate that CNN with ResNet is superior for classifying poisonous and non-poisonous ornamental plants compared to CNN without ResNet
Web-Based E-Commerce Application Development with Real-Time Notifications via Telegram Bot Luthfi Nurul Huda
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.334

Abstract

This research aims to develop a web-based e-commerce system equipped with real-time notifications through a Telegram bot. The background of the study originates from issues in sales transactions that are still conducted through chat messages, which often lead to delayed confirmations and irregular order recording. The system being developed is expected to simplify the transaction process, improve data organization, and enhance the efficiency of communication among system users. The research employs an Agile approach with development stages that adapt to user needs. The system is designed to support three types of users with different access rights: customers, employees, and owners. The main features implemented include product management with ready-stock and pre-order categories, order management, payment confirmation, transaction history, sales reports, and automated notifications via a Telegram bot. Testing was conducted using Black Box Testing to evaluate system functionality, along with external testing to obtain feedback from users. The test results indicate that the system operates according to its functions and provides a good level of user satisfaction. Based on these findings, it can be concluded that the system is capable of improving the effectiveness and efficiency of business processes while supporting the digitalization of small and medium enterprises, particularly in the integrated management of ready-stock and pre-order product models.
Implementation Of Information Technology In Sustainable Building Construction Management Edison Hatoguan Manurung; Tiodorus Situmorang
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.502

Abstract

The construction industry accounts for approximately 36% of global energy consumption and 39% of energy-related carbon emissions, necessitating urgent sustainable transformation through digital technologies. This research aims to analyze and evaluate how information technology implementation can effectively support sustainable construction management by enhancing coordination, improving resource efficiency, reducing environmental impacts, and enabling data-driven decision-making throughout the building lifecycle. The study employs a systematic literature review approach with descriptive analysis, examining 45 peer-reviewed publications from academic databases including Scopus, Web of Science, and Google Scholar published between 2019 and 2025. Data analysis utilized qualitative content analysis techniques to identify recurring themes regarding IT applications in sustainable construction practices. The findings reveal four critical contributions: BIM technology reduces design errors by 40-60% and construction waste by 15-20%; digital project management platforms achieve 12-18% time savings and 8-15% cost reductions; IT-enabled sustainability assessment tools reduce operational energy consumption by 25-35%; and cloud-based collaborative platforms reduce coordination issues by 30-40%. This research demonstrates that integrated information technology systems serve as indispensable enablers of sustainable construction management, offering practical solutions to resource efficiency, environmental impact reduction, and project coordination challenges while maintaining economic viability.
Predicting Potential Deposit Customers Using CatBoost with Feature Selection and Hyperparameter Optimization Jiawei Han; Zainal Arifin
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.671

Abstract

The banking industry requires increasingly targeted marketing strategies to identify potential customers for deposit products. This study aims to build a predictive model for identifying potential deposit customers using the CatBoost algorithm combined with feature selection based on feature importance and hyperparameter tuning using Hyperopt. This study employs an experimental method using the Bank Marketing Dataset from Kaggle, a secondary dataset consisting of 45,211 data points and 17 attributes, including a target variable indicating the customer’s decision to subscribe to a deposit product or not. The research stages included Exploratory Data Analysis (EDA), preprocessing, data cleaning, class balancing using SMOTE-NC, feature selection, hyperparameter tuning, modeling, and model evaluation. The EDA results revealed outliers in the duration and previous features, as well as class imbalance in the target variable. After preprocessing, the model was built using 10 selected features: duration, month, contact, day, outcome, balance, housing, pdays, age, and job. Evaluation results using a confusion matrix showed that the CatBoost model achieved an accuracy of 92.8%, a sensitivity of 91.0%, and a specificity of 94.8%. These findings demonstrate that the combination of CatBoost, feature selection, and hyperparameter tuning is capable of producing an accurate and efficient predictive model for handling both numerical and categorical data. This model can help banks develop more effective, cost-efficient, measurable, and data-driven deposit marketing strategies, while also supporting business decision-making that is more adaptive to customer behavior.
Comparison of XGBoost LightGBM and CatBoost for Online Store Customer Churn Prediction Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.672

Abstract

Customer churn is a critical issue in digital business because it can reduce revenue, increase customer acquisition costs, and weaken a company’s competitive position. In the context of online stores, customers have many alternative shopping platforms, requiring companies to understand customer behavior patterns to minimize churn risk. This study aims to develop an online store customer churn prediction model using tree-based gradient boosted models by comparing XGBoost, LightGBM, and CatBoost algorithms. This study applies an experimental method using a secondary dataset obtained from Kaggle. The dataset consists of 25,000 observations and 13 variables related to online store customer transactions and characteristics. The research stages include problem identification, data collection, preprocessing, missing value handling, removal of irrelevant variables, data visualization, outlier detection, feature engineering, model selection and training, model evaluation, and result analysis. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC curve. The results show that XGBoost achieved the best performance with an accuracy value of 0.80032 and an ROC-AUC value of 0.66. Feature importance analysis indicates that the Product_Category_Clothing variable was the most influential feature in the model performance. These findings show that tree-based gradient boosted models, particularly XGBoost, can be used to help online store companies identify customers who are likely to churn and design more targeted customer retention strategies.
Shallot Leaf Disease Detection Using Convolutional Neural Network and Support Vector Machine Rania Majdoubi; Beny Yusman
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.673

Abstract

Shallot is one of the important horticultural commodities in Indonesia with high economic value and increasing market demand. However, shallot productivity often decreases due to plant diseases, especially purple blotch and moler disease. Manual disease identification through visual observation still has limitations because it depends on farmers’ experience and may lead to diagnostic errors. This study aims to compare the performance of Convolutional Neural Network and Support Vector Machine methods in detecting shallot leaf diseases based on leaf images and to implement the best-performing model into a Flask-based web application. The dataset used in this study consists of 520 shallot leaf images divided into two classes: purple blotch and moler. The data were collected from primary sources through direct image acquisition in agricultural fields and secondary sources from Kaggle and Roboflow. The research stages include literature study, data collection, preprocessing, image resizing, data annotation, dataset splitting, Convolutional Neural Network model training, Support Vector Machine training, model evaluation, and Flask web implementation. The results show that the Convolutional Neural Network model achieved a training accuracy of 98% and a validation accuracy of 98%, while the Support Vector Machine model achieved an accuracy of 95%. These results indicate that both methods can effectively detect shallot leaf diseases. The Flask web implementation can also assist users in detecting diseases more practically and quickly
Eye Disease Classification Using VGG-19 and TensorFlow on Fundus Images Luthfi Nurul Huda; Abdelghani Dahou
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.674

Abstract

Eye disorders are health problems that can reduce visual quality and may lead to blindness if they are not detected at an early stage. The use of deep learning technology has become a promising approach to support the classification of eye diseases based on medical images. This study aims to classify eye diseases from fundus images using a Convolutional Neural Network architecture based on VGG-19 with the support of TensorFlow and Keras frameworks. The dataset used in this study was obtained from Kaggle and consists of eight classes, namely Normal, Cataract, Diabetes, Glaucoma, Hypertension, Myopia, Age Issues, and Other. The research stages include data collection, image preprocessing, dataset generation, training and testing data splitting, VGG-19 model design using transfer learning, model training, model testing, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix. The testing results show that the model achieved an accuracy of 78.24% on the testing data with a loss value of 0.0983. Meanwhile, evaluation using k-fold cross validation obtained an accuracy of 91.35% with a margin of ±2.25%. The confusion matrix results indicate that Cataract and Myopia achieved the best classification performance, while the Normal class still showed a low recall value. These findings indicate that TensorFlow-based VGG-19 can be used for eye disease classification, although further development is still needed to improve the model’s generalization ability

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