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Khatulistiwa : Journal of Artificial Intelligence
Published by Yayasan Literasi Altruis Khatulistiwa
ISSN : -     EISSN : 30899710     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Khatulistiwa : Journal of Artificial Intelligence is a Journal of Published by The Altruistic Equatorial Literacy Foundation, 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 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 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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. Diana Indri Rukmana; Zainal Arifin; Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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 Hatoguan Manurung, Edison; Situmorang, Tiodorus
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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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.

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