Khatulistiwa : Journal of Artificial Intelligence
type: channel
description: 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.
publisher: CV Narasi Khatulistiwa Indonesia
language: en
publication name: Khatulistiwa : Journal of Artificial Intelligence
issn: 3163-852X
items: Predicting Potential Deposit Customers Using CatBoost with Feature Selection and Hyperparameter Optimization
items: Rainfall Prediction in East Java Using Attention-Based LSTM
items: Eye Disease Classification Using VGG-19 and TensorFlow on Fundus Images
items: Shallot Leaf Disease Detection Using Convolutional Neural Network and Support Vector Machine
items: Comparison of XGBoost LightGBM and CatBoost for Online Store Customer Churn Prediction
items: Implementation Of Information Technology In Sustainable Building Construction Management
items: Web-Based E-Commerce Application Development with Real-Time Notifications via Telegram Bot
items: Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods
items: Classification of Rice Leaf Disease Images Using Convolutional Neural Network (CNN) Algorithm
items: Web-Based Classification Optimization of Grape Leaf Diseases Using Transfer Learning in CNN to Improve Model Accuracy and Efficiency
items: Hybrid Method Optimization For Classifying Heart Disease Using Knn And Pca Algorithms Based On Web Streamlite
items: Classification of Forest Fire-Prone Areas Using the K-Nearest Neighbor Algorithm: A Case Study of Baluran National Park
items: Application of Siamese Neural Network for Offline Signature Verification Based on Similarity Level
items: Siamtek Unuja Security Analysis Using Two-Factor Algorithm Biometric Scan With Fingerprint
items: Pancasila-Based AI Ethics: Preventing Digital Manipulation and Deepfakes in Society
items: Spam SMS Classification Analysis Using Naive Bayes with Python Language
items: Sentiment Analysis of Hate Speech Using SVM Method
items: Plant Disease Identification Using Deep Learning: A Systematic Literature Review
items: Sentiment Analysis of Movie Reviews Using Spark on IMDB Review Dataset
items: Corn Plant Disease Detection Using CNN Model with Resnet50 Architecture
items: Application of Random Forest and XGBoost for Credit Card Fraud Detection with Unbalanced Data

Predicting Potential Deposit Customers Using CatBoost with Feature Selection and Hyperparameter Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: Heart disease
  topic: keywords
subject: KNN
  topic: keywords
subject: PCA
  topic: keywords
subject: classification
  topic: keywords
subject: Streamlit
  topic: keywords
creator: Jiawei Han
creator: Zainal Arifin
rights: Copyright (c) 2026 Jiawei Han, Zainal Arifin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 17
doi: 10.67490/kjarti.v3i1.671

Rainfall Prediction in East Java Using Attention-Based LSTM
type: item
description: Rainfall is one of the most influential meteorological parameters affecting agriculture, transportation, infrastructure, public health, and disaster mitigation. Accurate rainfall prediction is highly important, especially in East Java, one of the largest rice-producing regions in Indonesia. This study aims to develop a daily rainfall prediction model using the Attention-Based Long Short-Term Memory (LSTM) method. The dataset used in this study was obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), covering the period from January 1, 2000, to December 31, 2023, with a total of 8,764 daily records. The variables include average temperature, relative humidity, rainfall, sunshine duration, and average wind speed. The research stages include data collection, preprocessing, missing value handling, outlier replacement using the Z-Score method, correlation-based feature selection, Min-Max normalization, lagged feature construction, data splitting, Attention-Based LSTM model design, model training, model testing, and Streamlit application integration. The best model was obtained using a batch size of 16 and 100 epochs, achieving a validation MSE of 0.007038 and a validation RMSE of 0.083893. On testing data, the model achieved an MSE of 0.008076 and an RMSE of 0.089871. These results indicate that Attention-Based LSTM can model daily rainfall patterns effectively and has potential as a data-driven rainfall prediction support system
subject: Articles
  topic: section
subject: SMS fraud
  topic: keywords
subject: promotional SMS
  topic: keywords
subject: normal SMS
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Classification
  topic: keywords
creator: Beny Yusman
rights: Copyright (c) 2026 Beny Yusman (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 56
ending page: 66
doi: 10.67490/kjarti.v3i1.675

Eye Disease Classification Using VGG-19 and TensorFlow on Fundus Images
type: item
description: 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
subject: Articles
  topic: section
subject: Customer Churn
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Tree-Based Gradient Boosted Models
  topic: keywords
subject: XGBoost
  topic: keywords
creator: Luthfi Nurul Huda
creator: Abdelghani Dahou
rights: Copyright (c) 2026 Luthfi Nurul Huda, Abdelghani Dahou (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 31
ending page: 42
doi: 10.67490/kjarti.v3i1.674

Shallot Leaf Disease Detection Using Convolutional Neural Network and Support Vector Machine
type: item
description: 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
subject: Articles
  topic: section
subject: Convolutional Neural Network
  topic: keywords
subject: Disease Detection
  topic: keywords
subject: Leaf Image
  topic: keywords
subject: Shallot
  topic: keywords
subject: Support Vector Machine
  topic: keywords
creator: Rania Majdoubi
creator: Beny Yusman
rights: Copyright (c) 2026 Rania Majdoubi, Beny Yusman (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 43
ending page: 55
doi: 10.67490/kjarti.v3i1.673

Comparison of XGBoost LightGBM and CatBoost for Online Store Customer Churn Prediction
type: item
description: 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.
subject: Articles
  topic: section
subject: Customer Churn
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Tree-Based Gradient Boosted Models
  topic: keywords
subject: XGBoost
  topic: keywords
creator: Fuadz Hasyim
rights: Copyright (c) 2026 Fuadz Hasyim (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 18
ending page: 30
doi: 10.67490/kjarti.v3i1.672

Implementation Of Information Technology In Sustainable Building Construction Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Building information modeling
  topic: keywords
subject: construction management
  topic: keywords
subject: digital transformation
  topic: keywords
subject: information technology
  topic: keywords
subject: sustainability
  topic: keywords
creator: Edison Hatoguan Manurung
creator: Tiodorus Situmorang
rights: Copyright (c) 2025 Edison Hatoguan Manurung, Tiodorus Situmorang (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 104
ending page: 112
doi: 10.67490/kjarti.v2i2.502

Web-Based E-Commerce Application Development with Real-Time Notifications via Telegram Bot
type: item
description: 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.
subject: Articles
  topic: section
subject: Agile
  topic: keywords
subject: Bot Telegram
  topic: keywords
subject: Django
  topic: keywords
subject: E-Commerce
  topic: keywords
creator: Luthfi Nurul Huda
rights: Copyright (c) 2026 Luthfi Nurul Huda (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 89
ending page: 103
doi: 10.67490/kjarti.v2i2.334

Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods
type: item
description: 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
subject: Articles
  topic: section
subject: Image Classification
  topic: keywords
subject: Poisonous Ornamental Plants
  topic: keywords
subject: CNN
  topic: keywords
subject: ResNet
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Flask
  topic: keywords
creator: Ahmad Halimi
creator: Eka Herliana Agustini
creator: Nur Diyana Kholidah
rights: Copyright (c) 2026 Ahmad Halimi, Eka Herliana Agustini , Nur Diyana Kholidah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 77
ending page: 88
doi: 10.67490/kjarti.v2i2.326

Classification of Rice Leaf Disease Images Using Convolutional Neural Network (CNN) Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: image classification
  topic: keywords
subject: rice leaves
  topic: keywords
subject: deep learning
  topic: keywords
subject: Flask
  topic: keywords
creator: Meirike Diana Eka Lestari
rights: Copyright (c) 2026 Meirike Diana Eka Lestari (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 68
ending page: 76
doi: 10.67490/kjarti.v2i2.325

Web-Based Classification Optimization of Grape Leaf Diseases Using Transfer Learning in CNN to Improve Model Accuracy and Efficiency
type: item
description: 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.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Grape Leaves
  topic: keywords
subject: EfficientNetB0
  topic: keywords
subject: Classification
  topic: keywords
creator: Zainal Arifin
creator: Diana Indri Rukmana
creator: Fuadz Hasyim
rights: Copyright (c) 2026 Diana Indri Rukmana, Zainal Arifin, Fuadz Hasyim (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 54
ending page: 67
doi: 10.67490/kjarti.v2i2.323

Hybrid Method Optimization For Classifying Heart Disease Using Knn And Pca Algorithms Based On Web Streamlite
type: item
description: 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.
subject: Articles
  topic: section
subject: Heart disease, KNN, PCA, classification, Streamlit
  topic: keywords
creator: Khofiyatul
creator: Moh Ainol Yaqin
creator: Cahyuni Novia
rights: Copyright (c) 2026 Khofiyatul, Moh Ainol Yaqin , Cahyuni Novia (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 48
ending page: 53
doi: 10.67490/kjarti.v2i2.295

Classification of Forest Fire-Prone Areas Using the K-Nearest Neighbor Algorithm: A Case Study of Baluran National Park
type: item
description: Forest and land fires are one of the most recurring and destructive natural disasters in Indonesia, particularly during the dry season when rainfall is significantly low. Among the most affected areas in East Java is Baluran National Park, a region highly vulnerable due to its dominant savanna ecosystem. In response to the urgent need for effective forest fire risk prediction, this study aims to classify fire-prone areas using weather-related data and machine learning techniques. The research focuses on the application of the K-Nearest Neighbor (K-NN) algorithm to predict fire risk levels based on meteorological parameters. The dataset used in this study was obtained from Visualcrossing.com and consists of 211 weather records with 32 explanatory variables, such as maximum and minimum temperature, wind speed, sea-level pressure, solar radiation, and solar energy, along with one target variable representing fire risk level (categorized as High, Medium, or Low). The research method involves several stages: data preprocessing (handling missing values and converting nominal data into numeric), transformation (splitting into training and testing sets using the Percentage Split technique), and classification using K-NN implemented on Google Colab. The K-NN algorithm was configured with K = 3, using Euclidean Distance as the distance metric. The classification process produced a high accuracy rate of 98%, indicating the robustness and effectiveness of K-NN in classifying forest fire risks based on weather data. This model was further validated by comparing predicted outputs against actual values in the testing dataset, showing high consistency. The results suggest that the K-NN algorithm is highly applicable for environmental classification problems and can support decision-making systems in early warning and disaster mitigation efforts. This study contributes to the growing field of data-driven disaster risk management and highlights the potential of machine learning in en…
subject: Articles
  topic: section
subject: Forest Fires
  topic: keywords
subject: Baluran
  topic: keywords
subject: K-Nearest Neighbor
  topic: keywords
subject: Classification
  topic: keywords
subject: East Java
  topic: keywords
creator: M Noer Fadli Hidayat
rights: Copyright (c) 2025 M Noer Fadli Hidayat (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 39
ending page: 47
doi: 10.67490/kjarti.v2i1.258

Application of Siamese Neural Network for Offline Signature Verification Based on Similarity Level
type: item
description: The increasing demand for secure and accurate identity verification systems has led to the development of various biometric technologies, one of which is signature verification. Despite the rise of digital authentication methods, signatures remain a widely accepted and legally binding form of identity verification, especially in paper-based systems. This research explores the application of the Siamese Neural Network (SNN) method for offline signature verification based on image similarity levels. The study aims to reduce human error, speed up verification time, and increase accuracy in identifying genuine and forged signatures. The dataset used in this study consists of 210 signature images collected from 14 respondents, including 7 with genuine signatures and 7 with forged signatures (categorized as random, unskilled, and skilled forgeries). Preprocessing steps such as scanning, resizing, and CSV data generation were conducted to optimize input for the SNN model. The model was trained using contrastive loss to learn signature similarity representations and was evaluated using a confusion matrix. The training dataset included 147 image pairs, and the testing set contained 63 image pairs, resulting in 168 prediction possibilities. The SNN achieved an accuracy rate of 94%, correctly predicting 159 cases while misclassifying 8 due to image quality and unclear signature strokes. These results indicate that the Siamese Neural Network is effective for offline signature verification and demonstrates strong potential for real-world implementation in identity authentication systems. This research contributes to the field of computer vision, particularly in biometrics, by providing an efficient, learning-based approach to signature validation using deep learning techniques.
subject: Articles
  topic: section
subject: Signature, Siamese Neural Network, Pre-processing, Confusion Matrix
  topic: keywords
creator: Ahmad Halimi
rights: Copyright (c) 2025 Ahmad Halimi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 27
ending page: 38
doi: 10.67490/kjarti.v2i1.257

Siamtek Unuja Security Analysis Using Two-Factor Algorithm Biometric Scan With Fingerprint
type: item
description: Student data security is a critical priority in academic information systems, particularly at Nurul Jadid University (UNUJA), which utilizes the SIAMTEK platform to manage practicum and final project activities. This study aims to analyze the effectiveness of implementing Two-Factor Authentication (2FA) using biometric fingerprint scanning to enhance the security of the SIAMTEK application. The research employed an experimental approach that included literature review, system analysis, system design, development, and testing. The authentication process consisted of two stages: verification via username and password, followed by a fingerprint scan. System testing was conducted using a Likert scale questionnaire distributed to 4th and 6th semester students, who are active users of the SIAMTEK application. The results indicated a 62% satisfaction index for the user interface and a 65% index for the functionality of the 2FA system. These results suggest that users are moderately satisfied with the added layer of security. Unlike One-Time Password (OTP) methods, which depend on mobile credit or connectivity, fingerprint biometric scanning offers a cost-effective and secure solution without requiring additional user expenses. This study also reviewed previous research on 2FA and Multi-Factor Authentication (MFA), highlighting the vulnerabilities of single-factor systems, particularly to phishing attacks and unauthorized access. The findings confirm that implementing fingerprint-based 2FA in the SIAMTEK system is both feasible and beneficial, significantly reducing risks of data breaches and account misuse. The integration of biometric technology as an additional authentication layer positively contributes to digital academic system security and supports data protection efforts for students' personal and academic information.
subject: Articles
  topic: section
subject: Two-factor authentication (2FA);
  topic: keywords
subject: biometrics
  topic: keywords
subject: fingerprint
  topic: keywords
creator: zainal Arifin
creator: Fuadz Hasyim
rights: Copyright (c) 2025 zainal Arifin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 17
ending page: 26
doi: 10.67490/kjarti.v2i1.256

Pancasila-Based AI Ethics: Preventing Digital Manipulation and Deepfakes in Society
type: item
description: Deepfake is an artificial intelligence technology that is developing very rapidly and has a major impact on all aspects such as the industrial sector, education, and digital communications. In the surge in the use of Deepfake, it is not free from its misuse which can harm many people without discrimination. The absence of regulations that specifically regulate the use of Deepfake and AI technology in Indonesia has caused the legal system to be unable to quickly anticipate technological advances. Education that has not yet instilled digital ethics skills has also caused Indonesia to be a country behind in the readiness of AI regulations and ethics. The many problems and negative impacts that arise, this reality shows that the development of policies and digital ethics approaches have not been able to keep up with the development of AI technology and still need a lot of improvement. Therefore, this study aims to overcome the existing shortcomings by offering an AI ethics approach based on noble values ​​in Indonesia. Pancasila as the foundation of the state includes principles such as humanity, social justice, deliberation, and responsibility, which are very appropriate to face ethical challenges in this digital era. This study uses descriptive qualitative methods and non-participatory observation. This study examines the spread of Deepfake content on digital platforms and offers a Pancasila-based ethical framework to address the challenges of AI use. The findings show that Deepfake content is mostly used for non-consensual pornography, political manipulation, and digital fraud. Low digital literacy and the absence of specific regulations exacerbate social impacts such as polarization and loss of public trust. Pancasila, with its principles of humanity, unity, deliberation, and justice, can be the ethical foundation for strengthening regulation, education, and technology in the development of responsible and inclusive AI.
subject: Articles
  topic: section
subject: Ethics;
  topic: keywords
subject: Deepfake;
  topic: keywords
subject: Pancasila;
  topic: keywords
subject: Technology;
  topic: keywords
subject: AI
  topic: keywords
creator: Anna Sakila
creator: Maulidiah Yasmin
creator: Nidaurrohmah
creator: Royhan Ammar Sulthon
rights: Copyright (c) 2025 Anna Sakila, Maulidiah Yasmin, Nidaurrohmah , Royhan Ammar Sulthon (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 8
doi: 10.67490/kjarti.v2i1.254

Spam SMS Classification Analysis Using Naive Bayes with Python Language
type: item
description: Short Message Service (SMS) continues to be widely used in Indonesia, both by official institutions and private entities, despite the growing prevalence of internet-based communication technologies. This study aims to classify SMS messages into three categories—normal SMS, promotional SMS, and fraudulent (spam) SMS—using the Naïve Bayes algorithm. The dataset used in this study comprises 1,143 records, obtained from an open-source platform on GitHub. The research stages include dataset collection, text preprocessing (consisting of case folding, tokenization, filtering, normalization, and stemming), term weighting using two text representation techniques: Count Vectorizer and TF-IDF, and classification using the Multinomial Naïve Bayes algorithm. Classification performance was evaluated using a confusion matrix, along with accuracy, precision, recall, and F1-score metrics. The results show that both combinations—Multinomial Naïve Bayes with Count Vectorizer and with TF-IDF—performed well in classifying SMS messages. The Count Vectorizer model achieved an accuracy of 93%, while the TF-IDF model demonstrated competitive precision and recall values. These findings confirm that the Naïve Bayes algorithm, when paired with appropriate text representation techniques, can serve as an effective solution for automatic SMS classification systems, particularly for short messages in the Indonesian language. This research also opens opportunities for exploring more advanced classification algorithms in future studies.
subject: Articles
  topic: section
subject: SMS fraud (spam); promotional SMS; normal SMS; Naïve Bayes; classification.
  topic: keywords
creator: Beny Yusman
rights: Copyright (c) 2025 Beny Yusman (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 9
ending page: 16
doi: 10.67490/kjarti.v2i1.253

Sentiment Analysis of Hate Speech Using SVM Method
type: item
description: This research analyses the sentiment of hate speech on social media using the Support Vector Machine (SVM) method. The Indonesian dataset from Kaggle is processed through text normalisation, filtering, and stemming to ensure the data is suitable for use in machine learning models. The SVM model was compared with Naive Bayes and Random Forest. Results showed SVM excelled with 75.40% accuracy, compared to Naive Bayes (67.34%) and Random Forest (46.64%). Performance evaluation is done with a confusion matrix that measures accuracy, precision, recall, and F1-score. The advantage of SVM lies in its ability to find optimal decision boundaries in a multidimensional feature space, making it more effective in handling complex interactions between features compared to Naive Bayes and Random Forest. The findings show that SVM is more effective for the classification of hate speech on social media. This research contributes to the development of automated monitoring systems that are more accurate and efficient in detecting and classifying hate speech content, thus improving countermeasures on social media platforms.
subject: Articles
  topic: section
subject: Hate Speech
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: SVM
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: NLP
  topic: keywords
creator: Hayatul Kamalia
rights: Copyright (c) 2025 Hayatul Kamalia (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 35
ending page: 40
doi: 10.67490/kjarti.v1i1.235

Plant Disease Identification Using Deep Learning: A Systematic Literature Review
type: item
description: This research aims to analyze and summarize recent approaches in plant disease identification and classification using deep learning techniques. Through a systematic literature review, we evaluate the various methodologies, neural network architectures, and datasets used in recent studies in this field. Our findings show that the use of deep learning, especially by utilizing complex neural network architectures, has led to significant improvements in plant disease identification accuracy. One of the key findings is the highest accuracy achieved by the Inception Net CNN architecture-based Deep Learning method in detecting diseases in tomato plants, reaching 99.89%. These results confirm that deep learning approaches have great potential to optimize plant disease management and improve agricultural productivity globally.
subject: Articles
  topic: section
subject: Plant diseases
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Classification
  topic: keywords
subject: Identification
  topic: keywords
subject: Systematic literature review
  topic: keywords
creator: Mochammad Faid
rights: Copyright (c) 2025 Mochammad Faid (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 26
ending page: 34
doi: 10.67490/kjarti.v1i1.232

Sentiment Analysis of Movie Reviews Using Spark on IMDB Review Dataset
type: item
description: Analisis sentimen pada ulasan film telah menjadi topik penting dalam penelitian berbasis teks, terutama untuk mendeteksi polaritas sentimen seperti positif, negatif, atau netral. Penelitian ini mengevaluasi kinerja dua algoritma, Support Vector Machine (SVM) dan Logistic Regression (LR), dalam mengklasifikasikan ulasan film dengan menggunakan dataset IMDb yang tersedia untuk umum di Kaggle. Data tersebut dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian, melalui tahapan preprocessing seperti case folding, tokenisasi, penghilangan stop words, stemming, dan ekstraksi fitur dengan menggunakan Word2Vec. Hasil evaluasi menunjukkan bahwa SVM memiliki akurasi sebesar 76%, mengungguli LR yang mencapai 69%. Keunggulan SVM terletak pada kemampuannya untuk menemukan hyperplane yang optimal dalam ruang berdimensi tinggi, yang sesuai dengan sifat data teks yang jarang dan berbentuk vektor. Sebaliknya, meskipun LR lebih sederhana dan lebih cepat untuk dilatih, model ini menunjukkan kinerja yang lebih rendah karena keterbatasannya dalam menangani hubungan non-linear. Preprocessing terbukti memberikan kontribusi yang signifikan dalam meningkatkan kualitas data input, sementara representasi Word2Vec memberikan fitur-fitur yang berarti untuk mendukung analisis sentimen. Penelitian ini menggarisbawahi pentingnya memilih algoritma yang tepat untuk analisis sentimen berbasis data besar, dengan hasil yang menunjukkan bahwa SVM lebih unggul dalam menangani data teks berskala besar. Penelitian ini berkontribusi dalam memahami efektivitas metode pembelajaran mesin dalam analisis sentimen ulasan film, sekaligus memberikan dasar untuk penelitian di masa depan yang dapat memperluas metode dan set data ke domain lain.
subject: Articles
  topic: section
subject: fraud detection
  topic: keywords
subject: credit card
  topic: keywords
subject: random forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: machine learning
  topic: keywords
creator: Luthfi Nurul Huda
rights: Copyright (c) 2025 Luthfi Nurul Huda (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 17
ending page: 25
doi: 10.67490/kjarti.v1i1.231

Corn Plant Disease Detection Using CNN Model with Resnet50 Architecture
type: item
description: Plant diseases are a significant problem in agricultural production. Plants affected by the disease can experience growth disorders, declining yields, and declining quality. This study aims to detect diseases in corn plants using a Convolutional Neural Network (CNN) model with ResNet50 architecture. Several scenarios with hyperparameter variations are tested to determine their effect on model accuracy. The first scenario using Adam's optimization algorithm, GlobalAveragePooling2D operation, dropout 0.5, and batch size 64 resulted in an accuracy of 85.97%. The second scenario uses the Flatten operation and results in 85.45% accuracy with a 0.5 dropout and 87.54% with a 0.2 dropout. The use of the SGD optimization algorithm in the third and fourth scenarios resulted in an accuracy of 61.30% and 60.09%, respectively. However, in the fifth scenario, with a dropout of 0.2, the accuracy increases to 73.25%. The results show that hyperparameter variations have a significant influence on model performance.
subject: Articles
  topic: section
subject: Predictions
  topic: keywords
subject: CNN
  topic: keywords
subject: ResNet50
  topic: keywords
subject: Corn Plant Diseases
  topic: keywords
creator: Indra Irawanto Irawan
rights: Copyright (c) 2025 Indra Irawanto Irawan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 8
ending page: 16
doi: 10.67490/kjarti.v1i1.230

Application of Random Forest and XGBoost for Credit Card Fraud Detection with Unbalanced Data
type: item
description: Credit card fraud detection is a very important issue in electronic transaction security, especially due to the significant class imbalance between fraudulent and non- fraudulent transactions. This study aims to explore the application of two machine learning algorithms, namely Random Forest and XGBoost, in detecting fraudulent transactions on a highly imbalanced credit card dataset. The dataset used consists of credit card transactions involving more than 284,000 transactions, with only about 0.172% of them being fraudulent. The features used in these models have been processed using Principal Component Analysis (PCA) to reduce dimensionality and improve computational efficiency. Both models are evaluated using metrics such as precision, recall, F1-score, and confusion matrix to measure their performance in detecting fraud. The experimental results show that XGBoost manages to provide better performance in terms of recall and F1-score for detecting fraudulent transactions compared to Random Forest. Although the accuracy of both models is very high, XGBoost shows better ability in handling class imbalance, with higher recall in the fraud class. The findings provide insights into the effectiveness of machine learning algorithms in solving fraud detection problems that are often hampered by data imbalance, as well as their contribution to improving the security system of credit card- based financial transactions.
subject: Articles
  topic: section
subject: fraud detection
  topic: keywords
subject: credit card
  topic: keywords
subject: random forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Hafid
rights: Copyright (c) 2025 Hafid (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 7
doi: 10.67490/kjarti.v1i1.229