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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
 1   2   3 
Application of Random Forest and XGBoost for Credit Card Fraud Detection with Unbalanced Data Hafid
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 1 (2024): December 2024
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v1i1.229

Abstract

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.
Corn Plant Disease Detection Using CNN Model with Resnet50 Architecture Indra Irawanto Irawan
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 1 (2024): December 2024
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v1i1.230

Abstract

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.
Sentiment Analysis of Movie Reviews Using Spark on IMDB Review Dataset Luthfi Nurul Huda
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 1 (2024): December 2024
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v1i1.231

Abstract

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.
Plant Disease Identification Using Deep Learning: A Systematic Literature Review Mochammad Faid
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 1 (2024): December 2024
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v1i1.232

Abstract

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.
Sentiment Analysis of Hate Speech Using SVM Method Hayatul Kamalia
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 1 (2024): December 2024
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v1i1.235

Abstract

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.
Spam SMS Classification Analysis Using Naive Bayes with Python Language Beny Yusman
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.253

Abstract

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.
Pancasila-Based AI Ethics: Preventing Digital Manipulation and Deepfakes in Society Anna Sakila; Maulidiah Yasmin; Nidaurrohmah; Royhan Ammar Sulthon
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.254

Abstract

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.
Siamtek Unuja Security Analysis Using Two-Factor Algorithm Biometric Scan With Fingerprint zainal Arifin; Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.256

Abstract

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.
Application of Siamese Neural Network for Offline Signature Verification Based on Similarity Level Ahmad Halimi
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.257

Abstract

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.
Classification of Forest Fire-Prone Areas Using the K-Nearest Neighbor Algorithm: A Case Study of Baluran National Park M Noer Fadli Hidayat
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.258

Abstract

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 enhancing environmental monitoring systems.

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