cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yahya Auliya' Abdillah
Contact Email
khatulistiwakjarti@gmail.com
Phone
-
Journal Mail Official
khatulistiwakjarti@gmail.com
Editorial Address
Jalan Timur, SMA Nurul Jadid, Dusun Krajan, RT 015 / RW 007, No. 89, Desa Karanganyar, Kecamatan Paiton, Kabupaten Probolinggo, Jawa Timur, Indonesia.
Location
Kab. probolinggo,
Jawa timur
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol 1 No 2 (2025): Juni 2025" : 5 Documents clear
Spam SMS Classification Analysis Using Naive Bayes with Python Language Beny Yusman
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 2 No 1 (2025): Desember 2025 Vol 1 No 2 (2025): Juni 2025 Vol 1 No 1 (2024): Desember 2024