https://ejournal.unitomo.ac.id/index.php/ijair/issue/feedInternational Journal of Artificial Intelligence & Robotics (IJAIR)2023-12-31T00:10:55+07:00Anik Vega Vitianingsih[email protected]Open Journal Systems<hr> <p>Accredited by Minister of Education, Culture, Higher Education, and Research, Republic Indonesia, Number <strong><a href="https://drive.google.com/file/d/1N-OfAfF_yizw64Xar6hxY1ljQD9Q3IRv/view?usp=share_link">1429/E5.3/HM.01.01/2022</a></strong> as <strong>Ranking 4 (SINTA 4)</strong>.</p> <p><strong>International Journal of Artificial Intelligence & Robotics (IJAIR)</strong> is one of the journals published by the Informatics Engineering Department of Dr. Soetomo University, which was established in November 2019. <strong>IJAIR</strong> is a journal with a Double-Blind Peer Review process, dedicated to the publication of quality research results in the fields of Computer Science and Technology Artificial Intelligence & Robotics, but not implicitly limited. All publications in the IJAIR journal are open access which allows articles to be freely available online without any subscription and free of charge. The journal publishes twice a year in November and May, containing 5 articles for each issue. All of the articles in this journal registered with unique <strong>DOI</strong>, provided by <strong>Crossref</strong>.</p> <p><a href="http://u.lipi.go.id/1571393645"><strong>ISSN (online) : 2686-6269</strong></a></p> <p><strong>Focus and Scope</strong>:</p> <p>Machine Learning & Soft Computing, Data Mining & Big Data, Computer Vision & Pattern Recognition dan Robotics.</p>https://ejournal.unitomo.ac.id/index.php/ijair/article/view/6907Keyword Security Implementation Based on Hill Cipher Optimized Using Genetic Algorithms2023-11-28T10:44:14+07:00Mayang Arinda Yudantiar[email protected]Purwanto Purwanto[email protected]Sri Winarno[email protected]<p>In the process of exchanging data and information, the most important task is to maintain data and information security and reach out to interested parties. One way this can be achieved is through encryption, a process better known as cryptography. Cryptography can scramble messages so that, even if intercepted, the message cannot be immediately read. One example of an encryption algorithm is the Hill Cipher. The Hill Cipher uses an m-by-m-sized matrix as the key for the encryption and decryption process, making it a challenging algorithm to crack. The key provided for the Hill Cipher encryption and decryption process cannot be arbitrary. The keys with mismatched determinants cannot be used, as they can prevent the encrypted message from being restored to its original form. Optimization can be carried out to overcome these obstacles using a genetic algorithm. Genetic algorithms can determine the keys to encrypt and decrypt the Hill Cipher. A key with the appropriate composition for the Hill Cipher will be obtained through the genetic algorithm's evaluation function. This research aims to enhance message security by using the correct composition to generate Hill Cipher encryption and decryption keys. The research results indicate that out of 10 tests conducted with different lengths of original text, eight succeeded, while two failed to complete the encryption and decryption process.</p>2023-11-30T00:00:00+07:00Copyright (c) 2023 Mayang Arinda Yudantiar; Purwanto Purwanto, Sri Winarnohttps://ejournal.unitomo.ac.id/index.php/ijair/article/view/7167Sentiment Analysis to Measure Public Trust in the Government Due to the Increase in Fuel Prices Using Naive Bayes and Support Vector Machine2023-11-28T10:44:20+07:00Zakaria Zakaria[email protected]Kusrini Kusrini[email protected]Dhani Ariatmanto[email protected]<p>The study examines public sentiment on the government's fuel price policy using an experimental approach and Twitter data obtained through API scraping. It applies sentiment analysis methods like Naïve Bayes, SVM, and Majority Voting. SVM achieved 85% accuracy, excelling in identifying negative sentiments, while Majority Voting reached 70% by considering confidence levels. Naïve Bayes struggled with neutral sentiments. They are combining methods to enhance the understanding of public sentiments on fuel price changes. The study highlights sentiment analysis' effectiveness in gauging reactions to fuel policies, with SVM offering more profound insights into sentiments related to fuel price hikes. Challenges remain in identifying neutral sentiments due to social media text brevity. These findings underscore the contextual importance of interpreting sentiment analysis. Leveraging these insights, governments can understand public perceptions better and devise improved communication strategies for sensitive economic policies like fuel price hikes, fostering better government-citizen interactions. The study aims to guide stakeholders in comprehending public perspectives within public policy, emphasizing the relevance of sentiment analysis for policy evaluation.</p>2023-11-24T05:56:38+07:00Copyright (c) 2023 Zakaria Zakaria, Kusrini Kusrini, Dhani Ariatmantohttps://ejournal.unitomo.ac.id/index.php/ijair/article/view/7179Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter2023-11-30T13:06:53+07:00Mochamad Mobed Bachtiar[email protected]Iwan Kurnianto Wibowo[email protected]Yusuf Rifa’I[email protected]Daniswara Prasetya Subagja[email protected]Nanda Alfi Syahriyah[email protected]<p>The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.</p>2023-11-30T12:38:41+07:00Copyright (c) 2023 Mochamad Mobed Bachtiar, Iwan Kurnianto Wibowo, Yusuf Rifa’I, Daniswara Prasetya Subagja, Nanda Alfi Syahriyahhttps://ejournal.unitomo.ac.id/index.php/ijair/article/view/7318Optimizing Long Short-Term Memory to Predict Currency Rates2023-12-30T22:54:14+07:00Yarham Syahabi Lubis[email protected]Muhammad Rizqy Septyandy[email protected]Mika Debora Br Barus[email protected]<p>As a travel destination, Saudi Arabia attracts individuals worldwide, including tourists, investors, and immigrant workers, for various purposes, including trip planning, investment decisions, and remittance transfers. Indonesia and Pakistan are the biggest countries that send <em>Umrah</em> and <em>Hajj</em> pilgrims. We need to predict currency rates in 3 pairs of currencies that are frequently used by travel agencies, <em>Hajj</em> and <em>Umrah</em> pilgrims, such as the Saudi Riyal (SAR) against the Pakistani Rupee, the SAR against the Indonesian Rupiah (IDR), and the United States Dollar (USD) against the IDR. This study utilizes Long Short-Term Memory (LSTM) models, the machine learning approach for predicting currency pairs exchange rates. Previous studies succeeded in predicting USD/IDR rates using the LSTM time series-machine learning approach, but the root mean square error (RMSE) value was the worst 271. The research aims to optimize the LSTM to predict the currency rate in the future using historical data obtained from investing.com. We use Python to predict the currency rate pairs, following an experimental investigation with adjustments to the batch size, epoch, and prediction days. The experimental results show that SAR/PKR has a smaller mean square error (MSE) of 0.94, RMSE of 0.97, and MAE of 0.61, while SAR/IDR and USD/IDR Excel with Models 2 and 1 have smaller MSEs of 317.79 and 6654.41, RMSEs of 17.82 and 81.57, and MAEs of 10.54 and 50.12, respectively.</p>2023-12-04T12:27:37+07:00Copyright (c) 2023 Yarham Syahabi Lubis, Muhammad Rizqy Septyandy, Mika Debora Br Barushttps://ejournal.unitomo.ac.id/index.php/ijair/article/view/7499Water Quality Monitoring for Smart Farming Using Machine Learning Approach2023-12-31T00:10:55+07:00Yana Hendriana[email protected]Restiadi Bayu Taruno[email protected]Zulkhairi Zulkhairi[email protected]Nur Azmi Ainul Bashir[email protected]Joang Ipmawati[email protected]Ilham Unggara[email protected]<p class="AbstractText" style="margin-bottom: 0cm; line-height: normal;"><span lang="EN" style="font-size: 8.0pt;">Water quality in fish farming environments has been a topic of research investigation for numerous years. While most studies have concentrated on managing water quality in fish ponds, there is a lack of research on implementing these practices on a commercial scale. <span class="rynqvb">Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations.</span> In our study, we gathered weekly data from two fish ponds in the Lintangsongo smart farming area over six months. To deal with the limited dataset, we utilized methods for reducing dimensionality, like the pairwise comparison of correlation matrices to eliminate the highest correlated predictors. We used techniques of feature selection, including XGBoost classification, <span class="rynqvb">and apart from that,</span> we used Recursive Feature Elimination (RFE) to determine the importance of features. This analysis identified ammonium and calcium as the top two predictors. These nutrients played a vital role in maintaining the paired cultivation system and promoting the robust development of Nile tilapia fish and water spinach. This process of detecting and distributing nutrients persists until the desired quantities of ammonium and calcium are reached. During each cycle, 0.7 g of ammonium sulfate and calcium nitrate are distributed, and the nutrient levels are assessed. Vernier sensors were employed for assessing nutrient values, and a system of actuators was integrated to supply the necessary nutrients to the smart farming environment using the closed-loop concept. This research investigates water quality management practices in fish farming, assesses their impact on fish health and profitability, identifies key water quality predictors, and implements a closed-loop system for nutrient delivery.</span></p>2023-12-31T00:10:06+07:00Copyright (c) 2023 Yana Hendriana, Restiadi Bayu Taruno, Zulkhairi Zulkhairi, Nur Azmi Ainul Bashir, Joang Ipmawati, Ilham Unggara