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International Journal of Artificial Intelligence and Robotics (IJAIR)
Published by Universitas Dr. Soetomo Surabaya
ISSN : -     EISSN : 26866269     DOI : 10.25139
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Artificial Intelligence & Robotics (IJAIR) is One of the journals published by Informatics Department, Universitas Dr Soetomo, was established in November 2019. IJAIR a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in November and May. Focus and Scope: Machine Learning & Soft Computing, Data Mining & Big Data, Computer Vision & Pattern Recognition dan Robotics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 75 Documents
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Keyword Security Implementation Based on Hill Cipher Optimized Using Genetic Algorithms Yudantiar, Mayang Arinda; Purwanto, Purwanto; Winarno, Sri
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.6907

Abstract

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.
Sentiment Analysis to Measure Public Trust in the Government Due to the Increase in Fuel Prices Using Naive Bayes and Support Vector Machine Zakaria, Zakaria; Kusrini, Kusrini; Ariatmanto, Dhani
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7167

Abstract

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.
Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter Mobed Bachtiar, Mochamad; Wibowo, Iwan Kurnianto; Rifa’I, Yusuf; Prasetya Subagja, Daniswara; Syahriyah, Nanda Alfi
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7179

Abstract

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.
Optimizing Long Short-Term Memory to Predict Currency Rates Lubis, Yarham Syahabi; Rizqy Septyandy, Muhammad; Debora Br Barus, Mika
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7318

Abstract

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 Umrah and Hajj pilgrims. We need to predict currency rates in 3 pairs of currencies that are frequently used by travel agencies, Hajj and Umrah 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.
Water Quality Monitoring for Smart Farming Using Machine Learning Approach Hendriana, Yana; Taruno, Restiadi Bayu; Zulkhairi, Zulkhairi; Bashir, Nur Azmi Ainul; Ipmawati, Joang; Unggara, Ilham
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7499

Abstract

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. Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations. 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, and apart from that, 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.
Prototype of pH and Water Temperature Control System in Discus Fish Farming Using IoT-based Sugeno Fuzzy Ahmad al-Badawi, Abdullah; Ikhsan, Muhammad; Siddik Hasibuan, Muhammad
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.7524

Abstract

Challenges in cultivating discus fish often arise from abrupt pH and temperature fluctuations attributed to manual and sluggish intervention. An IoT-based prototype for automatic pH and water temperature regulation was developed to address this. The study aimed to evaluate the efficacy of the prototype in controlling pH levels and water temperature and to explore the application of IoT-based fuzzy logic in discus fish cultivation. Test data from the implemented tools and sensors revealed an error comparison value of 0.0132% and an accuracy level of 99.986% for pH measurement. In comparison, temperature sensing yielded an error value of 0% with 100% accuracy. The IoT-based fuzzy Sugeno system demonstrated regular and effective operation in regulating pH and water temperature in discus fish cultivation, showcasing superiority over manual handling systems in mitigating sudden environmental changes.
Design of Pedal Bicycle Prototype using the PID Controller as an Alternative Energy Generator Rizanty, Reyhan; Wirateruna, Efendi S; Habibi, Anang
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.7761

Abstract

In recent years, electricity consumption in Indonesia rose to 1.109 kWh, as the Ministry of Energy and Mineral Resources reported. An alternate method for generating electrical energy is harvesting the energy produced via exercising on a stationary bike. By employing Arduino Mega 2560pro-powered torque control using the PID (Proportional – Integral – Derivative) technique, we can effectively save the generator's power in the battery and modify the paddle load to match the user's desired settings. The design incorporates a repurposed bicycle that has been rebuilt, along with the addition of a transmission gear, a controller box housing a control circuit, a relay, and an inverter. Additionally, it is equipped with a display and buttons. This system can generate a paddle load ranging from 1 to 17 in normal mode and 1 to 10 in PID mode. The system has a maximum current output of 3.2A and a battery capacity of 24VDC. This DC voltage is then transformed into a 220 VA AC voltage suitable for residential electrical use using an inverter. The PID controller will regulate the current flowing into the battery, ensuring it remains steady even with a consistent wood load. PID control can reach a set point at the settling time, 7 s, with an overshoot and a steady-state error of 0%. Every motor achieved the Pulse Width Modulation (PWM) value set to the ideal current. As the RPM increases, the PWM decreases until it reaches the preset set point with a constant current value.
Design of Predictive Control System for Lane Change in Autonomous Vehicle Mega Permatasari, Shervind Maharani; Widjiantoro, Bambang Lelono; Indriawati, Katherin
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.8141

Abstract

The automobile business is introducing a lot of autonomous vehicles in the modern day. Lane changes are one of the most complicated urban scenarios in which autonomous vehicles are used. Self-driving automobiles must thus interact with human-driven vehicles in a certain way. In this work, we concentrate on the autonomous vehicle's lane-changing control system for obstacle avoidance. This study employs a predictive control system as its methodology. The vehicle's next movements can be predicted by this control system. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. It is clear from the numerical simulation results that the predictive control system executes control actions on lane changes correctly, avoiding collisions with the running vehicle obstacles. RMSE (Root-Mean Square Error) is a performance metric that is derived from the difference between the vehicle's lateral position and the reference trajectory value. The RMSE of the planned predictive control is 0.9681.
Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review Zangana, Hewa Majeed; Mohammed, Ayaz Khalid; Mahmood Mustafa, Firas
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.8217

Abstract

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.
Analysis of the Concept of Solving the Sudoku Game Using Backtracking and Brute Force Algorithms Indriyono, Bonifacius Vicky; Wardani, Ratna; Susanti, Titin; Wulandari, Laili; Fathurrohim, Moh.; Sari Pratiwi, Rifka; Wulan Safitri, Endah
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.8377

Abstract

Sudoku is a puzzle game that arranges numbers from one to nine on a board consisting of 9x9 squares. This game has a rule: the numbers arranged on a board cannot be the same in each row or column. The main goal of this Sudoku game is to complete the board and win by entering numbers into each empty box. Therefore, in this Sudoku game, there is only one valid solution. At the start of the game, several numbers are provided, which serve as clues for players to fill in the next box. The number of clues at the start of the game determines whether the Sudoku game level is difficult. The fewer the clues given, the more difficult it is to solve Sudoku. Solving Sudoku problems cannot be done manually, so several algorithms are needed, which require a lot of repetition and search functions. In this research, we will compare concepts in solving Sudoku using the Backtracking algorithm and the backtracking algorithm using the brute force technique. This comparison is seen from the effectiveness of solving Sudoku. The results of this research conclude that to solve Sudoku logic quickly and efficiently, the backtracking algorithm can be used compared to the Brute Force algorithm. This is because the backtracking algorithm has better complexity, and the solution steps are fast and “smart”. In contrast, the brute algorithm takes longer to complete because the brute force algorithm has to search for all the possibilities. The average time required to complete 10 9 x 9 puzzle tests with backtracking is 10910.96 ms, while the brute force algorithm takes longer, around 15871.90 ms.

Page 7 of 8 | Total Record : 75

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