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International Journal of Artificial Intelligence and Robotics (IJAIR)

ijair
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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 60 Documents

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Prediction of IDR-USD Exchange Rate using the Cheng Fuzzy Time Series Method with Particle Swarm Optimization Juwairiah Juwairiah; Winaldi Ersa Haidar; Heru Cahya Rustamaji
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Currently, much research on machine learning about prediction has been carried out. For example, to predict the exchange rate of the rupiah against the United States currency, namely the United States Dollar (USD). The continuing trend of USD depreciation has attracted many researchers to explore currency trading, especially in establishing an efficient method for predicting fluctuating exchange rates. The rapid development of time series prediction methods has resulted in many methods that can predict data according to needs. In this study, we apply the Fuzzy Time Series Cheng method with Particle Swarm Optimization (PSO) to predict the IDR exchange rate against USD. The data used in this research is sourced from Bank Indonesia in the form of time series data on the selling and buying exchange rate. The FTS Cheng method forecasts the IDR exchange rate against USD. In contrast, the PSO algorithm optimizes the interval parameter to increase the forecasting accuracy. Based on the implementation and the results of the tests, the results show that using the PSO algorithm can produce the best optimization interval parameters and increase the accuracy value. From the results of 10 trials with training data, testing data, and different iterations, it was obtained that the MAPE test for predicting the rupiah exchange rate against the US dollar using FTS Cheng with 60% training data and 40% testing data resulted in the lowest MAPE of 0.610145%. Furthermore, 70% of the training and 30% of the testing data resulted in the lowest MAPE of 0.313388%. Then the FTS Cheng and PSO testing with 60% training data and 40% testing data, and an iteration value of 200 resulted in the lowest MAPE of 0.394707%. Furthermore, 70% of training data and 30% of testing data and an iteration value of 90 resulted in the lowest MAPE of 0.263666%.

Smart Room Lighting System for Energy Efficiency in Indoor Environment Rafika Rizky Ramadhani; Mike Yuliana; Aries Pratiarso
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

The building sector absorbs 40% of global energy sources. Energy demand in the building sector is dominated by around 60 – 70% electricity, mainly used for air conditioning, water pumping machines, and lighting. On average, artificial lighting can consume 37% of the total electrical energy needs. Meanwhile, sunlight enters the room through the morning window from noon until the afternoon. Using unnecessary or excessive room lighting when there is a natural light source in the room consumes a relatively large total energy requirement of the building. There is a need for a smart lighting system specifically for indoors for efficient energy management and a lighting control system integrated with IoT, which utilizes the intensity of natural light in a room. In this paper, we proposed that the Smart Room Lighting System uses the fuzzy logic method based on ESP32 to control the lighting in the room to save electricity usage for a room lamp. The result of the tool's design, it can control the light starting from bright, dim, and lights go out. The results obtained by the Smart Room Lighting System can reduce power consumption by up to 93% and energy by up to 70%.

Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset Wisnalmawati Wisnalmawati; Agus Sasmito Aribowo; Yunie Herawati
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Sentiment analysis aims to categorize opinions using an annotated corpus to train the model. However, building a high-quality, fully annotated corpus takes a lot of effort, time, and expense. The semi-supervised learning technique efficiently adds training data automatically from unlabeled data. The labeling process, which requires human expertise and requires time, can be helped by an SSL approach. This study aims to develop an SSL-Model for sentiment analysis and to compare the learning capabilities of Naive Bayes (NB) and Random Forest (RF) in the SSL. Our model attempts to annotate opinion documents in Indonesian. We use an ensemble multi-classifier that works on unigrams, bigrams, and trigrams vectors. Our model test uses a marketplace dataset containing rating comments scrapping from Shopee for smartphone products in the Indonesian Language. The research started with data preparation, vectorization using TF-IDF, feature extraction, modeling using Random Forest (RF) and Naïve Bayes (NB), and evaluation using Accuracy and F1-score. The performance of the NB model outperformed previous research, increasing by 5,5%. The conclusion is that SSL performance highly depends on the number of training data and the compatibility of the features or patterns in the document with machine learning. On our marketplace dataset, better to use Random Forest.

Body Temperature and Heart Rate Monitoring System Using Fuzzy Classification Method M. Yayan Nurhadiansyah; Rahardhita Widyatra Sudibyo; Moch. Zen Samsono Hadi
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Climbing becomes one of the extreme sports that test endurance with nature, just like in a mountainous environment. In addition to the excitement and fun that climbing provides, climbers enjoy the opportunity to view breathtaking natural scenery and breathe in the fresh air drawn directly from the surrounding environment. Because of the temperature in the cold mountains, there are frequent and common obstacles Not realized by the climbers, such as hypothermia. Hypothermia is a condition in which the body temperature drops below 35oC. When body temperature is below normal 37oC, nervous system function and other body organs will experience interference. If not soon Left untreated, hypothermia can lead to heart failure, disturbances respiratory system, and even death. To anticipate things requires a system that functions to know the condition of mountaineer health. The system to be created uses the Mamdani fuzzy logic method, which decides whether the climber is healthy. The fuzzy logic method is used for decision-making based on body temperature and heart rate values. Implementation of the system in the form of a prototype containing sensors and mini-computers located at the climbing post, with data transmission using a node sent from post x to the main post to be uploaded to the database so that it can be known by the admin or rescue team when climbers need help in critical situations. This is done so that the condition can be monitored.

Expert System for Detecting Diseases of Potatoes of Granola Varieties Using Certainty Factor Method Bonifacius Vicky Indriyono; Moch. Sjamsul Hidajat; Tri Esti Rahayuningtyas; Zudha Pratama; Iffah Irdinawati; Evita Citra Yustiqomah
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

The low productivity of potatoes is caused by many factors, including the very low quality of the seeds used, poor storage, climate, capital, limited farmer knowledge, and attacks by plant-disturbing organisms, especially diseases. Not only that, many farmers are still unfamiliar with the various diseases that can attack potato plants, or their knowledge about potato plant diseases is incomplete. This study aims to design and develop an expert system web-based application technology using the Certainty Factor (CF) method to detect potato disease symptoms. The CF method defines a measure of the capacity of a fact or provision to express the level of an expert's belief in a matter experienced by the concept of belief or trust and distrust or uncertainty contained in the certainty factor. The results showed that the CF method could function optimally in detecting potato plant diseases which can help farmers based on the symptoms that appear with an accuracy value of 94%.

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application Dewi Mutiara Sari; Bayu Sandi Marta; Muhammad Amin A; Haryo Dwito Armono
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

Implementation of PSO algorithm on MPPT PV System using Arduino Uno under PSC Efendi S Wirateruna; Mohammad Jasa Afroni; Annisa Fitri Ayu
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i1.6029

The availability of fossil energy sources decreases as consumers' demand for electrical energy increases rapidly. Currently, the utilization of renewable energy sources is crucial. PV is a renewable energy source that converts photon energy into DC current. Maximum power point tracker (MPPT) control technology for photovoltaics has advanced significantly. PV is unique in that its P-V characteristic curve is non-linear. Conditions of partial shading can cause the P-V curve to have multiple peaks. This research will design MPPT PV using the Particle Swarm Optimization (PSO) algorithm in partially shaded conditions with an Arduino Uno and boost converter. Conventional algorithms, incremental conductance (IC), and Perturb and Observe (P&O) are implemented as a comparison. The purpose of implementing the PSO algorithm is to find the global peak of power to minimize power losses of PV. It leads to optimal power in case of partial shading conditions. Two PV modules are arranged in series for MPPT in a partially shaded environment. The examination was conducted in a darkened room with spotlights. The mean absolute percentage error of the current sensor, INA219, and the voltage sensor, voltage divider, was less than 1% during testing. The MPPT PV system test results indicate that the PSO algorithm can extract approximately 1.64 Watts of average power. In contrast, the IC and P&O algorithms can extract about 1.25 Watts and 1.41 Watts, respectively. When no algorithm exists in the control system, the extracted power is approximately 1.13 watts. Thus, the PSO algorithm tracks global or optimal power under partial shading conditions.

Utilizing Virtual Humans as Campus Virtual Receptionists Moh. Zikky; Marvel Natanael Suhardiman; Kholid Fathoni
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i1.6175

To imitate human-like behavior is one of the greatest feats a computer software could achieve. Computers can produce close-to-realism avatars with similar looks and behaviors in this modern era. One of the works that computer software could achieve now is conveying information in a place that a receptionist usually does. Therefore a computer software capable of that is called a Virtual Receptionist. This paper aims to explore the use of virtual humans as virtual receptionists and compare it to human receptionists to find both advantages and disadvantages. This research utilizes a virtual human model that imitates the behavior of a human receptionist. Its movements are based on real-life movements recorded with motion capture. It could also communicate with users by processing the voice input using speech-to-text technology recorded by a microphone. The recorded input will then be analyzed to determine whether it contains information stored in a database. The virtual human will then show the user the answer to their question accordingly. Utilizing virtual humans can make the process more interactive and exciting because of its futuristic feel. This way, campuses can have appealing introductory media and support campuses to be more open to the public in the future. However, the agent can only respond with prepared answers and not generate its own when necessary. Transcribed text will be analyzed for words that indicate the user's required information. In this case, the information would be the information of research laboratories in the post-graduate building of the EEPIS campus.

Classification Appropriateness Recipient Help Non-Cash Food Using Learning Vector Quantization (LVQ) Method Ayu Lestari; Anang Aris Widodo; Nanda Martyan Anggadimas
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i1.6287

Help Non-Cash Food is a program from the Government that is used to overcome poverty. The program is not functioning as well as it could because the procedure of receiving aid is not uniform, and individuals responsible for making choices are having trouble determining which families are qualified to receive the assistance. To overcome this problem, a classification system is needed to classify the eligibility of Non-Cash Food Assistance recipients so that the results are more efficient and accurate. This research uses the Learning Vector Quantization (LVQ) method with Python. This research aims to implement the LVQ method for the eligibility classification of non-cash food assistance recipients. System design is a stage that contains the process from start to finish of running this system which is described in the form of a flowchart, including system requirements that support this research, both software and hardware. In the process of analyzing the results and tests that are used as evaluation material in the process of finding a solution to a problem and making decisions in the process of planning activities, it is necessary to assess whether or not the LVQ approach is practicable to apply based on the findings of the research. In this study, 200 datasets were used with three epoch values and a learning rate of 0.1. The data set was randomly divided into a training portion of 80% and a testing portion of 20%. So that the results of this research using the LVQ method on the eligibility classification of recipients of Non-Cash Food Assistance obtain an accuracy of 97.5%.

Comparative Analysis of the Performance Testing Results of the Backtracking and Genetics Algorithm in Solving Sudoku Games Bonifacius Indriyono; Natalinda Pamungkas; Zudha Pratama; Ery Mintorini; Imelda Dimentieva; Pita Mellati
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i1.6501

Games that hone thinking skills and logical accuracy have recently been very popular. One of them is the game Sudoku. Sudoku is a game that hones logic through puzzles arranged in rows and columns. Sudoku is also defined as a puzzle game that aims to arrange several numbers in a grid from one to nine on a grid consisting of 9x9 squares. The concept of this Sudoku game is to enter numbers into the rows and columns provided. The rule of this game is that the numbers arranged on the board cannot be the same in every row, column, and 3x3 square in the grid. In another sense, each number entered must appear once in each row and column. When running Sudoku, several numbers are already instructions for players to fill in the next boxes. The number of clues at the beginning of the game determines the difficulty level players face. The fewer clues, the more difficult the Sudoku is to solve. This study aims to compare how to solve Sudoku using genetic algorithms, backtracking, and the completion time needed. The tests' results show that the genetic and backtracking algorithms can solve Sudoku games quickly. Still, the backtracking algorithm has the advantage of being relatively shorter, and the process is not so complicated that the backtracking algorithm can be an alternative solution to solving Sudoku logic games.

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Anik Vega Vitianingsih

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+6281332765765

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ijair@unitomo.ac.id

Editorial Address
Jl. Semolowaru no 84, Surabaya, 60118

Location
Kota surabaya,

Jawa timur

INDONESIA

Abstract

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Anik Vega Vitianingsih

Contact Email vega@unitomo.ac.id

Phone +6281332765765

Journal Mail Official ijair@unitomo.ac.id

Editorial Address Jl. Semolowaru no 84, Surabaya, 60118

Location Kota surabaya, Jawa timur INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Anik Vega Vitianingsih

Contact Email
vega@unitomo.ac.id

Phone
+6281332765765

Journal Mail Official
ijair@unitomo.ac.id

Editorial Address
Jl. Semolowaru no 84, Surabaya, 60118

Location
Kota surabaya,

Jawa timur

INDONESIA