Pub Date : 2022-12-05DOI: 10.25139/ijair.v4i2.5290
M. Y. Nurhadiansyah, Rahardhita Widyatra Sudibyo, Moch. Zen Samsono Hadi
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.
{"title":"Body Temperature and Heart Rate Monitoring System Using Fuzzy Classification Method","authors":"M. Y. Nurhadiansyah, Rahardhita Widyatra Sudibyo, Moch. Zen Samsono Hadi","doi":"10.25139/ijair.v4i2.5290","DOIUrl":"https://doi.org/10.25139/ijair.v4i2.5290","url":null,"abstract":"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.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116981946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-03DOI: 10.25139/ijair.v4i2.5312
B. Indriyono, Moch. Sjamsul Hidajat, Tri Esti Rahayuningtyas, Zudha Pratama, Iffah Irdinawati, Evita Citra Yustiqomah
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%.
{"title":"Expert System for Detecting Diseases of Potatoes of Granola Varieties Using Certainty Factor Method","authors":"B. Indriyono, Moch. Sjamsul Hidajat, Tri Esti Rahayuningtyas, Zudha Pratama, Iffah Irdinawati, Evita Citra Yustiqomah","doi":"10.25139/ijair.v4i2.5312","DOIUrl":"https://doi.org/10.25139/ijair.v4i2.5312","url":null,"abstract":"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%.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131856498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-03DOI: 10.25139/ijair.v4i2.5267
Wisnalmawati Wisnalmawati, A. Aribowo, Yunie Herawati
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.
{"title":"Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset","authors":"Wisnalmawati Wisnalmawati, A. Aribowo, Yunie Herawati","doi":"10.25139/ijair.v4i2.5267","DOIUrl":"https://doi.org/10.25139/ijair.v4i2.5267","url":null,"abstract":"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.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"53 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127998750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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%.
{"title":"Prediction of IDR-USD Exchange Rate using the Cheng Fuzzy Time Series Method with Particle Swarm Optimization","authors":"Juwairiah Juwairiah, Winaldi Ersa Haidar, Heru Cahya Rustamaji","doi":"10.25139/ijair.v4i2.5259","DOIUrl":"https://doi.org/10.25139/ijair.v4i2.5259","url":null,"abstract":"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%. \u0000 ","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125971009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.25139/ijair.v4i2.5266
Rafika Rizky Ramadhani, Mike Yuliana, Aries Pratiarso
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%.
{"title":"Smart Room Lighting System for Energy Efficiency in Indoor Environment","authors":"Rafika Rizky Ramadhani, Mike Yuliana, Aries Pratiarso","doi":"10.25139/ijair.v4i2.5266","DOIUrl":"https://doi.org/10.25139/ijair.v4i2.5266","url":null,"abstract":"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%.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131806420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-02DOI: 10.25139/ijair.v4i1.4328
Solikhun Solikhun, Verdi Yasin, Donni Nasution
The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.
{"title":"Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia","authors":"Solikhun Solikhun, Verdi Yasin, Donni Nasution","doi":"10.25139/ijair.v4i1.4328","DOIUrl":"https://doi.org/10.25139/ijair.v4i1.4328","url":null,"abstract":"The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125562555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-02DOI: 10.25139/ijair.v4i1.4335
Novrido Charibaldi, Nur Heri Cahyana, Muhamad Setiawan Wicaksono
Hypertension is when a person's blood pressure exceeds the reasonable limits determined by experts. A person who suffers from high blood pressure or hypertension risks developing non-communicable diseases that can endanger the sufferer's life, such as stroke and heart attack. One of the causes that can increase and worsen hypertension is an unhealthy lifestyle. Due to a lack of knowledge in regulating food composition, it is difficult for ordinary people to vary the composition of food in the next few days, which is usually done by simply avoiding foods ordered by doctors or experts. The Improved Particle Swarm Optimization (IPSO) method was chosen because it can be used to solve the problem of optimizing optimal food composition. In addition, the IPSO method can also remember the worst position ever visited so that particles can pass through a bad position and always try a better position. Based on the research conducted, the IPSO method succeeded in producing recommendations for the composition of foods consumed by people with hypertension consisting of 3 portions, namely breakfast, lunch, and dinner. Breakfast and lunch contain staple foods, plant sources, animal sources, vegetables, fruits, or complementary foods. At the same time, dinner contains only staple foods, animal sources, plant sources, and vegetables. This research found that the iteration that can produce optimal results is 400 iterations and the most optimal particles are 10 particles. This happens because the price of food ingredients is included in the calculation.
{"title":"Optimization of Foodstuffs for Patients with Hypertension Using the Improved Particle Swarm Optimization Method","authors":"Novrido Charibaldi, Nur Heri Cahyana, Muhamad Setiawan Wicaksono","doi":"10.25139/ijair.v4i1.4335","DOIUrl":"https://doi.org/10.25139/ijair.v4i1.4335","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000Hypertension is when a person's blood pressure exceeds the reasonable limits determined by experts. A person who suffers from high blood pressure or hypertension risks developing non-communicable diseases that can endanger the sufferer's life, such as stroke and heart attack. One of the causes that can increase and worsen hypertension is an unhealthy lifestyle. Due to a lack of knowledge in regulating food composition, it is difficult for ordinary people to vary the composition of food in the next few days, which is usually done by simply avoiding foods ordered by doctors or experts. The Improved Particle Swarm Optimization (IPSO) method was chosen because it can be used to solve the problem of optimizing optimal food composition. In addition, the IPSO method can also remember the worst position ever visited so that particles can pass through a bad position and always try a better position. Based on the research conducted, the IPSO method succeeded in producing recommendations for the composition of foods consumed by people with hypertension consisting of 3 portions, namely breakfast, lunch, and dinner. Breakfast and lunch contain staple foods, plant sources, animal sources, vegetables, fruits, or complementary foods. At the same time, dinner contains only staple foods, animal sources, plant sources, and vegetables. This research found that the iteration that can produce optimal results is 400 iterations and the most optimal particles are 10 particles. This happens because the price of food ingredients is included in the calculation. \u0000 \u0000 \u0000 \u0000","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116628259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-31DOI: 10.25139/ijair.v4i1.4327
Efendi S Wirateruna, Annisa Fitri Ayu Millenia
Fossil energy sources experience a decrease each year when the demand increases significantly. In the case of environmental issues, renewable energy sources (RES) can be energy alternatives. The photovoltaic module is RES with unique characteristics, especially partial shading conditions. This condition leads to the PV characteristic curve experiencing multiple peaks. The paper conducted the simulation of the PV solar panel module using MATLAB Simulink. The Maximum Power Point Tracking (MPPT) PV is also described based on a particle swarm optimization (PSO) algorithm. The proposed algorithm can address multiple peak curve problems due to partial shading conditions. For comparison, the conventional algorithm, perturb & observe, is presented. The PV module is divided into three group cells with irradiance differences for each group to illustrate the partial shading condition. The result shows that the PSO algorithm guarantees optimal and fast response for the operating PowerPoint. It needs about 0.04 seconds to maintain at the optimal power point, 129 Watt, compared with the perturb and observe algorithm performance that only kept at the lower operating power point, 67 Watt at 0.06 second. Thus, the PSO algorithm can tackle the partial shading condition with a fast response to maintain the maximum PowerPoint. Therefore, the PSO algorithm is the proper solution for tracking the optimum operating power point under partial shading conditions.
{"title":"Design of MPPT PV using Particle Swarm Optimization Algorithm under Partial Shading Condition","authors":"Efendi S Wirateruna, Annisa Fitri Ayu Millenia","doi":"10.25139/ijair.v4i1.4327","DOIUrl":"https://doi.org/10.25139/ijair.v4i1.4327","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000Fossil energy sources experience a decrease each year when the demand increases significantly. In the case of environmental issues, renewable energy sources (RES) can be energy alternatives. The photovoltaic module is RES with unique characteristics, especially partial shading conditions. This condition leads to the PV characteristic curve experiencing multiple peaks. The paper conducted the simulation of the PV solar panel module using MATLAB Simulink. The Maximum Power Point Tracking (MPPT) PV is also described based on a particle swarm optimization (PSO) algorithm. The proposed algorithm can address multiple peak curve problems due to partial shading conditions. For comparison, the conventional algorithm, perturb & observe, is presented. The PV module is divided into three group cells with irradiance differences for each group to illustrate the partial shading condition. The result shows that the PSO algorithm guarantees optimal and fast response for the operating PowerPoint. It needs about 0.04 seconds to maintain at the optimal power point, 129 Watt, compared with the perturb and observe algorithm performance that only kept at the lower operating power point, 67 Watt at 0.06 second. Thus, the PSO algorithm can tackle the partial shading condition with a fast response to maintain the maximum PowerPoint. Therefore, the PSO algorithm is the proper solution for tracking the optimum operating power point under partial shading conditions. \u0000 \u0000 \u0000 \u0000","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124455839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-30DOI: 10.25139/ijair.v4i1.4326
Y. Chandra, Irfan, Anargya Satya Rifisyah Putro
From the android developer website, it can be seen that the number of android users in the world is increasing. Almost 70% of the world's people use Android as their gadget. Nevertheless, today's average person uses android gadgets only to send messages, social media, and telephone. Furthermore, they do not realize they can increase the ease and sophistication of other things in the world and are very useful for everyday things and certain things in life. In today's modern era, many communication types of equipment have an intelligent system or what is commonly referred to as a smartphone. Modern society uses branded gadgets as a lifestyle. Today, the average person uses android gadgets only to send messages, social media, and telephone. When using Bluetooth, applications are generally used to exchange data. However, now Bluetooth is not only used to communicate with telephones or computers but can also be used to command an electronic device according to the needs of its users. The purpose of this research is to create a robot simulation that can be controlled using Bluetooth to move an item to facilitate human work, relieve heavy tasks that have a high risk, for example carrying goods in the factory and reduce accidents in terms of carrying goods, and able to be controlled things remotely as desired by utilizing Bluetooth media using the Arduino Uno-based L293D Motor Shield Driver.
{"title":"Cargo Simulation Robot Prototype with Bluetooth Based Motor Driver Shield Using Arduino Uno Microcontroller","authors":"Y. Chandra, Irfan, Anargya Satya Rifisyah Putro","doi":"10.25139/ijair.v4i1.4326","DOIUrl":"https://doi.org/10.25139/ijair.v4i1.4326","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000From the android developer website, it can be seen that the number of android users in the world is increasing. Almost 70% of the world's people use Android as their gadget. Nevertheless, today's average person uses android gadgets only to send messages, social media, and telephone. Furthermore, they do not realize they can increase the ease and sophistication of other things in the world and are very useful for everyday things and certain things in life. In today's modern era, many communication types of equipment have an intelligent system or what is commonly referred to as a smartphone. Modern society uses branded gadgets as a lifestyle. Today, the average person uses android gadgets only to send messages, social media, and telephone. When using Bluetooth, applications are generally used to exchange data. However, now Bluetooth is not only used to communicate with telephones or computers but can also be used to command an electronic device according to the needs of its users. The purpose of this research is to create a robot simulation that can be controlled using Bluetooth to move an item to facilitate human work, relieve heavy tasks that have a high risk, for example carrying goods in the factory and reduce accidents in terms of carrying goods, and able to be controlled things remotely as desired by utilizing Bluetooth media using the Arduino Uno-based L293D Motor Shield Driver. \u0000 \u0000 \u0000 \u0000","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116829317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-30DOI: 10.25139/ijair.v4i1.4323
Nico Nico, N. Charibaldi, Yulianti Fauziah
One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.
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