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Anik Vega Vitianingsih
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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 5 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2022): May 2022" : 5 Documents clear
Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center Nico Nico; Novrido Charibaldi; Yuli Fauziah
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.721 KB) | DOI: 10.25139/ijair.v4i1.4323

Abstract

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.
Cargo Simulation Robot Prototype with Bluetooth Based Motor Driver Shield Using Arduino Uno Microcontroller Yudi Irawan Chandra; Irfan; Anargya Satya Rifisyah Putro
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (648.439 KB) | DOI: 10.25139/ijair.v4i1.4326

Abstract

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.
Design of MPPT PV using Particle Swarm Optimization Algorithm under Partial Shading Condition Efendi S Wirateruna; Annisa Fitri Ayu Millenia
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.745 KB) | DOI: 10.25139/ijair.v4i1.4327

Abstract

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.
Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia Solikhun Solikhun; Verdi Yasin; Donni Nasution
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (355.569 KB) | DOI: 10.25139/ijair.v4i1.4328

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.
Optimization of Foodstuffs for Patients with Hypertension Using the Improved Particle Swarm Optimization Method Novrido Charibaldi; Nur Heri Cahyana; Muhamad Setiawan Wicaksono
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.257 KB) | DOI: 10.25139/ijair.v4i1.4335

Abstract

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.

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