OPTIMIZATION OF TEST KEEPER SCHEDULING USING GENETIC ALGORITHM AT INFORMATICS DEPARTMENT PETRA CHRISTIAN UNIVERSITY

before mid or final exam, there will be a manual process to schedule the test keepers for every exam session. The test keepers are lecturer assistants (assistant is an appointed student to help lecturer in class). For an exam session, the keeper can be 1 up to 3 people, depending on the exams participant. These manual process is considering many factors, i.e. the assistants batch (year), the average of exams participant batch(year), gender combination of the keeper, evenness of the exam keeping of every assistant, the character of the assistant itself, and the exam schedule of the assistant. These factors are considered upon picking every exam sessions keeper, which is taking a lot of time and knowledge, and this process is done twice a semester by an exam coordinator (lecturer). In this paper, will be designed an application that is using genetic algorithm to automatically assign the test keepers for every exam. The result of the application is tested during the mid-exam and final-exam early semester of 2016, and the application is giving a good result, with the accuracy of 90.23%, in which the 9.77% is some minor changes that is required to make the test keepers more suitable

Development of Interactive Learning Media for Simulating Human Digestive System

This proceedings volume contains papers presented at the fifth International Conference on Soft Computing, Intelligent System and Information Technology (the 5th ICSIIT) held in Bali, Indonesia, 26-29 September 2017. Main theme of this international conference is “Building Intelligence through IoT and Big Data”, and it was organized and hosted by Informatics Engineering Department, Petra Christian University, Surabaya, Indonesia. The Program Committee received 106 submissions for the conference from across Indonesia and around the world. After peer-review process by at least two reviewers per paper, 64 papers were accepted and included in the proceedings. The papers were divided into ten groups: Classification and Correlation Techniques, Feature Extraction and Image Recognition Methods, Algorithms for Intelligent Computation, Distributed Systems and Computer Networks, Mobile and Pervasive IoT Applications, Assessments of Integrated IS/IT, Simulation and Virtual Reality Applications, Smart Assistive Technologies, Smart Mobile Applications, Case Studies of Knowledge Discovery and Management

Optimization of Units Movement in Turn-Based Strategy Game

Each game has an artificial intelligence that is used to fight the player, which will provide more challenge. But in some strategy games, unit movements are usually done using simple considerations. For example the rest of unit lives, unit strength, and so forth. In this study, a turn based strategy game is designed using genetic algorithm to control the movement of the enemy armies. In each turn, the enemy will move based on the potential level of produced damage to and from the opponent, the distance between the units, and the distance to the opponent�s building. The genetic algorithm�s chromosome for each unit contains the following information: the position where the unit will move, who is the target, and the distance to the armies� centroid. Distance to centroid (midpoint) is used to force the units to remain in the set. The genetic algorithm process is used to control when and where the units will move or attack. From the test results, the genetic algorithm can create a more powerful enemy than the randomly moving enemy because it creates a higher winning chance of enemy units and acts more efficiently, in terms of the usage of money, the damage produced to the opponent, and the received damage

Development of Interactive Learning Media for Simulating Human Digestive System

The learning process can be done by utilizing multiple media, such as sounds, images, and animation. In general, the available learning media only use one medium that is either images or texts. Combining some elements of these mediums will complement each other. In this research, an interactive learning media application for digestive system will be created. There are 3 topics in this application, i.e. illness, digestive system, and test. The simulation was made interactive, with interactivities such as hover, click and drag with the mouse, and type using keyboard. The test menu contains multiple choice and hangman. This application was created with Adobe Flash CS6 and ActionScript 3 as the programming language. This application is tested to several users using questionnaire. Based on the results of the survey, memory and subjective satisfaction has a 85% of score because of easyness of use and easy navigation. A hurried user often had difficulties in doing the test because they didn’t read the instructions give

Development of Interactive Learning Media for Simulating Human Blood Circulatory System

This proceedings volume contains papers presented at the fifth International Conference on Soft Computing, Intelligent System and Information Technology (the 5th ICSIIT) held in Bali, Indonesia, 26-29 September 2017. Main theme of this international conference is “Building Intelligence through IoT and Big Data”, and it was organized and hosted by Informatics Engineering Department, Petra Christian University, Surabaya, Indonesia. The Program Committee received 106 submissions for the conference from across Indonesia and around the world. After peer-review process by at least two reviewers per paper, 64 papers were accepted and included in the proceedings. The papers were divided into ten groups: Classification and Correlation Techniques, Feature Extraction and Image Recognition Methods, Algorithms for Intelligent Computation, Distributed Systems and Computer Networks, Mobile and Pervasive IoT Applications, Assessments of Integrated IS/IT, Simulation and Virtual Reality Applications, Smart Assistive Technologies, Smart Mobile Applications, Case Studies of Knowledge Discovery and Management

Classification of instagram fake users using supervised machine learning algorithms

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users

Optimization of Units Movement in Turn-Based Strategy Game

Each game has an artificial intelligence that is used to fight the player, which will provide more challenge. But in some strategy games, unit movements are usually done using simple considerations. For example the rest of unit lives, unit strength, and so forth. In this study, a turn based strategy game is designed using genetic algorithm to control the movement of the enemy armies.In each turn, the enemy will move based on the potential level of produced damage to and from the opponent, the distance between the units, and the distance to the opponent’s building. The genetic algorithm’s chromosome for each unit contains the following information: the position where the unit will move, who is the target, and the distance to the armies’ centroid. Distance to centroid (midpoint) is used to force the units to remain in the set. The genetic algorithm process is used to control when and where the units will move or attack. From the test results, the genetic algorithm can create a more powerful enemy than the randomly moving enemy because it creates a higher winning chance of enemy units and acts more efficiently, in terms of the usage of money, the damage produced to the opponent, and the received damage

3D LIDAR City Model Application and Marketing Plan Development

Abstract—The main goal of this research is to create a prototype application in Unity3D using 3D City Model generated from LIDAR/Point Cloud data with Virtual Reality feature enabling the use of Google Cardboard accessories. This application has a purpose of showing Yado-VR 3D city model potential/use-case into their new potential customer. Aside from the application development, a marketing plan also constructed in this research to give Yado-VR a business direction to approach their new marke

Effectiveness of a nutritional mobile application for management of hyperphosphatemia in patients on hemodialysis A multicenter open-label randomized clinical trial

This study aims to determine the effectiveness of a phosphate mobile app (PMA), MyKidneyDiet-Phosphate Tracker ©2019, on hemodialysis (HD) patients with hyperphosphatemia. A multicenter, open-label, randomized controlled trial design allowed randomization of patients with hyperphosphatemia to either the usual care group (UG; receiving a single dietitian-led session with an education booklet) or the PMA group (PG). Thirty-three patients in each intervention group completed the 12-week study. Post-intervention, serum phosphorus levels were reduced in both groups (PG: −0.25 ± 0.42 mmol/L, p = 0.001; UG: −0.23 ± 0.33 mmol/L, p 0.05). Patients in both groups increased their phosphate knowledge (PG: 2.18 ± 3.40, p = 0.001; UG: 2.50 ± 4.50, p = 0.003), without any treatment difference (p > 0.05). Dietary phosphorus intake of both groups was reduced (PG: −188.1 ± 161.3 mg/d, p 0.05). The serum calcium levels of patients in the UG group increased significantly (0.09 ± 0.20 mmol/L, p = 0.013) but not for the PG group (−0.03 ± 0.13 mmol/L, p = 0.386), and the treatment difference was significant (p = 0.007). As per phosphate binder adherence, both groups reported a significant increase in Morisky Medication Adherence Scale scores (PG: 1.1 ± 1.2, p 0.05). HD patients with hyperphosphatemia using the PMA achieved reductions in serum phosphorus levels and dietary phosphorus intakes along with improved phosphate knowledge and phosphate binder adherence that were not significantly different from a one-off dietitian intervention. However, binder dose adjustment with meal phosphate content facilitated by the PMA allowed stability of corrected calcium levels, which was not attained by UC patients whose binder dose was fixed

Optimization of Auto Equip Function in Role-Playing Game Based on Standard Deviation of Characters Stats using Genetic Algorithm

Genetic algorithm is a well-known optimization solution for an unknown, complex case that cannot be solved using conventional methods. In Role-Playing Games (RPG), usually the main features are characters stats and equip items. Character has stats, namely strength, defense, speed, agility, life. Also, equip items that can boost characters stats. These items retrieved randomly when an enemy dead. A problem arise when the player have so many items that we cannot choose the best. Latest items doesnt always mean best, because usually in RPGs, items dont always boost all stats equally, but often it reduces certain stat while increasing the other. Based on this, a function is built in this research, to auto equip all items, based on the standard deviation of characters stats after equipping. The genetic algorithm will evaluate the best combination of gloves, armors and shoes. This algorithm involves the process of evaluating initial population (items combination), selection, crossover, mutation, elitism, creating new population. The algorithm stops when the best fitness is getting stable in successive 3 generations. After the auto equip process, the character is getting significantly stronger compared to using default equip items, measured by the remaining life after fighting with several enemies