scholarly journals Perancangan dan Implementasi Penjadwalan Mata Kuliah Menggunakan Algoritma Steepest Ascent Hill Climbing (Studi Kasus : Fakultas Psikologi UKSW)

d'CARTESIAN ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Klaudius Nikotino P ◽  
Magdalena Ineke Pakereng ◽  
Ramos Somya
Keyword(s):  
Teaching And Learning ◽  
Hill Climbing ◽  
Learning Activity ◽  
Scheduling System ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Course Scheduling ◽  
Optimum Solution

Abstract Schedule of courses has been one of the most important part in a university’s teaching and learning activity.  The  large number of  courses and  lecturers  that  are involved,  make some clashes on course’s schedule or class room’s schedule could happens frequently,  so it requires  an application to simplify the process of developing the course schedule. Steepest ascent hill climbing algorithm is an algorithm that generates all possible solutions, and then checks every solution to obtain optimum solution. The result of this research is this course scheduling system can facilitate courses schedulling, in addition  steepest ascent hill climbing algorithm is able to obtain optimum scheduling courses solutions.  Keywords : Course Schedulling, Steepest Ascent Hill Climbing Abstrak Jadwal mata kuliah telah menjadi salah satu bagian terpenting dalam proses belajar mengajar sebuah universitas.  Banyaknya jumlah mata kuliah   dan  dosen yang terlibat, mengakibatkan sering terjadinya bentrokan  jadwal mata kuliah   atau pun dalam pemakaian ruang perkuliahan,  sehingga diperlukan sebuah aplikasi guna mempermudah proses penyusunan jadwal mata kuliah tersebut.  Algoritma  steepest ascent hill climbing  adalah algoritma yang mengumpulkan seluruh solusi yang mungkin, dan men gecek setiap solusi untuk memperoleh solusi yang optimal. Hasil dari penelitian ini adalah aplikasi yang dibangun dapat digunakan untuk memudahkan penjadwalan mata kuliah selain itu algoritma  steepest ascent hill climbing mampu memperoleh solusi penjadwalan mata kuliah yang optimal.   Kata Kunci : Penjadwalan Mata Kuliah, Steepest Ascent Hill Climbing.

scholarly journals IMPLEMENTASI ALGORITMA HILL CLIMBING PADA PENENTUAN JARAK TERPENDEK KOTA WISATA DI INDONESIA

2019 ◽  
Vol 1 (3) ◽  
pp. 127-132
Author(s):  
Desti Fitriati ◽  
Nura Meutia Nessrayasa
Keyword(s):  
Optimization Problems ◽  
Complex Problem ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Shortest Route ◽  
Shortest Distance ◽  
Optimization Function ◽  
The Right

Searching and determining the shortest route is a complex problem, looking for the shortest route from a number of attractions and the distance between attractions. With varying access paths, the shortest route search becomes the right choice using a website-based app that provides the closest route on a map using the SAHC (Steepest Ascent Hill Climbing) algorithm. Steepest Ascent Hill Climbing is a method of an algorithm that is widely used for optimization problems. One application is to find the shortest route by maximizing or minimizing the value of the existing optimization function. In research ii study using 34 provinces in Indonesia and every province, there are 5 most popular tour, accuracy value obtained in research determination of the shortest distance of tourist city in Indonesia is 93,3%.  

Empirical Evaluation of Hill Climbing Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 27-40 ◽  
Author(s):  
Manju Khari ◽  
Prabhat Kumar
Keyword(s):  
Collective Behavior ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Research Community ◽  
Hill Climbing ◽  
Test Cases ◽  
Average Percentage ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Better Than

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

scholarly journals Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle

2018 ◽  
Vol 3 (2) ◽  
pp. 36
Author(s):  
Hairul Anam ◽  
Feby Sabilhul Hanafi ◽  
Ahmad Fauzal Adifia ◽  
Ahmad Firdaus Ababil ◽  
Saiful Bukhori
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Optimal Time ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
The Hill

Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

scholarly journals Design and Application of an Improved Genetic Algorithm to a Class Scheduling System

2021 ◽  
Vol 16 (01) ◽  
pp. 44
Author(s):  
Xiangliu Chen ◽  
Xiao-Guang Yue ◽  
Rita Yi Man Li ◽  
Ainur Zhumadillayeva ◽  
Ruru Liu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Strong Support ◽  
Colleges And Universities ◽  
Hill Climbing ◽  
Teaching Management ◽  
Scheduling System ◽  
Course Scheduling ◽  
Class Scheduling ◽  
Classroom Resources

The current expansion of national colleges and universities or the increase in the number of enrolments requires teaching management to ensure the quality of teaching. The problem of scheduling is a very complicated prob-lem in teaching management, and there are many restrictions. If the number of courses scheduled is large, it will be necessary to repeat the experiment and make adjustments. This kind of work is difficult to accomplish accu-rately by manpower. Moreover, for a comprehensive university, there are many subjects, many professional settings, limited classroom resources, limited multimedia classroom resources, and other factors that limit and constrain the results of class scheduling. Such a large data volume and com-plicated workforce are difficult to complete accurately. Therefore, manpow-er scheduling cannot meet the needs of the educational administration of colleges and universities. Today, computer technology is highly developed. It is very economical to use software technology to design a course schedul-ing system and let the computer complete this demanding and rigorous work. Common course scheduling systems mainly include hill climbing al-gorithms, tabu search algorithms, ant colony algorithms, and simulated an-nealing algorithms. These algorithms have certain shortcomings. In this re-search, we investigated the mutation genetic algorithm and applied the algo-rithm to the student’s scheduling system. Finally, we tested the running speed and accuracy of the system. We found that the algorithm worked well in the course scheduling system and provided strong support for solving the tedious scheduling work of the educational administration staff.

scholarly journals Pengembangan Model Optimasi Artificial Neural Network pada Penjadwalan Produksi Snack Tortilla

2020 ◽  
Vol 11 (2) ◽  
pp. 67-77
Author(s):  
Aldi Yoga Pradana ◽  
◽  
Widya Setiafindari ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hill Climbing ◽  
Mean Flow ◽  
Swarm Optimization ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Mean Flow Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Production on May 2019, PT X produced 29,159 kg of Tortillas to be marketed domestically and abroad. The large amount of production shows the high consumer interest in Tortilla, which makes PT X produce large quantities in 1 month. Production of 29,159 kg was completed in 3 weeks with 3 shifts in 7 working days in the first and third week, and 6 working days in the second week. Inaccurate production planning makes Tortilla production exceed warehouse capacity, indicating that the production process is still running even though the number in June is as much as 17,346 kg and 26,835 kg in July resulting in overproduction of 6% in May 2019 and 15% in June 2019 so that there was an increase in July 2019 to 50%. The implementations of the Artificial Neural Network (ANN) method based on Particle Swarm Optimization (PSO) using the Steepest Ascent Hill Climbing Algorithm (SAHC) areoptimize the final mean flow time by 51%, reduction in makespan by 0.5 in MayJune and 0.1 in July, and a reduction in lateness by 13% after reprocessing results in an optimization that can overcome the problem of overproduction.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

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