Article Per Year (5 Year)
p-Index From 2020 - 2025
2.63
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Jurnal Jaring SainTek Journal of Computer Science Contributions (Jucosco) Jurnal Komtika (Komputasi dan Informatika)
Purnomo, Rakhmat
Unknown Affiliation
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
A Review on AMRR and Improved Round Robin Algorithms: Comparative Study Putra, Tri Dharma; Purnomo, Rakhmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13563

Abstract

Round Robin Algorithm is a dominant algorithm in real time system. Improved round robin and average max round robin, which is also called AMRR are two types with a breakthrough. Improved round robin is an algorithm where if the remaining burst time of the process is less than the quantum, then the running process will continue to be executed. Afterwards the next iteration will be executed as its turn. So, each iteration will have a vary of quantum. It is called a dynamic time quantum. Different with improved round robin, in AMRR, in every iteration, the quantum will be calculated. So, for every iteration, the quantum might be different, depending upon the quantum calculation of the rest burst time. The first stage of this algorithm is to calculate the average of the existing burst times. Then this average is added with the maximum existing burst time. This addition then will be divided, then we get the quantum. This calculation will be executed again after the iteration finish. Based on our analysis, with quantum 10 in these two algorithms. It is can be shown that the improved round robin is less efficient than AMRR, because its average turnaround time and average waiting time is lower. The average turnaround time is 17.25 ms for AMRR compared to 23.25 ms in improved round robin. And the average turnaround time is 9 ms for AMRR compared to 15 ms in improved round robin.
Median-Average Round Robin (MARR) Algorithm for Optimal CPU Task Scheduling Purnomo, Rakhmat; Putra, Tri Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13920

Abstract

Abstract: In operating systems, multitasking or multiprocessing terms are used. If more than one task operating consecutively, but the users feel that they are running simultaneously, than it is called multitasking. Round robin algorithm is a noted algorithm in multitasking. Several modifications of classical round robin algorithm have been proposed by experts. The idea behind these modifications are to get lower turnaround time and lower waiting time. The main topic’s discussion is about median-average round robin (MARR) algorithm. In this algorithm, the processes are arranged in ascending order. Then we get the median of the burst time. Afterwards, calculation of the average burst time is done. The summation of average and median, divide by two is the time quantum. So, the time quantum will be dynamic, based on each iteration of round robin. First iteration can have different time quantum compared to the second and so on. Each iteration will have one time quantum. Three analysis’s are given. Each with five processes. In the first analysis, time quantum for 1st iteration is 11 and the 2nd iteration is 4. The average turnaround time is 29. The average waiting time is 19. For the second analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 8. The average turnaround time is 24.2. The average waiting time is 13.6. For the third analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 9. The average turnaround time is 23.2. The average waiting time is 12.8.
Research on Sobel Edge Detection Algorithm of Grayscale Images to Analyse Car Number Plate Putra, Tri Dharma; Purnomo, Rakhmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14538

Abstract

Image processing is a very important subject to be discussed in computer science. Many applications of image processing are already in the field. Image processing techniques are applied in color and grayscale images. The application of image processing are ranging for military, medical and many other applications. One most important thing to analyse image and enhance its quality is doing edge detection. Edge detection in image is a well known approach to be used to detect discontinuity in grayscale image. Edge detection functions to identify edge line in images. Sobel algorithm is one of most known algorithm, others are prewitt, canny, homogeneity algorithms. Image can be made sharper and will enhance its quality. To detect number plate of cars, an edge detection algorithm needs to be applied. In number plate, to recognize the cars number plate, the image should be clear and clean from dirt. Sometimes we can not recognize the plate number if it is too blur or has many dirt. So in its application we need a strong edge detection algorithm to recognize car number plate easily. In this journal, five car’s images are presented. Each with the original image, grayscale image and the image after edge detected by sobel algorithm. It is concluded that this algorithm is quiet good in the implementation. But in the result, there are poor quality image also. For PSNR of images after edge detected, their values are between 19 and 20 dB, which are not good.
Research and Analysis of Exchange Sort Algorithm in Data Structure Purnomo, Rakhmat; Putra, Tri Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15005

Abstract

Exchange sort is different from bubble sort. Exchange sort compares an element with other elements in the array, and swaps elements if necessary. So there is an element that is always the center element (pivot). Here is its theoretical description: Comparison, the algorithm compares each element with its adjacent element. Then continue until all elements are compared. Swap: If the elements are in the wrong order (for example, in ascending order, if the left element is greater than the right), they are swapped. This swapping continues until all match numbers are swapped. Iteration, this process of comparing and swapping, is repeated for each pair of adjacent elements in the array. Looping, this process is repeated a number of times (traversing) the array until no more swapping is required, indicating that the array is sorted. It is concluded that for the six numbers in these three case studies, the iterations needed are 5 iterations each. The swaps counts needed are 7, for case study 1. The swap counts needed are 12 for case study 2 and the swap counts are 8 for case study 3. In this research and analysis, the order, all of them is descending, although it can be made ascending. In modern days, exchange sort plays a very important role in terms of sorting algorithms. This paper is only research and analysis. For novelty, the analysis is given with a clear step-by-step procedure of the algorithm.
Pelatihan Talents Mapping Pada Guru-Guru SMK Negeri 11 Bekasi Priatna, Wowon; Purnomo, Rakhmat; Fadjriya, Andry; Kustanto, Prio
Journal Of Computer Science Contributions (JUCOSCO) Vol. 2 No. 1 (2022): Januari 2022
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/27aesk67

Abstract

Talents Mapping is an application tool for recognizing one's talents based on 34 talent themes adopted from Gallup's research, so that someone can also find out their personal strength and typology's strength. Talend mapping training is motivated by a request from the leadership of SMK Negeri 11 Bekasi so that teachers can get to know their respective talents so that in teaching and educating students more optimally and teachers after receiving this activity can teach back to their students. This training begins by recording teachers' emails to create a classroom account to ask preliminary aptitude test questions and share material. The training was conducted at the Computer Lab of SMK Negeri 11 on 12 July 2020 with 46 teachers participating. The results of the training for teachers using the lecture method, filling out the instruments, and comments from resource persons. The teachers fill in the questions about themselves at www.temubakat.com, then the answers are mapped using talents mapping to find out the potential talents of each teacher.
Segmentasi Berbasis Data Time Series Penjualan Produk Kopi Menggunakan Algoritma K-Means Anggaini, Meri; Herlawati, Herlawati; Purnomo, Rakhmat
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.15336

Abstract

Coffee shops are businesses in the Food and Beverage (F&B) sector that contribute 7.15% to Indonesia's economy. The high demand for coffee has led to increasingly fierce competition. Kanae Coffee & Space in Bekasi faces challenges in maintaining customer loyalty and managing unpredictable demand. This study aims to apply the K-Means algorithm to cluster coffee products based on time series sales data, using the 6-step CRISP-DM methodology. The number of clusters was determined using the elbow method and confirmed with a silhouette coefficient of 0.5916 (good structure). The analysis resulted in five clusters with distinct characteristics: Cluster 0 (very low demand, stable trend, very high price), Cluster 1 (very high demand but sharply declining trend, very low price), Cluster 2 (moderately high demand, moderately stable trend, moderate price), Cluster 3 (moderate demand, slowly declining trend, moderately high price), and Cluster 4 (low demand, stable trend, moderately low price). These segmentation results are expected to serve as the basis for more effective marketing strategies and product management.
Co-Authors Anggaini, Meri Fadjria, Andri Fadjriya, Andry Herlawati Herlawati Kustanto , Prio Nur Rahman, Nur Paduloh Paduloh Priatna , Wowon Putra, Tri Dharma Widyantoro, Murwan