Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal The IJICS (International Journal of Informatics and Computer Science) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer
Nurhasan Nugroho
Universitas Bina Bangsa, Banten
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Multi-Criteria Decision Making Using Additive Ratio Assessment in Digital Voice Recorder Selection System Nurhasan Nugroho
The IJICS (International Journal of Informatics and Computer Science) Vol 6, No 2 (2022): July 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v6i2.4686

Abstract

Digital Voice Recorder has many uses, usually used for interviews, recording voices and songs, recording meeting results, and can be used for learning. Currently, various Digital Voice Recorder products have been circulating and have different functions and specifications that are created according to the needs of users. For this reason, users must be observant in choosing a Digital Voice Recorder to support their work. So, we need a system that can provide recommendations and help in making decisions to choose the right Digital Voice Recorder. This study aims to develop a decision support system with Multiple Criteria Decision Making (MCDM) using Additive Ratio Assessment (ARAS) to assist in selecting a Digital Voice Recorder, so that it can assist in selecting the best solution appropriately and according to user needs. The ARAS method is used as a model that can select the best alternative based on the utility level of each alternative to determine the best alternative. Based on the case studies conducted, the utility values of each alternative were obtained, namely: Zoom Handy Recorder with a value of 0.5672, Sony PX470 with a value of 0.6147, Ruizu X52 with a value of 0.4664 and Tascam DR-22ML with a value of 0.9096. So, the best alternative is the Tascam DR-22ML. Based on testing through the black-box testing method, it shows that the system built has been running well
Implementasi Metode Composite Performance Index (CPI) Pada Sistem Pendukung Keputusan Pemilihan SSD Eksternal Nurhasan Nugroho
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2553

Abstract

The need for data storage is currently increasing, so external or portable data storage is developing. Likewise for SSDs, currently the External SSD market has been circulating which offers practicality, speed and large capacity. However, problems arise in selecting an External SSD, it will take time and effort to find information about product specifications and match the wishes and needs of the buyer. The purpose of this study is to implement the Composite Performance Index (CPI) method in a decision support system for choosing an External SSD, so that it can make it easier for users to determine alternatives quickly and precisely. The CPI method is used to solve decision problems with a number of alternatives through a combined index to rank alternatives from several criteria. The results of the calculations in the case study produced the highest score obtained by the alternative Kingston XS2000 SSD (A3) with a value of 130,001, followed by Lacie Rugged SSD Pro (A1) with a value of 108,334, Samsung SSD T7 Touch (A4) with a value of 100 and Seagate SSD Portable (A2) with a score of 93,335. The calculation of the CPI method obtained from the system obtains the same calculation results as the manual results. Based on testing with the black-box testing technique, all test cases are declared valid, this means that the system can work properly.
Implementasi Algoritma Greedy dan Algoritma A* Untuk Penentuan Cost Pada Routing Jaringan Ristasari Dwi Septiana; Dimas Abisono Punkastyo; Nurhasan Nugroho
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 2 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i2.576

Abstract

The current increase in internet development raises new problems in terms of path optimization on the internet. This makes network path optimization a major problem in choosing the shortest route. The purpose of this research is to understand and compare the process of finding the shortest route using two algorithms, namely Greedy and A*. The A* algorithm has an advantage in overcoming network workloads compared to the Greedy algorithm. In implementation, both algorithms have the same results in determining the delivery path. However, the A* algorithm is more effective for use on large and complex networks because it has more certain and accurate calculations. From the test results, it was found that the A* algorithm has better performance than the greedy algorithm in the test. Where the final cost value of the greedy algorithm is 49, while for the A* algorithm is 48
Decision Support System for Selection of Virtual Reality Head-Mounted Display Using the WASPAS Method Nurhasan Nugroho
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3272

Abstract

Virtual reality is a technology that allows one to carry out simulations by presenting three-dimensional visuals and atmosphere through a device called a Head-Mounted Display (HMD) or VR HMD. To make a VR HMD selection, users must know one by one the specifications of the product to be selected, this of course makes the selection process long and makes it difficult to make a choice. This study aims to develop a decision support system by implementing the Weighted Aggregated Sum Product Assessment (WASPAS) approach for selecting VR HMDs, in order to make it easier for users to make decisions. The WASPAS method can determine the best alternative through prioritization that is relevant to the weighting used. Based on the case study conducted, the best alternative results were Shinecon 6.0 VR Box with a value of 0.9021, followed by Enric VR Box with a value of 0.8965, Yoko VR Box with a value of 0.8179, VRPark VR Box with a value of 0.8158 and BoboVR Z5 VR Box with a value of 0.7827. The built decision support system has produced valid calculations, this is because the calculations obtained by the system with manual calculations produce the same value. On the results of usability testing get an average value of 90% and fall into the good category. This shows that the developed system is easy to use and feasible to implement.
Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm Nuke L Chusna; Nurhasan Nugroho; Umbar Riyanto; Ahmad Ari Aldino
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4104

Abstract

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Co-Authors Ahmad Ari Aldino Chusna, Nuke L Dimas Abisono Punkastyo Ristasari Dwi Septiana Umbar Riyanto