cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282370070808
Journal Mail Official
mesran.skom.mkom@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Computing and Informatics Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2808375X     DOI : -
Core Subject : Science,
Computer Science & IT
Fokus kajian Journal of Computing and Informatics Research mempublikasikan hasil-hasil penelitian pada bidang informatika, namun tidak terbatas pada bidang ilmu komputer yang lain, seperti: 1. Kriptografi, 2. Artificial Intelligence, 3. Expert System, 4. Decision Support System, 5. Data Mining, dan lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 3 No 2 (2024): March 2024" : 5 Documents clear
Penerapan Metode Jaringan Saraf Tiruan Dalam Memprediksi Produksi Daging Domba Menurut Provinsi Listy Oktaviani; Sandy Erlangga; Bintang Aufa Sultan; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.992

Abstract

Prediction is the process of estimating future needs. This research aims to predict the amount of sheep meat production by province. Lamb is a source of protein which is also a high value commodity. However, along with the increase in lamb production in Indonesia, the level of lamb meat consumption in Indonesia has tended to fluctuate in recent years. Imports are the step most often taken by the government to meet domestic sheep meat needs. By using Artificial Neural Networks and the backpropagation algorithm, the amount of sheep meat production will be predicted based on provinces in order to determine steps to fulfill domestic sheep meat needs based on the amount of sheep meat consumption in the community. This research uses data from 2001 to 2022 with 1 target, namely data for 2023.
Klasifikasi Peminatan Topik Keilmuan Dalam Penyelesaian Studi Menggunakan Algoritma Naive Bayes Waldi Setiawan; Dedy Hartama; Muhammad Ridwan Lubis; Ihsan Syajidan; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1200

Abstract

Academic expertise is a subject of study taught at the university level to assist students in completing their thesis writing, thereby enabling them to successfully complete their graduate studies. The chosen academic specialization aligns with the vision and mission of each program and can have a positive impact on the university. Students' chosen fields of expertise in completing their studies may either align or not align with the program's vision and mission. The variables used in this research are GPA, MKRV1, MKRV2, and Academic Expertise. The aim of this research is to determine how many students select an academic topic that aligns with the program's vision and mission, particularly in this case, the Computer Science program, as they complete their studies. The Naïve Bayes algorithm is employed in this research, yielding an accuracy rate of 98.11%. This research can provide valuable insights for STIKOM Tunas Bangsa Pematang Siantar to understand the extent to which students from other programs choose academic expertise that aligns with the vision and mission of each program.
Sistem Pendukung Keputusan Pemilihan Smartphone Kelas Midrange 2023 dengan Menggunakan Metode MAUT Afri Nirmalasari Halawa; Helfrida Hotmaria Sihite; Muhammad Syahrizal
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1201

Abstract

Smart phones (HP) have become a basic need for society in various aspects of life, from communication, entertainment, information, to work. There are various kinds of cellphones on the market, with various features and specifications offered. The price of cellphones is getting more and more expensive, so consumers' needs for using cellphones are also different. Midrange cellphones are suitable for users who want a cellphone with fairly good specifications, but at a relatively affordable price. Midrange is a category of cellphone that is between the entry-level and high-end categories. Midrange cellphones usually have better specifications than entry-level cellphones, but are not as expensive as high-end cellphones. Therefore, an appropriate midrange class cellphone selection process is needed for people to get a cellphone that suits their needs at an affordable price. In the selection process for midrange class cellphones, a decision-making technique known as SPK (Decision Support System) is required. In this research, the MAUT method is used for the midrange class HP selection process. The level of importance of each criterion is determined by the researcher himself. The final result obtained is that the A5 is the highest midrange class cellphone with a final value (Ui) of 0.865. The second ranked alternative is A13 with a final value (Ui) of 0.857 and the third ranked alternative is A17 with a final value (Ui) of 0.657.
Implementation of the Simple Additive Weighting (SAW) Method in Selection of Students Recipients of Single Tuition Fee Assistance Riki Sulistio; Ahmad Fahreza Nasution; Muhammad Tawaf Akbar; Rima Tamara Aldisa; Bister Purba
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1202

Abstract

Higher education in Indonesia, including at Budi Darma University, continues to be committed to creating an inclusive and equitable learning environment for all students. In line with this determination, Budi Darma University is implementing the Single Tuition Assistance (BUKT) program to support educational accessibility. Obstacles in the selection process, which is subjective and lacks a structured framework, can lead to inequality in the distribution of aid. This can result in students who should receive greater support being overlooked, while those who may need less may receive greater aid. In the BUKT selection process, there are a number of requirements that must be met, such as parents' income, PKH card ownership, completeness of documents, parents' dependents and home ownership. It is hoped that the use of a decision support system can be a solution to overcome this challenge. Decision Support Systems (DSS) integrate computer technology, mathematical models, and data to provide structured and organized support within a decision-making framework. The Simple Additive Weighting (SAW) method is a multi-criteria decision making method that allows relative weighing between criteria to determine the final score for each alternative. By applying SAW in the selection of BUKT recipient students, it is hoped that more objective and data-based decisions can be obtained. The research results produced the best alternative with a value of 100.00 in the alternative with code A5 in Fitri's name, so that Fitri was declared entitled to receive single tuition assistance.
Utilizing K-Means Clustering to Understanding Audience Interest in SEO-Optimized Media Content Erlin Windia Ambarsari; Dedin Fathudin; Gravita Alfiani
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1207

Abstract

This study observes k-means clustering for segmenting SEO data to understand audience interests, identifying the elbow method as crucial for determining the optimal number of clusters. It highlights notable differences in content engagement across clusters, emphasizing the need for refined SEO strategies and a deeper understanding of audience segmentation. Despite challenges like SEO's dynamic nature and data reliance, this methodology provides a strong foundation for enhancing content strategies. Future research suggestions include cross-platform data integration, longitudinal studies, sentiment analysis, content experimentation, user experience (UX) focus, and monitoring algorithm updates to develop more adaptive content and SEO strategies aligned with changing audience behaviors.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 5 No 1 (2025): November 2025 Vol 4 No 3 (2025): July 2025 Vol 4 No 2 (2025): March 2025 Vol 4 No 1 (2024): November 2024 Vol 3 No 3 (2024): July 2024 Vol 3 No 2 (2024): March 2024 Vol 3 No 1 (2023): November 2023 Vol 2 No 3 (2023): July 2023 Vol 2 No 2 (2023): March 2023 Vol 2 No 1 (2022): November 2022 Vol 1 No 3 (2022): July 2022 Vol 1 No 2 (2022): March 2022 Vol 1 No 1 (2021): November 2021 More Issue