cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ichwanul Muslim Karo Karo
Contact Email
cs@unimed.ac.id
Phone
+6285262688968
Journal Mail Official
jids@unimed.ac.id
Editorial Address
Gedung 77, FMIPA di Jalan Willem Iskandar, Pasar V Medan Estate, Percut Sei Tuan, Deli Serdang
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Informatics and Data Science (J-IDS)
Published by Universitas Negeri Medan
ISSN : -     EISSN : 29640415     DOI : https://doi.org/10.24114/j-ids.xxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Journal of Informatics and Data Science (J-IDS) is a scientific journal managed by the Computer Science Study Program, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia which contains scientific writings on pure research and applied research in the field of computer science and data science as well as summarizing general developments in related theories, methods and applied sciences. Focus dan Scope J-IDS covers: Artificial Intelligence Science Computation Data Mining Data Science Big Data Natural Language Processing Computer Vision Expert System Text and Web Mining Parallel Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 33 Documents
 1   2   3   4 
Implementing Combined FEFO and FIFO Methods in Inventory System (Case Study: UD Ilham Pilly Beef Merchant) Ramadhan, Ilham; Usman, Ari; Sarudin, Sarudin
Journal of Informatics and Data Science Vol. 2 No. 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.51505

Abstract

Stock inventory is an important aspect of supply chain management. The success of the company's operations in maintaining stock availability and avoiding losses due to damage or expiration of goods is very dependent on the use of the right method of managing inventory. The purpose of this study is to combine the FEFO (first expired first out) and FIFO (first in first out) methods in the UD. Ilham Pilly Beff Merchant stock inventory system to avoid losses due to expired goods and increase stock rotation because the FEFO and FIFO methods are operational management in determining inventory. The results of this study are that the system that has been designed can facilitate managers in the process of collecting data on incoming and outgoing goods so that the risk of managing product stocks can be minimized and with an inventory system that has been built
Sentiment Analysis of Twitter Users Regarding Taxation Topics in Indonesia Utilizing Multinomial Naive Bayes Tarigan, Dewan Dinata; Al Idrus, Said Iskandar
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.52465

Abstract

The country's income is heavily dependent on taxes, which contribute to improved public well-being. Public confidence in tax authorities plays a key role in increasing tax receipts. Therefore, it is important to measure this level of confidence. One of the methods used is sentimental analysis, which helps to understand public views on regulations, services, performance, and tax policies. One of the purposes of this study is to measure the sentiment of Twitter users towards taxation in Indonesia. Sentiment analysis involves data collection processes, initial data processing, separation of datasets, feature extraction, classification, and evaluation. The classification model used is Multinomial Naive Bayes with a comparison of 80% training data and 20% test data. The results show that 89.65% of tweets about taxation in Indonesia have negative sentiment. The model evaluation was carried out on two test scenarios, namely initial data and randomly under-sampleed data. Classification on initial data achieved accuracy of 89.97%, precision of 46.68%, and sensitivity of 33.61%. Whereas on undersampling data results, accuration reached 53.28%, accurateness of 52.66%, and sensibility of 52.52%. Analysis showed significant differences between the two scenarios in which undersammpling techniques resulted in a more balanced distribution of data. Despite this, the model still faces difficulties in classifying positive and neutral data due to the dominance of negative sentiment.
Classification of North Sumatra Batak Ulos Based on Ethnicity Using Convolutional Neural Network Algorithm Approach Kiswanto, Dedy
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.60388

Abstract

Ulos is a piece of cloth produced through a weaving process that reflects a rich cultural heritage and has high value. The patterns contained in woven ulos often contain philosophical meanings, reflecting the traditional values, beliefs and history of the communities that produce them. However, in reality there are still many Batak young men and women and the general public who are not yet able to distinguish between types of ulos. This research aims to help identify types of uos with the hope of providing deeper insight into the diversity of ulos based on ethnicity in North Sumatra. The dataset used in this research consists of 600 datasets which are divided into 6 types of ulos. Before the classification process is carried out, the data is cleaned through data preprocessing by cropping the image data to produce the same image data size. The research results show a classification accuracy rate of 96%. This finding confirms that the Convolutional Neural Network (CNN) method can be applied to classify ulos based on ethnicity. This has important implications in increasing understanding and appreciation of the traditional arts of the Batak tribe and supporting efforts to preserve this valuable cultural heritage

Page 4 of 4 | Total Record : 33

 1   2   3   4 

Filter by Year

2022 2024


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2024): November 2024 Vol. 3 No. 1 (2024): JUNE 2024 Vol 3, No 1 (2024): JUNE 2024 Vol 2, No 1 (2023): VOL 2, NO 1 (2023): VOL 2, NO 1 (2023) Vol 2, No 2 (2023): November Vol. 2 No. 2 (2023): November Vol 1, No 2 (2022): Vol 1, No 2 (2022) Vol 1, No 1 (2022): Vol 1, No 1 (Juni 2022)