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INDONESIA
Jurnal ULTIMATICS
Published by Universitas Multimedia Nusantara
ISSN : 20854552     EISSN : 2581186X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Jurnal ULTIMATICS merupakan Jurnal Program Studi Teknik Informatika Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang analisis dan desain sistem, programming, algoritma, rekayasa perangkat lunak, serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, kecerdasan buatan, pemrograman sistem mobile, serta topik lainnya di bidang Teknik Informatika. Jurnal ULTIMATICS terbit secara berkala dua kali dalam setahun (Juni dan Desember) dan dikelola oleh Program Studi Teknik Informatika Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 7 Documents
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Search results for , issue "Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika" : 7 Documents clear
Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method Farhan Hashfi; Dedy Sugiarto; Is Mardianto
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2384

Abstract

Tweets from users in the form of opinions about a product can be used as a company evaluation of the product. To obtain this evaluation, the method that can be used is sentiment analysis to divide opinions into positive and negative opinions. This study uses 1000 data from Twitter related to an internet service provider company where the data is divided into two classes, namely 692 positive classes and 308 negative classes. In the Tweet there are still many words that are not standard. Therefore, previously carried out the initial process or preprocessing to filter out non-standard words. Before doing the classification, the data needs to be divided into training data and test data with a ratio of 90:10, then processed using the Support Vector Machine and Naïve Bayes techniques to get the results of the classification of positive opinions and negative opinions. The level of accuracy in the classification using the Support Vector Machine is 84% ​​and using Naïve Bayes is 82%.
The Influence of KMS Trello on the Intern Performance CDC UIN Jakarta Jihan Fadhilah; Evy Nurmiati
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2450

Abstract

Trello is software used for knowledge and project management. The ease of appearance and integration with Google Drive makes the UIN Jakarta Career Center interested in using Trello as a workspace for their intern. The problem of this research stems from the number of internals with non-technological backgrounds and this is the first time using this tool. So the purpose of this study is to see the effect of Trello software on internal performance and to see the difference in influence between technology and non-technological internals by using a research method, namely a descriptive qualitative approach through interviews with several selected sources
The Development of UMS Building Catalogue Information System Ganno Tribuana Kurniaji; Mohammad Faqih Eza 'Ammar; Yusuf Sulistyo Nugroho
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2525

Abstract

Universitas Muhammadiyah Surakarta (UMS) as one of the biggest private university in Indonesia has a large number of new students per year. However, most new students often get confused to identify and even do not know the location of the buildings that handle various activities in the early period of lectures, such as student orientation, English pre-tests, etc. This is due to lack of information provided by the university to the new enrolled students. To overcome this problem, this work aims to develop a web-based building catalogue by implementing a framework, namely Ionic. This system is built to help the new students getting the building-related information in UMS and find out the location of the building. By following the waterfall model to build the system, the results show that we have successfully developed the building catalogue information system. The main page of the information system displays a list of buildings that are within the scope of UMS, starting from Building A to Building L completed with photos and detailed information about the building. The system is helpful for new students to get the information of all buildings in UMS.
Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text Aprilia Nurkasanah; Mardhiya Hayaty
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2540

Abstract

Text Mining is a part of Neural Language Processing (NLP), also known as text analytics. Text mining includes sentiment analysis and emotion analysis which are often used in analysis on social media, news, or other media in written form. The emotional breakdown is a level of sentiment analysis that categorises text into negative, neutral, and positive sentiments. Emotion is categorized into several classes, In this study, emotion is categorized into 5 classes namely anger, fear, happiness, love, and sadness. This study proposed feature extraction using Lexicon and TF-IDF on the emotion recognition dataset of Indonesian texts. InSet Lexicon Dictionary is used as the corpus in performing the feature extraction. Therefore, InSet Lexicon was chosen as the dictionary to perform feature extraction in this study. The results show that InSet Lexicon has poor performance in feature extraction by showing an accuracy of 30%, while TF-IDF is 62%.
Elicitation of Needs Using User Personas to Improve Software User Experience Ahmad Raihan Djamarullah; Wahyu Andhyka Kusuma
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2633

Abstract

In the development of a software, the elicitation of requirements in the software development process is a very important phase because at this stage it will determine how a system is made to work properly and according to the plan. User Persona is an HCI technique that works by gathering information about a user to understand the characteristics of that user. The purpose of this research is to solve the problem of uploading practicum modules which are sometimes late so that students or practitioners lack time to study and complete the given practicum assignments. The use of user personas applied at the elicitation stage of software requirements facilitates the needs analysis stage so that it is easier to understand what needs to be developed from the software according to the needs of the user.
E-Business Software Product Line Methodology Based on SMEs Characteristics Ni Made Satvika Iswari; Eko Kuswardono Budiardjo; Zainal Arifin Hasibuan
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2642

Abstract

The growth of Small and Medium Enterprises (SMEs) will certainly have a positive influence on economic development in a country. However, many SMEs struggle to survive, grow, and show limited productivity. It was recorded that there were around 63.9% of SMEs in Indonesia whose turnover had decreased by more than 30% during the COVID-19 pandemic. Among the factors that cause the low survival rate of SMEs, a factor that is considered critical is the lack of success in the use of e-business. The use of e-business by SMEs is not a one-size-fits-all solution, because SMEs have various characteristics. In this study, an E-Business Software Product Line Methodology based on SMEs characteristics was proposed using Software Product Line Engineering (SPLE) approach. In general, the proposed methodology was sufficient to describe the aspects needed in building a software development methodology. Aspects that had not been described were those related to software project management. The proposed methodology is useful for building an e-business application platform that can be customized based on SMEs characteristics.
Teakwood Grade Identification with GLCM and K-NN with Adaboost Optimization Nirma Ceisa Santi; Hastie Audytra
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2679

Abstract

Teak is one type of tree that has many functions and uses. Teak wood has a very high quality to be used as raw material for the manufacture of home furniture such as tables, chairs, cabinets, and others. But middle testers (Perhutani staff) who test the quality of wood grade have limitations if the classification uses the five senses of sight and also there are still many furniture entrepreneurs who are often mistaken about teak wood quality assessment. This resulted in a lack of quality grade teak wood used as raw material for making home appliances or for furniture and trade needs under the Perhutani Corporation, especially the Cepu Kph. The teak wood image data is then acquired through preprocessing data ready to be processed. By using GLCM as an image feature extraction both training data and testing data. After the image characteristics are obtained, the image is classified by the K-Nearest Neighbor method with adaboost optimization. The final result is obtained in the form of wood grade quality classification namely grade A, B, C and D according to the class

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