Garuda - Garba Rujukan Digital

Rancang Bangun Aplikasi e-Commerce Dropship Berbasis Web Alexander Waworuntu
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): 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.v12i2.1823

Inabay is a small business that provides various types of products from stationery to food supplements which are distributed through dropship mechanism. The transaction process with drop shippers are still carried out conventionally with a large number of drop shippers/resellers, causes resellers unable to monitor product stock in real-time. Therefore, Inabay develops a web-based e-commerce application that can be used by resellers to make purchases of goods and monitor the movement of stock quantities. The application development process adapts the Rapid Application Development method and uses the PHP programming language with Laravel framework and MariaDB database. User acceptance of the application is evaluated using the Technology Acceptance Model with the results of 88% perceived ease of use and 96% perceived usefulness.

Development of a Mouse Pad Selection Recommendation System Using the Simple Additive Weighting (SAW) Approach Kandoko, Charoline; Waworuntu, Alexander
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3505

In the current era of technological advancement and growth, especially in the realm of virtual entertainment, specialized equipment like mouse pads has become increasingly essential. Often, the vast array of mouse pad types and brands available can be overwhelming, making it challenging for consumers to choose one that meets their specific needs. This research aims to design and develop a mouse pad recommendation system to assist individuals in selecting the most suitable mouse pad. The study employs the Simple Additive Weighting method, a weighted sum approach for problem-solving, enabling users to receive tailored recommendations based on criteria such as size, thickness, stitching, material, and price across various brands. User satisfaction was measured using the End User Computing Satisfaction (EUCS) method, achieving a satisfaction percentage of 88.67%. This indicates that the system is effective as a mouse pad recommendation tool. The mouse pad recommendation system has been successfully constructed using the Simple Additive Weighting method, following a comprehensive process of design, development, and system testing.

Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm Samudera, Elfajar Bintang; Waworuntu, Alexander; Lumba, Ester
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3653

Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%. Index Terms—Analog Television; Digital Television; Sentiment; Twitter; Random Forest

Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development Waworuntu, Alexander
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): 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.v16i2.3936

The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.

Development of a Mouse Pad Selection Recommendation System Using the Simple Additive Weighting (SAW) Approach Kandoko, Charoline; Waworuntu, Alexander
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3505

In the current era of technological advancement and growth, especially in the realm of virtual entertainment, specialized equipment like mouse pads has become increasingly essential. Often, the vast array of mouse pad types and brands available can be overwhelming, making it challenging for consumers to choose one that meets their specific needs. This research aims to design and develop a mouse pad recommendation system to assist individuals in selecting the most suitable mouse pad. The study employs the Simple Additive Weighting method, a weighted sum approach for problem-solving, enabling users to receive tailored recommendations based on criteria such as size, thickness, stitching, material, and price across various brands. User satisfaction was measured using the End User Computing Satisfaction (EUCS) method, achieving a satisfaction percentage of 88.67%. This indicates that the system is effective as a mouse pad recommendation tool. The mouse pad recommendation system has been successfully constructed using the Simple Additive Weighting method, following a comprehensive process of design, development, and system testing.

Public Sentiment Analysis on the Transition from Analog to Digital Television Using the Random Forest Classifier Algorithm Samudera, Elfajar Bintang; Waworuntu, Alexander; Lumba, Ester
ULTIMATICS Vol 16 No 1 (2024): 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.v16i1.3653

Television is one of the most popular media for entertainment and information. Analog television is the most widely used type among the public. However, with technological advancements, analog television is becoming obsolete and is being replaced by digital television, which offers better performance. On November 2, 2022, the Government officially mandated the transition from analog to digital broadcasting. This Analog Switch Off program has elicited various pro and con opinions among the public. Twitter, a widely used social media platform, facilitates rapid communication and information dissemination among users. This study aims to classify public sentiment regarding the Analog Switch Off policy as either positive or negative. The classification model used is the Random Forest algorithm, with the Lexicon Inset for data labeling, Count Vectorizer and TF-IDF Vectorizer for data vectorization and weighting, and various train-test data splits. The study achieved the best classification performance using the Count Vectorizer method, with an 80%:20% train-test data ratio, yielding an accuracy of 88%, precision of 88%, recall of 88%, and an F1-score of 88%. Index Terms”Analog Television; Digital Television; Sentiment; Twitter; Random Forest

Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development Waworuntu, Alexander
ULTIMATICS Vol 16 No 2 (2024): 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.v16i2.3936

The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.

Title

Found 7 Documents
Search
Journal : Jurnal ULTIMATICS

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 7 Documents Search Journal : Jurnal ULTIMATICS

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 7 Documents
Search
Journal : Jurnal ULTIMATICS