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Implementation of Scrum Method for Designing Website-Based E-commerce Application (Case Study: Putra Prabu Workshop) Wijaya, William; Tobing, Fenina Adline Twince
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.v15i2.3487

The internet is a technology that has now become a major necessity in the world. There are many applications designed using the internet to meet daily needs, such as educational, commercial and other applications. according to DataIndonesia.id the number of motorized vehicles in Indonesia, which according to vehicles in Indonesia reached 141.99 million units in 2021. Bengkel Putra Prabu is a workshop that operates in the city of Prabumulih, Putra Prabu has several problems, such as lack of intensive advertising. Scrum is a software engineering method using agile principles that relies on team collaboration, incremental products and an iteration process to realize the final result. The results show user acceptance of the system system was 76.06% for the Perceived Ease Of Use category, 73.51% for the Perceived Usefulness category, 71.53% for the Atitude Toward Using category, and 71.89% for the Behavioral Intentional category. The conclusion of this research the system that has been created is well received by users.

Implementation of SAW Method for Design and Development Apartment Recommendation System in Tangerang Using Mobile-Based Nugraha, Achmad Ilyasa; Kusnadi, Adhi; Tobing, Fenina Adline Twince
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.v15i2.3492

The house is no longer the sole type of residence available while looking for a place to live. Apartments are a solution for those who need a place to live in locations with limited land, such as Tangerang, in today’s period. However, criteria are needed to choose an apartment based on a person’s needs, thus in this project, we will develop and create an apartment recommendation system in Tangerang using the SAW approach to make it easy for people to choose the best apartment. The user’s choice will be determined by the recommendation system based on their interests, activity, and other data. To put the recommendation system into action, the FMADM method must be employed. A Simple Additive Weighing (SAW) approach is required to complete this FMADM, which is a mechanism for computing the number of performance appraisals for each alternative based on all criteria. This recommendation system is called APARTKU, and it was created with HTML5, CSS, and AngularJS, as well as the Ionic Framework and the Firebase Database. The system was then put to the test by administering questionnaires to 32 respondents using the DeLone and McLean methodologies, and the results were tallied using the Likert Scale method, yielding a score of 90.64 percent, based on the interval on the Likert Scale technique, these results imply that the application has been constructed and designed very well.

Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
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.v16i1.3610

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.

Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
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.v16i2.3874

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.

Enhancing Intelligent Tutoring Systems through SVM-Based Academic Performance Classification and Rule-Based Question Recommendation Tobing, Fenina Adline Twince; Haryanto, Toto
ULTIMATICS Vol 17 No 1 (2025): 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.v17i1.4178

The aims to automatically classify students' academic performance levels using Support Vector Machine (SVM) algorithm and automatically recommend questions based on classification results. Dataset consists of six assignment scores per student, averaging students into three performance levels: Beginner, Intermediate, and Advanced. Before training, the data undergoes preprocessing involving normalization with Standard Scaler and splitting into training and testing sets. The model is trained using Radial Basis Function (RBF) kernel with hyperparameter tuning to optimize its performance. The evaluation results show that the model achieved an accuracy of 91.67%, with a precision of 93.06%, a recall of 91.67%, and an F1-score of 91.89%. The best performance was found in the Intermediate class, the dominant category in the dataset, while performance in the Advanced category was relatively lower due to limited sample size. Following classification, a rule-based recommendation system is used to suggest questions that match the student's predicted level of competence. This approach supports a more adaptive and personalized learning environment. The findings demonstrate that the SVM algorithm effectively supports intelligent learning systems such as the Intelligent Tutoring System (ITS). Future work should include data balancing techniques, expansion of dataset size, and comparative analysis with other algorithms to enhance model generalization.

Implementation of Scrum Method for Designing Website-Based E-commerce Application (Case Study: Putra Prabu Workshop) Wijaya, William; Tobing, Fenina Adline Twince
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.v15i2.3487

The internet is a technology that has now become a major necessity in the world. There are many applications designed using the internet to meet daily needs, such as educational, commercial and other applications. according to DataIndonesia.id the number of motorized vehicles in Indonesia, which according to vehicles in Indonesia reached 141.99 million units in 2021. Bengkel Putra Prabu is a workshop that operates in the city of Prabumulih, Putra Prabu has several problems, such as lack of intensive advertising. Scrum is a software engineering method using agile principles that relies on team collaboration, incremental products and an iteration process to realize the final result. The results show user acceptance of the system system was 76.06% for the Perceived Ease Of Use category, 73.51% for the Perceived Usefulness category, 71.53% for the Atitude Toward Using category, and 71.89% for the Behavioral Intentional category. The conclusion of this research the system that has been created is well received by users.

Implementation of SAW Method for Design and Development Apartment Recommendation System in Tangerang Using Mobile-Based Nugraha, Achmad Ilyasa; Kusnadi, Adhi; Tobing, Fenina Adline Twince
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.v15i2.3492

The house is no longer the sole type of residence available while looking for a place to live. Apartments are a solution for those who need a place to live in locations with limited land, such as Tangerang, in today's period. However, criteria are needed to choose an apartment based on a person's needs, thus in this project, we will develop and create an apartment recommendation system in Tangerang using the SAW approach to make it easy for people to choose the best apartment. The user's choice will be determined by the recommendation system based on their interests, activity, and other data. To put the recommendation system into action, the FMADM method must be employed. A Simple Additive Weighing (SAW) approach is required to complete this FMADM, which is a mechanism for computing the number of performance appraisals for each alternative based on all criteria. This recommendation system is called APARTKU, and it was created with HTML5, CSS, and AngularJS, as well as the Ionic Framework and the Firebase Database. The system was then put to the test by administering questionnaires to 32 respondents using the DeLone and McLean methodologies, and the results were tallied using the Likert Scale method, yielding a score of 90.64 percent, based on the interval on the Likert Scale technique, these results imply that the application has been constructed and designed very well.

Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
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.v16i1.3610

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.

Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
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.v16i2.3874

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.

Design and Evaluation of an AI-Driven Gamified Intelligent Tutoring System for Fundamental Programming Using the Octalysis Framework Dzaky Fatur Rahman; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona
ULTIMATICS Vol 17 No 2 (2025): 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.v17i2.4514

This research aims to address the challenges of student motivation and engagement in fundamental programming education by implementing the Octalysis Gamification Framework within an Intelligent Learning System. Traditional learning methods often fail to visualize abstract concepts or provide personalized feedback, leading to student demotivation. To overcome this, a platform named "Starcoder" was designed and built, integrating two conceptual pillars: the eight core drives of the Octalysis Framework and an AI-supported Intelligent Tutoring System (ITS). The system employs the Next.js framework and integrates the Gemini AI API (M.E.C.H.A.) to provide real-time, adaptive feedback and remedial learning paths. The effectiveness of the platform was evaluated using the Hedonic-Motivation System Adoption Model (HMSAM) with 54 respondents, comparing the gamified platform against traditional classroom methods. Evaluation results demonstrate that the platform significantly outperforms traditional methods, achieving an 86.44% score in Perceived Usefulness and an 85.56% score in Curiosity. Notably, Behavioral Intention to Use increased by 15.56% compared to the baseline. These findings demonstrate that the comprehensive integration of gamification frameworks with generative AI agents effectively enhances student motivation and immersion in technical education. Future work should focus on expanding the AI's capability to dynamically adjust gamification elements in real-time based on student performance.

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