INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi

Analysis and Design of Customer Relationship Management System on the SMEs Using Iconix Process

Thu, 01 Aug 2024 00:00:00 +0700

Background: Integrating Customer Relationship Management (CRM) systems is crucial for small and medium enterprises (SMEs) to enhance customer relations and profitability. Many SMEs in Indonesia, including Go-Sumber Plastik, still need to fully utilize CRM systems, which are essential for managing customer data, improving satisfaction, and retaining customers. Objective: The purpose of this research is to analyze and design a web-based CRM system for Go-Sumber Plastik using the Iconix Process methodology to enhance user interaction and overall system effectiveness. Methods: The study employed the Iconix Process methodology, which includes a use case, robustness, sequence diagrams, a GUI prototype, and a test plan. The design was tested using Maze to measure user interaction efficiency and satisfaction. Results: The research revealed significant challenges in user understanding of the CRM system, particularly in managing activities and adding customer information. Tasks such as reporting and logging in had good user performance. The overall user interaction score was 81.1, indicating moderate effectiveness of the initial design. Conclusion: The results underscore the necessity for a more intuitive and streamlined CRM system interface for Go-Sumber Plastik. Implementing an effective CRM system can improve SMEs' competitiveness and profitability by systematically enhancing communication, managing customer data, and boosting business performance. Future research should focus on refining the user interface to reduce error rates and improve task completion efficiency. Enhanced visibility and user guidance are recommended to optimize system usability and customer satisfaction.

Technology Acceptance Analysis Using UTAUT: A Study of QRIS Acceptance during the Pandemic

Thu, 01 Aug 2024 00:00:00 +0700

Background: The COVID-19 pandemic situation has compelled society to practice physical distancing. One of the government's efforts is to encourage the use of the QRIS payment method to minimize direct physical contact during transactions. Objective: The purpose of this research is to analyze the primary driving factors in the adoption of QRIS technology. The research urgency is to determine the most contributing predictor from the variables within the UTAUT model among the people of Jabodetabek. Methods: This research used the quantitative method by conducting an online survey among 384 respondents distributed across the Jabodetabek region. The sampling technique utilized was non-purposive sampling with criteria including domicile, age, reasons, frequency, and experience of QRIS usage. Conclusion: The results of the factor analysis test indicate that the performance expectancy and effort expectancy variables are combined into one variable, while the social influence variable is divided into two independent variables. The research findings reveal that the perceived risk variable is the predictor with the most significant contribution in the context of the pandemic situation. Future researchers are expected to be able to develop the research model in other contexts with different goals.

Enhancing the Decision Tree Algorithm to Improve Performance Across Various Datasets

Pandu Pratama Putra, M Khairul Anam, Sarjon Defit, Arda Yunianta — Thu, 01 Aug 2024 00:00:00 +0700

Background: The Village Fund is an initiative by the central government to promote equitable regional development. However, it has also led to corruption. Many Indonesians share their opinions on the Village Fund on social media platforms like X, and news coverage is extensive on portals like detik.com. Objective: This study aims to classify data from social media and news coverage to enhance understanding. Methods: The research improves the decision tree algorithm by integrating other algorithms and techniques such as XGBoost and SMOTE. Ensuring high accuracy is vital for the credibility of machine learning classifications among the public. The study uses two different datasets, necessitating varied testing approaches. For the news portal dataset, a single test with seven labels is conducted, followed by enhancement with XGBoost. The X dataset undergoes two tests with datasets of 1200 and 3078 entries, using three labels. Conclusion: The evaluation results indicate that the highest accuracy achieved with the news portal data was 82%, thanks to a combination of decision tree algorithms with various parameters and the balancing effect of SMOTE. For the Twitter dataset with 3078 entries, the highest accuracy reached 95%, attributed to the application of ensemble techniques, particularly boosting.

A Prototype Design of a Vertical Axis Wind Turbine as One of the Renewable Energy Sources in Brunei

Muhammad Azim Mahmood, Sri Hastuty, Iwona Gołdasz, Wahyu Caesarendra — Thu, 01 Aug 2024 00:00:00 +0700

Background: According to the Asia Wind Energy Association, Brunei can harness the power of wind energy to meet its future demands for a reliable energy source that is both renewable and non-polluting. Objective: A preliminary study to design and manufacture wind turbines needs to be initiated earlier especially in the Brunei with has potential wind energy. Methods: This preliminary study compares several Vertical Axis Wind Turbine (VAWT) types and examines the optimal design in terms of mechanical parts for wind speed characteristics in Brunei. The project focuses on the engineering design stages to obtain a selected design that differs from other available designs. Results: The preliminary study successfully generated a small amount of electricity from the mechanical rotation of the VAWT. Conclusion: Although the preliminary study can generate a small amount of electricity, several design parameters need to be improved in further study. Proper manufacturing technologies are also needed to fabricate a better VAWT.

Sentiment Analysis of YouTube Users on Blackpink Kpop Group Using IndoBERT

Slamet Riyadi, Lathifah Khansa Salsabila, Cahya Damarjati, Rohana Abdul Karim — Thu, 01 Aug 2024 00:00:00 +0700

Background: The Korean Pop (K-Pop) phenomenon has become an important part of popular culture worldwide, with Blackpink being one of the most influential groups. Analyzing sentiment toward Blackpink is urgent, given its growing popularity and wide influence among fans worldwide. In the present technological era, social media platforms such as YouTube have evolved into a space where artists and their fans may interact with each other. As a consequence, social media has become a powerful tool for assessing the emotional tone and sentiment conveyed by individuals. Objective: This research aims to explore the trend of public sentiment towards Blackpink and evaluate how well the IndoBERT model analyzes the sentiment of Indonesian texts. Methods: The objective of this study is to examine the pattern of public sentiment towards Blackpink and assess the proficiency of the IndoBERT model in analyzing the sentiment of Indonesian writings. Results: The findings demonstrated that the IndoBERT model had an exceptional level of precision, achieving a 98% accuracy rate. In addition, it obtained a f1, recall, and accuracy score of 95%. The remarkable results demonstrate the efficacy of the IndosBERT technique in evaluating the emotion of Indonesian-language literature towards Blackpink. Conclusion: This study enhances the knowledge of how fans and audiences react to K-pop material and establishes a foundation for future research and advancement. The impressive precision of the IndoBERT model showcases its capacity for sentiment analysis in Indonesian literature, making it a useful tool for future research endeavors.

Recommendation System for Determining the Best Banner Supplier Using Profile Matching and TOPSIS Methods

Sat, 31 Aug 2024 00:00:00 +0700

Background: Choosing a banner supplier is a significant challenge for digital printing companies due to the various advantages offered by each supplier, often leading to selections based on subjective aspects such as price and quality. Objective: This research aims to develop a system that determines the best banner supplier to minimize production inefficiencies and maximize profits by comparing two calculation methods, Profile Matching and TOPSIS. Methods: A quantitative study was conducted using transaction data from the last six months. The parameter criteria used in this system include price, quality, delivery, availability, and payment terms. The study compares the effectiveness of Profile Matching and TOPSIS methods in identifying the best supplier. Results: The study results show that the TOPSIS method is superior, yielding 100% accuracy, 84% recall, and a 92% F1-score, outperforming the Profile Matching method. This demonstrates that the correct method and algorithm effectively provide the best alternative recommendations. Conclusion: The results indicate that using the TOPSIS method leads to more accurate and objective decisions based on predetermined criteria. The findings suggest that further research should focus on refining these methods to enhance decision-making in supplier selection.

Optimization of Machine Learning-Based Automatic Target Detection and Locking System on Robots

Mokhammad Syafaat, Siti Sendari, Ilham Ari Elbaith Zaeni, Samsul Setumin — Sat, 31 Aug 2024 00:00:00 +0700

Background: In recent years, the world of robotics has made significant progress in improving the operational capabilities of robots through target detection and locking systems. These systems play a crucial role in improving the efficiency and effectiveness of critical applications such as defense, security, and industrial automation. However, the main challenge faced is the limitations of the existing system in adapting to unstable environmental conditions and dynamic changes in targets. Objective: This research aims to overcome these challenges by developing a more adaptive and responsive target detection and locking system by integrating two leading machine learning technologies: Convolutional Neural Networks (CNN) for target detection and Long Short-Term Memory (LSTM) for target tracking. Methods: This study uses a quantitative approach to evaluate the effectiveness of the integration of CNNs and LSTMs in target detection and locking systems. Results: The results of the study showed a detection accuracy rate of 95% and a locking accuracy of 90%. The system is proven to be able to adapt to changing operational conditions in real-time and provide consistent performance in a variety of complex and dynamic scenarios. Conclusion: The conclusion of this study is that the integration of CNN and LSTM technologies in target detection and locking systems in robots significantly improves the performance and efficiency of the system, enabling a wider and more complex application.

Performance of Deep Feed-Forward Neural Network Algorithm Based on Content-Based Filtering Approach

Fikri Maulana, Erwin Budi Setiawan — Sat, 31 Aug 2024 19:36:30 +0700

Background: Selecting a restaurant in a diverse city like Bandung can be challenging. This study leverages Twitter data and local restaurant information to develop an advanced recommendation system to improve decision-making. Objective: The system integrates content-based filtering (CBF) with deep feedforward neural network (DFF) classification to enhance the accuracy and relevance of restaurant recommendations. Methods: Data was sourced from Twitter and PergiKuliner, with restaurant-related tweets converted into rating values. The CBF combined Bag of Words (BoW) and cosine similarity, followed by DFF classification. SMOTE was applied during training to address data imbalance. Results: The initial evaluation of CBF showed a Mean Absolute Error (MAE) of 0.0614 and a Root Mean Square Error (RMSE) of 0.0934. The optimal DFF configuration in the first phase used two layers with 32/16 nodes, a dropout rate of 0.3, and a 20% test size. This setup achieved an accuracy of 81.08%, precision of 82.89%, recall of 76.93%, and f1-scores of 79.23%. In the second phase, the RMSprop optimizer improved accuracy to 81.30%, and tuning the learning rate to 0.0596 further increased accuracy to 89%, marking a 9.77% improvement. Conclusion: The research successfully developed a robust recommendation system, significantly improving restaurant recommendation accuracy in Bandung. The 9.77% accuracy increase highlights the importance of hyperparameter tuning. SMOTE also proved crucial in balancing the dataset, contributing to a well-rounded learning model. Future studies could explore additional contextual factors and experiment with recurrent or convolutional neural networks to enhance performance further.

Utilizing Apache Jena Fuseki for Ontology-Based Smartphone Knowledge Representation

Sat, 31 Aug 2024 10:28:47 +0700

Background: Smartphones are a fundamental require for everybody since smartphones can offer assistance someone's work through different highlights and certain innovation contained within the smartphone. Some people's need for information about smartphones makes people confused in choosing smartphone products because there are many smartphone brands available on the market, as a result, many people still buy smartphones that do not suit their needs and preferences. That is why ontology-based knowledge representation is becoming increasingly important in the field of smartphone technology to improve data organization, data retrieval, and interoperability. Objective: This research aims to develop a smartphone ontology using the Apache Jena Fuseki server which functions as a data collection tool and facilitates knowledge management about smartphones. Methods: This ontology was built using the methontology method, namely an ontology development method that is superior in providing a detailed description of each required activity. This smartphone ontology was developed using the Protégé 5.5.0 application which consists of 4 classes, 9 object properties, 15 data properties, and 92 individuals. Results: The research results show that the ontology built can help users search for smartphones that suit their criteria and needs. This research also succeeded in developing an android and semantic web-based application that allows users to search for smartphones more easily and efficiently, strengthening the benefits of the developed ontology in supporting smartphone purchasing decisions. Conclusion: The contribution of this research is to help customers, by providing recommendation the smartphone that best meets the requirements or best fits the given knowledge representation.

Sentiment Analysis of Sirekap Tweets Using CNN Algorithm

Handoko Handoko, Ahmad Asrofiq, Junadhi Junadhi, Ari Sukma Negara — Sat, 31 Aug 2024 10:55:01 +0700

Background: The research investigates the application of deep learning models for sentiment analysis on Twitter data related to Indonesia's Sirekap system. Sentiment analysis is crucial for understanding public opinion and enhancing the transparency and reliability of election result recapitulation processes. Objective: The objective of this study is to compare the performance of Convolutional Neural Networks (CNN) and CNN-LSTM models in analyzing sentiments from tweets about the Sirekap system. The study aims to identify the most effective model and preprocessing techniques to improve sentiment classification accuracy. Methods: A comprehensive data preprocessing pipeline was implemented, including cleansing, case folding, tokenizing, normalization, stopword removal, and stemming. To address class imbalance, the SMOTE technique was applied. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained word embeddings were used to enhance model performance. Results: The CNN model achieved an accuracy of 85.90%, outperforming the CNN-LSTM model, which achieved 79.91% accuracy. Additionally, the CNN model demonstrated superior precision, recall, and F1-score metrics compared to the CNN-LSTM model. The thorough preprocessing and handling of class imbalance significantly contributed to the enhanced performance of the CNN model. Conclusion: The research emphasizes the effectiveness of deep learning approaches, particularly CNNs, in sentiment analysis tasks. The findings highlight the importance of comprehensive preprocessing and class imbalance handling. The use of pre-trained word embeddings and various evaluation metrics ensures robust model performance. These insights contribute to improving the accuracy and efficiency of sentiment classification, thereby enhancing the reliability and transparency of election result recapitulation processes.