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INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Published by Universitas Nusantara PGRI Kediri
ISSN : 2580409X     EISSN : 25496824     DOI : https://doi.org/10.29407/intensif
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
INTENSIF Journal is a publication container for research in various fields related to information systems. These fields includeInformation System, Software Engineering, Data Mining, Data Warehouse, Computer Networking, Artificial Intelligence, e-Bussiness, e-Government, Big Data, Application Development, Geograpic Information System, Information Retrieval, Information Technology Infrastructure, Knowledge Management System, Enterprise Architecture.Published periodically in February and August.
Arjuna Subject : -
Articles 168 Documents
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Technology Acceptance Analysis Using UTAUT: A Study of QRIS Acceptance during the Pandemic Prasetya, Denny; Aritonang, Eva Reh Ulina; Manggalaningwang, Jody; Maharani, Nadya Ayu; Ivander, Yohanes; Mukhamadiyev, Abdinabi; Rahardjo, Alexandra Rianti Grandi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.21982

Abstract

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.
Mengungkap Wawasan: Pendekatan Penemuan Pengetahuan untuk Membandingkan Teknik Pemodelan dalam Topik Riset Kesehatan Digital Rohajawati, Siti; Rahayu, Puji; Misky, Afny Tazkiyatul; Sholehah, Khansha Nafi Rasyidatus; Rahim, Normala; Setyodewi, R.R. Hutanti
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 1 (2024): February 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i1.22058

Abstract

This paper introduces a knowledge discovery approach focused on comparing topic modeling techniques within the realm of digital health research. Knowledge discovery has been applied in massive data repositories (databases) and also in various field studies, which use these techniques for finding patterns in the data, determining which models and parameters might be suitable, and looking for patterns of interest in a specific representational. Unfortunately, the investigation delves into the utilization of Latent Dirichlet Allocation (LDA) and Pachinko Allocation Models (PAM) as generative probabilistic models in knowledge discovery, which is still limited. The study's findings position PAM as the superior technique, showcasing the greatest number of distinctive tokens per topic and the fastest processing time. Notably, PAM identifies 87 unique tokens across 10 topics, surpassing LDA Gensim's identification of only 27 unique tokens. Furthermore, PAM demonstrates remarkable efficiency by swiftly processing 404 documents within an incredibly short span of 0.000118970870 seconds, in contrast to LDA Gensim's considerably longer processing time of 0.368770837783 seconds. Ultimately, PAM emerges as the optimum method for digital health research's topic modeling, boasting unmatched efficiency in analyzing extensive digital health text data.
Enhancing the Decision Tree Algorithm to Improve Performance Across Various Datasets Putra, Pandu Pratama; Anam, M Khairul; Defit, Sarjon; Yunianta, Arda
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22280

Abstract

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 Mahmood, Muhammad Azim; Hastuty, Sri; Gołdasz, Iwona; Caesarendra, Wahyu
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22334

Abstract

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 Riyadi, Slamet; Salsabila, Lathifah Khansa; Damarjati, Cahya; Karim, Rohana Abdul
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22678

Abstract

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.
Performance of Deep Feed-Forward Neural Network Algorithm Based on Content-Based Filtering Approach Maulana, Fikri; Setiawan, Erwin Budi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22904

Abstract

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 Wardhana, Helna; Susilowati, Dyah; Aguswandi, Lalu Heri; Maulana, Muhammad; Karim, Abdul
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22962

Abstract

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 Accuracy for 2024 Indonesian Election Tweets Using CNN-LSTM With Genetic Algorithm Optimization Abdullah, Athallah Zacky; Setiawan, Erwin Budi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.22999

Abstract

Background: The 2024 Indonesian Presidential Election is ideal for analyzing public sentiment on Twitter. Data collection began with crawling from the data source to create a dataset, which included 62,955 entries from Twitter, 126,673 entries from IndoNews, and a combined Tweet+IndoNews dataset totaling 189,628 entries. Objective: This study aims to explore sentiment using a hybrid model integrating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) methods, with feature expansion via Word2Vec optimized by a Genetic Algorithm (GA). Methods: The research evaluates the effectiveness of the hybrid CNN-LSTM model in analyzing sentiment from 2024 Indonesian Presidential Election tweets, aiming for higher accuracy and deeper insights compared to traditional methods. Results: The hybrid CNN-LSTM model, optimized with a Genetic Algorithm, significantly enhances accuracy, achieving the highest accuracy of 84.78% for the news data, marking a 3.59% increase. Conclusion: This study illustrates the innovative application of a hybrid CNN-LSTM model with Word2Vec feature expansion and Genetic Algorithm optimization for sentiment analysis in a national election context, demonstrating how advanced techniques can improve accuracy and efficiency in sentiment analysis. 
Sentiment Analysis of Sirekap Tweets Using CNN Algorithm Handoko, Handoko; Asrofiq, Ahmad; Junadhi, Junadhi; Negara, Ari Sukma
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.23046

Abstract

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.
Student Dropout Prediction Using Random Forest and XGBoost Method Putra, Lalu Ganda Rady; Prasetya, Didik Dwi; Mayadi, Mayadi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.21191

Abstract

Background: The increasing dropout rate in Indonesia poses significant challenges to the education system, particularly as students advance through higher education levels. Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. Methods: A quantitative study was conducted using a dataset of 4,424 instances with 34 attributes, categorized into Dropout, Graduate, and Enrolled. The performance of Random Forest and XGBoost was compared based on accuracy, specificity, and sensitivity. Results: Random Forest achieved the highest accuracy at 80.56%, with a specificity of 76.41% and sensitivity of 72.42%, outperforming XGBoost. While XGBoost was slightly less accurate, it remained a competitive approach for student attrition prediction. Conclusion: The findings highlight Random Forest's robustness in handling extensive datasets with diverse attributes, making it a reliable tool for identifying at-risk students. This study underscores the potential of machine learning in addressing educational challenges. Future research should explore advanced ensemble techniques, such as the Ensemble Voting Classifier, or deep learning models to further enhance prediction accuracy and scalability. 

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