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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology Jurnal Abdi Masyarakat Indonesia
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

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An Ensemble and Filtering-Based System for Predicting Educational Data Mining Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.44

Abstract

When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.
Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns Saputra, Jeffri Prayitno Bangkit; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.83

Abstract

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).
Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis Batmetan, Johan Reimon; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.162

Abstract

This research examines public sentiment regarding electric vehicle incentives through sentiment analysis of online comments. These incentives include tax deductions and other financial rewards offered to promote the adoption of electric vehicles. In this study, the researchers collected and analyzed over 1,000 comments from various online platforms to understand the public's perspective on these incentives. The study employs Support Vector Machine (SVM), a powerful machine learning algorithm, as the main method and utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze comment texts. The research findings depict significant variation in public sentiment regarding electric vehicle incentives. Approximately 57.3% of comments express negative sentiment towards these incentives, while 33.2% are positive, and the rest are neutral. There is strong support for these incentives, particularly from a financial standpoint. However, some dissatisfaction is expressed, especially regarding electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment. Easy access to charging infrastructure also plays a crucial role in shaping positive sentiment. Environmental issues also contribute to a positive view of electric vehicle incentives. Policy recommendations arising from this research emphasize the need to consider these factors when designing and implementing electric vehicle incentives. Improvement efforts in pricing, infrastructure, and environmental education can help enhance electric vehicle adoption in society. This research provides valuable insights into public sentiment towards electric vehicle incentives and the factors influencing such sentiment. The results can serve as a foundation for better decision-making to support the development of sustainable and environmentally friendly electric vehicles.   
The Empirical Study of Usability and Credibility on Intention Usage of Government-to-Citizen Services Cheng, Tsang-Hsiang; Chen, Shih-Chih; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 2: MAY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i2.30

Abstract

E-government allows governments to service citizens in a more timely, effective, and cost-efficient method. The most popular benefits of Government-to-Citizen (G2C)are the simple posting of forms and registrations, serve citizens, improvement of education information and e-voting. This paper analyzes the influence of website usability and the credibility on both citizen satisfaction and citizen intention to use an e-government website, as well as the impact of citizen satisfaction on citizen intentions. To prove the validity of our proposed research model, empirical analysis was performed with 366 valid questionnaires using Partial Least Square. The results of the research show that credibility of website e-government usage had significant effects on citizen satisfaction which in turn affects citizen intention to use, and citizen satisfaction also significantly affected citizen intention to use. However, the usability of e-government websites slightly influences citizen satisfaction and citizen intention to use.
Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government Hariguna, Taqwa; Ruangkanjanases, Athapol
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.127

Abstract

This study explores the potential of text analysis and classification techniques to improve the operational efficiency and effectiveness of e-government, particularly within law enforcement agencies. It aims to automate the analysis of textual crime reports and deliver timely decision support to policymakers. Given the increasing volume of anonymous and digitized crime reports, conventional crime analysts encounter challenges in efficiently processing these reports, which often lack the filtering or guidance found in detective-led interviews, resulting in a surplus of irrelevant information. Our research involves the development of a Decision Support System (DSS) that integrates Natural Language Processing (NLP) methods, similarity metrics, and machine learning, specifically the Naïve Bayes' classifier, to facilitate crime analysis and categorize reports as pertaining to the same or different crimes. We present a crucial algorithm within the DSS and its evaluation through two studies featuring both small and large datasets, comparing our system's performance with that of a human expert. In the first study, which encompasses ten sets of crime reports covering 2 to 5 crimes each, the binary logistic regression yielded the highest algorithm accuracy at 89%, with the Naive Bayes' classifier trailing slightly at 87%. Notably, the human expert achieved superior performance at 96% when provided with sufficient time. In the second study, featuring two datasets comprising 40 and 60 crime reports discussing 16 distinct crime types for each dataset, our system exhibited the highest classification accuracy at 94.82%, surpassing the crime analyst's accuracy of 93.74%. These findings underscore the potential of our system to augment human analysts' capabilities and enhance the efficiency of law enforcement agencies in the processing and categorization of crime reports.
Survey Opinion using Sentiment Analysis Hariguna, Taqwa; Sukmana, Husni Teja; Kim, Jong Il
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.10

Abstract

Sentiment analysis or opinion mining is a computational study of the opinions, judgments, attitudes, and emotions of a person towards an entity, individual, issue, event, topic, and attributes. This task is very challenging technically but very useful in practice. For example, a business always wants to seek opinion about its products and services from the public or the consumers. Additionally, potential consumers want to learn what users think they have when using a service or purchasing a product. To get public opinion on food habits, ad strategies, political trends, social issues and business policy, this is a very critical factor. This paper will explain a survey of key sentiment-extraction approaches.
Knuth Morris Pratt String Matching Algorithm in Searching for Zakat Information and Social Activities Riawan, Fendi; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 3, No 1: JANUARY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i1.49

Abstract

Algorithms are one of the components that need to be considered in the development of information systems. Determination of the algorithm is adjusted to the purpose of the system to be built. One algorithm that can be used is string matching. The string matching algorithm will play a role in searching for a string consisting of several characters (usually called a pattern). The method used in this research is string matching knuth morris pratt (KMP) which is used to search zakat information and social activities in the search engine system. KMP is a string matching algorithm with good performance. The results showed the performance of string matching using the KMP algorithm with 5 trials of input pattern on zakat information with execution times of 0.03 ms, 0.03 ms, 0.02 ms, 0.02 ms and 0.03 ms. And 5 times the input pattern experiment on social activities with execution time of 0.02 ms, 0.02 ms, 0.03 ms, 0.03 ms and 0.02 ms. Thus the average execution time of the KMP algorithm in string matching is 0.026 ms and 0.024 ms.
Health and Socio-Demographic Risk Factors of Childhood Stunting: Assessing the Role of Factor Interactions Through the Development of an AI Predictive Model Hariguna, Taqwa; Sarmini, Sarmini; Azis, Abdul
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.612

Abstract

Stunting is a significant global health problem, especially in developing countries such as Indonesia. This study aims to develop and evaluate an artificial intelligence (AI)-based predictive model to identify the risk of stunting in children using the CatBoost algorithm which is a combination of Weighted Apriori and XGBoost. This model is designed to utilize the advantages of each algorithm in handling data with variable weights to improve prediction accuracy. Feature analysis shows that "Height (cm) Age (months)" are the main indicators in classifying children's nutritional status. Model evaluation shows high accuracy of 94.85%, precision of 95%, recall of 94.85%, and F1 Score of 94.84%. Kappa Coefficient and Matthews Correlation Coefficient (MCC) reached 93.13% and 93.19%, respectively, while ROC-AUC reached 99.70%. These findings indicate that the CatBoost model can provide highly accurate results in detecting the risk of stunting and offer in-depth insights into risk factors that can improve the effectiveness of health interventions. This study fills the gap in the literature by integrating the Weighted Apriori and XGBoost algorithms, providing a significant contribution to early detection of stunting and supporting government efforts to reduce the prevalence of stunting in Indonesia and other regions.
High-Accuracy Stroke Detection System Using a CBAM-ResNet18 Deep Learning Model on Brain CT Images Tahyudin, Imam; Isnanto, R Rizal; Prabuwono, Anton Satria; Hariguna, Taqwa; Winarto, Eko; Nazwan, Nazwan; Tikaningsih, Ades; Lestari, Puji; Rozak, Rofik Abdul
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.569

Abstract

Stroke is a brain dysfunction that occurs suddenly as a result of local or overarching damage to the brain, lasts for at least 24 hours, and causes about 15 million deaths each year globally. Immediate medical treatment is essential to reduce the potential for further brain damage in stroke patients. Medical imaging, especially computed tomography (CT scan), plays a crucial role in the diagnosis of stroke. This study aims to develop and evaluate a deep learning architecture based on Convolutional Block Attention Module (CBAM) and ResNet18 for stroke classification in CT images. This model is designed through data preprocessing, training, and evaluation stages using a cross-validation approach. The results showed that the CBAM-ResNet18 integration resulted in a high accuracy of 95% in distinguishing stroke and non-stroke cases. The accuracy rate reached 96% for nonstroke identification (class 0) and 94% for stroke (class 1), with recall rates of 96% and 93%, respectively. Outstanding classification ability is demonstrated by an Area Under the Curve (AUC) value of 0.99. In comparison, the standard ResNet18 model shows significant fluctuations in validation loss and difficulty in generalization, with training accuracy only reaching 64-68%. On the other hand, CBAM-ResNet18 showed a significant performance improvement with a validation accuracy of 95%, a validation loss of 0.0888, and good generalization on new data. However, the limitations of the dataset and the interpretation of the results indicate the need for further validation to ensure the generalization of the model. These results show the great potential of the CBAM-ResNet18 architecture as an innovative tool in stroke diagnostic technology based on CT imaging analysis. This technology can support faster and more accurate clinical decision-making, as well as open up opportunities for further research related to the development of artificial intelligence-based systems in the medical field.
Enhancing Digital Marketing Strategies with Machine Learning for Analyzing Key Drivers of Online Advertising Performance Berlilana, Berlilana; Hariguna, Taqwa; El Emary, Ibrahiem M. M.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.658

Abstract

The rapid growth of digital advertising has underscored the need for data-driven strategies to optimize campaign performance. This study applies machine learning techniques to analyze online advertising data, aiming to identify key performance drivers and provide actionable insights for optimizing marketing strategies. The dataset includes metrics such as clicks, displays, costs, and revenue, which were preprocessed, analyzed, and modeled using ensemble methods, including Random Forest and Gradient Boosting. These ensemble methods were chosen for their ability to handle high-dimensional data, mitigate overfitting, and capture complex, nonlinear relationships between variables. Random Forest, with its bagging approach, enhances generalization by reducing variance, while Gradient Boosting incrementally corrects errors by focusing on hard-to-predict instances, improving overall predictive performance. Descriptive analysis revealed significant variability in campaign outcomes, with cost and user engagement emerging as primary predictors of revenue. Machine learning models demonstrated strong predictive accuracy, with Random Forest achieving 92% accuracy and an F1-score of 89%. Visualizations such as feature importance charts, correlation heatmaps, and learning curves validated the robustness of the models and highlighted key insights, including inefficiencies in cost allocation and the limited impact of certain categorical features like placement. The study emphasizes the potential of machine learning to optimize digital marketing strategies by identifying critical factors that influence campaign success. The findings provide a scalable framework for resource allocation, audience targeting, and strategic decision-making in online advertising. Future research could further enhance predictions by incorporating additional features, such as audience demographics and temporal trends, to provide deeper insights into campaign dynamics.
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Anton Satria Prabuwono Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Jawad, Haider Mahmood Johan Reimon Batmetan Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nurwanti, Aulia Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Subhan Mahendrasyah, Muhammad Susanti, Nindi Nofi Tikaningsih, Ades Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding