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All Journal JOIN (Jurnal Online Informatika) ADI Journal on Recent Innovation (AJRI) IJIIS: International Journal of Informatics and Information Systems
Oh, Lee Kyung
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Industrial & Manufacturing Engineering

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ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA Ramadhan, Febrian Wahyu; Sukmana, Husni Teja; Oh, Lee Kyung; Wardhani, Luh Kesuma
ADI Journal on Recent Innovation (AJRI) Vol 1 No 1 (2019): AJRI (ADI Journal on Recent Innovation)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v1i1.91

Abstract

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.
A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms for Diabetes Detection Based on Health Datasets Nurwicaksana, Satria; Oh, Lee Kyung; Sukmana, Husni Teja
International Journal of Informatics and Information Systems Vol 7, No 4: December 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i4.230

Abstract

Diabetes is a chronic, progressive condition whose global prevalence continues to rise, creating substantial public health and economic burdens. Early diagnosis and timely intervention are critical to preventing severe complications and improving long-term patient outcomes. In recent years, artificial intelligence (AI) particularly machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering capabilities in automated pattern recognition and disease classification. This study aims to evaluate and compare the predictive performance of three supervised ML algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Decision Tree for classifying and predicting diabetes based on two primary physiological indicators: glucose level and blood pressure. The dataset employed was sourced from Kaggle, comprising 995 patient records containing relevant clinical attributes. The research methodology involved several stages, including data preprocessing to ensure quality and consistency, data partitioning into training and testing subsets using an 80:20 split ratio, model training, and performance evaluation. Each algorithm’s effectiveness was measured using accuracy, precision, recall, and F1-score metrics. The experimental findings demonstrate that the Decision Tree algorithm achieved the highest classification accuracy (94.47%), outperforming SVM and Naïve Bayes, both of which recorded 92.96% accuracy. Moreover, the Decision Tree exhibited balanced precision and recall values, underscoring its robustness in identifying both diabetic and non-diabetic cases with minimal misclassification. These outcomes indicate that the Decision Tree model provides an optimal balance between predictive accuracy and interpretability, making it particularly suitable for clinical decision-support applications.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Widiyanti, Nur Febriana; Sukmana, Husni Teja; Hulliyah, Khodijah; Khairani, Dewi; Oh, Lee Kyung
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.898

Abstract

In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.
Co-Authors Husni Teja Sukmana Khairani, Dewi Khodijah Hulliyah Luh Kesuma Wardhani Nurwicaksana, Satria Ramadhan, Febrian Wahyu Widiyanti, Nur Febriana