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Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder Anna Hendri Soleliza Jones; Cicin Hardiyanti
IJISTECH (International Journal of Information System and Technology) Vol 5, No 1 (2021): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i1.111

A personality disorder is a condition of a person with an extreme personality that causes the sufferer to have unhealthy and different thoughts patterns and behavior from other people. The personality disorders discussed in this study consisted of 110 diseases with 300 case data and 68 symptoms. Based on Basic Health Research (Riskesdas) 2018 data, it shows that more than 19 million people aged 15 years and over were affected by mental-emotional disorders. Data from the Statistics Indonesia in 2019 that the population of Indonesia is around 265 million people, while according to the Indonesian Clinical Psychologist Association, the number of verified professional psychologists is 1,599 clinical psychologists out of a total membership of 2,078 as of January 2019. However, this figure does not meet the standards of the World Health Organization (WHO), which is that psychologists serve 30 thousand people. This shows that Indonesia still lacks around 28,970 psychologists. The unequal distribution of professional psychologists has made psychologists need a long time to provide a diagnosis because of the number of patients being inversely proportional to the availability of psychologists in Indonesia. Moreover, there is not enough patient knowledge about the symptoms they feel. This study aims to produce a system for diagnosing personality disorders. This study is a case based reasoning to solve problems that have occurred in previous cases using K-Nearest Neighbor to classify data based on the closest distance using the calculation of the Euclidean Distance. Algorithm testing for the system used the Confusion Matrix test. Based on the results of testing data in the 60 case data using K-nearest Neighbor and the calculation of the Euclidean Distance with a score of K=3, it is known that 60 data have 100% similarity to cases with a personality disorder. Meanwhile, testing new cases with 10 case data that were not in the knowledge base was also conducted showing that 9 cases had 100% similarity to the previous case, while another case had 90% similarity to the previous case.

Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder Anna Hendri Soleliza Jones; Cicin Hardiyanti
IJISTECH (International Journal of Information System and Technology) Vol 5, No 1 (2021): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

A personality disorder is a condition of a person with an extreme personality that causes the sufferer to have unhealthy and different thoughts patterns and behavior from other people. The personality disorders discussed in this study consisted of 110 diseases with 300 case data and 68 symptoms. Based on Basic Health Research (Riskesdas) 2018 data, it shows that more than 19 million people aged 15 years and over were affected by mental-emotional disorders. Data from the Statistics Indonesia in 2019 that the population of Indonesia is around 265 million people, while according to the Indonesian Clinical Psychologist Association, the number of verified professional psychologists is 1,599 clinical psychologists out of a total membership of 2,078 as of January 2019. However, this figure does not meet the standards of the World Health Organization (WHO), which is that psychologists serve 30 thousand people. This shows that Indonesia still lacks around 28,970 psychologists. The unequal distribution of professional psychologists has made psychologists need a long time to provide a diagnosis because of the number of patients being inversely proportional to the availability of psychologists in Indonesia. Moreover, there is not enough patient knowledge about the symptoms they feel. This study aims to produce a system for diagnosing personality disorders. This study is a case based reasoning to solve problems that have occurred in previous cases using K-Nearest Neighbor to classify data based on the closest distance using the calculation of the Euclidean Distance. Algorithm testing for the system used the Confusion Matrix test. Based on the results of testing data in the 60 case data using K-nearest Neighbor and the calculation of the Euclidean Distance with a score of K=3, it is known that 60 data have 100% similarity to cases with a personality disorder. Meanwhile, testing new cases with 10 case data that were not in the knowledge base was also conducted showing that 9 cases had 100% similarity to the previous case, while another case had 90% similarity to the previous case.

Gender inequality in HDI and per capita expenditure: A probabilistic distribution and spatial data analysis Fadilah, Zainal; Purwaningsih, Tuti; Inderanata, Rochmad Novian; Konate, Siaka; P, Cicin Hardiyanti
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1214

Men and women have different habits or lifestyles, which inevitably leads to variances in other areas. As a result, gender statistics emerged. In this example, researchers seek to discover if there are discrepancies in HDI and per capita expenditure in Indonesia between men and women. To determine this, data from reliable sources is required; thus, researchers use data from the official BPS website, bps.go.id. The data comes from many tables, so the researcher will join them so that they may be studied. The data used in this scenario are HDI data by gender in 2020 and Per Capita Expenditure data by gender in 2020. Researchers employed graphical tools, such as boxplots and thematic charts, to examine whether there are differences in HDI and per capita expenditure between men and women in Indonesia. Aside from that, researchers used the two-sample t-test approach to see if there were variations in HDI and per capita expenditure between men and women. Researchers will utilize Python software to run this hypothesis test. According to the findings of the investigation, there is still gender imbalance in Indonesia in terms of HDI and per capita expenditure. As a result, it is intended that this research can be utilized as a reference in analyzing existing policies to ensure that there is no gender discrepancy in terms of HDI and per capita expenditure between men and women. It is also envisaged that this research would be beneficial to many people.

Evaluation of Success and Failure Factors for Maternal and Child Health in Integrated Healthcare Center Information Systems (IHCIS) Using the HOT-Fit Method Hardiyanti, Cicin; Kusumadewi, Sri; Kurniawan, Rahadian
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.152-166

Background: Maternal and child health in Integrated Healthcare Center Information Systems (IHCIS) has been implemented in several community health centers. Some have been implemented successfully, but others have failed. Many factors influence the success and failure of IHCIS implementation. Thus, knowing these factors can be used to assist the decision-making process in implementing IHCIS. Objective: This research aims to determine the factors affecting the success and failure of IHCIS for maternal and child health using the HOT-Fit (Human, Organization, Technology, and Fit) model. Methods: This research begins with preliminary research to identify the problem, determine research variables, and collect data. It uses quantitative and qualitative approaches. A quantitative approach is conducted at locations that have successfully implemented IHCIS. The data collection instrument uses a questionnaire. A qualitative approach was conducted in locations that were still experiencing failure in implementing IHCIS. Data collection techniques through direct interviews. Results: Organizational factors do not fully determine the success or failure of the information system. Factors supporting the success of IHCIS are human (user satisfaction and system use) and technological factors (quality of information and the quality of service). However, technology (system quality and information quality) is the main factor in the failure of IHCIS implementation. Problems with system quality include the system login, limited access to the internet, and an information system that is not in accordance with requirements. The perceived information obstacle is that the system is not yet integrated, and the information produced is incomplete. Conclusion: To satisfy requirements, the information and system qualities must be enhanced. Implementing IHCIS requires an appropriate strategy according to local circumstances and conditions. This approach involves human, organizational, and technological factors. Keywords: Community Health Workers, Evaluation, HOT-Fit, Integrated Healthcare Center Information Systems, Success Factors

Optimizing breast cancer classification using SMOTE, Boruta, and XGBoost Hardiyanti P, Cicin
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.2109

Breast cancer remains one of the leading causes of death among women worldwide. This study aims to develop a clinical data-based breast cancer classification framework by integrating the Synthetic Minority Oversampling Technique (SMOTE), the Boruta feature selection algorithm, and the XGBoost classifier. The proposed approach is tested using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, consisting of 569 samples and 30 numerical features. SMOTE addresses class imbalance, Boruta selects the most relevant diagnostic features, and XGBoost is the main classification algorithm due to its tabular and imbalanced data robustness. Model validation is conducted through Repeated Stratified K-Fold Cross Validation with 30 repetitions to ensure statistical stability. The resulting model achieves excellent classification performance, with an average accuracy of 0.9608 ± 0.0274, precision of 0.9465 ± 0.0481, Recall of 0.9512 ± 0.0524, and F1-score of 0.9475 ± 0.0374. The ROC-AUC value reaches 0.9926 ± 0.0094, the PR-AUC is 0.9906 ± 0.0113, and the Matthews Correlation Coefficient (MCC) is 0.9179 ± 0.0575, indicating a well-balanced model. Clinically, this model can aid early diagnosis by effectively reducing irrelevant diagnostic attributes, retaining only 10 key features without compromising accuracy, thereby offering a lightweight yet reliable diagnostic tool. However, limitations include the relatively small dataset and the absence of hyperparameter tuning. Future research should explore larger datasets, advanced ensemble methods, and interpretability techniques such as SHAP or LIME to improve clinical transparency and adoption.

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