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All Journal Jurnal Ilmiah METADATA Jurnal Indonesia Sosial Sains JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Abdul Jamil
Universitas Muhammadiyah Brebes
Religion Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Physics Social Sciences

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SCIENTIFIC ARTICLE PLAGIARISM DETECTION USING THE QUADWORD APPROACH Hari Purwanto; Betesda Sinaga; Iswandir Iswandir; Mohamad Adila Rossa; Abdul Jamil
Jurnal Ilmiah METADATA Vol. 8 No. 1 (2026): Edition January 2026
Publisher : LPPM YPITI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47652/metadata.v8i1.936

Abstract

Plagiarism is defined as the act of taking or presenting another person's work as one's own, regardless of intent. This infraction can be substantiated when supported by robust evidence. Academic works are often targeted by plagiarism, as the constrained writing proficiency among undergraduate students may foster such occurrences. A principal preventive endeavor to mitigate plagiarism within academic works involves the deployment of plagiarism detection applications. A primary preventive measure to mitigate plagiarism in academic works is the utilization of plagiarism detection software. Third-party applications typically require the purchase of a license to optimally access their full features. Cognizant of this limitation, the author developed an alternative application, although its capabilities are restricted to the detection of internal manuscripts. The application engineered herein implements the Rabin-Karp algorithm, leveraging a quadword approach. The concept of the quadword itself incorporates a phrase-based technique, functioning by generating a collection or set comprising four consecutive words within the source text
Peningkatan Performa Classification and Regression Tree Menggunakan Bagging pada Diagnosis Penyakit Jantung Kokom Hera Fitriyana; Fitri Ayuning Tyas; Abdul Jamil
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.12439

Abstract

Heart disease is one of the leading causes of death worldwide, necessitating fast and accurate diagnostic methods for effective prevention. One approach that can be used is data mining, particularly classification methods to analyze health data. The Classification and Regression Tree (CART) algorithm is known for its interpretability but has a drawback in terms of model stability against data variation. To address this issue, the Bootstrap Aggregating (Bagging) technique is applied to improve the model’s stability and accuracy. This study aims to implement and evaluate the effectiveness of the Bagging technique in enhancing the performance of the CART algorithm for heart disease diagnosis. The data used in this study consists of three datasets available on the Kaggle platform: Heart Disease, Heart Disease Cleveland, and Heart Disease Prediction. The model is built under two conditions: using default parameters and using parameters optimized through the Grid Search method. The research process includes data preprocessing (data type adjustment, handling missing values, and outlier detection), training of two types of classification models (single CART and CART with Bagging), and evaluation based on accuracy metrics. The results show that the application of the Bagging technique consistently improves the accuracy of the CART algorithm. Under default parameters, accuracy increased from 72.89% to 78% (Heart Disease), 81.89% to 85.78% (Heart Disease Cleveland), and 77.44% to 82.44% (Heart Disease Prediction). With tuned parameters, accuracy increased from 75% to 84% (Heart Disease), 77% to 83% (Heart Disease Cleveland), and remained at 83% (Heart Disease Prediction). Therefore, the Bagging technique is proven effective in enhancing the accuracy and stability of the CART model for heart disease diagnosis.
The Qualitative Study of Information System Utilization in Organizational Decision-Making Joko Sarono; Abdul Jamil; Emmie Fatkhunnajah; Nurkomar Hidaya; Purwanto Purwanto
Jurnal Indonesia Sosial Sains Vol. 7 No. 3 (2026): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v7i3.2282

Abstract

Background: The use of information systems has become a crucial element in supporting organizational decision-making amidst the complexity of the business environment and demands for efficiency. Objective: This study aims to explore how information systems are utilized in organizational decision-making processes, as well as to identify the benefits, challenges, and factors contributing to their success. Methods: The study used a qualitative approach with a case study method in an organization that has implemented an integrated information system. Data were collected through in-depth interviews, observations, and analysis of internal organizational documents. Results: The results indicate that information systems play a significant role in improving decision quality by providing accurate, timely, and relevant data. However, the use of information systems has not been fully optimized due to limited user competency, resistance to change, and data integration constraints. Conclusion: This study provides a conceptual contribution to understanding the role of information systems in organizational decision-making and provides practical implications for system managers and organizational leaders in improving the effectiveness of information technology utilization.
Co-Authors Betesda Sinaga Emmie Fatkhunnajah Fitri Ayuning Tyas Hari Purwanto Iswandir Iswandir Joko Sarono Kokom Hera Fitriyana Mohamad Adila Rossa Nurkomar Hidaya Purwanto Purwanto