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All Journal Jurnal Bumigora Information Technology (BITe) Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Jurnal Sains Informatika Terapan (JSIT) Insand Comtech : Information Science and Computer Technology Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Journal of Engineering and Science Application
Guterres, Juvinal Ximenes
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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Journal : Proceeding of International Conference Health, Science And Technology (ICOHETECH)

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PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

Abstract

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.
Optimization of MySQL Database in the Development of Solo Batik Mall Srirahayu, Agustina; Pamekas, Bondan Wahyu; Guterres, Juvinal Ximenes
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/kjy3qe33

Abstract

This study aims to design and optimize a MySQL database for an online batik mall system to support the digitalization of batik micro, small, and medium enterprises (MSMEs). The research employed three stages: analysis, design, and implementation. The analysis phase identified actors (sellers, buyers, and administrators) and business process needs. The design stage focused on database structures and optimization strategies, including indexing, query optimization, caching, normalization, and denormalization. The implementation involved building the database, applying optimization techniques, and evaluating performance. The optimization of MySQL significantly improved query execution speed, reduced system response time, and enhanced resource efficiency. The system was able to manage transactions, product searches, and reporting more effectively, supporting both operational and strategic needs of the online batik mall. The MySQL-based online batik mall system provides an efficient solution for data management, thereby enhancing the competitiveness of batik MSMEs in the digital era.
PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

Abstract

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.
Co-Authors Abu Tholib Agustina Srirahayu Andi, Moh syaiful Arnes Yuli Vandika Costa, Nuno da da Silva, Recardina Freitas Freitas, Abrao Gusmao, Adozinda Soares Guterres, Anita Hairani Hairani Jauhari, Burhanuddin Khairan marzuki Lalu Ganda Rady Putra Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Maulindar, Joni Muhammad Najibulloh Muzaki Muhammad Syarif Hartawan Noviyanty Pamekas, Bondan Wahyu Riska Rosita Sales, Jacinto Defatima Shudiq, Wali Ja'far Silva, Delfim Da Siswanto, Yoyok Adi Soares, Domingas Solihin Solihin Sucipto Sucipto Sukron, Moh