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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA
Rozi Meri
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Computer Science & IT Engineering

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APLIKASI SISTEM PAKAR DALAM MENDIAGNOSIS PENYAKIT KULIT PADA MANUSIA BERBASIS VISUAL Rozi Meri
JURNAL ILMIAH INFORMATIKA Vol 9 No 02 (2021): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v9i02.4439

Abstract

Health is a blessing that is very important for humans, most of us, many of us don't really care about skin diseases caused by fungi such as tinea versicolor, water fleas, ringworm and scabies. In general, people are quite aware of how to deal with the symptoms of skin diseases suffered. But it would be better to include medical participation in detecting a disease symptom, because many disease symptoms are considered trivial by some people but can be fatal to human skin. So it is necessary to make an application based on medical knowledge to diagnose skin diseases in humans which is used as a tool in obtaining information about skin diseases in humans and provide recommendations as the first action that must be taken to repeat skin diseases in humans. The knowledge base is structured in such a way into a database with several tables. Drawing conclusions in this expert system using the forward chaining inference method. The expert system will provide questions to the user in the form of symptoms of several diseases and the user will answer these questions. Until the user will get a solution from the results of the question earlier.  
Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction Raja Ayu Mahessya; Dian Eka Putra; Rostam Ahmad Efendi; Rayendra; Rozi Meri; Riyan Ikhbal Salam; Dedi Mardianto; Ikhsan; Ismael; Arif Rizki Marsa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6606

Abstract

Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. Despite achieving a higher overall accuracy (82%), the logistic regression model failed to identify any kyphosis cases, rendering it clinically ineffective. Conversely, the decision tree model demonstrated superior clinical utility by successfully identifying 33% of kyphosis cases while maintaining 71% accuracy. Feature importance analysis established starting vertebral position as the dominant predictor (importance=0.554), followed by patient age (0.416), with vertebrae count contributing minimally (0.030). The decision tree identified critical thresholds for risk stratification: operations beginning at or above T8-T9, particularly in children aged 5-9 years, carried a substantially elevated kyphosis risk. Our methodological approach emphasizes sensitivity over conventional accuracy metrics, recognizing that missing high-risk patients have greater clinical consequences than unnecessary monitoring. This study demonstrates the capacity of decision tree models to extract clinically meaningful patterns from small, imbalanced surgical datasets that elude conventional statistical approaches.
Co-Authors Arif Rizki Marsa Dedi Mardianto Dian Eka Putra Ikhbal Salam, Riyan Ikhsan Ismael Raja Ayu Mahessya Rayendra Rostam Ahmad Efendi