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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Rayendra
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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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.
IMPLEMENTASI INCREMENTAL METHOD PADA RANCANG BANGUN WEBSITE PENERBIT PNP PRESS Ikhbal Salam, Riyan; Ikhsan; Rayendra; Ismael; Eka Putra, Dian; Ramadhani
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 1 (2025): TEKNOIF APRIL 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.1.1-7

Abstract

PNP Press publisher is one of the publishers in Indonesia which is under the auspices of the Padang State Polytechnic State University. This publisher publishes two publishing models, namely magazines and books. The types of books published at PNP Press are scientific books, texts, and references. Meanwhile, the types of articles published in magazines are popular articles and articles from several columns. With the increasing demand for book and magazine publications at this publisher, a system is needed that can manage the publication process effectively and efficiently. This research aims to design and implement a website that can facilitate the publication and distribution process of magazines and books at PNP Press using the Incremental method. This method was chosen because it can accommodate various needs during the system development process. Using the Incremental Method in building publisher websites has proven capable of producing PNP Press publisher websites that suit user needs and ensure the system can be implemented effectively, namely in the form of books published using the open monograph press (OMP) and magazines using the open journal system (OJS).
Implementasi X-StuntAI untuk Deteksi Dini dan Interpretasi Risiko Stunting pada Balita Rayendra; Yunaldi, Andri
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 6 No 2 (2026): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v6i2.1954

Abstract

Stunting pada balita masih menjadi persoalan kesehatan utama di Indonesia karena berdampak pada pertumbuhan fisik, perkembangan kognitif, dan kualitas sumber daya manusia di masa depan. Penelitian ini bertujuan mengimplementasikan X-StuntAI, yaitu framework berbasis Explainable Artificial Intelligence (XAI) dengan pendekatan Explainable Ensemble Learning, untuk mendeteksi dini stunting sekaligus menginterpretasikan faktor risiko yang memengaruhi hasil prediksi. Metode yang digunakan meliputi preprocessing data, pembagian data latih dan data uji, penerapan beberapa algoritma machine learning, serta evaluasi performa model menggunakan akurasi. Hasil penelitian menunjukkan bahwa algoritma K-Nearest Neighbors (KNN) memberikan performa terbaik dengan akurasi sebesar 99,53%, diikuti Support Vector Machine (SVM) sebesar 99,48%, SVM dengan hyperparameter tuning menggunakan GridSearchCV sebesar 99,40%, Logistic Regression sebesar 74,81%, dan Gaussian Naive Bayes sebesar 55,41%. Temuan ini menunjukkan bahwa X-StuntAI mampu memberikan klasifikasi yang sangat baik sekaligus membantu interpretasi faktor risiko stunting secara lebih transparan. Ke depan, framework ini berpotensi dikembangkan menjadi sistem pendukung keputusan yang lebih aplikatif untuk membantu tenaga kesehatan dalam deteksi dini dan pencegahan stunting pada balita.
Co-Authors Arif Rizki Marsa Ayu Mahessya, Raja Dedi Mardianto Dian Eka Putra Eka Putra, Dian Ikhbal Salam, Riyan Ikhsan Ismael Ramadhani Rostam Ahmad Efendi Rozi Meri Yunaldi, Andri