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All Journal Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Proceeding of International Conference on Special Education in South East Asia Region
Saon, Sharifah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Social Sciences

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Determining Program Study Using AHP with Dynamic Criterias and Weights Based on GIS-Mobile Kurniawan, Mohammad Rizky; Kurniawati, Ayuningtyas; Saon, Sharifah
Letters in Information Technology Education (LITE) Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (615.079 KB) | DOI: 10.17977/um010v3i12020p030

Abstract

This research aim to develop a decision support system based on GIS-Mobile Apps using Analytical Hierarchy Process (AHP) Algorithm and softmax function for dynamic weight. The stages of AHP dynamic criteria in this system is the preparation of a hierarchy, prioritization, consistency, and the weight of priority. ). The use of AHP in this system involves four criteria which keywords, department accreditation, accreditation of colleges and colleges location distance that can be set by the user dynamically. Experience Programming (XP) is model development that choosed by author for process development system. The step begin with planning, design, coding, and testing. The result of this research is a GIS-Mobile Apps to determine a list of recommended program study with the greatest weight from user input criteria.
Android Application Prototype of Basic BISINDO Introduction and Practice for General People Fadlilah, Umi; Mahamad, Abd Kadir; Handaga, Bana; Saon, Sharifah; Ratih, Koesoemo; Rahmawati, Laili Etika; Thamrin, Husni
Proceeding of International Conference on Special Education in South East Asia Region Vol. 1 No. 1 (2022): Technology and Education for Student with Special Needs
Publisher : Angstrom Centre of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57142/picsar.v1i1.30

Abstract

One of the sign languages used in Indonesia is BISINDO (Indonesian Sign Language) created by the deaf. BISINDO is like the mother tongue for the deaf in Indonesia, although it is not as popular as SIBI (Indonesian Sign System). Therefore, the researchers propose to develop an Android-based prototype to promote BISINDO to others and help the deaf in daily communication. This prototype was built using coding in React Native framework and code editor of Visual Studio Code, then it was compiled to an Android Smartphone. The features include BISINDO's basic dictionary, translation from hand gesture to text, voice to sign language, text to sign language, and voice to text. This prototype is expected to introduce basic BISINDO vocabulary and practice using BISINDO. This prototype ia expected to be developed further into a more useful application, especially to assist communication between deaf people and the general public, but not as a substitution of BISINDO teachers.
The Development of Stacking Techniques in Machine Learning for Breast Cancer Detection Van FC, Lucky Lhaura; Anam, M. Khairul; Bukhori, Saiful; Mahamad, Abd Kadir; Saon, Sharifah; Nyoto, Rebecca La Volla
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.416

Abstract

This study addresses the challenges of accurately detecting breast cancer using machine learning (ML) models, particularly when handling imbalanced datasets that often cause model bias toward the majority class. To tackle this, the Synthetic Minority Over-sampling Technique (SMOTE) was applied not only to balance the class distribution but also to improve the model's sensitivity in detecting malignant tumors, which are underrepresented in the dataset. SMOTE was effective in generating synthetic samples for the minority class without introducing overfitting, enhancing the model's generalization on unseen data. Additionally, AdaBoost was employed as the meta model in the stacking framework, chosen for its ability to focus on misclassified instances during training, thereby boosting the overall performance of the combined base models. The study evaluates several models and combinations, with K-Nearest Neighbors (KNN) + SMOTE achieving an accuracy of 97%, precision, recall, and F1-score of 97%. Similarly, C4.5 + Hyperparameter Tuning + SMOTE reached 95% in all metrics. The stacking model with Logistic Regression (LR) as the meta model and SMOTE achieved a strong performance with 97% accuracy, precision, recall, and F1-score all at 97%. The best result was obtained using the combination of Stacking AdaBoost + Hyperparameter Tuning + SMOTE, reaching an accuracy of 98%. These findings highlight the effectiveness of combining SMOTE with stacking techniques to develop robust predictive models for medical applications. The novelty of this study lies in the integration of SMOTE and advanced stacking methods, particularly using AdaBoost and Logistic Regression, to address the issue of class imbalance in medical datasets. Future work will explore deploying this model in clinical settings for accurate and timely breast cancer detection.
Co-Authors Anam, M Khairul Bana Handaga Husni Thamrin Koesoemo Ratih Kurniawati, Ayuningtyas Laili Etika Rahmawati Lucky Lhaura Van FC, Lucky Lhaura Mahamad, Abd Kadir Mohammad Rizky Kurniawan Rebecca La Volla Nyoto Saiful Bukhori Umi Fadlilah, Umi