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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Informatika: Jurnal Pengembangan IT
Widi Utomo, Emanuel Pinesthi
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Analisis Pengaruh SMOTE terhadap Kinerja Model KNN untuk Prediksi Risiko Stroke Paramita, Cinantya; Simbolon, Calvin Samuel; Pamungkas, Azriel Sebastian; Triono, Justin Matthew; Widi Utomo, Emanuel Pinesthi; Subhiyakto, Egia Rosi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8809

Abstract

Penelitian ini membahas masalah ketidakseimbangan data dalam klasifikasi risiko stroke, di mana kasus non-stroke secara signifikan lebih rendah daripada kasus stroke. Ketidakseimbangan kelas cenderung menimbulkan bias terhadap kelas mayoritas, yang menyebabkan berkurangnya efektivitas klasifikasi. Untuk mengatasi hal ini, SMOTE (Synthetic Minority Over-sampling Technique) digunakan untuk mengatasi ketidakseimbangan kelas dalam dataset dan algoritma K-Nearest Neighbor (KNN) digunakan untuk klasifikasi. Dataset mengalami preprocessing, aplikasi SMOTE, dan algoritma KNN dilatih dan dievaluasi menggunakan metrik standar termasuk akurasi, presisi, recall, dan F1-score. Penerapan SMOTE bersama dengan KNN menghasilkan peningkatan yang signifikan dalam hasil klasifikasi, mencapai akurasi 91,87%, presisi 94,27%, recall 89,20%, dan F1-score 91,66%. Temuan ini menegaskan bahwa pendekatan yang diimplementasikan berkinerja baik dalam mendeteksi risiko stroke meskipun ada set data yang tidak seimbang. Tujuan dari penelitian ini adalah untuk menginformasikan kemajuan teknologi deteksi dini stroke yang lebih kuat dan mendukung peningkatan dalam penyediaan layanan kesehatan.
Implementasi MobileNetV2 pada Aplikasi Mobile untuk Penilaian Objektif Kondisi Fisik Ponsel Bekas Pamungkas, Azriel Sebastian; Triono, Justin Matthew; Widi Utomo, Emanuel Pinesthi; Paramita, Cinantya
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.8947

Abstract

The lack of attention to electronic waste (e-waste), particularly regarding mobile phones, has a serious impact on global environmental issues. One of the main obstacles in the economic circulation of these devices is the subjectivity and technical difficulty in accurately assessing the physical condition of used phones. This research aims to address these challenges through the development of a circular economy platform prototype based on a mobile application that provides objective and automated phone condition assessment services. The system is designed using React Native Expo and integrates the MobileNetV2 Deep Learning model via TensorFlow Lite. Transfer learning methods are applied to a dataset covering various mobile phone brands such as Samsung, Xiaomi, and OPPO to train the model to recognize physical damage on the screen and body. Test results indicate that the system is capable of providing objective assessment with high precision for devices in prime condition (Grade A) at 0.95. However, objectivity for severely damaged phones (Grade D) remains a challenge with a precision of 0.22 due to training data imbalance. Nevertheless, the application prototype successfully presents a transparent real-time scanning feature. This research contributes to providing a technical solution that bridges the trust gap through automated assessment standardization, thereby minimizing manual inspection subjectivity and promoting supply chain efficiency in the electronic circular economy.
Co-Authors Egia Rosi Subhiyakto, Egia Rosi Pamungkas, Azriel Sebastian Paramita, Cinantya Simbolon, Calvin Samuel Triono, Justin Matthew