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All Journal JURNAL INFORMATIKA Jurnal Riset Informatika
Ahmad Rifa'i
Universitas Duta Bangsa Surakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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ANALYSIS OF CLASSIFICATION ALGORITHM IN UNBALANCED DIABETES DATASET Ahmad Rifa'i; Herin Dwibima Aprianto; Lubna
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.458

Abstract

Diabetes mellitus is a metabolic disease that is spreading rapidly and has the potential to be life-threatening worldwide. This condition occurs when the body experiences a decline in its ability to process glucose, triggering metabolic disorders. The use of machine learning algorithms is one effective approach to predicting or detecting diabetes based on the severity of a patient's symptoms. This study uses the Diabetes dataset from Kaggle and compares the performance of several classification algorithms in unbalanced data conditions and after data balancing using the SMOTE, Random Under Sampling, Random Over Sampling, and Near Miss resampling techniques. The results show that model performance is greatly influenced by data balance conditions and the resampling method used. In the original unbalanced data condition, Artificial Neural Network (ANN) provided the best results with the highest accuracy of 96.98%, indicating that ANN is the most adaptive to class imbalance. After resampling, the performance pattern changed: with SMOTE, Random Under Sampling, and Random Over Sampling, the Random Forest algorithm consistently produced the highest accuracy of 96.52%, 89.84%, and 96.26%, respectively, demonstrating its superiority in utilizing balanced data. Meanwhile, in the Near Miss method, the best performance was achieved by Logistic Regression with an accuracy of 94.41%, indicating that minority sample selection based on proximity is more suitable for linear models. Therefore, selecting the right combination of resampling methods and machine learning algorithms is an important factor in obtaining optimal diabetes predictions.
Efektivitas Pengembangan Quick Rigging Tools dalam Pembuatan Film Animasi 3D Herin Dwibima Aprianto; Ahmad Rifa'i; Anip Moniva
JURNAL INFORMATIKA Vol 15, No 1 (2026): Jurnal Informatika
Publisher : Informatics Engineering Department, Dayanu Ikhsanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55340/jiu.v15i1.2701

Abstract

Proses rigging merupakan tahap penting dalam produksi film animasi 3D karena menentukan kesiapan karakter untuk dianimasikan, kualitas deformasi mesh, serta efisiensi kerja animator. Pada pipeline manual, pembuatan skeleton, kontrol IK/FK, penamaan joint, mirroring, dan inisialisasi skin weight sering memerlukan waktu panjang serta berpotensi menimbulkan inkonsistensi teknis. Penelitian ini bertujuan untuk mengevaluasi Quick Rigging Tools sebagai pendekatan semiotomatis untuk mendukung pembuatan rig karakter 3D pada Autodesk Maya Rigging Builder. Metode penelitian menggunakan pendekatan kuantitatif komparatif berbasis pengujian gerak. Data sudut gerak diperoleh dari satu skenario animasi tendangan dengan empat bagian tubuh, yaitu tangan kanan, tangan kiri, kaki kanan, dan kaki kiri, masing-masing 69 frame. Sudut hasil rigging QR Maya dan rig Studio dibandingkan dengan video referensi menggunakan circular angular error. Analisis statistik mencakup rata-rata ± standar deviasi, RMSE, uji normalitas Shapiro-Wilk, serta uji Wilcoxon signed-rank karena selisih error tidak berdistribusi normal. Hasil menunjukkan error gabungan QR Maya sebesar 10,718 ± 9,380 derajat, sedangkan studio sebesar 11,236 ± 9,715 derajat. Perbedaan kualitas gerak tidak signifikan secara statistik (Wilcoxon, p = 0,787), sehingga Quick Rigging Tools belum dapat dinyatakan lebih unggul secara statistik dibandingkan dengan metode pembanding pada aspek kualitas gerak animasi. Meskipun demikian, temuan deskriptif menunjukkan bahwa Quick Rigging Tools memiliki potensi untuk meningkatkan efisiensi workflow, konsistensi rig, dan kesiapan pipeline melalui standardisasi skeleton, controller, penamaan, dan validasi rig.
Co-Authors Anip Moniva Herin Dwibima Aprianto Lubna