Calculate machine learning classification accuracy quickly and accurately from your confusion matrix. Enter your True and False predictions below to generate detailed metrics.
Note: Accuracy shows the proportion of true results among the total number of cases examined. For highly imbalanced datasets, accuracy can be misleading. Consider the additional metrics below.
In machine learning and statistics, Accuracy adalah metrik evaluasi yang mengukur rasio total prediksi yang benar (baik kelas positif maupun negatif) dibandingkan dengan keseluruhan sampel data yang dievaluasi. Metrik ini sangat umum digunakan untuk menilai seberapa baik sebuah model klasifikasi bekerja.
Accuracy Formula
Rumus dasar untuk menghitung tingkat akurasi diperoleh dari nilai-nilai yang ada di dalam Confusion Matrix:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
True Positive (TP): Jumlah data kelas positif yang diprediksi dengan benar sebagai positif.
True Negative (TN): Jumlah data kelas negatif yang diprediksi dengan benar sebagai negatif.
False Positive (FP): Jumlah data kelas negatif yang salah diprediksi sebagai positif (Type I Error).
False Negative (FN): Jumlah data kelas positif yang salah diprediksi sebagai negatif (Type II Error).
How to Use Accuracy Calculator
Siapkan data Confusion Matrix dari hasil evaluasi model klasifikasi Anda.
Masukkan nilai TP, TN, FP, dan FN ke dalam kolom input yang tersedia di atas. Pastikan nilai berupa bilangan bulat positif.
Klik tombol Calculate Accuracy.
Kalkulator akan langsung memproses data di browser Anda dan menampilkan persentase akurasi, visualisasi grafis, serta detail metrik tambahan.
Accuracy Calculation Example
Sebagai contoh, apabila model klasifikasi kita menghasilkan prediksi berikut: TP = 90, TN = 85, FP = 10, dan FN = 15. Maka perhitungan akurasinya adalah:
Total Prediksi Benar = (90 + 85) = 175
Total Sampel = (90 + 85 + 10 + 15) = 200
Accuracy = 175 / 200 × 100% = 87.5%
Advantages and Limitations of Accuracy
Keuntungan utama dari metrik Accuracy adalah kemudahannya untuk dipahami; metrik ini memberikan gambaran langsung mengenai seberapa sering model menebak dengan benar. Namun, metrik ini dapat menyesatkan pada dataset yang tidak seimbang (imbalanced dataset). Jika 99% data Anda adalah kelas A dan 1% kelas B, model yang selalu menebak kelas A akan mendapat akurasi 99%, namun gagal total mendeteksi kelas B. Oleh karena itu, metrik tambahan seperti Precision, Recall, dan F1-Score disediakan pada kalkulator ini sebagai bahan pertimbangan.
Who Can Use This Tool?
Accuracy Calculator dirancang khusus untuk mahasiswa, dosen, peneliti, data scientist, dan praktisi machine learning yang membutuhkan alat hitung cepat, aman, dan tanpa perlu menulis script evaluasi secara manual.
Frequently Asked Questions
1. What is an Accuracy Calculator?
It is a web tool designed to instantly calculate the accuracy percentage of a classification model based on the True Positive, True Negative, False Positive, and False Negative values from a Confusion Matrix.
2. How is accuracy calculated from a confusion matrix?
Accuracy is calculated by dividing the sum of correct predictions (TP + TN) by the total number of predictions made (TP + TN + FP + FN), then multiplying by 100 to get a percentage.
3. What are TP, TN, FP, and FN?
They stand for True Positive (correctly predicted positive), True Negative (correctly predicted negative), False Positive (incorrectly predicted as positive), and False Negative (incorrectly predicted as negative).
4. Can accuracy be misleading for imbalanced datasets?
Yes. If one class heavily outweighs another, a model can simply guess the majority class every time and still achieve high accuracy, despite being practically useless for detecting the minority class.
5. What is the difference between accuracy and precision?
Accuracy measures all correct predictions against all cases, while Precision measures the proportion of correct positive predictions (TP) against all positive predictions made by the model (TP + FP).
6. What is the difference between accuracy and recall?
Recall (or Sensitivity) measures the proportion of actual positive cases that were correctly identified (TP / (TP + FN)), whereas accuracy looks at both positive and negative correctness overall.
7. Can this calculator be used for machine learning projects?
Absolutely! This tool is perfect for verifying evaluation scripts, doing quick homework checks, or documenting performance metrics for data science projects.
8. Is Accuracy Calculator free to use?
Yes, this tool is 100% free to use. We rely on standard advertisements to keep the tool accessible to everyone.
9. Are calculations processed in the browser?
Yes, all mathematical calculations are performed client-side using JavaScript. No data is sent to a server, ensuring complete privacy and speed.
10. How can I interpret my accuracy result?
A higher percentage means the model is generally predicting correctly more often. However, always review Precision, Recall, and F1-Score alongside accuracy to ensure the model isn't biased toward a specific class.
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