diff --git a/contents/approximate_counting/approximate_counting.md b/contents/approximate_counting/approximate_counting.md
index 5e06e43b5..e5b7c1f5e 100644
--- a/contents/approximate_counting/approximate_counting.md
+++ b/contents/approximate_counting/approximate_counting.md
@@ -366,6 +366,8 @@ As we do not have any objects to count, we will instead simulate the counting wi
 [import, lang:"cpp"](code/cpp/approximate_counting.cpp)
 {% sample lang="python" %}
 [import, lang:"python"](code/python/approximate_counting.py)
+{% sample lang="java" %}
+[import, lang:"java"](code/java/ApproximateCounting.java)
 {% endmethod %}
 
 ### Bibliography
diff --git a/contents/approximate_counting/code/java/ApproximateCounting.java b/contents/approximate_counting/code/java/ApproximateCounting.java
new file mode 100644
index 000000000..2dd49c0f0
--- /dev/null
+++ b/contents/approximate_counting/code/java/ApproximateCounting.java
@@ -0,0 +1,85 @@
+import java.lang.Math;
+import java.util.stream.DoubleStream;
+
+public class ApproximateCounting {
+    
+    /*
+     * This function taks
+     *   - v: value in register
+     *   - a: a scaling value for the logarithm based on Morris's paper
+     * It returns the approximate count
+     */
+    static double n(double v, double a) {
+        return a * (Math.pow(1 + 1 / a, v) - 1);
+    }
+    
+
+    /*
+     * This function takes
+     *   - v: value in register
+     *   - a: a scaling value for the logarithm based on Morris's paper
+     * It returns the new value for v
+     */
+    static double increment(double v, double a) {
+        double delta = 1 / (n(v + 1, a) - n(v, a));
+
+        if (Math.random() <= delta) {
+            return v + 1;
+        } else {
+            return v;
+        }
+    }
+
+
+    
+    /*
+     * This function takes
+     *   - v: value in register
+     *   - a: a scaling value for the logarithm based on Morris's paper
+     * It returns the new value for v
+     */
+    static double approximateCount(int nItems, double a) {
+        double v = 0;
+    
+        for (int i = 0; i < nItems; i++) {
+            v = increment(v, a);
+        }
+
+        return n(v, a);
+    }
+
+    /*
+     * This function takes
+     *   - nTrials: the number of counting trails
+     *   - nItems: the number of items to count
+     *   - a: a scaling value for the logarithm based on Morris's paper
+     *   - threshold: the maximum percent error allowed
+     * It terminates the program on failure
+     */
+    static void testApproximateCount(int nTrials, int nItems, double a, double threshold) {
+        double avg = DoubleStream.generate(() -> approximateCount(nItems, a))
+                     .limit(nTrials)
+                     .average()
+                     .getAsDouble();
+    
+        if (Math.abs((avg - nItems) / nItems) < threshold) {
+            System.out.println("passed");
+        } else {
+            System.out.println("failed");
+        }
+    }
+
+
+    public static void main(String args[]) {
+        System.out.println("[#]\nCounting Tests, 100 trials");
+        System.out.println("[#]\ntesting 1,000, a = 30, 10% error");
+        testApproximateCount(100, 1_000, 30, 0.1);
+
+        System.out.println("[#]\ntesting 12,345, a = 10, 10% error");
+        testApproximateCount(100, 12_345, 10, 0.1);
+    
+        System.out.println("[#]\ntesting 222,222, a = 0.5, 20% error");
+        testApproximateCount(100, 222_222, 0.5, 0.2);
+    }
+
+}