diff --git a/best-time-to-buy-and-sell-stock/se6816.java b/best-time-to-buy-and-sell-stock/se6816.java
new file mode 100644
index 000000000..d2a081c3e
--- /dev/null
+++ b/best-time-to-buy-and-sell-stock/se6816.java
@@ -0,0 +1,43 @@
+
+/**
+    dp를 이용한 방식?
+    prices의 길이 -> N
+    시간복잡도 : O(N^2) -> 시간 초과
+    공간복잡도 : O(N)
+ */
+class Solution2 {
+    public int maxProfit(int[] prices) {
+        int[] dp =new int[prices.length];
+        for(int i = 0; i < dp.length; i++) {
+            for(int j = 0; j < i; j++) {
+                dp[i] = Math.max(dp[i], prices[i] - prices[j]);
+            }
+        }
+    
+        return Arrays.stream(dp)
+                    .max()
+                    .getAsInt();
+    }
+}
+
+/**
+    이전 연산 값을 기억할 필요 없이 특정 인덱스 지점까지의 최소 값만 알면 되므로,
+
+    prices의 길이 -> N
+    시간복잡도 : O(N) 
+    공간복잡도 : O(1)
+ */
+class Solution {
+    public int maxProfit(int[] prices) {
+        int min=prices[0];
+        int profit=0;
+        for(int i=1; i<prices.length; i++){
+            if(prices[i] < min){
+                min=prices[i];
+                continue;
+            }
+            profit=Math.max(profit, prices[i] - min);
+        }
+        return profit;
+    }
+}
diff --git a/group-anagrams/se6816.java b/group-anagrams/se6816.java
new file mode 100644
index 000000000..99977e4a0
--- /dev/null
+++ b/group-anagrams/se6816.java
@@ -0,0 +1,28 @@
+/**
+	Map을 통해 사전순으로 정렬된 문자열의 집합을 구한 뒤 그 집합들을 리턴하는 방식
+	strs의 길이 -> N
+	시간 복잡도 : O(N)
+	공간 복잡도 : O(N)
+*/
+class Solution {
+    public List<List<String>> groupAnagrams(String[] strs) {
+        List<List<String>> result = new ArrayList<>();
+         Map<String, List<String>> hashMap = new HashMap<>();
+         for(int i=0; i<strs.length;i++){
+            char[] charArray = strs[i].toCharArray();
+            Arrays.sort(charArray);
+            String word = new String(charArray);
+            List<String> list = hashMap.getOrDefault(word, new ArrayList<>());
+            list.add(strs[i]);
+            hashMap.put(word,list);
+         }
+         for(Map.Entry<String, List<String>> entry : hashMap.entrySet()){
+            List<String> list = entry.getValue();
+            result.add(list);
+         }
+         return result; 
+    }
+}
+
+
+
diff --git a/implement-trie-prefix-tree/se6816.java b/implement-trie-prefix-tree/se6816.java
new file mode 100644
index 000000000..857c2162c
--- /dev/null
+++ b/implement-trie-prefix-tree/se6816.java
@@ -0,0 +1,92 @@
+/**
+	연결 리스트를 통해, 트리 구조를 만들고 탐색하는 방식
+*/
+class Trie {
+    public Map<Character, WordNode> wordMap;
+
+    public Trie() {
+        wordMap = new HashMap<>();
+    }
+    
+    public void insert(String word) {
+        WordNode wordNode = null;
+        char ch = word.charAt(0);
+        wordNode = wordMap.get(ch);
+
+        if(wordNode == null) {
+            boolean isFirstWord = word.length() == 1;
+            wordNode = new WordNode(ch, isFirstWord);
+            wordMap.put(ch, wordNode);
+        }
+
+        for(int idx = 1; idx < word.length(); idx++) {
+            char target = word.charAt(idx);
+            boolean isLeaf = word.length() - 1 == idx;
+            wordNode = wordNode.next.computeIfAbsent(target, key -> new WordNode(key, isLeaf));
+        }
+        wordNode.isLeaf = true;
+    }
+    
+    public boolean search(String word) {
+        
+        WordNode wordNode = null;
+        char ch = word.charAt(0);
+        wordNode = wordMap.get(ch);
+        if (wordNode == null) return false;
+        
+        
+        for(int idx = 1; idx < word.length(); idx++) {
+            char target = word.charAt(idx);
+            if (!wordNode.next.containsKey(target)) {
+                return false;
+            }
+            wordNode = wordNode.next.get(target);
+        }
+
+        return wordNode.isLeaf;
+    }
+    
+    public boolean startsWith(String word) {
+        
+        WordNode wordNode = null;
+        char ch = word.charAt(0);
+        wordNode = wordMap.get(ch);
+        if (wordNode == null) return false;
+        
+        
+        for(int idx = 1; idx < word.length(); idx++) {
+            char target = word.charAt(idx);
+            if (!wordNode.next.containsKey(target)) {
+                return false;
+            }
+            wordNode = wordNode.next.get(target);
+        }
+
+        return true;
+    }
+}
+
+class WordNode {
+    char ch;
+    Map<Character, WordNode> next;
+    boolean isLeaf;
+
+    public WordNode(char ch) {
+        this(ch, false);
+    }
+    
+    public WordNode(char ch, boolean isLeaf) {
+        next = new HashMap<>();
+        this.ch = ch;
+        this.isLeaf = isLeaf;
+    }
+
+}
+
+/**
+ * Your Trie object will be instantiated and called as such:
+ * Trie obj = new Trie();
+ * obj.insert(word);
+ * boolean param_2 = obj.search(word);
+ * boolean param_3 = obj.startsWith(prefix);
+ */
diff --git a/word-break/se6816.java b/word-break/se6816.java
new file mode 100644
index 000000000..e35702d3f
--- /dev/null
+++ b/word-break/se6816.java
@@ -0,0 +1,120 @@
+/**
+    Tries 자료구조를 구현하고, 이를 통해 단어를 탐색하는 방식
+    visited[] 방문 배열을 추가하여, 이전에 방문했던 인덱스에 대해서는 무시
+ */
+class WordMap {
+    public Map<Character, WordNode> wordMap;
+
+    public WordMap() {
+        wordMap = new HashMap<>();
+    }
+
+    public List<Integer> search(String word, int idx) {
+        List<Integer> idxList = new ArrayList<>();
+
+        WordNode wordNode = null;
+        char ch = word.charAt(idx);
+        wordNode = wordMap.get(ch);
+        if (wordNode == null) return idxList;
+
+        if(wordNode.isLeaf) {
+            idxList.add(idx + 1);
+        }
+        idx++;
+
+        for(; idx < word.length(); idx++) {
+            char target = word.charAt(idx);
+            if (!wordNode.next.containsKey(target)) {
+                break;
+            }
+            wordNode = wordNode.next.get(target);
+            if (wordNode.isLeaf) {
+                idxList.add(idx + 1);  
+            }
+        }
+
+        return idxList;
+    }
+
+    public void add(String word) {
+        WordNode wordNode = null;
+        char ch = word.charAt(0);
+        wordNode = wordMap.get(ch);
+
+        if(wordNode == null) {
+            boolean isFirstWord = word.length() == 1;
+            wordNode = new WordNode(ch, isFirstWord);
+            wordMap.put(ch, wordNode);
+        }
+
+        for(int idx = 1; idx < word.length(); idx++) {
+            char target = word.charAt(idx);
+            boolean isLeaf = word.length() - 1 == idx;
+            wordNode = wordNode.next.computeIfAbsent(target, key -> new WordNode(key, isLeaf));
+        }
+
+        wordNode.isLeaf = true;
+
+    }
+
+}
+
+class WordNode {
+    char ch;
+    Map<Character, WordNode> next;
+    boolean isLeaf;
+
+    public WordNode(char ch) {
+        this(ch, false);
+    }
+    
+    public WordNode(char ch, boolean isLeaf) {
+        next = new HashMap<>();
+        this.ch = ch;
+        this.isLeaf = isLeaf;
+    }
+
+}
+
+class Solution {
+    public static WordMap wordMap;
+    public boolean wordBreak(String s, List<String> wordDict) {
+        boolean[] visited = new boolean[s.length()];
+        initWordMap(wordDict);
+        if(s.length() == 1) return wordMap.wordMap.containsKey(s.charAt(0)) && wordMap.wordMap.get(s.charAt(0)).isLeaf;
+
+        Queue<Integer> que = new LinkedList<>();
+        boolean result = false;
+        que.add(0);
+        visited[0] = true;
+        loop:
+        while(!que.isEmpty()) {
+            int idx = que.poll();
+            List<Integer> idxList = wordMap.search(s, idx);
+            for(int i : idxList) {
+                if(i == s.length()) {
+                    result = true;
+                    break loop;
+                }
+
+                if(!visited[i]) {
+                    que.add(i);
+                    visited[i] = true;
+                }
+            }
+        }
+
+        return result;
+
+
+
+    }
+    
+    public void initWordMap(List<String> wordDict) {
+        wordMap = new WordMap();
+        for(String word : wordDict) {
+            wordMap.add(word);
+        }
+    }
+}
+