HWS: PHP

Assalamu'alaikum warahmatullahi wabarakatuh Selamat malam, langsung saja disini saya akan memberikan sedikit contoh penerapan metode KNN dengan menggunakan pemrograman PHP. sebelumnya apa itu definisi KNN? KNN merupakan sebuah metode untuk melakukan klasifikasi terhadap obyek baru berdasarkan (K) tetangga terdekatnya KNN termasuk algoritma supervised learning, dimana hasil dari query instance yang baru, diklasifikasikan berdasarkan mayoritas dari kategori pada KNN Kelas yang paling banyak muncul yang akan menjadi kelas hasil klasifikasi untuk penjelasan lebih lanjut tentang metode KNN nanti insyaAllah akan segera menyusul :) langsung saja ke contoh penerapan metode ini. contoh sederhana yaitu menentukan kualitas tisu berdasarkan daya tahan asam dan kekuatan. untuk lebih jelasnya dapat dilihat pada gambar berikut: <img alt="" height="123" 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w72UZccLFwE4HfZVrSyibvrjX+lxr/Mufc2tQvmtKcERAAAkDgiAGU8uvABGUBdAuATAdS+agXXiVsdSce6agutv9y+bhrHsHIJrVfwm/giANtFjiHvmg/it8Za+9l/M0p2v7wGyVuyYffx07WLUH0uPGh47VE6iI129eVuBJAvSS3qyizZsOPjswEPDYgqYgQgW1IiAMgLSGptjr+4MsRUPuS+CKItWX6+2ldbg5cWhFyHuLve+Fdq/MvsebyaAASErrbUlQgAAJJGBKCMVxfeNwOodpxtpwm8I4Ba26o2MpGn2mBp9lMP1kIDOvKe3/FJrVfwm5giANvwRxnpod6mr+S/+/yDN3c8ZR/XzzcC8G9jxRUBLHm4Mtq97SZNn2t3G1x7oyWcK17MH/1k32vPO8aUSiICCFES253OmUymMlzj8699cvRi3GlA1LEAJEtKBAB5Vl+v/fGeMJOF7OuFOXjKdeHDLTlYqC+gxu7bCl024ZeXxFdq7MusHS+Cc5CoGQ4AIDZEAMp4duF9MgCPBMAnAvB/SpEv+7etdIc5qfUK544lAnALFQJMn//8g9ee73zUvOQ8xEYwlHa/nBGA1f83DMMxkGOm/b6dQa2vOEo4VzzxyZs7nuqsXP7qs2kWIgKQKYlz1Cu79juWr9u8Y2+jDxf3E/2hgFIlJQKAtLl9j1QqRnAnTvQAeJmDZ7iDvdySg0X9AopStsafaCs/b/TmQXAwJKwWAIAFQwSgjHcX3jMD8EoAiADC/CbOCKD9vqeeqg3c7PXQo6q5Cz1b1vh2KDOLl2RXVe+FTSQCcPb/DcNwPq8pcGTAiCWc/urVX/3E9+nX7XcsX7PCe6SD2CMA6ZIYhmHMXfj01X/y38XtSx/aEfVekTgiAKmSEgFAmsejR4Onq5tM/uApOthHWXKw6BGAfNnSFQEINiwRAABogwhAGZ/h/OozAGuUHOe14aIIQKptHkMEsLDrFc4dUwTQft+LX007n97kEwLMHR/WQCMAACAASURBVN+51tmzXLxk2bLsxs7O51/b98nR/JXpgDUuSATgvSUczwjwHxkwSgkH33t8qaOV237H0mUr1z3a+dSONz/4/LuLxbmFGgsgSkls5opnP9+7Y7Pr2d2VJS7dEpAShRFLBNB4SYkAIC3iVQCRDp6BB/uISw4W8QsoUtnSFQGEPZIRAQBA0ogAlPH7VrTGU6pkAD4JgM/8tQF1AkYXDlGoRr/jk1qvcO4QS7aenuZ/6+NTn1avmP52532BIYAtJGhf8fieo1e8Lgr3bSgnGAEYRrHn8VoI4DcyYJi1Wzf6OkeqtN2a7j8+XW1QCXURQLSSeJsrnv38g9e2rLNfWOA7JHo4cUYADZRUfg8DIccCsL4vHAejaAfPoIN91CUHi/YFFK1sqYgAasdS/zFuwq4AALAgiACU8Q3GnRlANQFof/idYoj5a88OqpuhoUI1+h2f1HqFc4dYsu84xJ7z2p9fXB8C1B7xfOem93y3g++A2IlGAK4B5LxDgBBrr9UF+zS2AeoDdnKtpasuAohYEoHpr7Za9SO4vSuiKAIQlVR6DwMhnwjgPVXEg2fAwT7ykoNF+gKKWLZURAC2ZwIGPREg0oMiAACxIgJQxv/aOKt99A/PHfZNAPzmrzWuAk9CDr69sX3xkmXLlq1c94rtoeDy7Yak1iuaWzwQUa0JFrLFYw8B3FfMh7ri0da30ysCcD6yO9P+i911e7K2mPt2nvZchK2HbS9hqPUX39t0p8e8RqwRQJSSfJnrfHTdymVL77jXf3xv8SYKJ1oEIF1S6T0M2O5kC7ibqDZN45eL+x88A74IIi85WKQIIGLZUhEB2A4Zdz75kd/9Uf7fxACABUcEoEzAN78tA9jxO58EwHd+W/vcd1w3233fziVHaDcktV7B3LWr/L3bHvbB8EO3eOwdZWdDt7ZX/IKQueM7bKO06xYBOAMOrz1Zuwbc8y90zO4dAfg2Ah3DESxEBNB4SWpJl++AkLXwIPCMl1i0CEC+pLJ7GDDsXwQ+3wO+h9yIB89QEYDkkoPFFAFIlS0dEUDtfka/WhFpBwAAYkcEoExQ+G8139vb2zMZ74vr/ee33a6eWbz2hU8vOL5wp7/b8/iKWmd9Z2zthsTWW/uNV5fOdmrBNRL+XPHEG484RoUL3+KxXzJvDwFsF0NURhC0z1Q8YdsGmUym7mHRGkQAdY2xF50NNkfU85xjNPnp8x+/4BzXyl7C6nMtMpnMkk37nJXDmD7/8Y4NjqfYu54gHSUCcDeto5TE9ucv2bDDVccNY/qrV62Z/c+ChhPxRgDpkkrvYcAwHF8E7u+B6e8CDrkRD54BXwSRlxws6AtI+NUWsWwpiQDMCwB9asXchU9dx1wiAABIGhGAMoHX/9maBZlMpn3Te/V9iaD5XQOeL16yct2jnZ2dnY+uW25/8FD93d7RuuJJrbd2pj+z+CfmGnO1uwwcJ50yi5dkN3Z2dnZuzFbHRm+/b8eLjzTe4rEPnmfrQjlup8+037HcLE/no7bB2NuXPrSh0Q5tY9MEzhaieWVPczJLHu+xB1DOs8DVv9C2i5ds2vHsvR4ltA17ZdsRnZ0bs0vvqDUOH/rFnV7FbDwCsJ3Nbr9jzcbOzs7OrR8UopfEPXOljjvnzrQvfaInykAARuQIQL6k0nsYMAzDfRVN5aBs/xaoZNuuz1a0g2fQF0HUJQcL+gISf7VFK1tqIoC6WmF9E1sHI6tWEAEAQOKIAJQJvgXQngF4JgCiWwgHe3c85HzumdPiVVt63CcGI3/HJ7VeZ9sik3Hdxmw/4emyZMOrX01bi26sxWNfq+1c+dzx3QFbYPGqJ/Z8N107Ef0Pzx22LVKXCMDVfXRlNnMXen63wvsvbF/xu54Lc9a6XCUc/GjLqvqH0lVnXfrQjk8vzNVqvmMwPYkIwHk1QyZjHw8iQklEeziTySxe+0Kv5/MUGhLDcICyJZXew4BhGOaD7n0+Ye1LH9/3aTVkch2Mohw8jcAvgohLDhaw3jBfbVHKlqIIQHRc+ajXOpBFG0gVABAdEYAygi58rf/hnQCEGUVornjiwI7N61Yuq51MuGPpynWPPv/G5+e9HjwURwSQ0HrnLny641e2h57XDf43ff7zN7bUTqssXrJy3eZXP64URy4CcF5g4Lhgfvr8x69uXreydnrjjqUr123esfdINf2oDZHsuAhbnwjAceFmJtO+8W1nT3GueOLAjn+yTuC037E0u/H5PSfMx+sFdBDniif2PL8xW6sai5csy258/rVPvrMeu1id2bFOiQjAMIzp7/Y8YT/jbU+GZEti+/s3r1u5rLb0up0cUUxPBJAtqeweBiqmvzuwY7PzoLvljSMX5oIfAC978DR/F/RFEGnJwXzXG/Y5d7JlS1UEYBjWcWVZUK3gsAIAiSMCAAAAgEJWBNDgRRgAgPgRAQAAAEDOe5sWL1m2LLuxc5f/1RXFtzdWLgLgPgAASBwRAAAAAORYIxn4PojVNgip39MRAQALhwgAAAAAcuY+erI6bM6Sh9/4zjUkkOOpgHdueo9rAAAgcUQAAAAAkDX4zsO1J0S037G0+iRW+7MPM5n2pY+7nxgMAEgCEQAAAADkzX3/9iNBjyhtX/rQjhgepgoAiAMRAAAAACKa/u6T1xzPYs0sXrJs5brNOw5UnjcKANACEQAAAAAAAC2BCAAAAAAAgJZABAAAAAAAQEsgAgAAAAAAoCUQAQAAAAAA0BKIAAAAAAAAaAlEAAAAAAAAtAQiAAAAAAAAWgIRAAAAAAAALYEIAAAAAACAlkAEAAAAAABASyACAOSVy+V8Pt/X13cMUO/kyZP5fL5cLidd8QEAaCp/PDP2YG/hJx+fW/pf/bySff3k43MP9hb+eGYs6UrRzIgAAEnlcvnbb7+9ePHi+Pj4JKDe+Pj4pUuX+vr6SAEAAIjF6PWbP+8t3PXxudWfDtx/ML/2EK+EX/cfzK/+dOCuj8+tPZS/ME2DRwkiAEBSPp+/dOlSqVQql8s3APXK5XKpVLp06VI+n0+6+gMA0Ax+3lu4+5Pzifd7edW/7vnv82sP0eBRgggAkNTX1zc5OXnz5s35+fmky4KWMD8/f/PmzcnJyZMnTyZdFgAAUu+PZ8bMs8289Hzd9fE57ghQgQgAkHTs2LFyuUz/Hwtpfn6+XC4fO3Ys6YIAAJB6D/YWVn86kHhHl5ffa/WnAw/2FpKuJk2ICACQdOzYsRs3biRdCrScGzduEAEAABDdTz4+x/3/Or/uP5j/ycfnkq4mTYgIAJBEBIBEEAEAABCLpf/Vn3gvl1fwa+l/9SddTZoQEQAgiQgAiSACAAAgFkQA+r+IAFQgAgAkEQEgjGsNEi6QCAAAgFgQAej/IgJQgQgAkEQEgDCuXbu2d+/eMJ1/czLhAokAAACIBRGA/i8iABWIAABJRAAIw+zbXw2BCAAAgIVEBKD/iwhABSIAQBIRAMIwI4Bxp7a2tvE6RAAAACwkIgD9X0QAKhABJKu3K+PW1atudYVctm59vqs0J1ZXnGphVP7BSjUQAVzet2m1zaZ9lxWXDfowI4CiTVtV0UnXCKC3K5PJZHOFcJNWP9Dmwc38yf5/AAB0QQSg/4sIQAUigGR5RAChG9sSdIoAamVJa8eggQjgy+2rt3+puDjQlBkBjFa1OY3apD0C8O/oEwEAAHREBKD/iwhABSKAZNW1jCuZgPNEmiMZcPbM7Y1zj4l9mMuwJvGesboiz7568Cy13/o2+iuTdnW5J3LEFKI1VrZfrjpPV69tAao7HOEjABKAVmZGAEMh6BQB2D/27gjA/hmtfsqc8WJXr/9VAJUpyQMAAIkjAtD/RQSgAhFAsjxOjtneqrtGIJsrODMAW9vce2JvjgjAb0avSwYqJZWYxbME1XyhNo3fkn3e976KQrDuuISOAC7v27Rp0ybuAmhRZgQwGII2EYDXB9kZu9X9JmwEoPr+IgAAwiMC0P9FBKACEUCyPCIAnyaybcraFH7NadFlt66rALxnrLTpHb37hmYxf+OZRPj9Cb5r8Vmj/aIJx9gCAeuOTegI4Mvt9o7/5X2bSAFaiRkBhKRFBOA8T+/4VLs/oY7Pr/O44xUBFHJZ1R9LAABCk4gA3p8OWuDV4pWYur5XjpcNo1zapn5ej79o/va169cPfn85/ErXHRs/Xrr+LhFAShABJCtMBGA/62braWdzBfNXHtcQiM6Be/S0fdbiulsg6iwef6PHjQ2+f0LdGh3brz6M0CQCcPly+2ruC2gh6YsA3IeloIuNMnWhn38EkM1yCwAAQCcSEcDLV+bOT1Vel+fmDWN+Yqb2zrcXBtMZATj+isG52+V5wzBu/zg4tC7cQrYVbxlG+X0igJQgAkhW4I0AHlfj2s/LZXO9tlNq/hN7sHeQfWd0ZRG2boDELB6r9yms+5ceVxnXZqiPAEQXLcRINgK4vG8TEUALMSOAayE0ewQQ4tAEAMACingjwLbiLcO4dfxU/P3eBY8A3H/FumPXCrcMY/7W0XB/HRFAuhABJCtoOEBHL9Y1hFatS+zV5RWOt2Wb2n9G3/68xCwea/fNADy2ju8aUxEBuM/6f7md8QBaidm3n6hyPRFgwkaXCKCBGwEcxDcCLMwnEwCAcOKPAD4b+s9rN6/dmjcMwzDmyzdvnr9snUi/crxsGNPXnr08N3F73jCMmzdvHD93ee3h0b+XbpXnDWN+fmLy2rOfObrx+yoTz5dv3Dh+/kfbOfnLuZFy8eb8LcMwDOPmrVuDxeImZwTw5wuzIzfnDcO4ddv+21ARwNpD+XX5ubJhlK+NC9fouJVg+qqoeEQAySMCSFaIc2re/eRqN9p7VH/RqTZ7M9x3Rv/+vMQsPiu3lz3owge/NaYiAnCd9icBaDFmBDBuY/X/x510iQAaHA7Q41dBTwTgAYEAAF3EHQFc/u/SvDF/e+zazIeXJw8W54q3DMO4fXagUOuZ37p9ff7WheLUhyPXi7cM4/bNK3PzpdLswctTx2du3TKM6YnR2sTz87fmbw9NTH04XLp0Y94wbucvXFp7KL/2UOG1iduGMX9tunTw8uSHI7PDN+YNY37kypBz3lsXilMfXp45X543jPkff7zUUASw9tDYqZuGcaO0XbTGp/onv5y+bRg3T1+e/LB/SFQ8IoDkEQEkS9Bxt36dzRW8utfeYwbUT+zm7CD7zBjUn5eYxbluR9k8riRw9S381piOCMAwrwTgiQCtyYwAik5tbW3FOtpEAIbzQqOc64Ps+/hRayZrpBLPbr/vICEAACyomCOAE9ODt+YdIwL+MDtdGyPwyvGyYRi3Tpw2E4H8Pw7eMAyjPHOtenp87NRNw5ibea428e1TZwvV6wvGTt2wLu8fP31z/vr01X+0VnRk8sd56wx83bzmb2eued7Y7x8BDB6+blQv7w9eo+tGAMHERACJIwJIK56tlTjZsQDQWswIYDQEnSIAAACan/qxAK4WDKM0MVbrmVdOqufXHsqvvVg2DOPi5WpH/VDhP2esTv6V42XDmJvZYlva9uItw7h19FvPFV05XjaM0tQ/eq4ocHQA/wjA/JXfHf72NQrHAnBMTASQOCKAlCIBSB4RAMIwI4DBEIgAAABYSEoigMOX//n78Q8vz5yculE0BwWwn5y398Mvlg3DKFx0drntEYDrtPnFsmHMnx2o/vjZpc6TY+9enjp+rTxyY/6W4ZzX0eGXiwCGj84Zjo697xq9IgD/iYkAEkcEkD7WxbYEAMkiAkAY6XsoIAAArSH2sQDeuXbrpmEYhnHr9u2Z8s2LE+UxI94I4Paps/m1hwrbr9woVcYcvD1z8+bgteuDN2OPAMbP3rbmCl6jKwIQTEwEkDgiAEASEQDCCPM4QDvhAokAAACIRbwRgDmE/tWr492Hq9McmRo0IkQA16eftK1ue/GWYdw8fDy/9sRM0TDKpekXj1g3EYydijsCMP+cyvCEgjU6IwDRxEQAiSMCACQRASARRAAAAMQi3gjA/PHEaWuCwhPDN24ZESIA29iBa49eLdyqjg7gHkQg/9C569NGnBHAumPXCrcMY/7m4RO1ovqv0RkBiCYmAkgcEQAgiQgAiSACAAAgFjFfBTBw/bph3Lp54+Tw5IeXZ364fvvm/PzN+QgRgO0JgmM35435W8fNRODE9Mi8YczfOj8y+eHlqePTt8rmiuQjgPmJmbnzU5XX4Nztm4ZhGLcLFy9XJhOsMf/klZuGMT8yMfVh/5BwYiKAxBEBAJKIAJAIIgAAAGIR91gAhWcvz1WGAJy/fa00+97J4cPXDePG7L/LRQDT17ZfuTF1e94w5svl8qEfLlhTbjpXGjTH2JufL83NHTzz4zvX5o35Gx8fkYsAHG7dvl0szf7XyQv2yQLXmF97dOKH8vwto/JQQ8HERABJIwIAJBEBIBFEAAAAxCJiBMBrAV5EACoQAQCS+vr6SqVS0qVAyymVSidPnky6FAAApB4RgP4vIgAViAAASQMDA4ODg/Pz80kXBC1kfn5+cHAwn88nXRAAAFKPCED/FxGACkQAgKRyuXzixInBwcG5ubmky4KWMDc3NzQ0dOLECaocAADREQHo/yICUIEIAJBXLpcHBgb6+vqOAeqdPHkyn8/T/wcAIBZEAPq/iABUIAIAAAAA0HKIAPR/EQGoQAQAAAAAoOUQAej/IgJQgQgAAAAAQMshAtD/RQSgAhEAAAAAgJZDBKD/iwhABSIAAAAAAC2HCED/FxGACkQAAAAAAFoOEYD+LyIAFYgAAAAAALScn3x87v6Dyfdyefm97j+Y/8nH55KuJk2ICAAAAABAy3mwt7D604HEO7q8/F6rPx14sLeQdDVpQkQAAAAAAFrOH8+M3fXxucQ7urz8Xnd9fO6PZ8aSriZNiAgAAAAAQCtaeyh/z3+fT7yvy6v+dc9/n7/vYD7pCtKciAAAAAAAtKIL0+W1h/J3fXxu9acDjAugw+v+g/nVnw7c9fG5+w7mL0yXk64gzYkIAAAAAEDr+uOZsQd7Cz/5+NzS/+rnlezrJx+fe7C3wPX/ShEBAAAAgQEAAJKQ9BdgEyICAAAAAvo3wvQvISCBip1e7LtYsBlVIAIAAAAC+jfC9C8hIIGKnV7su1iwGVUgAgDklcvlfD7f19d3DFDv5MmT+Xy+XGZoHCRA/0aY/iUEJFCx04t9Fws2owpEAICkcrn87bffXrx4cXx8fBJQb3x8/NKlS319faQAWHj6N8L0LyEggYqdXuy7WLAZVSACACTl8/lLly6VSqVyuXwDUK9cLpdKpUuXLuXzPCYXC03/Rpj+JQQkULHTi30XCzajCkQAgKS+vr7JycmbN2/Oz88nXRa0hPn5+Zs3b05OTp48eTLpsqDl6N8I07+EgAQqdnqx72LBZlSBCACQdOzYsXK5TP8fC2l+fr5cLh87dizpgqDl6N8I07+EgAQqdnqx72LBZlSBCACQdOzYsRs3biRdCrScGzduEAFg4enfCNO/hIAEKnZ6se9iwWZUgQgAkEQEgEQQASAR+jfC9C8hIIGKnV7su1iwGVUgAgAkEQEgEUQASIT+jTD9SwhIoGKnF/suFmxGFYgAAElEAAjjWoOECyQCQCL0b4TpX0JAAhU7vdh3sWAzqkAEAEgiAkAY165d27t3b5jOvzmZcIFEAEiE/o0w/UsISKBipxf7LhZsRhWIAABJRAAIw+zbXw2BCAA6078Rpn8JAQlU7PRi38WCzagCEQAgiQgAYZgRwLhTW1vbeB0iAOgssBHW25XJZDJdvX6/yuYKqspVE6KEDp7FjUkhl61bn+8qzYnVFadaGJV/MNRp7KNXremivR3wmZXR20X98hCi72o7NDmPkyE3aStseSIAFYgAEICvliANRACX921abbNp32XFZYM+zAigaNNWVXTSJgKQ/uBXp22kv2dr/mj4KUeNbCNM3whAZbl0igBqZeEjlkoNRAANpD1xttNibvM1EdFhs+64VN2IITdpi2x5IgAViAAQgK+WIA1EAF9uX739S8XFgabMCGC0qs1p1EbTCKCxM4iFXLaBbpVv8wf6aeRUpL3TqVUEUB9sOVvcjmTA2TO3/x0eE/swl2FN4j1jdUWeffXgWWq/9f3sVCbt6nJP5IgpRGusbL9cdZ6uXtsC+NwqFbpiu+p0hX0/1+1m843KJF29Rl0g5fz8+n5MnCuIXGmbhqDv6jg+WHsk5Catnyxw3/l93tOACEAFIgAE4KslSPgIgASglZkRwFAIWkYAnh98e8vfo5HR1Ruyw2BO5Piwp6xh0kpC90O8ToBrGAE43qoLo7K5gvMby1bRvSf25tHEr5/Ra4tVSioxi2cJqvlCbRq/Jfu8730VhWDdiEW4iu19CK3fb65Qp67jGNBO86wb9e206JW2eYSKAHy/YYM3aUMRQCNHLf0QAahABIAAfLUECR0BXN63adOmTdwF0KLMCGAwBP0iAM8PvvOzls3WneT07lJ5fDidJ0gX7mQxpITtYDtrTeUnLSMAn+vv6/4Wj7Z10GIdXFcBeM/o3EgSs9jDNM8C+H0bC/ZLfUhi38X2X/DJVShMxbbSVkdldO/lulCrmg3Z9l/gqZq69fbW/+A7WehK20REfVdX29Ud0Yk2qec1e177LtTnXV9EACoQASAAXy1BQkcAX263d/wv79tECtBKzAggJK0igKAPvk8HrzYQQKMdhtZoDqZa2A62+5Ct640AHt16e3Bl+2bJ5grmrzyuIaj/hDh5tLx91uK6Ii7qLB5/o8eNDb5/Qt0afb+WU963SINwg1xkc4W6i7aCRsCoHHOz7pxX2E7zqPle7TT5SttMwvRdnXupoU0aOgIQfN51RwSgAhEAAvDVEkT2iQBfbl/NfQEtJLURQOAH37uDZ90G0GCHobKSZm8MplzzRQDuy9W8msiVDKDXFmf7T+zBXt99ZxSdu2toFo/V+xTW/UvbzQ/1M9R/okUXLSAuISq2s25631/p3NG+fUKJ2uisHVErbVNpoO/qvCkg3CYNHwH4fN5TgghABSIABOCrJYhsBHB53yYigBZiRgDXQtAsAgj84Ht38KzbABroMFQ/02lqjrSmZrsRwJZuOSql6/aX2peOVw0WDmFhm9p/Rt/vL4lZPNbu893quXV810gEkByJkThD7Bx3AladyqOeiWqjf8orUWmbS3Df1XX8sG/GcJvUMwLwvDjWQXT/knaIAFQgAkAAvlqChI0A3Gf9v9zOeACtxOzbT1S5nggwYaNZBBD4wffs4NVuAwjbYXCe9oDWmmI4QJ9yefyy7lx5UPwd7ioA3xn9v78kZvFZub3sQRc++K2RCCA5UtmWf9V2/Mb3qi2PWfxrfu03Xb0RK22TCTkcoOwmdU4WsO8aunZJP0QAKhABIABfLUEaGQ7QlgGQALQYMwIYt7H6/+NOukYArg++sylhGw6wkMu6u0mCDkNDfSkkTO6hgNlcTqcbAXwrm/3uF6+vE68PhNfEbs4Oss+MQd9fErM41+0om1cMby0+8K8jAkhOg3e4uJNVR9V372fHRHWVLuN6qKdvzbfqUjZXiFRpm4247+rzMQy3Sd2TBew73xWlARGACkQACMBXS5BGbgT4cvtqngjQoswIoOjU1tZWrKNvBOD+4Fufwi7bQwFtAwG4l+HTYfA6L0sEoC39G2GKSijo5AOK6f/Rgx/2XSzYjCoQAQCSZMcCQGsxI4DRELSJABrhGDcAzUz/RpiaEpIAIGH6f/Tgh30XCzajCkQAgCQiAIRhRgCDIaQkAvC/AQhNTf9GWOwltC5BIwBAgvT/6MEP+y4WbEYViAAASUQACCOFDwUU8XviB5qa/o0w/UsISKBipxf7LhZsRhWIAABJRAAII8zjAO2EC0w+AkBL0r8Rpn8JAQlU7PRi38WCzagCEQAgiQgAiSACQCL0b4TpX0JAAhU7vdh3sWAzqkAEAEgiAkAiiACQCP0bYfqXEJBAxU4v9l0s2IwqEAEAkogAkAgiACRC/0aY/iUEJFCx04t9Fws2owpEAIAkIgAkgggAidC/EaZ/CQEJVOz0Yt/Fgs2oAhEAIKmvr69UKiVdCrScUql08uTJpEuBlqN/I0z/EgISqNjpxb6LBZtRBSIAQNLAwMDg4OD8/HzSBUELmZ+fHxwczOfzSRcELUf/Rpj+JQQkULHTi30XCzajCkQAgKRyuXzixInBwcG5ubmky4KWMDc3NzQ0dOLECaocFp7+jTD9SwhIoGKnF/suFmxGFYgAAHnlcnlgYKCvr+8YoN7Jkyfz+Tz9fyRC/0aY/iUEJFCx04t9Fws2owpEAAAAQGAAAIAkJP0F2ISIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jbBESlgoFBZ+pWgp+n/04Id9Fws2owpEAAAAQCBcI6yQy2a6epUXxpOohL1dmaowReztEk1XyGUz2VyhOrX5X/FsQEPC9n/s9dGh8TpZyGU9l+TB9rGi4tcJ3Hf9W3e2tXWv72lgeT3ru61Z7P9vckQAKhABIB7lcjmfz/f19R0DgPicPHkyn8+Xy+WkD3KtLkQjrJDLJtkPCCxhIZe1SubbVXJoLAJoYDagIeHTt0xMEUDoBKC3y7ZK+2cManPyiwAAIABJREFUprgjADsiAERCBIAYlMvlb7/9dmhoiGY6gHiVy+Xh4eG+vj4OL8kKboRVev/Zri5NrwJw9IL8r1WwTmlmczn3HNZvCq53unq9rwIwJ6FPhGjC9H8KuWy2q0t8FYCjTvrVdqO3K9PVW824cl21et5r+79Rny1YnwJUhIoATq1v625r625r27m13/ql9WZ3W1v3oq0j5rt+VwGYi2pbf0rh35IgIgAViAAQg3w+PzQ0lHQpADStoaGhfD6fdClamigCyPWa/2oaAYS5CsB1Yb/V0XFMb/sh8EYATokiJuL+j3nWXngjgKNO2k7guy/fqSQAjrzA9wPhLAYRgFOICMD5qqQAZvfe7313BNB/cFET9/8NIgA1iAAQA07QAVCqXC6fPHky6VK0tPSPBeA+hen1a9tvrB/dpzqtvzEgAnBcIA1EIarY1cv2BRGAs046T9i7w4FaV98RGXj93/BcBkxhIoBFB81z/2aX3jrhb/E781/9/8jWRbWFNCciABWIABCDY8eOJV0EAE2O40yyUh4BOPo/vj0Yeweq+qfYrvivqetyOSIA+w0DQETCe3A8rkpx8KiT7g+q7SNhJQDOBdozg/oPEP1/bw2NBdCzx3Uxv/1aAL8IYNGipr4FwEQEoAIRAGJA0xyAahxnkpXuCMB1k7JXZykoAvDuzgfeCMBt0YhJUMW2D9wnvBHAVif9I4BaAhA2AijksiRePiQjgJGti1w3AvhGAK4JmhMRgApEAIgBTXMAqnGcSVbTRwChbwTwXEr9cIDcGI14BFRsz0tU6iqrV530uxHAN1PwiQC45SWQ3I0A5r39lfcrcYD/jQCGc/pmRASgAhEAYkDTHIBqHGeSle4IoP5GAI9ui/1iZvfoZ14zB0cA9I4Qj7D9H+FVAEZA7c1YIwY67xYIjACIuUQkhwN0PA4g+CqA+v83ISIAFYgAEAOa5gBU4ziTrJRHAIbtjuiAjrl1VtX/oYCuG6oDHgpICIA4xBkBOOqk86GA2VzBmQAII4Bw1yC0NOmHApo3BZjn9m0D/vt3+0+tb+ILAYgAVCACQAxomgNQjeNMsvRvhOlfQkDCQlRshq5Qg4NSLNiMKhABIAY0zQGoxnEmWfo3wvQvISBBScV2XKDCBf2qcFCKBZtRBSIAxICmOQDVOM4kS/9GmP4lBCQoqtjeN7cgVhyUYsFmVIEIADGgaQ5ANY4zydK/EaZ/CQEJVOz0Yt/Fgs2oAhEAYkDTHIBqHGeSpX8jTP8SAhKo2OnFvosFm1EFIgDEgKZ5S+rtymRzvdalhPahs83BtDP2hw/VX27o/W5jE6OFcJxJlv6NMP1LCEigYqcX+y4WbEYViAAQA5rmLcn+3Gz7A7Wdz9B2PGrbNgKR/SlFtf87BiWq/eA9MVoLx5lk6d8I07+EgAQqdnqx72LBZlSBCAAxoGneklx9cetH59jC7seEVyfz7Mm736zOTLcfHGeSpn8jTP8SAhKo2OnFvosFm1EFIgDEgKZ5S3I9R9jq+Ts7/e6nDTsmq7+FIFPHb2K0Go4zydK/EaZ/CQEJVOz0Yt/Fgs2oAhEAYkDTvCVFjABqv7d6+sJnE9snRqvhOJMs/Rth+pcQkEDFTi/2XSzYjCoQASAGNM1bUtCNALX3/W4EcKj2/UNd8C8MCtCcOM4kS/9GmP4lBCRQsdOLfRcLNqMKRACIAU3zluQYDrA2zp+70x9iOMBap945lKA1tffEaC0cZ5KlfyNM/xICEqjY6cW+iwWbUQUiAMSApnlL6u3KZLq6uuoe1ec+7+/3RL/qVf2uO/ztAwLUpvaZGC2E40yy9G+E6V9CQAIVO73Yd7FgM6pABIAY0DRvSQzTjwXFcSZZAwAAJCHpL8AmRASAGNA0b0lEAFhQHGeSpX8jTP8SAhKo2OnFvosFm1EFIgDEgKZ5SyICwILiOJMs/Rth+pcQkEDFTi/2XSzYjCoQASAGNM0BqMZxJln6N8L0LyEggYqdXuy7WLAZVSACQAxomgNQjeNMsvRvhOlfQkACFTu92HexYDOqQASAGNA0B6Aax5lk6d8I07+EgAQqdnqx72LBZlSBCAAxoGkOQDWOM8nSvxGmfwkBCVTs9GLfxYLNqAIRAGJA0xyAahxnkqV/I0z/EgISqNjpxb6LBZtRBSIAxKCvr69cLiddCgBNq1wunzx5MulStDT9G2H6lxCQQMVOL/ZdLNiMKhABIAYDAwPDw8NJlwJA0xoeHs7n80mXoqXp3wjTv4SABCp2erHvYsFmVIEIADEol8snTpwYGhriWgAA8SqXy8PDwydOnJibm0u6LC1N/0aY/iUEJFCx04t9Fws2owpEAIhHuVweGBjo6+s7BgDxOXnyZD6fp/+fOP0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkEDFTi/2XSzYjCoQAQAAAAH9G2GJlLBQKCz8StFS9P/owQ/7LhZsRhWIAAAAgICoEdbblbF09S5QoRzCNhMLuWwmmyuIJuvtEv0h9gX1dlX+K54NaIigYhdy2YyNR9VuvE4WctkQHxFr4cl+8HUWuO96jLY2o229aBnxTpZKRAAqEAEAAACBwEZYb5et61HIZRPpDIRrJpodptgjgAZmAxoiqNi9XaIK13CdDJ0AaPHB11kcfVciACIAJYgAAACAQFAjzN3FsE6JL6gwzcRCLpvt6vK/CsA6pZnN5ex/lO1Ma2VW2ztdvd5XAZiT0CdCNMEVW5wA+NZJv9peXaaZceW6avW81/Z/Q5cPvs4auwpg6yKjra3y6okyWb+xyPXbdCMCUIEIAAAACDTQCAt5pX3cxCU0T276Fs91Yb/V0XHMYPsh8EYATokiJoEVu5DLZrPufMrFs07aTuBX4iyrtlZTBXte4PuBcJaFCMCpgQhgfVutY2++tvZLTmZmBM3S/zeIANQgAgAAAAKhG2GJ9X1FJaxe3ezXUXGd0rR+dJ/qLOSytR6STwTguEAaiCL8PTg+V/B71UnnCXt3OFDr6jsiA6//G57LgClsBNC/1WhrMxZtrf7KPI2/XmYyc5pKLtAkiABUIAIAAAACDdxpn1A3ILiEtc6RTwTgfrva03eNtmY72+obAQSckAUa1Uj/x7Nz7lEnrRyrfr7anQWOj4Q9M6hfC/1/b2EjgJ717nP7bW1G2yKjv9HJFjXZLQAmIgAViAAAAIBAqMvsE+34BpXQfnZUIgLw/qsCbwTgtmjEpJH+j7trbxiGZ530jwBsYwuEjACS/uDrbMEjAOvVVOMCEgGoQAQAAAAExMOSJ90LCCih55l8d08p7I0AjqX6PxSQG6MRj8gjcXrVSb8bAXzDMp8IQIMPvs5kbwSINpk5FkAT3QtABKACEQAAABAQnGPXoBsQtpkYOBygc7B02y3Rju6P+UNwBEDvCPEQDQfoHMrfdywA67+etTdjjRjovFsgMALQ44Ovs0jDAVa6+hKTNdsDAokAVCACAAAAAlHPsasXOQIwbH+K/0MBXTdUBzwUkBAAcRBVbOtWf9ETAawfalcC2Gp7NldwDScoigA0+eDrrLGHAq6v79jLTmbeMtAsFwIQAahABAAAAAT0b4TpX0JAwkJUbIauUKOxCAA+OLarQAQAAAAE9G+E6V9CQIKSiu24QIUL+lXx3Xfm7fq+N/bDgWO7CkQAAABAQP9GmP4lBCQoqtjeN7cgVr77zrqYv7me3qcIx3YViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgMAAAABJSPoLsAkRAQAAAAH9G2H6lxCQQMVOL/ZdLNiMKhABAAAAAf0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkEDFTi/2XSzYjCoQAQAAAAH9G2H6lxCQQMVOL/ZdLNiMKhABAAAAAf0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkEDFTi/2XSzYjCoQAQAAAAH9G2H6lxCQQMVOL/ZdLNiMKhABAAAAAf0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkEDFTi/2XSzYjCoQAQAAAAH9G2H6lxCQQMVOL/ZdLNiMKhABAAAAAf0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkEDFTi/2XSzYjCoQAQAAAAH9G2GJlLBQKCz8StFS9P/owQ/7LhZsRhWIAAAAgICoEVbIZTMVXb0LVCansM3EQi6byeYKosl6u0R/iX1BvV2V/4pnAxoirNi9XdWPnnfFbrxOFnLZEB8R98qp+HUC912P0dZmtK0XLSPeyVKJCEAFIgAAACAQ3AizdTJC9rDjF66ZaEYVsUcADcwGNET40atVQu/a13CdDJ0A9HbZPkuFXJa67xJH35UIgAhACSIAAAAgENQIc/aFk8oAwjQTC7lstqvLv3zWKc1sLmfvN9mucajM6rrqwesqAHMS+kSIJvxHz4dfnfSr7UZvV6art7rwXFetnvfa/m/UZwuOOAJGo1cBbF1ktLVVXj1RJus3Frl+m25EACoQAQAAAIHQjTCNrwIwT276FtB1Yb/V0XHMYPsh8EYATokiJkEVO1Sn27NO2k7gV+Isq7ZWEgBHXuD7gbBJ7KOvrwYigPVttY69+draLzmZmRE0S//fIAJQgwgAAAAIhGiEVU+MJ9QLCDFaQTZX8O+ouE5pWj+6T3UWctlaD8knAnBcIA1EEVCxzSrYKxiHw6tOOrMDdzhQ6+o7IgOv/xuey4ApbATQv9VoazMWba3+yjyNv15mMnOaSi7QJIgAVCACAAAAAg00whK6HT64hLXbm30iAPfb1Z6+7Yr/mroswREBJBmEoOkIIgBXx95nLABXnbRyrPoZrQTA+ZGwZwb1q6H/7y1sBNCz3n1uv63NaFtk9Dc62aImuwXARASgAhEAAAAQaKQR5u5fLAzBLdNel/K7JvGNALy784E3AnBbNGIivAqg4Hyj7qPnUSf9I4BaAhA2AijksiRePhY8ArBeTTUuIBGACkQAAABAQHBDst8pxQUkPFnq4i5h2BsBHEv1fyggN0YjHo2MBRAYAdjrpN+NAL5hmU8EwC0vgWRvBIg2mTkWQBPdC0AEoAIRAAAAEAhshDk7HgmdAA/bTAwcDtA5WLrtlmhH98f8ITgCoHeEeDT20Qt+KKBv7c1YIwb6PNvDKwIg5hKJNBxgpasvMVmzPSCQCEAFIgAAACAQYrC9TLJ3wUeOAAzbX+H/UEDXDdUBDwUkBEAcGvjoBQ0HaPuhdiWArbZncwVnAiCMAEJdXNPaGnso4Pr6jr3sZOYtA81yIQARgApEAAAAQED/Rpj+JQQkLETFZugKNRqLAOCDY7sKRAAAAEBA/0aY/iUEJCip2I4LVLigXxXffWferu97Yz8cOLarQAQAAAAE9G+E6V9CQIKiiu19cwti5bvvrIv5m+vpfYpwbFeBCAAAAAjo3wjTv4SABCp2erHvYsFmVIEIAAAACOjfCNO/hIAEKnZ6se9iwWZUgQgAAAAI6N8I07+EgAQqdnqx72LBZlSBCAAAAAjo3wjTv4SABCp2erHvYsFmVIEIAAAACOjfCNO/hIAEKnZ6se9iwWZUgQgAAAAI6N8I07+EgAQqdnqx72LBZlSBCAAAAAjo3wjTv4SABCp2erHvYsFmVIEIAAAACOjfCNO/hIAEKnZ6se9iwWZUgQgAAAAI6N8I07+EgAQqdnqx72LBZlSBCAAAAAjo3wjTv4SABCp2erHvYsFmVIEIAAAACAwAAJCEpL8AmxARAAAAENC/EaZ/CQEJVOz0Yt/Fgs2oAhEAAAAQ0L8Rpn8JAQlU7PRi38WCzagCEQAAABDQvxGmfwkBCVTs9GLfxYLNqAIRAAAAENC/EaZ/CQEJVOz0Yt/Fgs2oAhEAAAAQ0L8Rpn8JAQlU7PRi38WCzagCEQAAABDQvxGmfwkBCVTs9GLfxYLNqAIRAAAAENC/EaZ/CQEJVOz0Yt/Fgs2oAhEAAAAQ0L8Rpn8JAQlU7PRi38WCzagCEQAAABDQvxGmfwkBCVTs9GLfxYLNqAIRAAAAENC/EaZ/CQEJVOz0Yt/Fgs2oAhEAAAAQ0L8Rpn8JAQlU7PRi38WCzagCEQAAABDQvxGmfwkBCVTs9GLfxYLNqAIRAAAAENC/EZZICQuFwsKvFC1F/48e/LDvYsFmVIEIAAAACIRthBVy2Uw2V1BbGE9BJeztytTp6g1cXG+XaBL7n9rbVfmveDagIZErduN1spDLhv0Q20tAxXcLPGz2b93Z1ta9vqeB5fWs77Zmsf+/yREBqEAEAAAABMI1wgq5bCajYQTg1NsVooyNRQANzAY0JHLFbrhOhk4AHCss5LLUfZe4IwA7IgBEQgQAAAAEwjTCCrlstqtLx6sA7II6RNYpzWwuZ5/MjDYqvym43unq9b4KwJyEPhGiiVyx/eqkX203ersyXb3VjCvXVavnvbb/e6zR+hSgIlQEcGp9W3dbW3db286t/dYvrTe729q6F20dMd/1uwrAXFTb+lMK/5YEEQGoQAQAAAAExI0w89ShnjcC1ASUz3Vhv9XRccxi+yHwRgBOiSImkSu2Z520ncCvxFlWba0kAI68wPcDEa4IrSpEBOB8VVIAs3vv9747Aug/uKiJ+/8GEYAaRAAAAEBA1AirXjuseQQQcAmA61fWj+5ZCrlsrYfkEwGEutUACCNqxfask84T9u5woNbVd0QGXv83PJcBU5gIYNFB89y/2aW3Tvhb/M78V/8/snVRbSHNiQhABSIAAAAgENwIq908rHUEEFQ49++qPX3bFf81dWmHIwKw3zAARBSxYnvWSSvHsk1Suz3AdteANYs9M6iPAOj/e2toLICePa6L+e3XAvhFAIsWNfUtACYiABWIAAAAgEBQI8w+epjOEUBg2YIiAO+ZAm8E4LZoxCRixfask/4RQC0BCBsBFHJZEi8fkhHAyNZFrhsBfCMA1wTNiQhABSIAAAAgENAI8zxPvvBnBMXNxOBuedgbASzBDwXkxmjEI2rF9qyTfjcC+MZ5PhEAt7wEkrsRwLy3v/J+JQ7wvxHAcE7fjIgAVCACAAAAAmEbYRpfBSB6Npr9Ymb36GfO7o/5Q3AEQO8I8YhcsT3rpPfgl47HAQojAGIuEcnhAB2PAwi+CqD+/02ICEAFIgAAACDQHBGAqGTWBQ3+DwV03VAd8FBAQgDEIXLF9quTzocCZnMFZwIgjAA0ufxHZ9IPBTRvCjDP7dsG/Pfv9p9a38QXAhABqEAEAAAABPRvhOlfQkDCQlRshq5Qg4NSLNiMKhABAAAAAf0bYfqXEJCgpGI7LlDhgn5VOCjFgs2oAhEAAAAQ0L8Rpn8JAQmKKrb3zS2IFQelWLAZVSACAAAAAvo3wvQvISCBip1e7LtYsBlVIAIAAAAC+jfC9C8hIIGKnV7su1iwGVUgAgAAAAL6N8L0LyEggYqdXuy7WLAZVSACAAAAAvo3wvQvISCBip1e7LtYsBlVIAIAAAAC+jfC9C8hIIGKnV7su1iwGVUgAgAAAAL6N8L0LyEggYqdXuy7WLAZVSACAAAAAvo3wvQvISCBip1e7LtYsBlVIAIAAAAC+jfC9C8hIIGKnV7su1iwGVUgAgAAAAL6N8L0LyEggYqdXuy7WLAZVSACAAAAAvo3wvQvISCBip1e7LtYsBlVIAIAAAACAwAAJCHpL8AmRAQAAAAE9G+E6V9CQAIVO73Yd7FgM6pABAAAAAT0b4TpX0JAAhU7vdh3sWAzqkAEAAAABPRvhOlfQkACFTu92HexYDOqQAQAAAAE9G+E6V9CQAIVO73Yd7FgM6pABAAAAAT0b4TpX0JAAhU7vdh3sWAzqkAEAAAABPRvhOlfQkACFTu92HexYDOqQAQAAAAE9G+E6V9CQAIVO73Yd7FgM6pABAAAAAT0b4TpX0JAAhU7vdh3sWAzqkAEAAAABPRvhOlfQkACFTu92HexYDOqQAQAAAAE9G+E6V9CQAIVO73Yd7FgM6pABAAAAAT0b4TpX0JAAhU7vdh3sWAzqkAEAAAABPRvhOlfQkACFTu92HexYDOqQAQAAAAE9G+EJVLCQqGw8CtFS9H/owc/7LtYsBlVIAIAAAACgkZYIZfN2GRzhQUqV03MJeztymS6ekVLrC6mt6vyX/FsQEOE/Z/eruBq3XidLOSyYT/CtpVT8esE7rseo63NaFsvWka8k6USEYAKRAAAAEBA0Ajr7Uq8/R9zCRuLABqYDWhIcMW2wqfKDx61r+E6GToB6O2ypQ6FXJa67xJH35UIgAhACSIAAAAgIOyHJN72j6OE1inNbC5n7zfZLiGo9Hhs73T1el8FYE6S+HZBygVVbM8Yys2vTvrV9uqHxVx4rqtWz3tt/zfqswVHHAGj0asAti4y2toqr54ok/Ubi1y/TTciABWIAAAAgEBgI6yQy2az7k7yQotcQteF/VZHx9HNsv0QeCMAp0QRk6CKHarT7VknbSfwK3GWVVurcZk9L/D9QNiEyiNaSwMRwPq2WsfefG3tl5zMzAiapf9vEAGoQQQAAAAEAhthjuuBG7mROE5RS+g6pWn96D7VWchlaz0knwjAuToggoCKbVbBXucVKXW86qQzO3CHA7WuviMy8Pq/4bkMmMJGAP1bjbY2Y9HW6q/M0/jrZSYzp6nkAk2CCEAFIgAAACDQSCMsmfvhI5bQfQqz2tN3DSNou4zANwJI8loINB1BBODq2PuMBeCqk1aOVT9j7ZYZx0fCnhnUr4b+v7ewEUDPeve5/bY2o22R0d/oZIua7BYAExGACkQAAABAoJFGmLt/sTAiljAoAgh144DzRgBui0ZMhFcBFJxv1H30POqkfwRgGzQjZARQyGVJvHwseARgvZpqXEAiABWIAAAAgIDghmQNRgWLWsKwNwJYgh8KyI3RiEcjYwEERgD2Oul3I4D9HpkwEQC3vASSvREg2mTmWABNdC8AEYAKRAAAAEBANNieczxx7cYCCFNC+8XM7tHPnN0f84fgCIDeEeLRWMUOfiigb+3NWCMGOu8WCIwAiLlEIg0HWOnqS0zWbA8IJAJQgQgAAAAIiBph1v3Gej4RwAhXQuvGf/+HArpuqA54KCAhAOIgqtj2yhkwHKDth9qVALbans0VXONkiiIAz2EyGBDArrGHAq6v79jLTmbeMtAsFwIQAahABAAAAAT0b4TpX0JAwkJUbIauUKOxCAA+OLarQAQAAAAE9G+E6V9CQIKSiu24QIUL+lXx3Xfm7fq+N/bDgWO7CkQAAABAQP9GmP4lBCQoqtjeN7cgVr77zrqYv7me3qcIx3YViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgID+jTD9SwhIoGKnF/suFmxGFYgAAACAgP6NMP1LCEigYqcX+y4WbEYViAAAAICA/o0w/UsISKBipxf7LhZsRhWIAAAAgMAAAABJSPoLsAkRAQAAAAH9G2H6lxCQQMVOL/ZdLNiMKhABAAAAAf0bYfqXEJBAxU4v9l0s2IwqEAEAAAAB/Rth+pcQkPD/t3fHyGkzYRiAuZM5Da3vwfgCblPT/3XqDHdIlZmM6pyCvxCWJVnSwnrX/oSeZ1IAwXiz+0F2X2mFwl4vY1eEbqxBBAAAJMSfhMVvIWRQ2Otl7IrQjTWIAACAhPiTsPgthAwKe72MXRG6sQYRAACQEH8SFr+FkEFhr5exK0I31iACAAAS4k/C4rcQMijs9TJ2RejGGkQAAEBC/ElY/BZCBoW9XsauCN1YgwgAAEiIPwmL30LIoLDXy9gVoRtrEAEAAAnxJ2HxWwgZFPZ6GbsidGMNIgAAICH+JCx+CyGDwl4vY1eEbqxBBAAAJMSfhMVvIWRQ2Otl7IrQjTWIAACAhPiTsG9pYdM0X/9L2ZT4bz3mGLsidGMNIgAAICE5CTsfn94cTs2XtGmocAvPx6en43npGc3p0L3Q+Xi9mf4xuEuisJvT4alnorTvr8nmdLj1Tdx7Vyn8DxbH7s/rj93u5fnnHa/38/ml+5H+7QcnAqhBBAAAJCxPwroV8PXOdywGCrfwvgjgjh+DuyTWP+djquDursmbE4DzsZc5NKeD2h8pHQH0iQD4FBEAAJCwNAmbXAt/uRIt7A5pHk6n/rqpd6T1+jK9R47n6bMA2qdYE/E5yWzrjjRrUJNz1f72mu275nR8r/Nz7/blY7YwyNm43BgB/H7evex2L7vdj9c/3V92D77sdi/713/to3NnAbQvtXv+XfHf8o1EADWIAACAhKVJWIyZ/6dbODqxv1voDPKD3p3FjQAOiVLI4vqnOR0Oh3E+NTJZk70D+Nc4q6vWt1ShnxfMviGGbYnwQRDJDRHA8M81BWiX93OPjyOAP7/2D7z+v4gA6hABAAAJC5OwduZ/Hh4W/3qfbeHokGZ3d3yoszkd3ldIMxHA4ARp+IzF9c+w0qbP4J+qyWEoNg4H3pf6g8hg6vZl8jVo3RIB7H+1x/7bJX13wL8zd+T/7fY1KMKdAAADI0lEQVS/1/37izwmEUANIgAAICGxwH4KfS2AW1o4PoT5ttIfXW2td7R1NgL4xmsi8njuWf9MvvcmarLLsT7+3PvOgsFbop8ZfPwt1v/T7roWwM//Rifz988FmIsA9vuH3gLQEgHUIAIAABKSx9ib4QNfvxz4ZAuXIoDp5fziRoAYmyN4APesfybfehM1OR8B9K4tcGME0JwOEq8ZmRHAv9f9aCPAbAQwesJjEgHUIAIAABLu2WkfLgK4qYW3bgQYvMj8lwLaGE0ZicJOX5BvqibnNgL0txLcEgHY8rIobyNAu7f/+vg1DpjfCHAZPv8RiQBqEAEAAAmpa5INrycebCPAbS3sn8w8vvrZcPnT3lmOAKyOKOO+wp69FkB3c7J6n7orBg53CyxGAGKulMzLAQ6+DmD5LICPtx+QCKAGEQAAkJCahPW3zIe7HODlcrmthd1z5r8UcLSheuFLAYUAlJAq7G6rf+obAbo772cC9Kr9cGpGlxNMRQCTl8lwQYC+7C8FbDcFtMf2exf8n1/2/35+4BMBRAA1iAAAgIT4k7D4LYQMX1HYLl1Rhw+lInRjDSIAACAh/iQsfgshQ5XCHpyg4oT+WnwoFaEbaxABAAAJ8Sdh8VsIGSoV9vTmForyoVSEbqxBBAAAJMSfhMVvIWRQ2Otl7IrQjTWIAACAhPiTsPgthAwKe72MXRG6sQYRAACQEH8SFr+FkEFhr5exK0I31iACAAAS4k/C4rcQMijs9TJ2RejGGkQAAEBC/ElY/BZCBoW9XsauCN1YgwgAAEiIPwmL30LIoLDXy9gVoRtrEAEAAAnxJ2HxWwgZFPZ6GbsidGMNIgAAICH+JCx+CyGDwl4vY1eEbqxBBAAAJMSfhMVvIWRQ2Otl7IrQjTWIAACAhPiTsPgthAwKe72MXRG6sQYRAACQ8BcAvsN3/wf4gEQAAAAAsAkiAAAAANgEEQAAAABsgggAAAAANkEEAAAAAJsgAgAAAIBNEAEAAADAJogAAAAAYBNEAAAAALAJIgAAAADYBBEAAAAAbIIIAAAAADZBBAAAAACbIAIAAACATfgfRftXfS1mekUAAAAASUVORK5CYII=" width="400" /> dari contoh sederhana diatas 6 buah data training (lebih banyak lebih bagus). selanjutnya dimasukkan daya tahan asam / detik dan kekuatan tisu kemudian klik proses. maka.. <img alt="" height="122" src="data:image/png;base64,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width="400" /> disini saya buat perhitungan dalam tabel agar lebih mudah dipahami dan dikembangkan lebih lanjut lagi. dari data training tersebut diatas ternyata menghasilkan jenis kualitas tisu yang jelek. cukup sekian dari saya. terima kasih app bisa download disini WAssalamu'alaikum warahmatullahi wabarakatuh NB : password silahkan sms/wa ke 085 725 626 073

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