Perhitungan Estimasi Ketidakpastian Pengujian Minyak dan Lemak dalam Air dan Air Limbah Secara Gravimetri (SNI 6989.10:2011)
Perhitungan Estimasi Ketidakpastian Pengujian Minyak dan Lemak
dalam Air dan Air Limbah Secara Gravimetri (SNI 6989.10:2011)
Berdasarkan tahapan metode SNI 6989.10:2011, kadar minyak dan lemak (oil and grease) ditentukan berdasarkan persamaan sebagai berikut:
![](data:image/png;base64,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)
Sehubungan dengan model matematika penentuan kadar minyak dan lemak tersebut, maka diagram sebab akibat adalah sebagai berikut:
![](data:image/png;base64,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)
Dari diagram sebab akibat tersebut terlihat bahwa sumber-sumber ketidakpastian berasal dari peralatan gelas dan timbangan yang digunakan dalam pengujian minyak dan lemak, karena itu kedua peralatan tersebut harus dikalibrasi. Adapun evaluasi masing-masing ketidakpastian adalah sebagai berikut:
1) ketidakpastian gelas ukur
Peralatan yang digunakan adalah gelas ukur 1000 mL yang telah dikalibrasi dengan ketidakpastian, U95% = 0,13 dan faktor cakupan, k = 2 pada tingkat kepercayaan 95%. Gelas ukur tersebut dikalibrasi pada suhu 200C sedangkan pada saat digunakan untuk pengujian minyak dan lemak suhu ruangan pengujian menunjukan 240C. Bila koefisien muai adalah 0,00021/0C maka evaluasi ketidakpastian baku adalah sebagai berikut:
![](data:image/png;base64,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)
2) ketidakpastian timbangan
Ketidakpastian timbangan dipengaruhi oleh pengulangan penimbangan (repeatability), kemampuan pengulangan pembacaan (readability) dan linearitas (linearity). Sertifikat kalibrasi timbangan yang digunakan menimbang minyak dan lemak menunjukan bahwa ketidakpastian U95% = 0,067 mg dan faktor cakupan, k = 2 pada tingkat kepercayaan 95%. Sehubungan saat menimbang kadar minyak dan lemak menggunakan cawan kosong serta cawan berisi minyak dan lemak, maka ketidakpastian saat menera sama dengan ketidakpastian saat menimbang cawan berisi minyak dan lemak. Oleh sebab itu, ketidakpastian penimbangan adalah sebagai berikut:
![](data:image/png;base64,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)
Bila diketahui berat konstan minyak dan lemak adalah 10 mg dan volume sampel 1000 mL maka hasil pengujian kadar minyak dan lemak adalah 10 mg/L. Dengan demikian, ketidakpastian gabungan dapat dihitung dengan menggunakan persamaan sebagai berikut:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyMAAAEoCAIAAADE6bl4AAAgAElEQVR4nO3dvW4bPd738TmBuzB8BjaQ3QNQgAtqE8Db3ltEnTsP4DbbucqmM+AFVBpwnlpIs41gVU+xiXvBdQS3vrZQlwOYu/hbFIfkcDij4bzp+6lsvYxIShZ/JjmcJAMAAEAcSdcFAAAAGC2SFgAAQCwkLQAAgFhIWgAAALGQtAAAAGIhaQEAAMRC0gIAAIiFpAUAABALSQsAACAWkhYAAEAsJC0AAIBYSFoAAACxkLQAAABiIWkBAADEQtICAACIhaQFAAAQC0kLaMMqTUS6yrJsM5+qn9Wd0/lm//i3RyTWHQCAQSFpAW1ZpUmSrrLNfDqdb1ZpsvstSVeb+XQfvCRlqXylQhqRCwCGh6QFtGSVJsl0Pk+n881bmprOV/N0vlH3bbJsl7N2qStTDyVkAcAQkbSAdmzm02SapvPV7pdkmqaSs/SgJUNYWrAyRrgAAINC0gJakQtMb6uw9HC1j1K7eUXtafoQFwBgSEhaQBtyC7HyYcoIWll+OXzrKcscU2vcrnYM0wE4CiQtoAV20MqPYRmpY5V2OIolK/bjHV1mTOVMgFivAgC9QdIC4isPWrul8VnnK+DjBq29VcqgFoBjQNICovMFLWPJlnZT0eyhunuay2bJdD5PkyRdya4Q6Wr/QuqHtwemqX564+54b7+3k4A28zkDWgCOAklr5HZ7MTF8oHQ6M1fGHM/avX/5TR+sRV377VDT1Waz2cyn6Xw+TVerNJmmqfyQrnIv8HaLevYuYLXQPCzUAnBMSFrDst/EskJP9bZhpoc2iFKxlzWGX94GTtIed6HuJGHUo6MQ4H6nrBVNegPruWtXNT1NTafp2w+71VFTtbGEDHvl8lbVoKXGzwrvK7p/M58StQAcA5LWUNi7Krk6OdfeS2VBS98ps0r399aR5g7tuq1fPEmi+42rCt9SxwSiFYz0sS01PDVNVzJ7WBi08sGyQtDyx3NzhzDnQ0haAI4ASWsQCk689w0o5B5UGrT0FT8h3V9xKOn5UIWvd3dmmraZg2tGY2pn7u1WYGkXUrSClhHHdolLmzLcR+x0Jb+n8ypvX36/Cv1Wc/NV80EELQDHgaTVf548FbD1UenUYf7iL0Hdnzfh9Xnu0JsjexG0ypgzcvslT6u399EZtORj4riAYn7JlDs2lRfIDlGuDcL0a2v3e+ATABpE0qpJdVp697GPK9ZghD5UUelfecexzDv1/tTswsrXaGkHUXNSgY932p1UZnSpjq0LtDYx66c/2bVlutywWq0KnuVusOEHrYNEWevuSlqO22pkOAAYB5LWAd5SjPTfu65EJmZy3bYx1eYYWSjkvxaLNYdnpYWAoKUVaL+EpzgVVrs6jNZE01zR9NE4o9S5QbbNPNWeqD3SHDbxHFA78MFBK39KgqnX02FxTip0RCjXfmBcJRvA0SJp1Sd9/Tzdb1S0339SywH52bmsUqfjX6NthR6r1wsIWu5xIMe25fpruBfmaNTWTDIoZe+AbkUrPUtZCUofLNQSa8HFbQpSU3nQGnESiLbbB0kLAPxIWrVt5tNkmqbaVNk0TdP9qJB+4pU17hTW6YSMaB0StPbFtK94HDqMZh7PLFCapsahquQs9zaf4QfMP6q4LQgCdQUmLWYPARwtklZduchhBxXjaivmOqOwPicg1pibXJpBK3gUZ18F72qt8iIZyc+5Wjq/tCw3NOXooe3oVTTq5dlkoqOg5ZtrHILSCrJOCwD8SFo12ddX8QSG/Jn74f2NJ9bYC+UrBi1HDHrLKeXnMvrKVDa25FkEFpCq9gd2LHsrqW7VoOU8iXLA67TicEUouz3Hf7oBABQhadXju5Cde2SmVjdTELVcA2OFQavgAnP+8yP1qrm60YLpOTNoOZKHcwxkrpa6mTXQhg21alizs84D5l+1atBiu6cg9ffTAoDjQNKqpTxorfYDIgdNTVm5xpio3NmVQeUDdUPRPpTmCNbbM81BG1fBnZN0xvhTYefqeJy+VkxbsZWmUofVfL7J5TZ7CNF5QP01S2ZRy4fXjl3R+GrBtKC1+o6cBeBYkbTq8AUtRxIytv2u3Oe4z+uzS6Tfad1QeuB0Zd/uCRvWXub5Q/h619xTCxpKT33pSkapCi//UnhAdbe7Iq46eCPmEXMkrbLPZcjHCABGj6QVlzmetet8+A8fAIBjQNKKyb3LgrroHAAAGDmSVkyubQ5YtAIAwPEgaUVmLQVizQoCmJ8bPjYAMFAkLaCnKlxNAADQVyQtoKeYaAaAESBpAf1E0AKAMSBpAW1Qm0ulq8zakNZ1iSOCFgCMAkkLaMvbrh+yk+puc3XZ9MPKVQQtABgHkhbQErmYwFyuDPS2p+3uqk3m5X8OuoQTAKA/SFpAOzbzaTJNU7lQtiSpNE3zV6nUH0vQAoAxIGkBrcjt2WBcHZOgBQCjRdIC2mBflbzoGuVFQWvzNtEY2yplFy8AaA5JC2iBHbT2WSYsaK3SttKPrNhv5aUAYPxIWkB85UFrtzTevTO8GcYil5WgBQCNIWkB0fmClrZky7pIpvvKh+ph6qZdOpunSZKu1IXN1QupH94emKZTbfZyd7y339sbPAOAY0DSAobFHHOSDbkylec2m81mPk3n82m6WqXJNE3lh1Q76XG+yd5uUc/eBSz1SABAE0hawNDoa+P13LULSXqamk7Ttx/mm9zD5cFm3iJoAUDDSFrA8GzmUysY6WNbanhqmq5k9rAwaOXnIQlaANAwkhYwILvhrF1kyg1j2UHLiGO7xKVNGe4nFdOV/J7OWaYFAM0haQEDoi5UrS+7SuTkRclazqAlw1b7Jxtr6Xc35Lf5AgA0gKQFHAlmBgGgAyQt4DgQtACgCyQt4AhYS98BAO0gaQEAAMRC0gIAAIiFpAUAABALSQsAACAWkhYAAEAsJC0AAIBYSFoAAACxkLQAAABiIWkBAADEQtICAACIhaQFAAAQC0kLAAAgFpIWAABALCQtAACAWEhaAAAAsZC0AAAAYiFpAQAAxELSAgAAiIWkBQAAEAtJCwAAIBaSFgAAQCwkLQAAgFhIWgAAALGQtAAAAGIhaQEAAMRC0gIAAIiFpAUAABALSQsAACAWkhYAAEAsJC0AAIBYSFoAAACxkLQAAABi6SZpPT8/39zcnJ+f397edlKAxrVTo5eXl5OTk+vra+NFn56ePM+6ubk5OTl5fn6OUaTAMlTlL/PLy8v9/f1kMrm4uCg91Ha7XSwWs9ks5MHtMN7Hvnl8fLy+vj45Oem6IF1SjRD4wX58fJzNZkkS9I0a6a8GQD91k7TOz8+TJEmSZDRJq50aGT20etEOk1ZgGaryl3k2m52cnCRJEhKebm9vpZAkrRBPT0/qPe26LJ3RGyHkg/34+DiZTMIbLdJfDYB+6uzL9ObmZkxJK+uoRtfX151/Xx9ehsViUfXpj4+P4eHp5eWl26TVz1DlIVGg61J0TMJT+CezUjztw18ugHZ09mV6e3s7sqTVSY3kRbv9vj6wDNvttsY0ytPTU6Xw1GHSur+/789wWqCLiwuSljRCpKTVh79cAO0gaTWGpFXjudvtturIgRhK0losFr2auAxE0spIWgAa0uqXqVoxc3Nz48wlarmDrGLZbreZtvz59vZ2u93KqPvJyclisdCfu91u1fHPz88fHx+NV99ut7L6xzi+sWL69vZWHnNzcxOpRv5X1Ot4f3+vl3+xWFxcXOjdtvF9nWjkRll7ayzsfXp6UstKJpOJaqvAprYbwV8Go63UeQMvLy9qwYq+ys1ZZr0xLy4u7NnDovdXlUo9WAos5EZ9Dbh6O2azmX6ELMvu7+9VgWezmb6MTK3sub6+fnl5kYroL6S6Yef76Dm4XrbHx0e15swom6H2n4OQkCGfAXmMeoBkXD1V6NWUW/QV38/Pz3K08/NzY+Hdy8uLOv7FxYW9LK/ZD6qnkVWbSHnkUHbS8j/dTlpF5c+0vxpP+0wmk5OTk5eXF3+lAPRce0nr+vp6MpnIV4l8Cxu5ZLFYSC+VZdn9/b18N2Xa8ufZbHZzc7PdbmW+KUkS/Tvo4uJC9c3ydL0rVceRAsjXnKyeUR3SZDJRBZDTiPxf3LVrpL/i7e3ty8vLdruVV7y+vpanbLdb+f5VX76qX/QkLckieodkr719fn5WdVcPULUOaWqDvwyqre7v7+WA0i+qIGs83VlmacyTkxM5rB441AOK3l9hPFi1fJZf/nxzc6N/hOT9EnKLfCQkbZyfn6syq8/ey8uLHLzopZ3vY9HBPWXzr/2q/eegnq5So90a8hHSU4WKX/KrKrO87+opqsWyLNtutycnJ5PJRD7t8qkQKnA3+0H1vIPZbnhV4rX89ckB1YfQ//TMSlqe8uttXtQ+GUkLGIuWkpbMoegdp/yrpzok6av0p8h3vXyvydNns5m6V76nVBKS7yl9FEq+i/UvKaNvS5JEvaKsmD4/P1f/1ss3qaczO7BGRa+od+3yEnqfbU+Z6TFF/jm2hzqMf82NZCO/qsGz0qa2lZZBBvD0W6RPknfHOY1ilFmaS28KOf/AeEOL3l/j3uvra7s60hHqgwr6+5VZE2p6DpbxFXWXGtNyFixzvY+egzvLZvfKugP/HDJtTEs/gqc1MitnSHLSB2XlKaoMElz0RC7BS/3a+AfV38gymqWqvN1upUaqAP6n2y3gL7/xa+ZqcwDj0FLSmkwm+ndoZq1qsudZ9P9upWfSey/j6TKXZD9A779PTk70vsf4WjQ6ntJlQAfWKOQV7Vp7klZRzMqs1HJ/f693ukaxS5vaVloGKYBNihSStCRX6Ue2myLw/XXGrMwVHeRDpfry2Wymv+N6CSU06L2mzv4g2YX3HNxZNqN2hsP/HEpbo7RI9is631P7AerXxj+onkaWXGUsGDAK7H+P7Bbwl7+0fQCMRhtJy3mOvfG9Yy9b0ZV+q8pL6P/g2t/jymKxsDe/qZS0Dq9RyCuGJy3jO91Q9A2ur5I5PGl5yhAY1Dxltjcd8LxBnvdXJmedxbCjQ9HeEE9PTzLAo0oo45dJkpyfn9sxLiRpeQ7uLJs/aR3+52C/omQRVebDk5YMSunxVIrhrE4jH1TFbmR7/NgusOfpzhbwl5+kBRyPNpKWs1+xc4lnT+qQb1V9NP7p6cmYiRCSBq6vr41lJVnFpHV4jUJeMTxpyUxNUWntb/CXlxdZhnJ/f29sA1Y7aXnKYPT6zqf7k5bdhznfAv/7K8OQRZ2ZHR0y13t0fn5+cXFhL5d+fn7W1z4bk0ohSctz8KpJKzv4z6G0NQ5PWplrMZkxqtTsBzUrbmTnc+0Ce94juwX85SdpAcejR0kryS96sI9Q+q2qulL7XKpstw6jqPOOkbQ8NQp5xUqzhxJ0nN2MnQn09mlw9rCoDEl+DVDR0z1lDklaIe+v1F1fjmO8on6LMYpjjME4+8XFYqFO91MvEZK0/AevkbSyw/4cikb4Gpw9zHbrxI0zUpXGP6ieRg5JWqUfAKMF/OUnaQHHo42kJT2WsYDX+b1jdEhqZXHIt+rNzY1nMax8Sza1TuvwGoW8YqWk5dmYyjkTp/rdBpNWURmkAMYR1L7wIUlLDqtPTRpNEf7+Fm1wZUcHeQnpWSVn+JfpKDK1pIcYf9IqPXiNpHXgn0PROq3wFfEhSUJOPCwqZLMfVH8jS+2MmWX9ASEfAKMF/OUnaQHHo6UV8Rf507gy69o18kUm/zTLl+/Ly8tsNpNTkwLXadmbBhU9Pjt4RfyBNQp5xUpJK9udcWYP2PjHhxpMWkVlkH5dDiK3Pz8/q1GfkKSlJijVA4z9tCq9v5KEnAMY+kdIRn3UThBGyxsjIvonQR4cnrT8B8+qJ63D/xwurPPgZBZM3XKRP5FQnalnvETpOi3PqXbNflD9jSxbTiT5NK//21D6HtkF9pefpAUcj5aSlpq1Ubs26PMa8hhJKjr1vWavAZLOUs1lqIUmBvVfu3TMk8lku93KAlV5gAwyyZe+ng9k1YjetTRbIxU+PK8oT5cyOx+jxpBUNeUBF9oJgGqfITXrIU9RxVY7ry4Wi+fn59KmNoSUQb2oTnU5aoGO7KXpLLN6FclqarPHZDfMEPL+qtZWx9eziBzw4uJC30FK5SdJErIBlWwKKh3z4+OjbFqrPgmyNZo++CGPlA1L9TVJ+vvoObiK7Pr8r9zin22v/eeQ7f7i1JiTFEl/OT37Pj093e7OtJ1MJovFQn+/ij6H6ik6fffRxj+onkbOdn+Sk8lE2lntkCerrEqfbr8pnvI/7XY0Ve+I3T4Z+2kBY9HezqWqdzw5Obm9vb29vZ1MJvf39/r3iNqCWU5Bl35I9akiy+9Cnuz+a9R3PtSpby4JLurIaqdQIw89PT0Zr1j0X2btGhl9jPMV7Vrbj9F/lS7HuMX5GBlPSnadiiyXlm3EA5taV1oGeZi+I7l0XeoIao9WCWfOMmfa1vlJklxfX8vmpTc3N/rOrknY+2u3f6aNaRXtqy5bpya7QUoVKJ+fn29vb+Wwxnbq+hNVhnB+uooOrm86miSJfMyMW5wfztp/Dqrkxj8PRn+v3g61vkq9L4GfQ5XGbPKeNvtB9b+D8gDj3b+4uJjNZhIcSz8A9pviKX9I+2QkLWAsxnNpM/vaKdvtdogX90Un7Bm6Qev/n4PEO+NGGWDzTFUDwOCMpGu5ubkp2jcy5PKFwJiS1iD+HIy9MJSnpyfPCjMAGJyRdC1JwVU4FosFY+8IMaak1f8/B5kvcxaGAS0AIzOSrkUWNNzurhmcZZkspPBsZwUoapP3cYym9P/PQRpcttRXs5yy4IkL/wEYmZEkLTkbSC2wPT8/t9fwAk6Ba8wHZBB/DrIkS9aMy7JxYhaAURpJ0gIAAOghkhYAAEAsJC0AAIBYSFoAAACxkLQAAABiIWkBAADEQtICAACIhaQFAAAQC0kLAAAgFpIWAABALCQtAACAWEhaAAAAsZC0AAAAYiFpAQAAxELSAgAAiIWkBQAAEAtJCwAAIBaSFgAAQCwkLQxbguPQ9QcNAGri+wvDRh8MAOgzeikMG0kLANBn9FIYNpIWAKDP6KUwbMeQtH7//v3hw4ckSc7Ozl5fX7suTkTHU1MAx2P8vRTG7RiS1sPDw3q9zrLs8vLyw4cPv3//7rpEsRxPTQEcj/H3UhixY4hZuvV6/f79+2MY7DmemgIYvePqqDAyx5a0Xl9fP378eAz543hqCmD0jqujwsgUJa3lcnl6eirzUP33+vp6dna2XC5LH7lerx8eHmKUobUWC6xsvJoCQMtIWhgwO2nJkurLy8tOynOIkJVJ3759a3zpUictVlrZGDUFgE6QtDBgRtL6/fv3p0+fhjKUZfMvTrq7uwsZ96qkwxbzVDZGTQGgKyQtDJiRtC4vL4feQ6/X60+fPtnDOXd3d3d3d1mWvb6+fv/+vamX67bFnJWNVFMA6ApJCwOmJy3VQw+dXZHLy0t1+b8GV1P1ocWMMkSqKQB0iKSFAVNJa0ynqrUzo9eTFhv6hC8AlCJpYcBU0urD8EyDlstl7CXq/WmxFioLAB0iaWHAJGmNb1wk9oBTr1qsJ6NrABAJSQsDJkmraBW5h6wH0odS+nb5l6hr1QNbrLVWGsGpDABQhKSFAZOkVXUi7PLycrFY6JtIyXaaPZlNE1Hn1EJarM1WYgIRwIiRtDBUapFWpRGR19fX9Xq9Xq9PT09VaOjhnvI1BurClbZYy60UtbIA0C2SFoZKLdK6urqqusrn7u5ODw2Xl5dnZ2e9WioUb/VSeIu11kos1QIwYiQtDJUkrRqdtFx/Rq03kpGbvs1exVu0HthibbZSr1boA0CzSFoYKkla//nPf/7yl7/897//DX+iPikmAzynp6c9XJH9xx9/fPv2rfHDBraYp5Xu7u5kc9G7uzsJZMbCeSHrugJX0EeqLAB0jqSFoZKk9e9///v8/LxS0rq7u1OzYJ8/f/769Wvfpg5FpPAR2GL+Vrq8vFTrt9br9efPn4sOEriCnqQFYKxIWhiq2klLbVUgVzK+vLyUFeJ9G9b6448//vWvfzV+2MAW87eSvqb+27dvzlGrSnOCkSoLAJ0jaWGoDklanskvmfM6Oztbr9eyqYGxu4HMnf3jH/84OztbLpf6VFqWn1lzPj68nJ0nraJW0iOUnlDlKWro6/X19erqKvCMQpIWgLEiaY2W5AN1vV7VR0qXqd84ULWTlt+ff/758+fPq6urq6urh4eH9+/fz2azh4cHaau7uzv1w9nZ2a9fv/RhG3sUx3h8pTnKbpOWh1pT//r6+uXLl2z3uVJZU36otEsWSQvAWJG0Rk6GGfShFElgg85YIlLSyrTdDS4vL//2t7/JD3d3d/q2TypGrNfrh4cH9Vz9V+fjw/U2aal6ffnyRbLjcrlUK99V0qq0zxlJC8BYkbRGzjg5f0yn00dKWqqJXl9f379/v1gs5AfJWxIdZAhHxQhZyaSOoH4tenygeuGjdBOHw1tMUqMxb6jOUpSaVt19g6QFYKxIWiOn74o0ppiVHXDRQz91QDUKpX6QeCFNKlOHX79+/f37tzzlzz//1H/9/fu38fiqpzfWuxpgacQ5vMWWy+XHjx9l3lCocay7uzv5sMljvn//HnhMLn0IYKxIWjX1/xLFisyF/fz5c3wxK4uQtOzJL3mv5fYkSU5PT799+3Z6evrx40e1Ek5GcfRfs91aePX4qrOHvU1a9pozVXdVx6obypO0AIwVSauO1i6+q87/cgp8ueVyKf39aGJWFjNp9Udvk1YMJC0AY0XSqmwolyhWRrMEXkfSKkLSAoBeIWnV1P9LFAtZm/Xu3bt+Fq+28KTlGRTsA08dw8OHf+zTCNkdtlgjlQWAYSFp1TGISxRn2ml0smBoTD1ZwphWAca0AKBXSFp1NHiJ4svLS8+04yHrtPQzDY25zhEgaRUhaQFAr5C06mjwEsWRuj1jQwdjEK416/X63bt3sqzt/fv3DU5fkrSKkLQAoFdIWnU0eIliY9PLRjiX59ubxQ+Fc4kPSasISQsAeoWkVYf/EsVCJuzUTJ++wZK62LDMPH78+LGp8xZlQwehX+hXvwCi/VpShdPT09lspoqq10uO8OHDh1+/fskPsj+nUUEVQKWz//XrlxpI088Y0Bvh4eHBPrIqmMpYdtgiaRUhaQFAr5C0olDXb8m0LsS+2PByuXz37t2PHz/UbpldUQu5JDDp11S5urrabDaLxeLLly9XV1ePj49SNaOCy+Xy69evMkW4Xq8/f/6cZdlyuZR6qVuMRliv18aRnRHBk7RiXPewJ3p73cMYuBoPgLEiaUWhhg1U5HJebFgPYd0mLVUAdUk7PW9JUeVay3KXXcFMS1HqKnifP39Wdy2Xy6JG0I/sLB5Jq0FttpiM/oYM2ZK0AIwVSSsWNcNYdLFhGS7qw+UI9QKomKV3kOpay3pFjAqqeqlV8GoaSx3f2Qj2kW0krQYFtphM8ioSgtWcsv7W+y2Xy5DzRUhaAMaKpBXL9+/f9d7FvtiwmllTQ0FdWS6XaoG/PdgmM4Nyu34KoVFBuevXr19XV1cycKVmDD1XXHYe2WYkLf3nfq46akSkpUvhLSbr84ycVPWC2XK+SMjDWKcFYJRIWlGof/1V52FfbNi+KG9X1OiUsRer2rJL9YKy4l5NLOoVVFdX/Pr1qzxGnVap9k21G8E+srOEJK0GhbeYvMv2qR7hG3bIhzykFiQtAGNF0opCrU/q7VV6lNJT1ZxKK9jslOhxJi3VyM2qOqalryCs+rYGTh1m0SoLAJ0jaTXP6Mm+fPnS56TlWYdexF9BWX/28+dPmTpsih629KSllrv5n66vKjMG5CRP2Ku21UIlGXtTiUEN+MnApHpW0e3+o3lECh+BLZa5Li1gjzzpK7fsSgVOHWYkLQDjRdKKQr+KTp/nRNT+W1XPfPRUsOo6nkCepBU4JifjK79+/fr06dPj4+PZ2ZmcRPn+/fvHx0f9ekpGFR4eHt69e6cvCZef9R1ii273H80j3nkSlVrMmAE3PifGnK/aUE29UODUYecnhQBAPCQtDENR0pLdX0Nyg+SAq6sr2XTj9PT0y5cvX79+/f37t52Z9MSgBw5jQk2N5xXd7j+aR71Z3RDhLXZ3d6eaRZ02oe61L6xuRMzwqcN4lQWAzpG0MAxFSSsLm3iSxDObzX78+JHtMkSapmq1mYoRerwQRoBQC/yNl3DeXnq0IlHXnwVO1aktdu1V8M6RS6Oy4VOHI15sBwAkLQyDJ2mF7PsqAzASp9TSInmWPhxlrwHPrEkxucWZlozbA4/mVGP9XLjAnXJlPZnMtxqVtZdwZfnTI5x1LxK1sgDQLZIWhsGTtNTGXR5qeCbbhQDVteuDTPaAk0z2GYnBnhN03h54NKeoux6EtJiqy9///ne7wPYcqDGZGDh0J9jiAcCIkbQwDJ6kVbrwyNgXyggBxtShPiNmnJaob9aqH6Todv/RPGKvWwpZquXf78256t9u1T///PPbt2/+wrBIC8C4kbQwGCpsGUkrK5sOk+EWNWqiRyvJE2ppvL7WShYnffz48cOHD4+Pjz9//vzw4YN6FTVTJiHDvj3Lr9wyjuYPWy3MppVOIOrzrfa9xrhg0bTpjx8/SmMlU4cAxo2khcHwJC3/uIg9dahChuQJ48RDfcd82c9Ctsj6/v272hZLP6Dzdv/RPNVsZ8uD0pGk0jMH1f5hdo30rcX8xWB/BwCjR9LCYKgdvJz3dn75yKYELldv5IU6b7HWKgsAXSFpYTD8SSsbxcLqlvc76LbF2NwBwDEgaWEwSpPW0KeiKl28uREdtlj7lQWATpC0MBilSSvbLY0a4shWVwM8nbQYo1kAjgdJC4MRkrRE0c6i/SSL9EO2M42ntRbrQ2UBoE0kLQxGeNICAKAn6LQAAABiIWkBAADEQtICAACIhaQFAAAQC0kLAAAgFpIWAABALCQtAACAWC/7YDcAAAnxSURBVEhaAAAAsZC0AAAAYiFpAQAAxELSAgAAiIWkBQAAEAtJCwAAIBaSFgAAQCwkLQAAgFhIWgAAALGQtAAAAGIhaQEAAMRC0gIAAIiFpAUAABALSQsAACAWkhYAAEAsJC0AAIBYSFoAAACxkLQAAABiIWkBAADEQtICAACIhaQFAAAQC0kLAAAgFpIWAABALCQtlEhwxLr+9AHA4PFNihJ0twAA1EYnihIkLQAAaqMTRQmSFgAAtdGJosTok9YqlSVJ0/mm66J0h0YAgEhG3onicCNPWqs0XWVZlm3m0+ONGTQCAEQz6k4UTRh50tpbpYnkjWNGIwBAw46kE0VNRxOzsv3AzlGjEQCgYcfTj6KOwqS1SpNBDH4Ez4dt5vMmq9O39glrh4YbAQBA0oKfM2n1LUWUCUgZq7TB5Ul9bZ+ydmi0EQAAgqQFHztpDXQhz2Y+LYwRqkpNDOn0vH0K26HRRgAAKCQt+JhJq+c5wstZ9s18ql175uC6DaF97DI23AgAAA1JCz75pOXPEbstmcq76vJHmhNw6hneRFA2bRc7B5UeP7yJtCfkH5tLRcZh9q3knwYcQh4EgLEgacFHT1qb+bS4f9YiQUneCXikJAbtnqCgZT3L5ptDPJi3fbIqTaQ/I/fAzXyq/b5K9UilHXMzn/rDVtR2AADoSFrw0ZKWbyAk3+fn80DlR67S6XSaSxjWUm1HWexnFb1+pOGckiOHN9H+GVaNjIS2mU/17KY1UmnWYlgLAFpC0oLPPmmFBy1fjih/5CqdzjerVE8Ym42RGOyT5BzPKi5rlIhRKWgFRC1njWTqcHfDKlWHtKpO1AKAniBpwUclLV/H7IpL7m6+9JFvGcqfmRxDXAHPKnx2I0qCS3gT7e4tqJG+TMucXrWOXx7lPDUCADSCpAWfXdLyLuwJ7+ZLHrmZp/IivphgFSXoWZ7nN6HsoNWSUEmNdmmrcLArCxjTYq0WALSDpAWfXdLyDtk0lLS0FeWeGGIGhLBnGYVofN6s7JhVkpa/Rm9Thm/nCOxnWCV97TNr+Zo15g8BoA0kLfi8JS3/+Ic1NVY4oOJ5ZO7MPW8MMZY7hTzLPETTCaN0fKhSExXXKLfLu5mm8rtilTdFhHYAAFhIWvB5S1r/b5r89Z/Fj2pgRbwVE9z7QtlBK+RZhn/+9X/+9/97q11VSftk4U3krZH5pMJ29i8C22m+HQAAFpIWCu1PPPznX/1JotFdHrLi0Sn/2FHgmFaEhFHWPlmdXR4yq0bWQJh7X66wnEXSAoBWkLRQKDxp6ckhHyKM9UOeR+oKMlPJJF2vk1aFJtpz7/KwawPHFaND1mdppSZpdYuzEoCjQNIahVVaMGdW+fIvugpJK9Pnvaz1SIlj5sxbqsJz7iqe4OfWTdLKqjTRG0eN9OnF3PYYQROnRqljJ62OLtAUdu2m7hV8oM0Z5D4WHUAFJK2h238tW1/a1S//klctaXmt5j36172zpOXVfhNFTlqdXaBp4EFrd19PSw2gMpLWKLjOZKu1MCinsaS1mc/71GX0MWl10URRk1Z3F2gKuXZTH3iHaB0zwwCGiqRVT37BTFYvyTRcnPwXc+XLv9iaSFrlO2i2r2bC8GyqflDS6qyJYiat7i7QFHDtpuryM7wyaKYvlwud5M0/gqAFHAeSVg2b+XQ6X+U7hFrTc02WyPhCr3r5F5ck6KKHw1Oz6/U8bZjtE/F6PJ1foCnormr2e8amK1VIyUuOYbn97/vJTGN1nj9oDfETBcCJpFXdZrXamNmm8f9BXWtNitec2EmrxoXwLCSt0KcNs32qtkOFz2THF2gKuquiVZok6fxtt9e3KPTWgvk/P6ukrpBJ0AKOCEmrJusf704H+0laVZC0RMQxrU4v0BR2V0Wb+TRJ07ezFiQKzXdr63JfBlZMcp0YXL4xHHOHwHiQtOo5fLl5owJmD2usBiJp+UZxjBPhBtg+bc4etneBpsC7KsqfC5hfqGX/12VegLLSiBZBCxgbklYt9j+xTfe0h84eNrsivjhJeJJIHxS0batjWl23QeHfeMTZw+4u0BR4V1W5UuZHqeyq5UtedQfe6oPPAPqNpFWHviprlU7TtM4irSZPOYu9y8Mwx2yKMHsoIo5pNbzLQ1acPzoIWo4t/u1lap7Pg79cBQ2gFqwBGBySVg3778L9eUjpKlulFXvb5vpnZ2rzX/emcFv5PZJW6NOG2T5Rk1b3F2hqMGjlTngxyih//VoQKvunpnwxfOkIIYBBIWnVYFxUZfdr1b62oe45P6dTtKuP9Ur5pOWcZiJphT5tmO0TN2llHV+gKVbQMjZ0eSu5fTpK5dnOgtnSknYB0H8krThc37Vvy2in0/1637nja7plKmMZYSsXvJq42kx/9HGP+C50e4XpXl2gqSnmkNTue4CcBBwzklYEm/lUmwxR415qf+ld0JpO091PPeh0SFqHHHGI7dNl0urZBZqaUbB9ccdbwADoGkkrBm0eQL5497NLu+yV3ymoD//ykrQOOeIQ26ejpNXHCzQ1w7nIqturRwDoAZJWFMbMiMpS+8Umao1Mb/7h7SBpFa7KV5OvUbqojpNWnVrXu6u01F3OHo6TvdiKlAUcPZJWDOaYlvo9TdPdjb1bweFLWs0Pu+2byMoc2iBAnPGA5leCh7ZPvVrXuyuk1P3I+AAwaiStppnnKfUmSZVpN2llWRZrG7BS3SWtLMsq17reXWGlJmkBQHQkrcbl5g8Gk7P8SSt3XZTmxNnaPuBVm04YldqnWq3r3RVYDpIWAERH0sKeHrbMpBWjUw64XGOETRvjJK3wQ1aqdb27mi80AKAmkhb2CpNWpIkmO3PY+1M2fxW4GFOhVdqnUq3r3RVY5iENuQLAUJG0sFectOKMf3SStKJUpcpBe5K0GNICgFaQtLBXnLTiDIAEzKM1v/lSnOG5Cu1Tqdb17gorMUELAFpA0sKeJ2lFiVpdrIiPNWcWftx+rIhn7hAA2kHSwp4vacXomjvY5SFewAg+ci92eSBoAUBLSFrY8yatCLvZO6e7tMDQeM6KujYptH2q1rreXWVFYOoQANpB0kKO2gnMdWeTAyHqIjKOfcf2W5I1O+4SeyCn/Pg1a13vrgPKCQBoCkkLOd6kpa6PPUgt5ItBtA85CwDaRNJCjj9pZYPtp1ubL+t5+zBvCAAtI2khpzRpZb0PE7aW40Vv24eYBQDtI2khJyRpZTF2bo+k+f24gvSufTpqBwAASQs5gUkLAACEoENFDkkLAIAG0aECAADEQtICAACIhaQFAAAQC0kLAAAgFpIWAABALCQtAACAWEhaAAAAsZC0AAAAYiFpAQAAxELSAgAAiIWkBQAAEAtJCwAAIBaSFgAAQCwkLQAAgFhIWgAAALGQtAAAAGL5P9DZcdCVFHuiAAAAAElFTkSuQmCC)
Bila faktor cakupan dinyatakan 2 pada tingkat kepercayaan 95%, maka ketidakpastian bentangan dinyatakan sebagai:
U95% = 2ugab = 2(0,0477) = 0,095 mg/L
Dengan demikian, kadar minyak dan lemak dalam air dilaporkan sebagai berikut:
Kadar minyak dan lemak dalam air limbah = (10,000 ± 0,095) mg/L
Berikut ini perbandingan faktor-faktor yang memiliki kontribusi ketidakpastian pengujian oil & grease dalam air limbah secara gravimetri (SNI 6989.10:2011):
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxEAAAB5CAIAAADJSjubAAAbT0lEQVR4nO2dvXoTx9uHRyZnIPsEDFVMyhRJiaDC/AsKqCypgQKlggPgCgUYLB8BXewTwIIuyDi1La4k7yFgp8YucwW/xaMdzT7zsbMz2i/v767inY8dzSg7N8/MjsQlAAAAAADIQlTdAAAAAACABgBnAgAAAADIBs4EAAAAAJANnAkAAAAAIBs4EwAAAABANnAmAAAAAIBs4EwAAAAAANnAmQAAAAAAsoEzAQAAAABkA2cCAAAAAMgGzgQAAAAAkA2cCQAAAAAgGzgTAAAAAEA2cCYAAAAAgGzgTAAAAAAA2cCZAhEAAABAA/nzzz+/fftW9SzaSOBMgQghDkFbkY+eqhtSOzqdDvXMdDqtui3ACoaptVy7do2G/t9//616Fm0kcKZAMF+2GTiTDUzGjQDD1FrgTJHAmQLBfNlm4Ew2MBk3AgxTa4EzRQJnCgTzZZuBM9nAZNwIMEytBc4UCZwpEMyXbQbOZAOTcSPAMLUWOFMkcKZAMF+2GTiTDUzGjQDD1FrgTJHAmQLBfNlm4Ew2MBk3AgxTa4EzRQJnCgTzZZtpoTPtjzaEEJvbGdnqPBnvj252xOYrj4btj252PLM2k1oP0y83Ox0hxN1XH2vXthj2f/lhpdMR4u7Lj1V2OpwpEjhTIE2cL2naExujfdPl7Pkwq2Kt5itLPmeydc/2Zly3h901rras1vpMxvujmx0hxMZob8ouic3tsNkkKa/Wydm+1+G3ddTWEaJSaUrMYePJXgGNCHCmeYPU9izsZllNTGqkb67XR399b0UW2XjyW9Ejtv/LDys0Lr9ZlU7Pk1wRQgjx/ZPfPk4Tg/reUU8hwJkigTMF0kRnojlam/finSmpGM5kIukd3scWhV0Wyx2UZTrT9mZHpAQpMR5/ZdreTKQm9XdTnWneNPV+80v1cabte520IEm/Wb4ybTzZm0739zO/uKovlaRNyS2lD80v3H21iB69vreSdqZEmciW9veVPN8r9ayUEYWCM0UCZwoEztRmgp1J7eWCnWm5FOhM+Y1JOpKH/yhlau9MxfiRiXhnKsCYDg+3yT88+0HRl6nUlxJCTcZWuOJOiQ/55Ck87gRnigTOFEhrnGl7U02ZV+E3z2dl9twiU0NCnGljg62V2Zxpnt/ZL/ujjXRJ7YITV8+bR60wZ0r+MKnM3KZkNClVhaf/bN9LdiaZnEmRFXZfL2daVG5O0mpepKRK+TuTR06yGbfKxDpT8oexHduLyIujgab0XM60/8vNlZQyOTKpUSBbJmMeMiJX8RKcyZ70+n/XVjri7svf/T0LzhQJnCmQK+1M8yubm0mEZGM0SgVLWD28hm1r5qTm0byErL8JsZYFIc60+MzzbtKcaT/dKcKYyrtuc3vf3JdsULJ73j5qem1WcjqTzZhk7Elp6DyHniQ2X+2NNmg/lGIKhoyLipKlr3Q108UtFs6U/CU2nuxN539sPnmykapgcWNHzTxJqVDLncRxkordDZ5bCGuV3T3inGl702xMqd1IpjboGRbJ23ydLcucEmeyGNEieVHhQq4Sl9J6TGoJX/ebJyXLbHdfzfckpT/u3Vcfp/L63Zcfp9NkEU5C6qPuCE/+W63n5cfp9FArK1PmtsRr9QDOFAmcKRDRAmdSnhebmxtiMcHyrTLG6dmY2aQFpjbVHNlsn8yLuFG635gzbS8EdV//09F1nHkBszPZe94xanptVnI506b8Qk7NGeg6Cyole5eUD7y3rzvTQjLSxr8x2pvOfUrWqOwdog+78BXpNEygtF5PlU3+Umvm0avtezdHe1NdhsgVmDPRn4lG8M1OJlkRQtgXzmKc6e5dtrGJZZi3SjqQdJ/kyjzAxIJKmjNlvjeXtqJ0uOm1cq+pGgxaWKZmI/KuTI2mh4eHr+/9MNrjzjTV9lNtLHxI2JyJpCf1Fp3TqxIbUndA7cnSFGF6/b9587KBM0UCZwpEtMKZbPEfy3xsnlDV1H0Wa8m52lcX5LPNJ7Oy1pb6+Gln0vuBvfimdZ31QiqkpzmTZ8/nGuIFeZxJTjQsyDRPX5iU/qIdX5uT793xcM+ijOvVOqYziz9fbXbS9TKFUq4YVuHSNe+N5ItnWo/oK248zsSqNRmVUDxmLjC2WE2EM8lRY1Xrd2Sv/mlLdvqbePn2Mx0ag0FzxWEbs+eOJKVJOpPMkl5jm2uPvprGZcq0NpdypnkeXpt28kD2fqZFkd/3Rj+sdDpBm5/gTJHAmQLxnC9rRW5nMkyQxg3Ntvx6Zuu92uFMKRFKfXJTFzodiFesXDDW6N/zuYaYkyvOpKxWKpkta2pC3diU6Uy6RJn3gKejPMyZ5qiWY9jqlFTB7EavWb2SNqcczmRusPbSv+FkgLzDxJBxJrmclQo0mU5VSl2zxsKUIrmdKbkNizft2aJIUnWSMiarSqI6srlGHyrTmdJxqLsvf/+4rSzN5TMnOFMkcKZARMucyby0Y5lQ7Zlb70zKqhetd6aX4lLdULIz5RpiGzn3M5nemeNhKEmeOJNBkFKXzGLG40zJ4uHCcAzOZAlSGWtOG4+s1sOZzMpRhTPJiFnKmkyBrZQzaWtvai9EORMhJWfjyW+vNq33YnEmqzMd8iCWYT9T4c5k2Ogk5IJcekcT9jOVBZwpENFAZ7KccmhfFbLuAHbOx67McCbNTexxprTjFuxMuYbYTu735nRr8jnfMjLOxFOta3NTefCTYasTq/dJ4mIde83pIlyl7M5kM6hqnEkJGmmnNalxptQGJp/jvWOcSX2Pbm/EjciW2eVMc6Q6pfd3F+9MPMs+28Q0b16iTp5vz8GZIoEzBeI5X9YLbV+xes0QVLJufXFOqK7McCblSmos9CAgu1KwM+UaYjsB5zPJuFIiSdqZl8Yq3M602CbN9nmLjdHe1FTc7EyLPeBpJ9LDSvQ3P2LAdmxB+hTLTGfiGap2JmV7U3LPRTpbaWN7vl3SpDmT62O8vrdyc6S9CUf1q/cy3iyHM6V3R5XoTPyK2ZmkNcGZygHOFIjnfFk3TO+T63OhNc5kKWWOM5kyw5kODw/TXZTxXpxNYkwVLyPO5DXEdoLOAefWZF44M/3WyryY6awB08v5STW2LVMGZ2JWZH1vzrkylwpZqW1h0SulKnOcyXbX8p1JtyZLGzUBsqbrziRLmD6H4RBwIVffzO/F6e/N2ZzJfMK4+k5cUjB94IB+1sC8qZnOlF6HsxxBME/7/dW9a/zj+S7OwZkigTMFIprpTIcmbdJ8xbVQNE9I/VaaLS6lZ4YzzVkoimvTmH1HuLHiyD3guYbYQpAzGWJNxlOYpulKOosU0/lM6To2RntUZK5eKaVKXpAzOlPKmvZGtAE6fUCT6YBKvWZmcalt4Gqa8Xwmd4OrcCZDrEmzIh5TMmiTYTFPP7TJGJvSvSgVVjJok2ZIfnvAhaI7mjOls4Y7U3qnd3IKk7r5++7Ll5sr8yMst9PHM+XYBg5nigTOFIhorDOBeOSzquqG1I6AybhR1OHne5fAVR8mYAXOFAmcKRDMl20GzmTjqk/GcCbQbOBMkcCZAsF82WbgTDau+mQMZwLNBs4UCZwpEMyXbQbOZOPqT8b7o5sdj98GrjdXf5iABThTJHCmQDBfthk4kw1Mxo0Aw9Ra4EyRwJkCEQAAAEAzgTOFAWcKpOovPAAAABAInCkMOFMgQqDr2ot87lTdkNohF32+fftWdVuAlZWVFRqm//77r+q2gFL57rvv4Ewx4KEfCObLNgNnsgFnagRwptYCZ4oED/1AMF+2GTiTDThTI4AztRY4UyR46AeC+bLNwJlswJkaAZyptcCZIsFDPxDMl20GzmQDztQI4EytBc4UCR76gWC+bDNwJhtwpkYAZ2otcKZI8NAPBPNlm4Ez2YAzNQI4U2uBM0WCh34gZc6Xk8lkfX397OystDsWQb/f7/V6FxcXS6/54uKi1+v1+/2l12wDzmQDztQI4EytBc4UCR76gajzZb/fFxbG43G/3+92u7PZLPhe4/E4sobKmc1m3W53MpkUVP9kMimzi2riTJPJhL5j7Lr8Qpav2vV0psFgIDvk9PQ0MvNgMOh0OltbW/QZLy4ubt++rf+/3+12T05OHAUrpInORL1H4/Lly5fgnMPh0F0PZdja2qLOubi4uHPnjvxiq+N7fHysDiUrWE/gTJHAmQKR8yUFOeBMbsbjcaHz99nZ2fr6um4PBVG5M6nfOvVTUz+wL2FxqqpTN2c6Ozu7fv0665CDg4PgzJPJhD5jr9c7Pz+3ldKdSS9YIc1yprOzsxs3brDufffunf4dc+f0qef9+/fUObdu3fr69autlNCcSS9YT+BMkcCZAhGm+ZJmrKUvEjXdmcpZOytu7U9HPjdLuJcOBe3W19ffvn0r0s7Evir0hSytWy7r50y7u7urq6vSXchvbNaSmZlCSvfv3+/1eg71oWwBBUujWc5E43J8fEx/ksT0ej1dTdw5M+uhkJIcJpv6ULaAgnUAzhQJnCkQOJM/NMcXHQQqc3mucmc6Ojq61NbmjIYUH+bMRa2cyWhIg8FAXzXzzEyT7tHREVMiBoWU1ACVZ8HSaJAzGQ1pMBio9uPIORwOKac7lf6kYfr06RMNk019KKSkBqg8C9YBOFMkcKZA/J1JNx5aqHr27JmcemndSt0Xpc58VMOLFy/UyDCLLrC4sUzV7yXSizUkNKw43d1RrU/NEpraWVLeTnC0U81QzvKcbEAJ93LAnMnYA8bOL45aOdNsNltdXd3Z2VEbowuNZ2aSqp2dnfPzc4f6UEhJ3QvlWbBMGuRMs9lsbW2NjYtuLZk5M+shqaJhYmEkFQopqXuhPAvWBDhTJHCmQIzzpb8zCSHkRZrVVAOgDHKeY/npLur2oKdPn8r6aSFMprKyNK2qqtHv92Vm1n5HtT41O3ogoBMc7XR0fkHU05mMW8Lb7ExkPJ7OlJl5MBiQ7uhLbyq6e3kWLJMGORNpjY8zuXNOJhN3PcPhkHRHX3pT+fz5M3Mvz4I1Ac4UCZwpkEhnUq+QjjhCJnoN7olQza+XdcuHY1cQq8pds4rxeq5O8GknnMnhTKXtjm+KM7GLmZnJhEie3OrDlvP8C5bJ1XCmXBcdzrSzs3NycrK2tkby5FYfuZxH9ZBC+RSsCXCmSOBMgVTrTO51KLczqa+wsd3ZekscH8Rds0q8M/m0E84EZ2Isy5meP39OXz/1cAGj+pAhsZw+BUsGzqSmPn/+nIZJPVzAqD5kSOwYAp+C9QHOFAmcKZBaOZO+18fTmS6TJbB+vy9fX5fhK0e1PjVL4p3J3U5H5xdEPZ0J+5kYy9rP9PjxYzV05FAf9ubdZDJR/4QzBVDCfqZHjx6pO8Ed6iPfvKNK3r9/v7a2Jv+EM7UBOFMg9XEm/W2pvHGmZ8+e0aE+7tfUK48zGdvp7vyCqKczeX79CqVWzkSbr2WYh2Ba45P54cOHwoI6B+tv3u3u7upnIeoFy6dBzkQ7rNlBoOzUAJ+cMlX9yJT64MEDn2HS37zb3d2VPcl48+ZNHf4X0IEzRQJnCqRkZ2KhAvWKHl3wdybH8dzuajNrdvdA3k7wOUYc783p+/Tdi61FUCtn0l9hc0R6/DPbrpMh2Q7MdN+9ZBrkTPp7arZwjjtn3nr06zS+xrM03QVrBZwpEjhTIOU7k5wdaaaU+dlrdOx9NJ84E/sXEqmJu9rMmlVsZw3kjTMZ2+m+S0HU05kute8GHdzQ5nPAaXFNagr9LorUGpoFZSzBnVliVB9duXTgTGHQCpo8X5t+GoXchY2gI6eeSj91ojuQUX105dKBM7UBOFMgJTvT+vr6hw8f5O4ipibqxiM6HjqXM7HX6OQ07Kg2s+a83eLjTLZ22tpTHNU6k/GoKma3NrMsmro506W2QMaOmmT7vh2ZJUb1sW0tzyxYCc1ypkttXKToZI4gUyJ3KmFUH+MGc5+CdQPOFAmcKZDKYwxLQT87u6DfOYn8YZPMdpa8CFWTOFMNqaEzAZ3GORNYFnCmSPDQD+RqzJd5TzEIJvKHTTzfHCwtpgJnsgFnagRwptYCZ4oED/1ArsZ8KRe5VIqI1kSGrzLbaTtOsyDgTDbgTI0AztRa4EyR4KEfyBWbL2mzcGknHwZTk3bCmWzAmRoBnKm1wJkiwUM/EMyXbQbOZAPO1AjgTK0FzhQJHvqBCCH+D7QV6UxVN6R2SGf6+++/q24LsCKd6a+//qq6LaBUrl27BmeKAc4UCGIMbQZxJhuIMzUCxJlaC+JMkeChHwjmyzYDZ7IBZ2oEcKbWAmeKBA/9QDBfthk4kw04UyOAM7UWOFMkeOgHgvmyzcCZbMCZGgGcqbXAmSLBQz8QzJdtBs5kA87UCOBMrQXOFAke+oFgvmwzcCYbcKZGAGdqLXCmSPDQD6SI+XIymZR5mHVpN438sTmdgn4Uzx84kw04UyOAM7UWOFMkeOgHYpsv2a985PoFNP1X1SKZTCbGNqg3WvpNGZG/BGf7CJE/YBdJac5EH9947jkdiS6E0JU3LGkp1N+ZBoOB7IHT01P/Iv1+X36oi4uL27dvUz3dbvfk5ETNr6b2er3z8/Plf4w46u9Mg8GAvkvr6+tfvnwJzjwcDh31uFNlhq2tLbWjLi4u7ty5QwW73e7x8bH6xZBJt27d+vr1a8iHLxI4UyRwpkD0+ZKCH0LDf2q/ks40Ho9jpmfbRyA3repHVEpwJvXrxD6m8df3qIvCkpZInZ3p7Ozs+vXrrAcODg7cpSaTCX0oaT+z2Wx1dVUIsbW11e/3KVXWI1Ml/nJWGnV2prOzsxs3brBhevfunfEb5cjsrsfnLu/fv6eOUu3n8+fPa2trbPSpoExShz5T+EoGzhQJnCkQkZ4v5Qynz+JPnz5trTPFL6LZPsJlAUt+/shnYkH1U3BufX397du3+peKDRnJEHVFWNISW15nZ9rd3V1dXZUxIVIodxyIIkb379/v9Xoy52AwUDVIrYfyq6mkUDs7O7XqkDo7Ew3T8fEx/Uly0+v1jDEbR2Z3PZl3oYiRHHp5fTgcXr9+XZqQLHh6enrnzh1Vkkih6jb0cKZI4EyBsPnSsYbiz9VzJpr7Y7rF4UwVLs+V4ExHR0eXpu+V0XX6/X632/3w4UNA0nI7sLbOZDSkwWCgr6yp0Mx6dHR0+/ZtmxXRFUrVDUlNLfQD5qK2zmQ0pMFgoPqNT+bJZELDrSYNh0Oqx1hQptLY0dB/+vSJho9ykkipYkRXer3e0dERMySZVKsVOjhTJHCmQNT5kqIpPitQcjcJweYwpi/j8ZiFjtW5k2ZTYxLL4+9MNB+rSexD9ft99QplePbsGbVBjyfpDWBFRLKxRu0ZtVsczhQvZMEU7UwS3ZmMn5qyvXjxIiBpuctztXUmY7yH1t1sy3OkWTs7O+fn56r37O7uqqXUmo13YXGpOlBbZ5rNZnpshtbI9OU5R+Zff/1VHwhZT+ZdSKpo6Jn30OjLxshg0snJiV4nC0rVAThTJHCmQNT50nOZgxxITlH65MecSV3UY1pGd8xc88rlTFSnrkRuZxLODVt6EIsVkeYne4/1kmNe9+yEIqjQmYwRTbr4+PHjgKSWOBPpUS5nGgwGamxJOpPc4r21tUU1bG1tUbXkTOpu8UuPaFb51NaZSFw8ncmR+dGjR/pwy3omk4n7LsPhkDxJjxXJXd5bW1sHBwcrKyu0Q5zkie0WZ7GrOgBnigTOFIg6X5L9yMmbbQYnP6CL+uKIesW9TKYHhJboTH/88YceKvN0Jkekh+XXP6PeLUwl4Uz+zvTzzz8HJC03UNdEZzLuOCH7IZ0yrq9RvIHMSX+fTtZJ2eBMnjg0SB8mR+affvrJ5kw7OzsOZ5IRI5In2/qaOvrUh9Kl3rx5I4d+ZWWFvVhXOXCmSOBMgTicSYVFcYQGUxD/mA0tZrkXBD2dSW+JzJbpTO4NMXCmSOBMyyKXM5H6yHCRLc50cHAwHo+ZEqkHDdA/mR4+fIi1OU8qd6bnz5/T0KsmZIwzvXv3bjweq1akHjRAQ//gwQOszV0x4EyBCL+1OakIpAK6MzniTHoRJihyYcsmLv5xphcvXujTJ5zJRoXOhP1MYeTazzSZTNQ37FRn0veA07YnhxVhP5M/le9nevTokbqaxpxJ3wNOO58cxzvBma4YcKZA2HxJUR+HnfhM8Prqm8/KHU2iRmOzBRLUF6ZktfpHgDPZqNCZjJ+aepVejsub1Kr35thOI3b6gHpdfhDGw4cPu90um27ZrvDM+1ZObZ2JFERd7rzUzgXwyUzvzbHdRbIeWVBPffDggW3obRu9aQ1OVzrjXSoHzhQJnCkQNl/KA3X05S11P5N7KU2dwzJ3iLOCxpqNATB2kbWQvbinn+iTy5mMe8CX5UztfG9O/yLJPvznn38CklpyPpP7jACfsrbTBC7t7nXp1KkKqa0zOV7m19/Yd2TWT0tS6/G/C7tuPHJJqhj7zttcqlrgTJHAmQLR50u5Uib/NU9X2DtiLHSk/tNfjzPJSY69bjabzX788Uf1HTrb5EcFZT1yvU9aiC5qsir1T7kfK5czGc8aWJYzFbG05EmFznSpfZHUAGFY0hKprTNdJitx6tGUQjm/m+TGuB9cX5sT2sHfMvXx48dSy+g3PVggpA7U1pkukzUyefQ29aE0DzZMjswsiX4FRdbjTpUY1+b0swZu3bp1enqqDr3xR1fqAJwpEjhTIMb5MvPnKdihSm6fUPcz0ZHQzKhkJT47wSXuwA9lliannhE1Ho9ZQCvTmfSFpDBnMra/0NM43RTtTMbdb/obi7ZvUUDSsqizM11qi25q+MfTmS5Nv8Gibw+X1O0YaKLOznSpDZOqMvowZWY2JmWmEnr8Sf/RFRIvtgFcCCFfoKsVcKZI4EyBFDdfXjEK+oWTgpaWPJGPxfJvXXNq7kyAqLkzgeKAM0WCh34gmC89KegXTigSU8nC3CWcyQ6cqRHAmVoLnCkSPPQDwXzpSfzP9BrR38grEziTDThTI4AztRY4UyR46AeC+bLNwJlswJkaAZyptcCZIsFDPxDMl20GzmQDztQI4EytBc4UCR76gQgAAACgmcCZwoAzBVL1Fx4AAAAIBM4UBpwpkKq/8AAAAEAgcKYw4EwAAAAAANnAmQAAAAAAsoEzAQAAAABkA2cCAAAAAMgGzgQAAAAAkA2cCQAAAAAgGzgTAAAAAEA2cCYAAAAAgGzgTAAAAAAA2cCZAAAAAACygTMBAAAAAGQDZwIAAAAAyAbOBAAAAACQDZwJAAAAACAbOBMAAAAAQDZwJgAAAACAbOBMAAAAAADZwJkAAAAAALKBMwEAAAAAZANnAgAAAADIBs4EAAAAAJDN/wM7zx5rUBZCUwAAAABJRU5ErkJggg==)
Berdasarkan faktor-faktor ketidakpastian pengujian minyak lemak maka dapat disimpulkan bahwa ketidakpastian baku timbangan mempengaruhi secara signifikan terhadap ketidakpastian bentangan. Sedangkan ketidakpastian baku gelas ukur tidak mempengaruhi secara signifikan karena kurang dari sepertiga ketidakpastian baku terbesar yang berasal dari timbangan.