?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=IDENTIFIKASI+KUPU-KUPU+MENGGUNAKAN+%0D%0AEKSTRAKSI+FITUR+GRAY+LEVEL+CO-OCCURRENCE+MATRIX+(GLCM)+%0D%0ADAN+KLASIFIKASI+K-NEAREST+NEIGHBOR+(KNN)%0D%0A&rft.creator=DEVI+MAHARANI%2C+1517051182&rft.subject=005+Pemrograman+komputer%2C+program+dan+data&rft.description=ABSTRACT%0D%0ATHE+IDENTIFICATION+OF+BUTTERFLY+USING+EXTRACTION+OF+%0D%0AGRAY+LEVEL+CO-OCCURRENCE+MATRIX+(GLCM)+FEATURES+AND+%0D%0ACLASSIFICATION+OF+K-NEAREST+NEIGHBOR+(KNN)%0D%0ABy%0D%0ADEVI+MAHARANI%0D%0AGita+Persada+Butterfly+Park%2C++the+only+breeding+of+in+situ+butterflies+engineered+in+%0D%0AIndonesia%2C+located+in+Lampung%2C+which+has+approximately+211+species+of+butterflies+%0D%0Athat+are+bred.++Butterflies+have+a+different+texture+on+wings+in++each+species.+The+%0D%0Alimited+ability+of+the+human+eye+inside+distinguishing+typical+textures+from+butterfly+%0D%0Aspecies++is++the++reason++for++making++pattern++recognition++based++on++butterfly+%0D%0Aidentification.+The+dataset+contains+600+the+images+of+the+butterfly+upper+side+wing+%0D%0Afrom++six++species%3A++Centhosia++penthesilea%2C++Papilio++memnon%2C++Papilio++nephelus%2C+%0D%0APachliopta++aristolochiae%2C++Papilio++peranthus%2C++and++Troides++helena.++The++preprocessing+stage+is+done+using+the+method+of+scaling%2C+segmentation%2C+and+grayscale.+%0D%0AThe+GLCM+method+is+used+to+recognize+the+characteristics+of+a+butterfly+image+%0D%0Ausing+pixel+distance+(d)++%3D+1+and+direction+0%0D%0Ao%0D%0A%2C+45%0D%0Ao%0D%0A%2C+90%0D%0Ao%0D%0A%2C+and+135%0D%0Ao%0D%0A.+The+features+used+%0D%0Ais++angular++second++moment%2C++contrast%2C++homogeneity%2C++and++correlation.++KNN+%0D%0Aclassification+method+in+this+study+using+the+values+of++k++%3D+1%2C+3%2C+5%2C+7%2C+9%2C+11%2C+13%2C+15%2C+%0D%0ADevi+Maharani%0D%0A17%2C++19%2C++21%2C++and++23.++Centhosia++penthesilea++and++Papilio++nephelus++class++can++be+%0D%0Aclassified+properly+compared+to+the+other+4+classes+and+required+a+classification+time+%0D%0Aof+2+seconds+at+each+orientation+angle.+The+highest+accuracy+is+91.1%25+with+a+value+%0D%0Aof++k++%3D+5+in+the+direction+of+angle+90%0D%0Ao%0D%0A.+Classification+errors+occurred+because+the+%0D%0Avalue+of+the+test+data+features+more+dominant+to+the+value+of+the+training+image+%0D%0Afeatures+in+different+classes+than+the+supposed+class+and+there+are+imperfect+test+data.%0D%0AKeywords%3A+butterflies%2C+Gita+Persada%2C+GLCM%2C+KNN%2C+Lampung%2C+pattern+recognition+%0D%0A%0D%0ATaman+Kupu-Kupu+Gita+Persada+merupakan+satu-satunya+penangkaran+kupu-kupu+%0D%0Ain++situ++rekayasa+di+Indonesia++yang+berada+di+Lampung++terdapat++kurang+lebih+211+%0D%0Aspesies++kupu-kupu++yang++dikembangbiakkan.++Kupu-kupu++memiliki++tekstur++yang+%0D%0Aberbeda+pada+sayapnya+disetiap+spesies+kupu-kupu.+Terbatasnya+kemampuan+mata+%0D%0Amanusia+dalam+membedakan+tekstur+yang+khas+pada+spesies+kupu-kupu+menjadi+%0D%0Aalasan+dalam+membuat+identifikasi+kupu-kupu+berbasis+pengenalan+pola.+Dataset+%0D%0Aberjumlah+600+citra++kupu-kupu+sisi+sayap+bagian+atas+terdiri+dari+6+spesies++kupukupu+yaitu++Centhosia+penthesilea%2C+Papilio+memnon%2C+Papilio+nephelus%2C++Pachliopta+%0D%0Aaristolochiae%2C++Papilio++peranthus%2C++dan++Troides++helena.++Tahap++preprocessing+%0D%0Adilakukan++dengan++menggunakan++metode++scaling%2C++segmentation++dan++grayscale.+%0D%0AMetode++GLCM++digunakan++untuk++mengenali++ciri++dari++citra++kupu-kupu++dengan+%0D%0Amenggunakan+jarak+piksel+(d)+%3D+1+dan+arah+sudut+0%0D%0Ao%0D%0A%2C+45%0D%0Ao%0D%0A%2C+90%0D%0Ao+%0D%0Adan+135%0D%0Ao%0D%0A.+Fitur+yang+%0D%0Adigunakan+yaitu++angular+second+moment%2C+contrast%2C+homogeneity++dan++correlation.+%0D%0ADevi+Maharani%0D%0AMetode+klasifikasi+KNN+pada+penelitian+ini+menggunakan+nilai+k+%3D+1%2C+3%2C+5%2C+7%2C+9%2C+11%2C+%0D%0A13%2C+15%2C+17%2C+19%2C+21+dan+23.+Kelas+Centhosia+penthesilea+dan+Papilio+nephelus+dapat+%0D%0Adiklasifikasi+dengan+baik+dibandingkan+dengan+4+kelas+lainnya+dan++membutuhkan+%0D%0Awaktu++klasifikasi++sebesar++2++detik++pada++setiap++sudut++orientasi.++Akurasi++tertinggi+%0D%0Asebesar+91%2C1%25+dengan+nilai+k+%3D+5+pada+arah+sudut+90%0D%0Ao%0D%0A.+Kesalahan+klasifikasi+terjadi+%0D%0Adikarenakan+nilai+fitur+citra+uji+lebih+dominan+dengan+nilai+fitur+citra+latih++pada+kelas+%0D%0Ayang++berbeda++dibandingkan++dengan++kelas++yang++seharusnya++dan++terdapat++data++uji+%0D%0Ayang+tidak+sempurna.%0D%0AKata+kunci+%3A+Gita+Persada%2C+GLCM%2C+KNN%2C+kupu-kupu%2C+Lampung%2C+pengenalan+pola+&rft.publisher=FAKULTAS+MIPA&rft.date=2019&rft.type=Skripsi&rft.type=NonPeerReviewed&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F57775%2F1%2FABSTRAK%2520%2528ABSTRACT%2529.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F57775%2F2%2FSKRIPSI%2520FULL.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F57775%2F3%2FSKRIPSI%2520FULL%2520TANPA%2520BAB%2520PEMBAHASAN.pdf&rft.identifier=++DEVI+MAHARANI%2C+1517051182++(2019)+IDENTIFIKASI+KUPU-KUPU+MENGGUNAKAN+EKSTRAKSI+FITUR+GRAY+LEVEL+CO-OCCURRENCE+MATRIX+(GLCM)+DAN+KLASIFIKASI+K-NEAREST+NEIGHBOR+(KNN).++FAKULTAS+MIPA%2C+UNIVERSITAS+LAMPUNG.+++++&rft.relation=http%3A%2F%2Fdigilib.unila.ac.id%2F57775%2F