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E-nose versi II memakai 16 sensor Metal Oxide Semiconductor (MOS) untuk menangkap pola aromanya. Fitur diekstrak dari 50 sampel hasil empat variasi perlakuan dengan lima kali pengulangan pada waktu pengambilan berbeda. Analisis komponen utama menggunakan PCA dengan statistik deskriptif (maksimum, minimum, median, rata-rata, standar deviasi). Model SVM tiga kernel dan Random Forest menghasilkan akurasi 100% pada data uji dan data latih.",{"@graph":63,"@context":112},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/contaminant-detection-in-mixed-formulations-of-metarhizium-sp-and-organic-materials-using-electronic-nose-using-machine-learning/127818/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/contaminant-detection-in-mixed-formulations-of-metarhizium-sp-and-organic-materials-using-electronic-nose-using-machine-learning/127818.png","ImageObject",300,407,{"name":89,"@type":90},"Hazel","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-20","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",5,{"@type":104,"mainEntity":105},"FAQPage",[106],{"name":107,"@type":108,"acceptedAnswer":109},"Model machine learning apa yang digunakan, dan bagaimana hasil akurasinya?","Question",{"text":110,"@type":111},"Penelitian menggunakan SVM dengan tiga jenis kernel serta Random Forest. 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Deteksi Kontaminan pada FormulasiCampuran Metarhizium sp dan Bahan Organik dengan Electronic NoseMenggunakan Machine Learning. Dibawah bimbingan Bambang Heru Isawantodan Agustin Sri Mulyatni.  \nPenggunaan campuran agensia hayati dan limbah bahan organik dapat dijadikanalternatif dari pupuk kimia sebagai produk bioinsektisida yang ramahlingkungan.Namun, keberadaan organisme agensia hayati dalam bahan organik dan rentankontaminasi pada komposisitertentu sulitdideteksi secara dini. Pada penelitian iniElectronic nose (E-nose) digunakan untuk deteksi kontaminan pada formulasicampuran Metarhizium anisopliae dan bahan organik berupa limbah ampas tebuberdasarkan pola aromanya. E-nose yang digunakan versiII dengan jumlah sensorsebanyak 16 sensor tipe Metal Oxide Semiconductor (MOS) . Fitur diekstrak daridata respon e-nose yang diambil dari 50 sampel campuran jamur agensia hayatidengan bahan organik yang terdiri dari empat variasi perlakuan. Penelitiandilakukan dengan empat sampel dengan perlakuan bahan organik saja, bahanorganik dengan M. anisopliae, bahan organik dengan M. anisopliae dan Aspergilusniger serta bahan organik dengan M. anisopliaedan Trichodermaharzianum. Totalterdapat 50 sampel dengan lima kali pengulangan dengan waktu pengambilan datayang berbeda. Untuk analisis komponen utama data menggunakan PrincipalComponent Analysis (PCA) dengan fitur statistik deskriptif nilai maksimum,minimum, median, rata-rata, dan standar deviasi. Model Machine learning yangdigunakan adalah Support Vector Machine (SVM) dengan tiga kernel yang berbedadan Random Forest (RF) . Akurasi dari variasi data ekstraksi fitur dan model iniuntuk data uji dan data tes masing-masing menghasilkan 100% dan 100% .  \nKata-kata kunci: agensia hayati, electronic nose, ekstraksi fitur, machine learning  \n## ABSTRACT\n\nINDRIANI LUTFIYYATUNNISA. Contaminant Detection in MixedFormulations of Metarhizium sp and Organic Materials with Electronic Nose UsingMachine Learning. Under supervised Bambang Heru Isawanto and Agustin SriMulyatni.  \nThe use of a mixture of biological agents and organic matter waste can be used asan alternative to chemical fertilizers as an environmentally friendly bioinsecticideproduct. However, the presence of biological agent organisms in organic materialsand susceptible to contamination in certain compositions is difficult to detect early.In this study, Electronic nose (E-nose) was used for contaminant detection in amixed formulation of Metarhiziumanisopliae and organic material in the form ofbagasse waste based on its aroma pattern. The E-nose used is version II with a totalof 16 Metal Oxide Semiconductor (MOS) type sensors. Features were extractedfrom e-nose response data taken from 50 samples of a mixture of biological agentfungi with organic materials consisting of four treatment variations. The researchwas conducted with four samples treated with organic matter alone, organic matterwith M. anisopliae, organic matter with M. anisopliae and Aspergilus niger andorganic matter with M. anisopliae and Trichodermaharzianum. There were a totalof 50 samples with five repetitions with different data collection times. For dataprincipal component analysis using Principal Component Analysis (PCA) withdescriptive statistical features of maximum, minimum, median, mean, and standarddeviation values. Machine learning models used are Support Vector Machine(SVM) with three different kernels and Random Forest (RF). The accuracy of theseextraction feature and model variations for test data and test data resulted in 100%and 100% respectively.  \nKeywords: biological agent, electronic nos","cbCaikXRacgF4Foo","https://ap.wps.com/l/cbCaikXRacgF4Foo","pdf",735256,10,"Indonesian","# ABSTRAK\n## Metode dan perangkat E-nose\n## Ekstraksi fitur dan analisis PCA\n## Model machine learning dan akurasi\n# LEMBAR PENGESAHAN SKRIPSI\n# LEMBAR PERNYATAAN\n# LEMBAR PERNYATAAN PERSETUJUAN PUBLIKASI KARYA ILMIAH","[{\"question\":\"Model machine learning apa yang digunakan, dan bagaimana hasil akurasinya?\",\"answer\":\"Penelitian menggunakan SVM dengan tiga jenis kernel serta Random Forest. Akurasi yang dilaporkan mencapai 100% untuk data uji dan 100% untuk data latih.\"}]","DETEKSI KONTAMINAN PADA FORMULASI CAMPURAN METARHIZIUM sp DAN BAHAN ORGANIK DENGAN ELECTRONIC NOSE - MENGGUNAKAN MACHINE LEARNING | PDF",15]