[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-124363-113":53,"doc-detail-124363-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","design-and-development-of-a-gelatin-raw-material-detection-device-using-a-machine-learning-based-gas-multisensor","RANCANG BANGUN ALAT DETEKSI BAHAN BAKU GELATIN MENGGUNAKAN MULTISENSOR GAS BERBASIS MACHINE LEARNING","","Kebutuhan gelatin untuk bahan makanan halal di Indonesia sangat tinggi. Gelatin berbahan dasar babi sulit dikenali setelah proses produksi, sehingga masih digunakan luas dalam industri makanan dan farmasi. Penelitian ini merancang bangun alat deteksi bahan baku gelatin menggunakan multisensor gas berbasis machine learning. Delapan sensor gas digunakan untuk mengidentifikasi senyawa organik volatil. PCA, LDA, dan SVM dipakai untuk analisis data pengukuran, menghasilkan akurasi tinggi pada klasifikasi gelatin tunggal dan prediksi gelatin campuran untuk autentikasi kehalalan produk pangan.",{"@graph":63,"@context":120},[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/design-and-development-of-a-gelatin-raw-material-detection-device-using-a-machine-learning-based-gas-multisensor/124363/",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/design-and-development-of-a-gelatin-raw-material-detection-device-using-a-machine-learning-based-gas-multisensor/124363.png","ImageObject",300,407,{"name":89,"@type":90},"Seraphina","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-18","2026-08-04",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",7,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan penelitian ini?","Question",{"text":110,"@type":111},"Merancang bangun alat deteksi bahan baku gelatin menggunakan multisensor gas berbasis machine learning untuk membantu identifikasi sumber gelatin dalam autentikasi kehalalan produk pangan.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Sensor gas apa saja yang digunakan dalam alat deteksi?",{"text":115,"@type":111},"Penelitian menggunakan delapan sensor gas: MQ3, MQ4, MQ6, MQ135, MQ136, MQ137, TGS822, dan MS1100.",{"name":117,"@type":108,"acceptedAnswer":118},"Model machine learning apa yang dipakai dan apa perannya?",{"text":119,"@type":111},"Data dianalisis dengan PCA untuk pemisahan pola nilai, LDA untuk klasifikasi gelatin tunggal, dan SVM untuk memprediksi komposisi gelatin campuran.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},124363,1785821828,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":77,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":127,"read_time":141},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","RANCANG BANGUN ALAT DETEKSI BAHAN BAKU GELATIN MENGGUNAKAN MULTISENSOR GAS BERBASIS MACHINE LEARNING  \nSKRIPSI  \nKarya tulis sebagai salah satu syarat Untuk memperoleh gelar Sarjana Sains Dari Universitas Andalas  \nFEBBY TRISHE ANANDA 2110442038  \nDEPARTEMEN FISIKA  \nFAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM  \nUNIVERSITAS ANDALAS  \nPADANG  \nRANCANG BANGUN ALAT DETEKSI BAHAN BAKU GELATIN MENGGUNAKAN MULTISENSOR GAS BERBASIS  \nMACHINE LEARNING  \nABSTRAK  \nKebutuhan gelatin untuk bahan makanan halal di Indonesia sangat tinggi. Gelatin berbahan dasar babi yang sulit dikenali setelah melalui proses produksi masih  \nbanyak digunakan dalam industri makanan dan farmasi. Penelitian ini merancang bangun alat deteksi bahan baku gelatin menggunakan multisensor gas berbasis machine learning. Delapan sensor gas yang terdiri dari MQ3, MQ4, MQ6, MQ135, MQ136, MQ137, TGS822, dan MS1100 digunakan untuk mengidentifikasisenyawa organik volatil. Sampel dikondisikan menjadi gelatin tunggal denganvariasi konsentrasi 1%, 3%, 5%, 7%, dan gelatin campuran. Pengukuran berlangsung selama 30 menit untuk setiap sampel. Data hasil pengukuran dianalisis menggunakan tiga model machine learning yaitu PCA, LDA, dan SVM. PCAuntuk pemisahan pola nilai yang akurat untuk setiap jenis gelatin, terutama padakonsentrasi 7% . LDA untuk mengklasifikasikan gelatin tunggal, hasil yang diperoleh terjadi peningkatan akurasi dari 90,38% pada konsentrasi 1% dan 95,56% pada konsentrasi 7% . SVM untuk memprediksi gelatin campuran berhasil mendeteksi komposisi gelatin campuran dengan akurasi 97,65% . Hasil ini menunjukkan bahwa alat yang dikembangkan berpotensi menjadi solusi alternatif untuk identifikasi sumber gelatin dalam autentikasi kehalalan produk pangan.  \nKata kunci : Gelatin, Halal, Machine learning, Sensor gas.  \nDESIGN AND DEVELOPMENT OF A GELATIN SOURCE DETECTION DEVICE USING GAS MULTISENSOR BASED ON  \nMACHINE LEARNING  \nABSTRACT  \nThe demand for halal food-grade gelatin in Indonesia is exceptionally high. However, porcine-based gelatin, which is difficult to identify post-production,  \nremains widely used in the food and pharmaceutical industries. This research designs and develops a gelatin raw material detection tool utilizing a machine learning-based multisensor gas system. Eight gas sensors, comprising MQ3, MQ4, MQ6, MQ135, MQ136, MQ137, TGS822, and MS1100, were employed to identify volatile organic compounds. Samples were prepared as single gelatins with varying concentrations of 1%, 3%, 5%, and 7%, as well as mixed gelatins. Measurements were conducted for 30 minutes for each sample. The obtained measurement data were analyzed using three machine learning models: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). PCA was utilized for accurate pattern separation of values for each gelatin type, particularly at 7% concentration. LDA was applied for classifying single gelatins, yielding an increase in accuracy from 90.38% at 1% concentration to 95.56% at 7% concentration. SVM successfully predicted mixed gelatin compositions with an accuracy of 97.65%. These results indicate that the developed tool has the potential to be an alternative solution for identifying gelatin sources in the halal authentication of food products.  \nKeywords: Gelatin, Halal, Gas sensor, Machine learning","cbCaicUlcjANn85u","https://ap.wps.com/l/cbCaicUlcjANn85u","pdf",295365,"Indonesian","# Abstrak\n# Metode dan Perancangan Alat\n## Sensor gas multisensor\n## Model machine learning (PCA, LDA, SVM)\n# Hasil dan Analisis\n## Klasifikasi gelatin tunggal\n## Prediksi gelatin campuran\n# Kesimpulan","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Merancang bangun alat deteksi bahan baku gelatin menggunakan multisensor gas berbasis machine learning untuk membantu identifikasi sumber gelatin dalam autentikasi kehalalan produk pangan.\"},{\"question\":\"Sensor gas apa saja yang digunakan dalam alat deteksi?\",\"answer\":\"Penelitian menggunakan delapan sensor gas: MQ3, MQ4, MQ6, MQ135, MQ136, MQ137, TGS822, dan MS1100.\"},{\"question\":\"Model machine learning apa yang dipakai dan apa perannya?\",\"answer\":\"Data dianalisis dengan PCA untuk pemisahan pola nilai, LDA untuk klasifikasi gelatin tunggal, dan SVM untuk memprediksi komposisi gelatin campuran.\"}]","RANCANG BANGUN ALAT DETEKSI BAHAN BAKU GELATIN MENGGUNAKAN MULTISENSOR GAS BERBASIS MACHINE LEARNING | PDF",5]