Perbandingan Algoritma K-Nearest Neighbor Untuk Klasifikasi Jenis Mangga Menggunakan Berdasarkan Fitur Gray Level Co-Occurrence Matric dan Fitur Warna

Indonesia merupakan negara dengan sumber daya manusia serta sumber daya alam yang memiliki pontesial untuk dapat membangun industri buah nusantra, serta mata pencaharian sebagian besar penduduk indonesia yakni petani. Produksi pertanian diantaranya padi, jagung dan lain-lain [1][2]. Budidaya tanaman kebun jenis buah-buahan di indonesiaa seperti alpukat, nanas, kelengkeng, pisang, mangga dan lain-lain. Sebagian besar penduduk indonesia sangat gemar menanam pohon mangga di halaman rimah atau kebun mereka. Akan tetapi dari kegemaran mereka menanam pohon mangga tidak jarang masyarakat tertipu dengan jenis mangga yang ditanam. Oleh sebab itu dibutuhkan suatu model atau metode untuk dapat mengklasifikasikan jenis mangga serta untuk mengetahui jenis mangga tersebut dapat dilihat dari ciri yang ada seperti bentuk tekstur dan warna. Terdapat beberapa metode yang telah diusulkan serta telah dikerjakan utnuk mengklasifikasikan jenis mangga, akan tetapi hasil rata-rata akurasi yang diperoleh kurang dari 80%. Dalam penelitian ini mengusulkan pendekatan menggunakan k-nearest neighbor dengan optimasi algoritma genetika serta menggunakan fitur gray level co-occurrence matrix dan fitur warna daun mangga jumlah dataset yang digunakan sebanyak 800 daun citra. Penggunaan algoritma genetika untuk optimasi berhasil meningkatkan nilai akurasi pada metode k-nearest neighbor. Akurasi tertinggi terdapat pada nilai k=3 yakni 93.50%. Sedangkan metode k-nearest neighbor tanpa menggunakan optimasi memperoleh akurasi sebesar 93.00% dengan nilai k=1

Apneic Events Detection Using Different Features of Airflow Signals

Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MITBIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis

Saving Bones: a direct comparison of FTIR-ATR, whole bone percent nitrogen, and NIR

89th Annual Meeting of the American-Association-of-Physical-Anthropologists (AAPA), Los Angeles, CA, APR 15-18, 202

Temporary Territories and Persistent Places: A Bioarchaeological Evaluation of the Association between Monumentality and Territoriality for Foraging Societies of the Prehistoric Ohio Valley

abstract: Federal legislation prioritizes the repatriation of culturally unidentifiable human remains to federally-recognized Indian tribes that are linked geographically to the region from which the remains were removed. Such linkages are typically based on a Eurocentric notion of the exclusive use and occupancy of an area of land - a space-based approach to land use. Contemporary collaborations between anthropologists and indigenous communities suggest, however, that indigenous patterns of land use are better characterized as place-based and are therefore more complex and fluid than is reflected in current legislation. Despite these insights, space-based approaches remain common within archaeology. One example is the inference of territorial behavior from the presence of monuments within the archaeological record. Drawing on osteological and mortuary data derived from a sample of Adena mounds located in northern Kentucky, this dissertation adopts a place-based approach in order to evaluate the archaeological association between monumentality and territoriality. The relative amounts of skeletal and phenotypic variability present at various spatial scales are quantified and compared and the degree to which mortuary and phenotypic data exhibit spatial structure consistent with the expectations of an isolation-by-distance model is assessed. Results indicate that, while burial samples derived from some mounds exhibit amounts of phenotypic variability that are consistent with the expectations of a territorial model, data from other mounds suggest that multiple groups participated in their construction. Further, the general absence of spatial structure within the phenotypic data suggests that the individuals interred in these mounds are perhaps better characterized as representing an integrated regional population rather than localized groups. Untested archaeological inferences of territoriality may therefore mischaracterize regional population dynamics. In addition, these results suggest that the prioritization criteria for the repatriation of culturally unidentifiable human remains may merit revision.Dissertation/ThesisDoctoral Dissertation Anthropology 201