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All Journal Aquacoastmarine J-Com (Journal of Computer)
Hafiz Muhammad
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KEBIASAAN MAKAN IKAN BILIH (Mystacoleucus padangensis Bleeker) DI SUNGAI NABORSAHAN, KECAMATAN AJIBATA, KABUPATEN TOBA SAMOSIR, SUMATERA UTARA Hafiz Muhammad; Yunasfi Djayus; Ani Suryanti
AQUACOASTMARINE Vol 2, No 2 (2014): JURNAL AQUACOASTMARINE VOLUME 2, NO 2, DESEMBER 2014
Publisher : Program Studi Manajemen Sumberdaya Perairan, Fakultas Pertanian Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.544 KB)

Abstract

The decline of fish populations bilih (Mystacoleucus padangensis Bleeker) caused by fishing activity and the availability of natural food in the River Naborsahan closely related to the physico-chemical factors of the river. The purpose of this study was to determine the feeding habits of fish bilih (Mystacoleucus padangensis Bleeker). This study was conducted in June - July with purposive random sampling method. The results showed that the natural food of bilih fish is phytoplankton from the genus Rhizosolenia, Synedra, Aulacoseira, Gonatozygon, Pinnularia, Closterium, Surirella, Gyrosigma, Melosira, Oscillatoria and zooplankton from genus Creseis, tubifex and Daphnia. Natural food that became the main food of bilih fish is phytoplankton from the genus Synedra with IP (Index Preporedence) > 40% fish is 98.9%. Natural feed becomes additional food of bilih fish <4% that is Rhizosolenia, Aulacoseira, Gonatozygon, Pinnularia, Closterium, Surirella, Gyrosigma, Melosira, Oscillatoria, Creseis, Tubifex and Daphnia. Measurement results of physico-chemical factors water good enough for the availability of natural food and life of bilih fish (Mystacoleucus padangensis Bleeker).Keywords: Fish bilih (Mystacoleucus padangensis Bleeker), Food Habits, Naborsahan River.
KLASTERISASI PENYAKIT MENULAR DI INDONESIA MENGGUNAKAN METODE K-MEANS CLUSTERING Muhammad, Hafiz; Anggraini, Sylvia
J-Com (Journal of Computer) Vol. 4 No. 1 (2024): Maret 2024
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v4i1.3033

Abstract

bstract: Disease is an abnormal condition in which the body or mind experiences discomfort or dysfunction in the person it affects. Every day the number of people suffering from infectious diseases always increases with different types of diseases. Therefore, there is a need for a grouping to help the government find information about the diseases most commonly suffered by citizens. In this research, patient disease data was grouped using multidimensional clustering data mining techniques. K-Means Clustering is a non-hierarchical data clustering method that groups data in the form of one or more clusters. Data that has the same characteristics is grouped in one cluster and data that has different characteristics is grouped in another cluster so that the data in one cluster has a small level of variation. This research aims to make the government pay more attention to areas that have high rates of infectious diseases both from the environment and other things. The results of research from 34 provinces and 8 infectious diseases show data where 32 areas are very vulnerable, 2 areas are vulnerable and 2 areas are quite vulnerable.Keywords: Data Mining, Disease Cases, K-MeansAbstrak: Penyakit adalah suatu keadaan abnormal dimana tubuh ataupun pikiran mengalami ketidaknyamanan atau disfungsi terhadap orang yang dipengaruhinya. Setiap harinya jumlah warga yang menderita penyakit menular selalu bertambah dengan jenis penyakit yang berbeda. Oleh sebab itu, perlu adanya pengelompokan untuk membantu pihak pemerintah menemukan informasi mengenai penyakit yang paling banyak diderita oleh warga. Pada penelitian ini dilakukan pengelompokkan data penyakit pasien menggunakan teknik data mining clustering multidimensi. K-Means Clustering merupakan salah satu metode data clustering non-hirarki yang mengelompokkan data dalam bentuk satu atau lebih cluster. Data-data yang memiliki karakteristik yang sama dikelompokkan dalam satu cluster dan data yang memiliki karakteristik berbeda dikelompokkan dengan cluster yang lain sehingga data yang berada dalam satu cluster memiliki tingkat variasi yang kecil. Penelitian ini bertujuan agar pemerintah lebih perhatian terhadap daerah yang memilki angka tinggi terhadap penyakit menular baik dari lingkungan maupun dari hal lainnya. Hasil dari penelitian dari 34 Provinsi dan 8 Penyakit menular menunjukkan data dimana 32 daerah dengan sangat rawan, 2 daerah rawan dan 2 daerah cukup rawan.Kata kunci: Data Mining, Kasus Penyakit, K-Means
Co-Authors Anggraini, Sylvia Ani Suryanti Yunasfi Djayus