scholarly journals Application of K-Means Clustering Method for City Grouping on Food Plant Productivity in North Sumatera

2019 ◽  
Vol 3 (2) ◽  
pp. 78
Author(s):  
Junita Fadilah ◽  
Ismail Husein
Keyword(s):  
Crop Productivity ◽  
Food Crop ◽  
Clustering Method ◽  
Sweet Potatoes ◽  
Green Beans ◽  
Or Groups ◽  
City District ◽  
Food Needs ◽  
Different Characteristics ◽  
Sufficient Food

The development of population increases every year, causing food needs to expand to meet food needs by increasing food crop productivity so that food availability can be sufficient. Food crops consist of rice, corn, green beans, peanuts, cassava, and sweet potatoes. Productivity in each region has different characteristics, and therefore it is necessary to group the areas so that solution can be implemented by each of the components of the region. The purpose of this study is to group districts/cities in North Sumatera Province based on food crop productivity using the k-means clustering method. Clustering k-means is a method of grouping non-hierarchical data that attempts to partition existing data into one or more clusters or groups so that data that has the same characteristics are grouped into one same characteristic are grouped into other groups. The result of this study is the formation of 3 city district clusters, namely, cluster 1 amounting to 1 regency/city, cluster 2 totaling seven districts/cities, and cluster 3 totaling 25 districts/cities.

scholarly journals PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)

2020 ◽  
Vol 9 (1) ◽  
pp. 112-121
Author(s):  
Besya Salsabilla Azani Arif ◽  
Agus Rusgiyono ◽  
Abdul Hoyyi
Keyword(s):  
Cluster Analysis ◽  
Crop Production ◽  
Secondary Data ◽  
Hierarchical Cluster ◽  
Food Crop ◽  
Sweet Potatoes ◽  
Green Beans ◽  
Homogeneous Groups ◽  
Cluster Analysis Method ◽  
Ward Method

Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that can maximize homogeneity in the cluster. The  Sum-of-Square (SSE) formula is used in this method to minimize variations in the clusters that are formed. In this research, squared euclid distance is used to measure the similarity between object pairs. The data used in this study are secondary data on food crop production, namely rice, corn, soybeans, peanuts, green beans, sweet potatoes, and cassava in Indonesia 2018. To determine the cluster, the elbow method is used to form optimal clusters using WSS formula. Based on the analysis results, it was found that the optimal cluster is four clusters. The first cluster consists of 9 Province, the second cluster consists of 20 Province, the third cluster consists of 1 Province, the fourth cluster consists of  2 Province, and the fifth cluster consists of 2 Province.Keywords: Food Crop, Cluster Analysis, Ward Method, Squared Euclid, Elbow Method

Analisis Nutrisi Umbi Ubi Jalar (Ipomoea batatas (L.) Lam.) untuk Konsumsi Bayo dan Anak-anak Suku Dani di Distrik Kurulu Kabupaten Jayawijaya

Agrotek ◽  
2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Andrew B. Pattikawa ◽  
Antonius Suparno ◽  
Saraswati Prabawardani
Keyword(s):  
Sweet Potato ◽  
Ipomoea Batatas ◽  
Nutritional Value ◽  
Animal Feed ◽  
Ash Content ◽  
Infants And Children ◽  
Chemical Analyses ◽  
Food Crop ◽  
Sweet Potatoes ◽  
Development Center

<em>Sweet potato is an important staple food crop especially for the local people of Central Highlands Jayawijaya. There are many accessions that have always been maintained its existence to enrich their various uses. Traditionally, sweet potato accessions were grouped based on the utilization, such as for animal feed, cultural ceremonies, consumption for adults, as well as for infants and children. This study was aimed to analyze the nutritional value of sweet potatoes consumed by infants and children of the Dani tribe. Chemical analyses were conducted at the Laboratory of Post-Harvest Research and Development Center, Cimanggu, Bogor. The results showed that each of 4 (four) sweet potato accessions which were consumed by infants and children had good nutrient levels. Accession Sabe showed the highest water content (72.56%), vitamin C (72.71 mg/100 g), Fe (11.85 mg/100 g), and K levels (130.41 mg / 100 grams). The highest levels of protein (1.44%), fat (1.00%), energy (154.43 kkal/100 gram), carbohydrate (35.47%), starch (30.26%), reducing sugar (3.44%), riboflavin (0.18 mg/100 g), and vitamin A (574.40 grams IU/100 were produced by accession Manis. On the other hand, accession Saborok produced the highest value for ash content (1.32%), vitamin E (28.30 mg/100 g), and ?-carotene (64.69 ppm). The highest level of crude fiber (1.81 %) and thiamin (0.36 mg/100 g) was produced by accession Yuaiken.</em>

SISTEM HUNIAN MASA DEPAN BERBASIS TEKNOLOGI UNTUK KEBUTUHAN MANUSIA

2021 ◽  
Vol 3 (1) ◽  
pp. 321
Author(s):  
Albert Utama ◽  
Sutarki Sutisna
Keyword(s):  
Urban Communities ◽  
Urban Areas ◽  
Human Population ◽  
Residential Buildings ◽  
Food Crop ◽  
Planting Methods ◽  
Use Of Technology ◽  
Livestock Animal ◽  
Food Needs ◽  
The Times

The Living Bot is a project where future residential buildings will adapt to the times. In the coming year, the human population will continue to grow, so that it will use the land as a place for various needs such as shelter, activities, and other things. Along with this increase in human population, the land will also shrink while the land itself is needed so that humans can meet their food needs either from farming (plants), or through livestock (animal). Therefore, The Living Bot created a system in which human implementation begins to adapt to the life in which they live by implementing a residential system that can produce their own food with plantings that maximize vertical land. This form of shelter can be used as a system so that its application can be carried out. Adaptations that are carried out are by changing the lifestyle of humans to the use of technology. The lifestyle that must adapt is by farming, assisted by A.I. technology. because humans in urban areas do not have a background in growing a food crop. Therefore technology is present in helping urban communities, also assisted by modern planting methods such as using hydroponics, aquaponics, aeroponics, and indoor planting techniques assisted by artificial light such as LEDs. Keywords: Adaptation; Techonology Abstrak The Living Bot merupakan sebuah proyek dimana bangunan hunian pada masa depan akan beradaptasi dengan perkembangan zaman. Pada tahun yang akan datang, populasi manusia akan terus bertambah, sehingga akan menggunakan lahan sebagai tempat untuk berbagai macam kebutuhan seperti tempat tinggal, aktivitas, dan hal lainnya. Seiring dengan pertambahan populasi manusia ini, lahan juga akan semakin menyempit sedangkan lahan sendiri diperlukan agar manusia dapat memenuhi kebutuhan pangannya baik dari hasil bertani (tumbuh-tumbuhan), ataupun melalui peternakan (hewani). Maka dari itu The Living Bot membuat suatu sistem yaitu dimana implementasi manusia mulai beradaptasi dengan kehidupan tempat tinggalnya dengan menerapkan sistem hunian yang dapat menghasilkan makanannya sendiri dengan penanaman-penanaman yang memaksimalkan lahan secara vertikal.Bentuk hunian seperti ini dapat dijadikan sebuah sistem sehingga penerapannya dapat dilakukan di berbagai hunian Adaptasi yang dilakukan adalah dengan mengubah gaya hidup manusia sampai kepada pengunaan teknologi. Adapun gaya hidup yang harus beradaptasi adalah dengan bercocok tanam, dengan dibantu oleh teknologi A.I. karena manusia yang ada di perkotaan tidak memiliki latar belakang dalam menanam sebuah tanaman pangan. Maka dari itu teknologi hadir dalam membantu masyarakat kota, juga dibantu oleh metode menanam yang modern seperti menggunakan hidroponik, akuaponik, aeroponik, dan teknik penanaman indoor yang dibantu oleh cahaya buatan seperti LED.

scholarly journals Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

2021 ◽  
pp. 243-248
Author(s):  
Yoni Aswan ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Hierarchical Data ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Mentawai Islands ◽  
Or Groups ◽  
Different Characteristics

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

COLOR CHANGES IN PIGMENT-FREE RESIDUES FROM GAMMA-IRRADIATED GREEN BEANS, BROCCOLI, SWEET POTATOES AND CARROTS

1960 ◽  
Vol 25 (6) ◽  
pp. 739-749 ◽  
Author(s):  
F. J. FRANCIS ◽  
G. E. LIVINGSTON ◽  
R. FRANCESCHINI ◽  
T. WISHNETSKY
Keyword(s):  
Sweet Potatoes ◽  
Green Beans ◽  
Color Changes ◽  
Gamma Irradiated

Projective analysis of staple food crop productivity in adaptation to future climate change in China

2017 ◽  
Vol 61 (8) ◽  
pp. 1445-1460 ◽  
Author(s):  
Qing Zhang ◽  
Wen Zhang ◽  
Tingting Li ◽  
Wenjuan Sun ◽  
Yongqiang Yu ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Productivity ◽  
Future Climate ◽  
Future Climate Change ◽  
Staple Food ◽  
Food Crop

scholarly journals Total Arsenic Determination and Speciation in Infant Food Products by Ion Chromatography-Inductively Coupled Plasma-Mass Spectrometry

2004 ◽  
Vol 87 (1) ◽  
pp. 244-252 ◽  
Author(s):  
Nohora P Vela ◽  
Douglas T Heitkemper
Keyword(s):  
Inductively Coupled Plasma ◽  
Inorganic Arsenic ◽  
Dry Weight ◽  
Food Products ◽  
Arsenic Species ◽  
Arsenic Content ◽  
Infant Food ◽  
Sweet Potatoes ◽  
Green Beans ◽  
Inductively Coupled

Abstract Health risk associated with dietary arsenic intake may be different for infants and adults. Seafood is the main contributor to arsenic intake for adults while terrestrial-based food is the primary source for infants. Processed infant food products such as rice-based cereals, mixed rice/formula cereals, milk-based infant formula, applesauce and purée of peaches, pears, carrots, sweet potatoes, green beans, and squash were evaluated for total and speciated arsenic content. Arsenic concentrations found in rice-based cereals (63–320 ng/g dry weight) were similar to those reported for raw rice. Results for the analysis of powdered infant formula by inductively coupled plasma-mass spectrometry (ICP-MS) indicated a narrow and low arsenic concentration range (12 to 17 ng/g). Arsenic content in purée infant food products, including rice cereals, fruits, and vegetables, varies from &lt;1 to 24 ng/g wet weight. Sample treatment with trifluoroacetic acid at 100°C were an efficient and mild method for extraction of arsenic species present in different food matrixes as compared to alternative methods that included sonication and accelerated solvent extraction. Extraction recoveries from 94 to 128% were obtained when the summation of species was compared to total arsenic. The ion chromatography (IC)-ICP-MS method selected for arsenic speciation allowed for the quantitative determination of inorganic arsenic [As(III) + As(V)], dimethylarsinic acid (DMA), and methylarsonic acid (MMA). Inorganic arsenic and DMA are the main species found in rice-based and mixed rice/formula cereals, although traces of MMA were also detected. Inorganic arsenic was present in freeze-dried sweet potatoes, carrots, green beans, and peaches. MMA and DMA were not detected in these samples. Arsenic species in squash, pears, and applesauce were not detected above the method detection limit [5 ng/g dry weight for As(III), MMA, and DMA and 10 ng/g dry weight for As(V)].

scholarly journals ANALISIS KETIMPANGAN WILAYAH DAN ARAHAN PENGEMBANGAN WILAYAH DI KABUPATEN BURU PROVINSI MALUKU

2018 ◽  
Vol 20 (4) ◽  
pp. 472
Author(s):  
Rifyan Ruman ◽  
Setia Hadi ◽  
Baba Barus
Keyword(s):  
Data Analysis ◽  
Agricultural Development ◽  
Health Facilities ◽  
Analysis Method ◽  
Sweet Potatoes ◽  
Green Beans ◽  
Location Quotient ◽  
Land Capability ◽  
Education And Health ◽  
Data Analysis Method

The purpose of this study was to determine the class-leading commodity and land capability and potential of land that can be used for agricultural development in Buru. Data analysis method used was overlying maps, Location Quotient (LQ) and Shift Share Analysis (SSA) to determine the main commodity. The result is elaborated as follow inequality in Buru can be seen from inadequate infrastructure especially the condition of road, education and health facilities. Based on analysis of LQ and SSA in the Buru Regency, Commodities priorities in this region are sweet potatoes, peanuts, green beans, peppers, onion, tomato, spinach, kale, squash, eggplant, beans, avocado, mango, jackfruit, durian, orange, papaya, banana, cashew, and clove. The potential cultivate land for each sub-districts as Namlea (22390.73 ha), District Waeapo (68615.62 ha), District of Waplau (22173.26 ha), District Batabual (7920.27 ha) and District Air Buaya (10985.77 ha) that can be utilized for the development of agriculture-based according to the vision of Buru and in accordance with the commodity that exist in each district.

scholarly journals JARINGAN SYARAF TIRUAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI KETERSEDIAAN KOMODITI PANGAN PROVINSI RIAU

2017 ◽  
Vol 2 (2) ◽  
pp. 196-209
Author(s):  
Eka Pandu Cynthia, Edi Ismanto
Keyword(s):  
Processed Food ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Sweet Potatoes ◽  
Green Beans ◽  
Productivity Level ◽  
Actual Output ◽  
Input Variables ◽  
Food Commodities ◽  
Food Commodity

A system for predicting the availability of food commodities can help in making decisions. Artificial Neural Network is a method that is able to carry out mathematical processes for predicting the availability of food commodities. With the Backpropagation algorithm, the previous data processing is used as input to predict the availability of food commodities. Data processed as input variables are Area of ​​Harvest, Productivity Level, Number of Production and Number of Consumption Needs. While the processed food commodities are types of Rice, Corn, Soybeans, Peanuts, Green Beans, Cassava and Sweet Potatoes. The data was taken from 2006 to 2013. The years 2006 to 2012 were used as input data, while for 2013 they were targeted data. Some stages of Backpropagation are initializing weights, activating, calculating input weights and output biases and changing weights and biases. This stage will obtain the output to be achieved with the smallest error approach so that the predicted results of the availability of food commodities are obtained. The training process uses Matlab software tools 6.1. The result is a prediction of the amount of food commodity availability by the training and testing process producing actual output as the target achieved.

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