Articles
<![CDATA[Application Of K-Means Clustering Algorithm On Population Growth In Simalungun Regency ]]>
<![CDATA[Murniati Rambe]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Irawan Irawan]]>
<![CDATA[ International Journal of Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science ]]>
Publisher : <![CDATA[Institute of Computer Science ]]>
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DOI: 10.35335/ijobas.v10i2.55
<![CDATA[Population growth is a condition when the population increases from previous years. Population growth has several variables, namely birth, death and migration rates. Positive population growth indicates an increase in population and vice versa. Population growth is caused by a high birth rate with a decrease in the death rate. The high rate of population growth and occurs in a fast period of time is what triggers a population explosion which is closely related to an increase in poverty, unemployment, crime, slum settlements, hunger and other social problems. An increase in the poverty rate occurs when high population growth is not matched by good economic growth accompanied by equitable distribution of income. An increase in unemployment occurs if the increase in population with reduced availability of adequate employment can lead to an increase in criminal cases. By knowing these problems, Data Mining is needed to classify aid receipts, build jobs. by using the K-Means method in clustering the population growth rate. The K-Means method can assist the Government in making decisions and the information needed to solve the problem of population growth and record all densely populated areas in an appropriate way.]]>
<![CDATA[A Oil Palm Harvest Grouping Using K-Medoids Algorithm ]]>
<![CDATA[Dessy Dwi Angraini]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Fitri Anggraini]]>
<![CDATA[ International Journal of Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science ]]>
Publisher : <![CDATA[Institute of Computer Science ]]>
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DOI: 10.35335/ijobas.v10i2.56
<![CDATA[Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.]]>
<![CDATA[Application Of Naive Bayes Algorithm In Classification Of Child Nutrition At The Simalungun Health Office ]]>
<![CDATA[Susi Septi Hardiani]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Dedi Suhendro]]>
<![CDATA[ International Journal of Basic and Applied Science Vol. 10 No. 3 (2021): December: Basic and Applied Science ]]>
Publisher : <![CDATA[Institute of Computer Science ]]>
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DOI: 10.35335/ijobas.v10i3.57
<![CDATA[Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.]]>
<![CDATA[Implementasi Moora Berbasis Web pada Penentuan Kelayakan Penerima Bantuan Siswa Miskin ]]>
<![CDATA[Aron Saputra Sirait]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Indra Gunawan]]>
<![CDATA[ SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 1 No. 1 (2021): April 2021 ]]>
Publisher : <![CDATA[Yayasan Pendidikan Penelitian Pengabdian ALGERO ]]>
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DOI: 10.54259/satesi.v1i1.2
<![CDATA[The Poor Student Assistance Program (BSM) is an aid from the government with the aim that poor children who excel are able to continue schooling. So that in order to minimize this assistance can be channeled properly on target, it is necessary to conduct a fairly strict selection based on the criteria that have been set. This study proposes the use of the website-based Moora method to overcome this problem. The MOORA method is very simple, stable, and powerful, even this method does not require an expert in mathematics to use it and does not require complicated mathematical calculations. The BSM assistance that will be discussed in this research is SD NEGERI 127696 Pematangsiantar City using 7 criteria, namely: Parent's Occupation, Income, Number of Dependents, Attendance, Last Semester Grades, Academic Achievement and Personality. The sample data used were 18 students. This study resulted in the highest value alternative on behalf of Ade Putri Mekaria Laila with an Optimization value of 2.22692. So it can be concluded that the Moora method can be used as a decision support method in the selection of BSM recipients.]]>
<![CDATA[Implementasi algoritma apriori pada sistem persediaan barang ]]>
<![CDATA[elfrida lucyana hutahaean]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Bahrudi Efendi Damanik]]>
<![CDATA[ JIKO (Jurnal Informatika dan Komputer) Vol 3, No 3 (2020) ]]>
Publisher : <![CDATA[Journal Of Informatics and Computer ]]>
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DOI: 10.33387/jiko.v3i3.2192
<![CDATA[Persediaan merupakan salah satu faktor penentu kelancaran dalam penjualan, maka dari itu persediaan seharusnya dikelola dengan baik. Jika persediaan produk kurang pelanggan akan kecewa dan pergi ke tempat lain dan jika persediaan berlebih akan menimbulkan penumpukan barang dan terjadi kerugian. Dengan adanya dukungan perkembangan teknologi, data-data transaksi yang ada dapat dimanfaatkan untuk mengelola persediaan barang dimasa yang akan datang yaitu menggunakan teknik Data Mining aturan Asosiasi. Aturan Asosiasi yang digunakan adalah Algoritma Apriori untuk menemukan aturan asosiatif kombinasi antara itemset. Perhitungan dilakukan dengan menentukan support dan confidence yang digunakan untuk menentukan stock barang yang perlu diperbanyak atau dikurangi agar efektif lagi dalam meminimalisir penumpukan barang dan kerugian.]]>
<![CDATA[Analysis of k-medoids clustering on toddler immunization in north sumatra province ]]>
<![CDATA[Lena Sapura]]>;
<![CDATA[M. Safii]]>
<![CDATA[ International Journal of Mechanical Computational and Manufacturing Research Vol. 11 No. 3 (2022): November: Mechanical Computational And Manufacturing Research ]]>
Publisher : <![CDATA[Trigin Publisher ]]>
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DOI: 10.35335/computational.v11i3.50
<![CDATA[Immunization is a process to increase the body's immune system by inserting a vaccine, namely a virus or bacteria that has been weakened, killed, or parts of the bacteria (virus) have been modified. The purpose of this study was to group children's immunization data according to districts/cities in North Sumatra Province using the K-Medoids Data Mining algorithm. The K-Medoids algorithm or pattern known as PAM (Partitioning Around Medoids) uses the partitioning clustering method to group a set of n objects into a number of K clusters. The data in this study is sourced from the Central Bureau of Statistics of North Sumatra. The grouping was carried out based on the number of recipients of the DPT-HB and Measles/MR vaccines from 33 districts/cities in North Sumatra Province. The results of the study are expected to be taken into consideration for local governments, especially the Ministry of Health in the distribution of immunization for children under five with the type of immunization to achieve the national target set by the Ministry of Health, which is 79.1%.]]>
<![CDATA[Analysis of the Feasibility Level of Determining Retail Prices of Staples Using the K-Means Clustering Method ]]>
<![CDATA[Akbar Fahri Hambali]]>;
<![CDATA[M. Safii]]>
<![CDATA[ Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research ]]>
Publisher : <![CDATA[Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara ]]>
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<![CDATA[The rate of economic growth in a region is highly dependent on the role and infrastructure of structured agriculture. Staples are also one of the state assets that can optimize state revenues through the success of a high production process so that staple commodities can be exported to other countries to increase economic competitiveness more optimally. One of the ways to stabilize the economy of a region is by determining proper retail prices for staple commodity commodities. This research examines the feasibility level analysis case for fixing the retail price of basic commodities in the city of Pematangsiantar using the methodK-Means Clusteringas a case solution. The source of the data in this study was obtained from official documents from the Central Bureau of Statistics in the city of Pematangsiantar with processing data on retail prices of basic commodities in 2018-2021 with data on 8 (eight) commodities. Data analysis in this study used 2 (two) cluster levels, namely the high cluster (C1) and the low cluster (C2). Based on the research results, it was found that 1 (one) commodity was included at a high level (cluster 1), namely salted fish. While t (seven) other commodities such as rice, cooking oil, sugar, salt, washing soap, wheat flour and cement are included in the low level cluster (C2). It is hoped that the research results can be input.]]>
<![CDATA[Analysis of Realization of Total Connected Power By Industrial Customer Using K-Means Clustering Method ]]>
<![CDATA[Tri Febri Damayanti]]>;
<![CDATA[M. Safii]]>
<![CDATA[ Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research ]]>
Publisher : <![CDATA[Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara ]]>
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<![CDATA[Electrical power is one of the primary needs for living things, especially humans. With the existence of electrical energy, human activities are getting easier and more practical. This study aims to assist the relevant government, especially PLN in the province of North Sumatra, in knowing the quality and quantity of actual connected power in the province of North Sumatra. Completion of cases in this study using the K-Means Clustering Data Mining Method. The data used in this study were obtained directly through the Central Statistics Agency (BPS) website for North Sumatra province with the url https://bps.sumut.go.id. The analysis in this study uses 2 (two) cluster levels, namely high realization (C1) and low realization (C2). The research results obtained are that there is 1 area that is included in the high cluster (C1) and there are 9 areas that are included in the low cluster (C2). It is hoped that the research results can become input, suggestions and efforts for the government, especially PLN in North Sumatra province to pay more attention to and increase the realization of electricity connected power in areas that are included in low clusters so that industrial processes can run effectively and efficiently and can support economic growth in the province of North Sumatra.]]>
<![CDATA[Application of Data Mining in Classification Fresh Milk Production by Province Using K-Means Algorithm ]]>
<![CDATA[Afifah Wulandari]]>;
<![CDATA[M. Safii]]>
<![CDATA[ Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research ]]>
Publisher : <![CDATA[Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara ]]>
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<![CDATA[The need for fresh milk is currently experiencing a fairly rapid development as can be seen in terms of the domestic market, here researchers want to increase the productivity and quality of fresh milk production. The data to be used is data from the Central Bureau of Statistics. The method in this study is the K-means clustering algorithm which is grouped into 2 clustering, namely high and low. The results of this study are 1 high-level cluster province, 24 low-level cluster provinces]]>
<![CDATA[Jaringan Saraf Tiruan Memprediksi Tingkat Penjualan Smartphone Di Wijaya Cell Pematangsiantar Menggunakan Metode Backpropagation ]]>
<![CDATA[Syahrial Azmi Pohan]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Sundari Retno Andani]]>;
<![CDATA[Muhammad Rafai]]>;
<![CDATA[Abdi Rahim Damanik]]>
<![CDATA[ SNASTIKOM Vol. 2 No. 1 (2023): SEMINAR NASIONAL TEKNOLOGI INFORMASI & KOMUNIKASI (SNASTIKOM) 2023 ]]>
Publisher : <![CDATA[Unit Pengelola Jurnal Universitas Harapan Medan ]]>
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<![CDATA[This study aims to optimize profits and minimize losses from smartphone sales at Wijaya Cell stores that have been achieved in the future. The data used in this study were obtained directly from the Wijaya Cell store by conducting observations and interviews. The Wijaya Cell store is a communication technology that sells smartphones to be marketed to the public. The data that will be processed from the sale of the Wijaya Cell smartphone uses the Backprogation method which is an Artificial Neural Network. The data used is annual smartphone sales data from 2018-2021. From the results of research with training data experiments, it was found that the best architecture was 3-8-1 with 92% accuracy, MSE training was 0.0099974. It is concluded that the Backprogation method can be implemented in predicting smartphone sales results. By doing this research, it is hoped that it can provide input to the Wijaya Cell Shop in optimizing profits and minimizing losses from smartphone sales in the future]]>
