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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol. 1 No. 1 (2023): January: Article Computer Science and Research" : 5 Documents clear
Analysis of the Feasibility Level of Determining Retail Prices of Staples Using the K-Means Clustering Method Akbar Fahri Hambali; M. Safii
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

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.
Analysis of Realization of Total Connected Power By Industrial Customer Using K-Means Clustering Method Tri Febri Damayanti; M. Safii
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

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.
Implementation of Clustering on Tobacco Import Data By Country Of Destination Using The K-Means Algorithm Syifa Aisyah Rahmah Sibuea; M. Safii
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Tobacco is a seasonal agricultural product which is not a food commodity, but a plantation commodity. This product is consumed not for food but as a spare time filler or "entertainment", namely as a raw material for cigarettes and cigars. Indonesia is currently one of the countries with the highest smoking rates in the world. The domestic tobacco industry is now growing rapidly, as well as making the types of cigarettes in the country more diverse. In this study, the authors used the k-means clustering data mining technique to classify copper import data according to their original purpose. The results of this study are copper import data clusters. The copper import cluster consists of two clusters, namely the high cluster and the low cluster.
Application of Data Mining in Classification Fresh Milk Production by Province Using K-Means Algorithm Afifah Wulandari; M. Safii
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

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
Implementation of data mining with the c4.5 algorithm for student majors (Case Study: SMA N 1 Bp.Mandoge) Nabilah putri; M. Safii
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 1 (2023): January: Article Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Classification of student majors is the process of grouping students according to abilities (values), talents and interests that are relatively the same so that the lessons that will be given to students will be more focused and directed. The process of classifying student data can be explored for patterns in the field of data mining, namely the process of obtaining relationships or patterns from large data so as to provide useful indications. SMA N 1 Bp.Mandoge is one of the educational institutions that started introducing majors and divided them into two choices of majors, namely "IPA" and "IPS". The curriculum currently used by SMA Negeri 1 Bp.Mandoge is Curriculum 2013, which regulates the process of sorting majors for class X (ten) students based on average junior high school report cards, junior high school national exam scores, and MTK, IPA, and social studies test scores. One method that can be used to solve data mining classification problems is the C4.5 Algorithm method. Algorithm C4.5 is used to construct a decision tree that divides a large data set into smaller record sets by applying a series of decision rules to classify the data. In this study, student majors were classified based on MTK, IPA, and Social Sciences academic test scores, average junior high school report cards for MTK, Science, and Social Studies subjects, SMP National Examination scores for MTK and Science subjects, and student interests. Based on the results of the research, the results of the classification of student majors that have been tested correspond to an accuracy rate of 89.74%. 5 is used to form decision trees that divide large data sets into smaller record sets by applying a series of decision rules to classify data. In this study, student majors were classified based on MTK, IPA, and Social Sciences academic test scores, average junior high school report cards for MTK, Science, and Social Studiessubjects, SMP National Examination scores for MTK and Science subjects, and student interests. Based on the results of the research, the results of the classification of student majors that have been tested correspond to an accuracy rate of 89.74%. 5 is used to form decision trees that divide large data sets into smaller record sets by applying a series of decision rules to classify data. In this study, student majors were classified based on MTK, IPA, and Social Sciences academic test scores, average junior high school report cards for MTK, Science, and Social Studies subjects, SMP National Examination scoresfor MTK and Science subjects, and student interests. Based on the results of the research, the results of the classification of student majors that have been tested correspond to an accuracy rate of 89.74%. and Social Studies, the average junior high school report cards for MTK, Natural Sciences, and Social Sciences subjects, the SMP National Examination scores for MTK and Natural Sciences subjects, and student interest. Based on the results of the research, the results of the classification of student majors that have been tested correspond to an accuracy rate of 89.74%. and Social Studies, the average junior high school report cards for MTK, Natural Sciences, and Social Sciences subjects, the SMP National Examination scores for MTK and Natural Sciences subjects, and student interest. Based on the results of the research, the results of the classification of student majors that have been tested correspond to an accuracy rate of 89.74%

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