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All Journal Jiko (Jurnal Informatika dan komputer) JURTEKSI International Journal of Basic and Applied Science Jurnal Sains Teknologi dan Sistem Informasi Prosiding Snastikom INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Journal of Computer Science and Research
M. Safii
STIKOM Tunas Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Mechanical Engineering Physics

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Journal : Journal of Computer Science and Research

 1 
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.
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
Co-Authors Abdi Rahim Damanik Afifah Wulandari Akbar Fahri Hambali Aron Saputra Sirait Bahrudi Efendi Damanik Dedi Suhendro Dessy Dwi Angraini elfrida lucyana hutahaean Fitri Anggraini Indra Gunawan Irawan Irawan Lena Sapura Muhammad Rafai Murniati Rambe Saputra Ramadani Sundari Retno Andani Susi Septi Hardiani Syahrial Azmi Pohan Tri Febri Damayanti Wanto, Anjar