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All Journal Jurnal Media Infotama Jurnal Sains dan Teknologi Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Majalah Ilmiah Kaputama JTIK (Jurnal Teknik Informatika Kaputama) MEANS (Media Informasi Analisa dan Sistem) Jurnal Manajemen Informatika Jayakarta Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Journal of Vision and Ideas (VISA) EXPLORER Bulletin of Multi-Disciplinary Science and Applied Technology Archive: Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Teknologi Komputer Sci-Tech Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Journal of Engineering, Technology and Computing (JETCom) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY International Journal of Health, Engineering and Technology Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Jurnal Penelitian Teknologi Informasi dan Sains Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Saturnus: Jurnal Teknologi dan Sistem Informasi International Journal of Information Engineering and Science
Buaton, Relita
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Journal : EXPLORER

 1 
Pengelompokan Bidang Usaha Terhadap Bantuan Produktif Usaha Mikro (BPUM) Berdasarkan Wilayah Deli Serdang Menggunakan Metode Clustering K-Means (Studi Kasus: Dinas Koperasi Dan UMKM Kabupaten Deli Serdang) Tiara Jelita; Relita Buaton; Magdalena Simanjuntak
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.783

Abstract

Micro Business Productive Assistance is a program that is assistance from the government to MSME workers throughout Indonesia. Every year, MSMEs can receive this assistance, without exception for those who have received it in previous years. The Office of Cooperatives and MSMEs of Deli Serdang Regency is a regional apparatus in North Sumatra Province which has the main task of carrying out government affairs in the field of cooperatives and small businesses including saving and loan business permits, empowerment and development of small businesses. Micro, Small and Medium Enterprises (MSMEs) are individual business entities which contributed significantly to increasing exports, increasing and equalizing income, forming national products and expanding employment opportunities. Based on these conditions, the authors provide a solution that needs to be built a clustering that can classify fields in each business owned by the community, because not all types of business fields in the community will receive this assistance, including agriculture and animal husbandry. Grouping data can apply the data mining process with the K-Means Algorithm clustering method which is a process of processing very large amounts of data using statistical methods, mathematics, and utilizing Artificial Intelligence technology to produce a group of data. By utilizing the data mining process using the clustering method, it is hoped that clustering can solve the problem of grouping business fields owned by the community. From the test results with 1004 data, which was carried out with MATLAB, it was found that group 1 had 383 data, group 2 had 261 data and group 3 had 360 data. Meanwhile, based on the results of the trial with RapidMiner, it was found that group 1 had 371 data, group 2 had 281 data and group 3 had 352 data.
Pengelompokan Data Mining Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode Clustering (Studi Kasus : Kantor Desa Payabakung Hamparan Perak) Fany Juliawati; Relita Buaton; Rusmin Saragih
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.793

Abstract

Poverty is a problem that is often faced by various countries in the world, including Indonesia. In an effort to overcome poverty and increase people's access to food, in 2017 the Government gradually created a program that was formed to reduce the burden on the community in meeting basic needs, with the Non-Cash Food Assistance Program (BPNT). The problem is that the assistance provided has not been distributed on target / the distribution of assistance has not been objective, due to limited data and information obtained regarding families receiving BPNT assistance, so that families who should be entitled to receive assistance cannot receive assistance due to limited data available. Therefore, the village office is required to record again the families who are entitled to receive BPNT assistance with the existing criteria. The solution offered is to create a system of k-means that displays the clustering results of recipients of Non-Cash Food Assistance, by utilizing a number of data owned by the agency, it can be grouped using data mining technology. The benefit is that data mining can help agencies gain knowledge. by processing existing BPNT beneficiary data. The use of data mining techniques in grouping BPNT recipients is expected to be useful in facilitating the process of searching system data, which was previously still manual. The data group for recipients of Non-Cash Food Assistance (BPNT) in the work group (X) are private employees, for the income group (Y) are 1,400,001 – 1,700,000 and in the home status group (Z) are self-owned homes, and Centroid 2 ( 1,552,861.44), the data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Plantation, for the income group (Y) is 800,001 – 1,100,000 and in the house status group (Z) is Owned house, and Centroid3 (4,592,351.64) data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Labor, for the income group (Y) is 500,001 – 800,000 and in the house status group (Z) is Rent house.
Klasifikasi Data Penduduk Pada Pemilihan Umum Di Kota Binjai Menggunakan Algoritma K-Means (Studi Kasus : KPU Kota Binjai) Windy Indah Sary Sinaga; Relita Buaton; Hermansyah Sembiring
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.794

Abstract

Population growth is something that continues in an environment both in rural and urban areas. The rapidly increasing number of residents must be re-recorded in a government agency. Likewise, the Binjai City KPU Office must re-record population data, especially residents in the city of Binjai who have the right to carry out the General Election in 2024 by involving the community that has been previously recorded. Problems were also found with data on residents who had moved domiciles but their personal data had already been recorded for general elections in 2024. With that, data collection had to be re-done to select population data so as to produce a new population data status so that data was not found that did not match what it should be. By observing the problems above Data Mining with the Clustering method is very appropriate to be used to generate knowledge of new population data groups to carry out general elections at the KPU Binjai, using the MATLAB application is also very appropriate to choose in this problem so that it can produce output from data mining that can be used in future decision making. This study aims to process data to produce population data in the city of Binjai in the implementation of general elections, implement a system so that it can classify new population data in the middle of old population data and design data grouping in determining population data groups based on criteria conditions at the KPU Office in Binjai city. By using the clustering method that has been used to process population data at general elections in the city of Binjai, it can produce new information from 1000 data that has been tested. From 1000 population data for general elections in Binjai City, 3 clusters are obtained with the results of 7 tests where cluster 1 totals 225 data, cluster 2 has 436 data and cluster 3 has 339 data.
Co-Authors Achmad Fauzi Ade Chairany Adek Maulidya Adinda Maudia Savira Ajisro Siringoringo Alma Diana Rangkuti Alma Diana Rangkuti Ambarita, Indah Ami Dilham Ana, Putri Andri Kristiawan Anisa Anisa Anisa Anisa Anisa Putri Pratiwi anjelia alsar anjeliaalsharlubis Anjelia Alsar Lubis Annatasia , Kristina Aprillianda Pasaribu Aula, Nurhasanah Auni Patrisyah Ayu Rahayu Febria Ayu Rahayu Febria Br. Ginting, Rosa Lina Budi Serasi Ginting Budi Serasi Ginting Cinta Apriliza Clara Rosa Wijaya David Jumpa Malem Sembiring Dea, Dea Puspita Deny Jollyta Deri Kurniawan Desva Karliana br Sembiring Dhea Agustina Akmal Dhea Alfiya Ningsih Dhovan Damara Santoso Dicha Mutia Dhani Dita Mawarni Diva Alifya Dwi ASTUTI Elviwani Fadillah Fadillah Fajar Amalia Putri Fany Juliawati Farid Reza Malau Fauzi, Achmad Febi Andini Fuji Dodo Aritonang Gultom, Imeldawaty Haryanto, Septian Hayati, Radhiah Heka Herawati Br Tarigan Herman Mawengkang Hermansyah Sembiring Hermansyah Sembiring Husnul K I Gusti Prahmana Indah Malasari Ivan Candra Dinata Kadim, Lina Arliana Nur Khadapi, Muammar Khair, Husnul Khairul, Habib Kristina Ananatasia Kristina Annatasia Leni Tri Ramadhayanti Lestari, Chintiya Wahyuni Indah Lidya Hasna lidya hasna Lishayani, Putri Lubis, Anjelia Alsar Lumbanbatu, Katen magdalena simanjuntak Magdalena Simanjuntak Magdalena Simanjuntak Malau, Farid Reza Marto Sihombing Mayaza, Suha Baby Melda Pita Uli Sitompul Mili Alfhi Syari Muhammad Arif Ridho Muhammad Zarlis, Muhammad Muhammad, Zarlis N Novriyenni Nadila Rahmawati Nike Alpio Rizky Ningsih, Novia Novita Anggraini Novriyenni Nur Fariza Khairani Nurhayati Nurlaila Nurlaila Nurlaila Nurlaila Nurul Syahrani Pardede, Akim Manaor Hara PASARIBU, TIO RIA Prahmana , I Gusti Prahmana, I Gusti Pramudhita, Chika Prisa Abela Purba, Ramen Antonov Putri Purwani, Dea Nanda Raja Rizki Alanta Nasution Ramadani, Suci Rani Lestari Rani Nuraini Rani Nuraini Ratih Ratih Puspadini Reza Alexandra Rianty Zabitha Siregar rifa'i, Muhammad Rohana, Sherly Rusmin Saragih, Rusmin Sany Lubis, Fauzan Al An Sari Suwandi, Ema Selfira Selfira Selfira Selfira, Selfira Sembiring, Hermansyah Sembiring, Indri Aurellia Apsari septian haryanto Septian Haryanto Sherly Eka Wahyuni Sihombing, Anton Sihombing, Marto Sihombing, Novena Putri Antonia Sima, Brema Arisma Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sinek Mehuli Br Perangin-Angin Siswan Syahputra Siti Nur Azizah, Siti Nur Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Astuti Sri Hardiningsih Sundari, Yeni Sundari, Yeni Suria Alamsyah Putra Syahputra, Siswan Syahputra, Suria Alam Syahril Effendi Syari, Milli Alfhi T. Reza Pahlevi Teuku Reza Pahlefi Tiara Jelita Windy Indah Sary Sinaga Windy, Windy Alfira Yani Maulita Yel, Mesra Yusnan Sepriadi Ginting Yusnan Sepriadi Ginting Yusrina, Eli Yuyun Arnia Zuliani - Zulkifli Zulkifli