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Buaton, Relita
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Clustering of Deleted Binjai City Government Asset Data Using the K-Means Algorithm Lestari, Chintiya Wahyuni Indah; Buaton, Relita; Syahputra, Suria Alam
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 2 (2025): Journal of Engineering, Tecnology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i2.306

Abstract

Regional assets are a crucial component in managing local government resources. However, their management often encounters various obastacles, such as the accumulation of unproductive assets and the difficulty of mapping assets that must be written off. The Binjai City Government, through the Regional Finance, Revenue, and Asset Management Agency (BPKPAD), is obliigated to manage its assets, including those that have reached the end of their useful life. However, without in-depth analysis, the management of written-off asset data can become disorganized, potentially hampering the transparency and efficiency of overall asset management. To address these issuses, this study applied the K-Means algorithm with 3, 4, and 5 clusters as a method for grouping deleted asset data. The data characteristics used included the type of item, year of acquisition, and method of acquisition. The test results showed that grouping with 3 clusters resulted in a cluster variance value of 208,6587, indicating a high level of data diversity. With 4 clusters, the cluster variance value decreased to 110,5156, resulting in a better and more compact grouping. Meanwhile, testing with 5 clusters provided the most optimal results, with a cluster variance value of 79,2477. This shows that the use of 5 clusters can minimize the spread of data within each cluster, resulting in higher similarity between data compared to 3 and 4 clusters. Therefore, the application of the K-Means Algorithm to deleted Binjai City Government asset data can assist the data analysis and grouping process, where the best results were obtained in testing with 5 clusters. Keywords: Asset Data, K-Means Clustering, MATLAB, Binjai City Government
Implementasi Metode Promethee Untuk Rekomendasi Destinasi Objek Wisata Unggulan Provinsi Sumatera Utara Purba, Ramen Antonov; David Jumpa Malem Sembiring; Sinek Mehuli Br Perangin-Angin; Relita Buaton
JST (Jurnal Sains dan Teknologi) Vol. 13 No. 1 (2024): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v13i1.82395

Abstract

Tidak semua objek wisata telah dipersiapkan dengan apik dan memiliki daya tarik yang cukup. Ini menyebabkan kesulitan bagi wisatawan dalam memilih destinasi yang tepat, yang sering kali berujung pada kekecewaan dan kerugian. Agar masyarakat dapat memilih objek wisata yang tepat, diperlukan media yang dapat memberikan rekomendasi. Tujuan penelitian ini yaitu, menganalisis efektivitas implementasi metode Promethee dalam memberikan rekomendasi destinasi objek wisata unggulan berdasarkan kriteria yang telah ditetapkan. Subjek yang terlibat dalam penelitian ini berjumlah sebanyak 32 orang. Penelitian ini termasuk penelitian deskriptif kuantitatif. Adapun metode pengumpulan data yang digunakan adalah kuesioner, sedangkan instrumen yang digunakan adalah lembar kuesioner. Setelah data dikumpulkan kemudian dianalisis menggunakan metode analisis data kuantitatif dengan metode promethee yang bertujuan untuk melakukan perangkingan destinasi wisata berdasarkan kriteria yang ditetapkan. Hasil analisis menunjukkan bahwa metode Promethee berhasil menghasilkan perangkingan destinasi wisata yang akurat dan stabil berdasarkan kriteria yang telah ditetapkan. Sehingga, dapat disimpulkan bahwa metode Promethee merupakan alat yang efektif dan andal untuk memberikan rekomendasi destinasi objek wisata unggulan. Penelitian ini memberikan kontribusi yang berharga dalam upaya meningkatkan pemasukan daerah melalui sektor pariwisata.
Application of Numerical Measure Variations in K-Means Clustering for Grouping Data Buaton, Relita; Solikhun, Solikhun
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3269

Abstract

The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265.
Data Mining Pengelompokan Akta Nikah Berdasarkan Usia Nikah atau Domisili Menggunakan Metode Clustering: Studi Kasus  Kemenag Langkat Nurlaila, Nurlaila; Buaton, Relita; Saragih, Rusmin
Sci-tech Journal Vol. 2 No. 1 (2023): Sci-tech Journal (STJ)
Publisher : MES Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (921.437 KB) | DOI: 10.56709/stj.v2i1.62

Abstract

One way to find out the marriage certificate data is to group the prospective bride and groom data that has been recorded at the Langkat Ministry of Religion. The analysis was carried out using the Clustering method using the K-Means method which was translated into a software. This software is used for grouping data. The output is to find out the grouping of marriage certificate data with the closest relationship between the age group of the groom, occupation and address. The results showed that the age of the groom-to-be between 19-28 years old had a civil servant job at the address/district of Kutambaru. Keywords: age, occupation, address/location and clustering
Co-Authors Achmad Fauzi 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 Eli Yusrina Elviwani Elviwani Ema Sari Suwandi 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 I Gusti Prahmana I Gusti Prahmana I Gusti Prahmana Indah Malasari Ivan Candra Dinata Kadim, Lina Arliana Nur Katen Lumbanbatu Khair, Husnul Kristina Ananatasia Kristina Annatasia Leni Tri Ramadhayanti Lestari, Chintiya Wahyuni Indah Lidya Hasna lidya hasna Lubis, Anjelia Alsar Magdalena Simanjuntak magdalena simanjuntak Malau, Farid Reza Marto Sihombing Melda Pita Uli Sitompul Mesra Yel Mili Alfhi Syari Muammar Khadapi Muhammad Arif Ridho Muhammad Rifa'i Muhammad Zarlis Muhammad Zarlis, Muhammad 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 Prahmana , I Gusti Prisa Abela Purba, Ramen Antonov Putri Lishayani Putri Purwani, Dea Nanda Raja Rizki Alanta Nasution Ramadani, Suci Rani Lestari Rani Nuraini Rani Nuraini Ratih Ratih Puspadini Reza Alexandra Rianty Zabitha Siregar Rohana, Sherly Rusmin Saragih, Rusmin Sany Lubis, Fauzan Al An Selfira Selfira Sembiring, Hermansyah septian haryanto Septian Haryanto Sherly Eka Wahyuni Sihombing, Anton Sihombing, Marto Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sinek Mehuli Br Perangin-Angin Siswan Syahputra Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Astuti Sri Hardiningsih suci ramadani Suha Baby Mayaza Sundari, Yeni Sundari, Yeni Suria Alamsyah Putra Syahputra, Suria Alam Syahril Effendi Syari, Milli Alfhi T. Reza Pahlevi Teuku Reza Pahlefi Tiara Jelita Tio Ria Pasaribu Windy Indah Sary Sinaga Windy, Windy Alfira Yani Maulita Yusnan Sepriadi Ginting Yusnan Sepriadi Ginting Yuyun Arnia Zarlis Muhammad Zuliani Zuliani Zulkifli Zulkifli