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All Journal Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Dimamu Indonesian Journal of Informatic Research and Software Engineering Jurnal AbdiMU Padma: Jurnal Pengabdian Kepada Masyarakat CONSEN: Indonesian Journal of Community Services and Engagement Journal of Islamic Economics and Business Coreid Journal INTERNAL (Information System Journal) Goodwood Akuntansi dan Auditing Reviu
Dewi, Sofia
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Physics Public Health Social Sciences Other

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Journal : Jurnal Dimamu

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Klasterisasi Pemilihan Paket Umrah Berdasarkan Musim untuk Menentukan Strategi Promosi Menggunakan Algoritma K-Means Rahayu, Ajeng; Dewi, Sofia
Jurnal Dimamu Vol. 4 No. 1 (2024)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/dimamu.v4i1.1326

Abstract

This research aims to classify Umrah package selection based on season using the K-Means algorithm, which is expected to help PT Pradana Grassindo Travel in determining more effective promotional strategies. The background to the problem includes problems in managing airline ticket prices and accommodation availability during the peak season, as well as extreme weather conditions in Saudi Arabia. The research method involved collecting primary data through interviews and literature study, the data obtained amounted to 149 data, which was then processed using the K-Means clustering method and using Microsoft Excel and RapidMiner software. The analysis results show that the K-Means algorithm is able to group pilgrim data into two clusters based on departure season, namely Summer and Winter. This research provides a significant contribution in improving promotional strategies and services for Umrah pilgrims.
Penerapan Data Mining Penyaluran Dana Bantuan Sosial Menggunakan Metode K-Means Clustering di Desa Rancaekek Kulon Rainaya, Suci; Dewi, Sofia
Jurnal Dimamu Vol. 4 No. 3 (2025)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/dimamu.v4i3.1618

Abstract

Data Mining simplifies and accelerates the process of distributing social assistance funds, namely direct cash assistance from village funds and has a significant impact. One of the causes of the problem is due to inappropriate and inaccurate data, so that assistance is often not on target and also the lack of limited access to technology to manage population data updates which causes social assistance not to reach groups of people who really need it. The purpose of this study is to apply Data Mining for the distribution of social assistance funds to the community in the Rancaekek Kulon Village Area and to find out the accuracy of the results of the application of data mining using Rapidminer Tools and also to increase the efficiency and speed in the process of distributing social assistance funds in Rancaekek Kulon Village. Data analysis and implementation with the K-Means Clustering algorithm was carried out by manual calculation using Microsoft Excel and then tested using RapidMiner software. The data used in this study is 526 data. The application of data mining using the K-Means Clustering algorithm can be used to group people who are eligible and unworthy of receiving assistance by producing 2 clusters, namely C0 (Not Feasible) and C1 (Feasible). The results obtained from the RapidMiner test were unfeasible community groups, namely C0 (Not Feasible) as many as 206 data and C1 (Feasible) as many as 320 data. Where the Davice Bouldin Index (DBI) was obtained from the formation of the cluster as much as 0.701 which means that the cluster is quite good because it is close to 0. Based on the calculation of data using Microsoft Excel, namely the Unfit Community group (C0) as many as 232 data and the Decent Community group (C1) as many as 294 data with the accuracy results obtained from the validation that has been carried out through the Davies Bouldin Index (DBI) validation on Microsoft Excel, which is 0.378
Co-Authors Abidin, Rachmat Anwar, Moh. Taufan Aprilia, Yulianti Arif Setyawan, Adhita Aryanti, Utami Asep Saepuloh Asmalasari, Ai Siti Desrian, Wanda Novianti Fadillah, Tifa Hidayat, Hilmi Sirojuddin Ipah Saripah, Ipah Jayatmi, Irma Komalasari, Esti Kresnawati, Ai Lidinillah, Siti Habibah Muhammad Prakarsa Al Qard Saleh Naasyiah, Rifdah Oktavianty, E Desy Prakarsa Al Qadr Saleh, Muhammad Putri, Magdalena Tri Rahayu, Ajeng Rainaya, Suci Rosita, Ita Sali Setiatin Salsabila, Jihan Salma Sopyanti, Sinta Srimulyani, Yeni Sudrajat, Juan Wendi Sulmainah, Alma Syawali, Fikri Ikhsan Tiara Anggraeni, Tiara Wijana, Miki Wijaya, Miki Wirahadi, Umar Yunengsih, Yuyun