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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EKSAKTA: Journal of Sciences and Data Analysis Katalogis Jurnal Raushan Fikr International Journal of Advances in Intelligent Informatics Al-Ahwal: Jurnal Hukum Keluarga Islam Prosiding Seminar Nasional INDOCOMPAC Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum JOIN (Jurnal Online Informatika) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Riset Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Dialog Jurnal Berdaya Mandiri Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Enthusiastic : International Journal of Applied Statistics and Data Science MAQASIDI Jurnal Ilmiah Mahasiswa Raushan Fikr Bulletin of Applied Mathematics and Mathematics Education Bulletin of Social Informatics Theory and Application Al-Hukama: The Indonesian Journal of Islamic Family Law Jurnal Akuakultur SEBATIN PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Muhammad Muhajir, Muhammad
Department Of Statistics, Universitas Islam Indonesia
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Journal : Unisda Journal of Mathematics and Computer Science (UJMC)

 1 
ANALISIS DISPARITAS SPASIAL MENGGUNAKAN DYNAMIC K-MEANS CLUSTER DAN LOCATION QUOTIENT PDRB KABUPATEN CILACAP TAHUN 2014 Rafendra Agustianda Putra; Muhammad Muhajir
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 3 No 1 (2017): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.849 KB) | DOI: 10.52166/ujmc.v3i1.458

Abstract

In an effort to increase the growth an area, the government tried to advance sectors that can grow the economy in an area, one of them is gross regional domestic product (GDRP) data. A region that has an average areas with low GRDP but in the region there are areas that have very high GRDP can cause imbalance in the region. Imbalance regions is a common aspect in economic activity in a region, that is Cilacap regency. One model that is representative enough to measure the degree of imbalance of development among regions is Index Williamson (IW) and there are several methods to provide solutions in solving the problem of imbalance in Cilacap regency, among others Dynamic K-Means Clustering and Location Quotient (LQ). In this research, the results of the analysis showed IW Cilacap regency in 2014, there is imbalance but low imbalance, there is still imbalance between district due to high differences in income and sector productivity differences are striking every district in Cilacap regency. Dynamic K-Means Cluster analysis results shows the results of iteration 3 times and the number of clusters by 2 namely Cluster 1 (C1) and Cluster 2 (C2). Characteristics of C1 members advance in Sek_2, Sek_3, Sek_4, Sek_5, Sek_6, Sek_7, Sek_8, and Sek_9 while C2 members advance in Sek_1. C1 the number of members as many as 6 districts and C2 the number of members as many as 18 districts. And the result of LQ analysis shows that agriculture sector of Cilacap regency is the best potential to be developed.
Perbandingan Rough Set dan Algoritma Apriori untuk Sistem Rekomendasi Perpustakaan muhammad muhajir; Jaka Nugraha; Rachmad Febrian
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 4 No 2 (2018): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (571.358 KB) | DOI: 10.52166/ujmc.v4i2.1241

Abstract

The recommendation system is a dynamic information filtering system that is produced according to user interests or behavior. The recommendation system in the library can provide book references based on user interests or characteristics. UII Central Library has an information system in the form of a database of book lending transactions that can be used for a recommendation system. The method is a rough set and apriori algorithm. This study compares 2 methods to get the best method that can be applied in the recommendation system. The results obtained by the number of rough set rules as many as 14 rules with an average coverage of 0,01111 and average accuracy of 0,87416 and the number of apriori algorithm rules as many as 23 rules with an average support of 0,00276 and average confidence of 0,87458. Based on the number rules, the average value of accuracy or confidence, the apriori algorithm mehod is a method that can be used for the recommendation system in the UII Central Library.
Implementasi Metode Improved K-Means dengan Algoritma Dbscan untuk Pengelompokan Film muhammad muhajir; Annisa Ayunda Permata Sari
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 6 No 01 (2020): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v6i01.1923

Abstract

The Indonesian film industry continues to experience an increase seen from the number of films that appear in theaters today with a box office increase of 28 percent each year in the past four years. Internet Movie Database (IMDb) is a website that provides information about films around the world, including the people involved in it from actors, directors, writers to makeup artists and soundtracks. In this case the researcher wants to conduct research on the characteristics of the film and the factors that make a film to be included in the IMDb Top 250. The data used in this study uses scraped data from the website. The method used is a non-hierarchical clustering method, namely kmeans and Dbscan. Where the Dbscan algorithm is used to determine the optimum number of clusters then proceed by grouping data based on centroids with k-means algorithm. From the analysis it was found that the factors that could influence a film included in the IMDB Top 250 were duration, number of votes, and films directed by Rajkumar Hirani and the optimal number of clusters using Dbscan algorithm obtained six clusters. With the improved k-means algorithm, the accuracy value for the cluster results is 87.2%.
Co-Authors Abdullah, Ahmad Hussain Aditya Rizq Herlandy Karjawan Akmalia Fitri Mafaza Alhaidar, Nihrul Bahi Ali, Asriyanti Alvita, Salma Annisa Ayunda Permata Sari Ayu, Devina Gilar Baehaqi Berky Rian Efanna Cahyani, Amalia Rizki Canas Rodrigues, Paulo Danardono Danardono Danardono, Danardono Dedi Rosadi Devi Andrian Fatahillah, Zahrul Guritno, Muhammad Wahyu Suryo Gusmayyeni, Gusmayyeni Hasibuan, Rizky Ardiansyah Henni Syawal Hermansah Hermansah Hermansah Iesje Lukistyowati Inggrid Septa Narendra Jaka Nugraha Julia Widiastuti Kamila, Juanita Jihan Makhtum, Ahmad Rohiqim Muhaimin Muhaimin Muhammad Fadli Kamil Mustain, Riefyal Arshyza Novendri Isra Asriny Nurirwan Saputra, Nurirwan Nurkamiden, Sukrin Pandri Ferdias Pandri Ferdias, Pandri Pratiwi, Lathifah Aliya Primananda, Dhea Laksmita Arsya Puspita, Ersa Riga Qurratul Uyun, Qurratul Rachmad Febrian Rafendra Agustianda Putra Rahmadi Yotenka Rahman, Iqbal Fathur Rahmatika, Tusriana Riefyal Arshyza Mustain Rohmatul Fajriyah Rosadi , Dedi Sari, Redita Anggita Syamsud Dhuha Uswatun Hasanah Widiastuti, Julia Wulandari, Kanth Yayi Diyah Palupi Yotenka, Rahmadi Zetta Nillawati Reyka Putri