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Ansari Saleh Ahmar
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VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : http://dx.doi.org/10.35580/variansiunm26374
Core Subject : Economy, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 6 Documents
 1 
Search results for , issue "Vol. 4 No. 3 (2022)" : 6 Documents clear
PENGEMBANGAN PAKET R UNTUK ANALISIS DISKRIMINAN BERBASIS GRAPHICAL USER INTERFACE WEB INTERAKTIF Nur Isra; Suwardi Annas; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (895.351 KB) | DOI: 10.35580/variansiunm24

Abstract

Penggunaan perangkat lunak berlisensi memerlukan biaya yang relatif mahal, dan sulitnya memperoleh perangkat lunak berlisensi menjadi salah satu penyebab meningkatnya penggunaan perangkat lunak bajakan. Salah satu upaya dalam mengurangi tingkat permasalahan perangkat lunak bajakan adalah melakukan pengembangan perangkat lunak yang memiliki lisensi publik bersifat open source seperti perangkat lunak R. Penelitian ini dilakukan untuk menyusun beberapa paket yang terdapat pada perangkat lunak R yang akan memudahkan pengguna dalam melakukan analisis statistika, khususnya untuk analisis diskriminan linear. Paket pendukung R tersebut yaitu paket R-Shiny yang mampu membuat tampilan berbasis Graphical User Interface. Pengembangan paket R dalam penelitian ini menggunakan metode waterfall. Paket ini bernama Linear Discriminant Analysis Application (LDA App). Berdasarkan pengujian yang dilakukan pada LDA App menunjukkan bahwa LDA App mampu menyelesaikan analisis statistika sesuai fungsinya. Perbandingan antara LDA App dan software statistika lainnya memiliki ouput yang sama, akurat, dan lebih efisien dalam melakukan analisis diskriminan linear.
ANALISIS HIERARCHICAL CLUSTERING MULTISCALE BOOTSTRAP (KASUS: INDIKATOR KEMISKINAN DI PROVINSI SULAWESI SELATAN TAHUN 2020) Musdalifah M. Ramly; Sudarmin Sudarmin; Bobby Poerwanto
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (805.252 KB) | DOI: 10.35580/variansiunm26

Abstract

Hierarchical cluster analysis is a statistical analysis used to group data based on their similarities. The single linkage, complete linkage and average linkage methods can be used to group data using distance techniques. There is a large difference in the number of poor people in urban and rural areas in South Sulawesi Province, so an analysis is needed to classify areas that have the same characteristics based on poverty indicators. For this reason, these three methods are used. However, the results of this analysis are only based on the similarity measure based on the distance technique used. Thus, the multiscale bootstrap method is used to obtain the validity of the resulting clusters. The results of the research using these three methods are four clusters with different characteristics. By using multiscale bootstrap, it is found that in single linkage there are four valid clusters, for complete linkage there is only one valid cluster and on average linkage there are three valid clusters. So it is found that single linkage is the best method in classifying these cases.
PENERAPAN METODE RANDOM FOREST UNTUK KLASIFIKASI VARIAN MINUMAN KOPI DI KEDAI KOPI KONIJIWA BANTAENG Suci Amaliah; Muhammad Nusrang; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (595.241 KB) | DOI: 10.35580/variansiunm31

Abstract

Random Forest (RF) adalah metode yang dapat meningkatkan hasil akurasi dalam membangkitkan atribut untuk setiap node yang dilakukan secara acak. Penelitian ini bertujuan untuk mengetahui tingkat akurasi metode RF dalam memprediksi varian minuman kopi di kedai Konijiwa Bantaeng yang paling diminati pelanggan. Berdasarkan hasil analisis diperoleh bahwa model dengan error klasifikasi terkecil adalah dengan menggunakan mtry 2 dan ntree 500. Model yang dihasilkan dievaluasi dengan menggunakan confusion matrix dimana diperoleh bahwa varian minuman kategori coffee based lebih diminati daripada signature coffee dengan nilai akurasi sebesar 94,12%.
Hybrid Hierarchical Clustering dalam Pengelompokan Daerah Rawan Bencana Tanah Longsor di Sulawesi Selatan Fithriyah Azzahrah; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.161 KB) | DOI: 10.35580/variansiunm38

Abstract

This study aims to describe and classify areas prone to landslides in South Sulawesi. The method used is Hybrid Hierarchical Clustering. The data used is landslide disaster data sourced from the National Disaster Management Agency (BNPB) for 2018-2020 in South Sulawesi. The variables used are the number of landslides, deaths, damaged houses, injured victims, and damaged public facilities. Grouping using the Hybrid Hierarchical Clustering method with mutual clusters using bottom-up and top-down methods. Grouping with bottom-up method produces 2 groups, top-down method produces 2 groups and 1 best mutual cluster. The ratio results in the bottom-up method is 0.84, the top-down method is 1.07 and the mutual cluster is 0.84. The grouping results obtained were 2 groups.
Analisis Sensitivitas Dalam Metode Analytic Hierarchy Process dan Pengaruhnya Terhadap Urutan Prioritas Pada Pemilihan Smartphone Android Yakoba Yusina Muanley; Aloisius Loka Son; Grandianus Seda Mada; Nugraha K. F. Dethan
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm32

Abstract

Smartphones nowadays have become a basic need for everyone because they provide many benefits and conveniences for users. People always want to have a smartphone with good quality. However, due to the lack of information along with the many types of smartphones in circulation, it often makes it difficult for users to choose a smartphone that suits their needs. To overcome these problems, it is necessary to have a method that can provide recommendations for appropriate decision-making for users. This study aims to apply the Analytic Hierarchy Process (AHP) method and sensitivity analysis in determining the priority order of smartphone selection by comparing one smartphone to another. The criteria for consideration are Facilities, Price, Battery, and RAM with alternative choices in the form of Xiaomi, Oppo, and Vivo brand smartphones. Data collection in this study was carried out by distributing questionnaires to 100 students of the Mathematics Study Program. The data were processed using the AHP method and sensitivity analysis. AHP is used to produce a more consistent ranking order of each alternative, while the sensitivity test is carried out to measure the stability of the calculation results if there is a change in decision-making. From the results of the analysis with AHP, it was found that Xiaomi was the first priority of the respondent's choice, followed by Vivo, and the last priority was Oppo with an inconsistency level of 0.02. Meanwhile, sensitivity testing shows that RAM is the most influential criterion for changing the order of alternative priorities, where Xiaomi remains the first priority, followed by Oppo, and Vivo is the last priority.
Peramalan Jumlah Produksi Kelapa Sawit Provinsi Kalimantan Timur Menggunakan Metode Singular Spectrum Analysis Meiliyani Siringoringo; Sri Wahyuningsih; Ika Purnamasari; Melisa Arumsari
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm46

Abstract

Singular spectrum analysis (SSA) is a nonparametric method that does not rely on assumptions such as stationary nature or residual normality. SSA separates time series data into its components, which are trend, seasonality, and error (noise). This study aimed to obtain forecasting results for the amount of oil palm production in East Kalimantan Province for the period January 2021 to December 2021 using SSA. Based on the results of the data analysis, in the process of forming the forecasting model with in-sample data, the parameter window length (L) was 24, which produced a MAPE value of 0.464%, and while the forecasting model validation process used out-sample data, it produced a MAPE value of 41.172%.

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