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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 69 Documents
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Peramalan Jumlah Kunjungan Wisatawan di Provinsi Nusa Tenggara Barat (NTB) Menggunakan Metode Arima Box-Jenkins Soraya, Siti; Fitriana Aziza, Istin; Juanda, M. Rizky Ujiana; Primajati, Gilang; Rahima, Phyta
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm150

Abstract

The development of tourism in West Nusa Tenggara Province (NTB) is supported by geographical conditions with scattered small islands (gilis), tropical climate, and cultural peculiarities of the Sasak Tribe, thus becoming an attraction in the development of global tourist destinations. Tourism development in NTB Province will be more attractive with the establishment of the Mandalika National Tourism Development Strategic Area (KSPPN). NTB Province can maximize its role with the momentum of this strategic policy through the development of new growth centers based on the tourism sector, collaborating with other sectors, and packaging the potential of villages to become thematic tourism villages. A method to forecast the number of tourist visits in NTB Province is needed in assisting the government in preparing proper facilities and infrastructure if there is a possible surge in tourist visits. The method used in this study is the ARIMA Box-Jenkins Method to forecast the number of tourist visits in NTB Province. The data used in this study is in the form of secondary data sourced from the Central Bureau of Statistics of NTB Province, namely from January 2020 to December 2022. The results showed that the ARIMA model formed was (1,1,1), this shows that the forecasting of the number of tourist visits in NTB Province meets the assumption of white noise.
GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) IN MODELING THE RISK FACTORS OF PNEUMONIA DISEASE AMONG TODDLERS IN THE CENTRAL SULAWESI PROVINCE Mar'ah, Zakiyah; Rais, Zulkifli; Haris, A. Sulfiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm151

Abstract

This research was conducted to map and model the number of Pneumonia cases in Central Sulawesi Province using the Geographically Weighted Negative Binomial Regression (GWNBR) approach. The data used were Pneumonia case data in Central Sulawesi Province obtained from the Health Publication of Central Sulawesi Province in 2021. The analysis results with the GWNBR method indicated that predictor variables significantly influencing the number of Pneumonia cases in each district/city of Central Sulawesi Province were Exclusive Breastfeeding Percentage (X1), Complete Basic Immunization Percentage (X2), Percentage of Toddlers Receiving Vitamin A (X3), and Percentage of Coverage of Toddler Services (X5). Meanwhile, the variable Low Birth Weight (X4) does not significantly affect the cases.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN DATA JUMLAH KEJADIAN DAN DAMPAK BENCANA BANJIR MENGGUNAKAN METODE FUZZY C-MEANS Hayati, Memi Nor; Goejantoro, Rito; Siringoringo, Meiliyani; Purnamasari , Ika; Yuniarti, Desi; Nida, Khairun; Messakh, Gerald Claudio
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm167

Abstract

Cluster analysis is a technique used to find groups of similar data objects. The Fuzzy C-Means (FCM) method is a data grouping method where the existence of each data in a cluster is determined by the degree of membership. This study aims to determine the optimal number of clusters based on the Modified Partition Coefficient (MPC) validity index and to determine the optimal grouping results of 34 provinces in Indonesia based on data on the number of events and the impact of floods in 2017-2021. The optimal number of clusters using the FCM method is based on MPC value consists of 2 clusters, namely the first cluster consisting of 27 provinces in Indonesia and the second cluster consisting of 7 provinces in Indonesia.
Statistical Downscaling Modeling with Time Lag Components for Forecasting Rainfall in Wet and Dry Seasons Meyliana, Sitti Masyitah; Mar'ah, Zakiyah; Hafid, Hardianti
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm180

Abstract

Climate change in Indonesia often poses a serious threat to the agricultural sector. The impacts can include reduced agricultural productivity. In this context, rainfall variables are frequently used in research related to the impacts of climate change. In this study, precipitation data from the global circulation model (GCM) outputs are used as predictor variables and rainfall data from the Indramayu station are used as response variables in statistical downscaling modeling. The cross-correlation function between these variables plays an important role in statistical downscaling modeling. The cross-correlation function can enhance the correlation between predictor variables and response variables. Therefore, this research aims to compare the rainfall prediction results using initial GCM data (GCM) and GCM data with lag components (lagged GCM) determined based on the cross-correlation function. The methods used in statistical downscaling modeling are partial least squares regression (PLSR) and principal component regression (PCR). The modeling results using data from the period 1993-2020 show that the PLSR model on lagged GCM data is the best compared to other models (PLSR on GCM data, PCR on GCM data, and PCR on lagged GCM data). This model produces the highest coefficient of determination and the smallest RMSE value. Furthermore, the PLSR model on lagged GCM data can predict the 2008 rainfall data, following the actual rainfall pattern with the smallest RMSEP value. In general, modeling using lagged GCM data provides better rainfall prediction results compared to GCM data
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana, Ruliana; Mustika M, Sitti Sri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm193

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method
Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur Bakri, Nurul Aulya; Annas, Suwardi; Aidid, Muhammad Kasim
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm194

Abstract

The Geographically Weighted Regression (GWR) method is a method used to analyze spatial heterogeneity, where the same independent variable gives unequal responses at different locations in a research area. The purpose of this study was to determine the environmental pollution factors that affect GRDP in the agricultural, forestry and fisheries sectors in East Java. The data used in this study are the GRDP of the Agriculture, Forestry and Fisheries sectors in East Java in 2020 along with the environmental pollution factors that are thought to influence it. The results of this study obtained a different model for each district/city. The GWR model shows better results than the multiple linear regression model, as seen from the smallest AIC value and the largest R2
Metode Radial Basis Function Neural Network Untuk Klasifikasi Kab/Kota Tertinggal Di Provinsi Sulawesi Selatan Ruliana, Ruliana; Rais, Zulkifli; Mar'ah, Zakiyah; Hasnita, Hasnita
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm197

Abstract

A disadvantaged area is an area that has the characteristics of tending to be left behind compared to other areas. Radial basis function neural networks are a part of Artificial Neural Networks, which use radial basis activation functions and are commonly used in classification cases. All districts/cities in South Sulawesi province have different characteristics from other districts/cities. Therefore, districts/cities are grouped into 2 groups to identify districts/cities that have characteristics that tend to be the same based on indicators of regional underdevelopment. The grouping results are then used as actual values ​​for classification using the RBFNN method, to determine the classification results and performance of the RBFNN method. In classifying districts/cities in South Sulawesi province based on indicators of regional underdevelopment using the radial basis function neural network method, an accuracy value of 91% was obtained using a comparison of 55% training data and 45% testing data and an f-measure value of 92% was obtained
Transformasi Wavelet Diskrit Daubechies Fungsi Soft Thresholding untuk Prediksi Data Inflasi di Indonesia Mohammad Reyfi Syahnaz Anugrah; Purnamasari, Ika; A’yun, Qonita Qurrota
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 02 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm233

Abstract

Metode prediksi klasik pada umumnya mensyaratkan data harus bersifat stasioner, akan tetapi umumnya terdapat beberapa data runtun waktu yang bersifat nonstasioner. Salah satu metode yang mampu menganalisa data non-stasioner dengan baik adalah transformasi wavelet diskrit (TWD). Estimasi thresholding dilakukan untuk menghapuskan noise pada data, dengan menggunakan fungsi dan parameter threshold yang sangat berpengaruh pada kemulusan hasil estimasi. Penelitian ini bertujuna untuk memprediksi inflasi di Indonesia pada bulan Mei 2012 hingga Desember 2022 menggunakan TWD daubechies soft thresholding dan parameter adaptive, serta mengetahui level resolusi terbaik dari perolehan nilai akurasi prediksi menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa nilai prediksi baik pada level 1 sampai dengan level 5 sangat mendekati pola data aktual, dengan MAPE kurang dari 5%. Nilai MAPE terkecil yaitu 2,81% terdapat pada level resolusi pertama, dengan kategori akurasi prediksi sangat baik.
Rancangan Acak Kelompok (RAK) pada Analisis Pengaruh Pupuk Bokashi Kotoran Ayam terhadap Pertumbuhan dan Produksi Tanaman Bawang Daun -, Syilfi; Hidayat, Rahmat
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 02 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm257

Abstract

This research focused on assessing the impact of chicken manure bokashi on the growth and yield of leeks and determining the optimal dosage for maximizing these outcomes. The study took place from June to August 2023 in Lasalepa Village, Lasalepa District, Muna Regency, using a Randomized Block Design (RBD) with a single factor: chicken manure bokashi fertilizer, tested at five different levels. These levels included a control with no bokashi (B0) and four varying doses: 5 tons/ha (0.6 kg/plot, B1), 10 tons/ha (1.2 kg/plot, B2), 15 tons/ha (1.8 kg/plot, B3), and 20 tons/ha (2.4 kg/plot, B4). Each treatment was repeated three times, resulting in a total of 15 experimental units. The parameters measured included plant height, the number of leaves per clump, the number of tillers per clump, and wet weight per clump. Data were analyzed using analysis of variance (ANOVA), and any significant effects were further explored using the Least Significant Difference (LSD) test at a 95% confidence level. The findings indicated that chicken manure bokashi had a highly significant effect on all measured parameters. The highest yield was observed with the application of 20 tons/ha (2.4 kg/plot) of bokashi, which resulted in an average wet weight of 27.49 grams per clump, equivalent to 3.66 tons/ha.
Implementasi Metode Support Vector Machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store Yolanda, Anne Mudya; Mulya, Ridho Tri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 02 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm258

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja metode Support Vector Machine (SVM) dalam mengklasifikasikan ulasan pengguna aplikasi Sayurbox yang terkenal di Indonesia. Data ulasan diperoleh melalui scraping dari Google Play Store antara tahun 2017 hingga 2023. Ulasan dan rating yang diberikan pengguna digunakan sebagai indikator untuk mengevaluasi kepuasan terhadap layanan yang disediakan. Dalam penelitian ini, metode SVM digunakan untuk memproses data ulasan tersebut. Hasil klasifikasi menunjukkan bahwa metode SVM mencapai akurasi sebesar 89,29%. Selain itu, berdasarkan Confusion Matrix, nilai precision yang diperoleh adalah 91,42%, recall 95,58%, dan f1-score 93,50%. Temuan ini menunjukkan bahwa SVM merupakan metode yang efektif dalam mengklasifikasikan ulasan pengguna, yang dapat memberikan wawasan berharga untuk meningkatkan kualitas pelayanan Sayurbox.

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