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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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PENERAPAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) UNTUK ANALISIS SENTIMEN TERHADAP DATA ULASAN APLIKASI E-COMMERCE LAZADA PADA GOOGLE PLAYSTORE Rais, Zulkifli; Muhammad Kasim Aidid; Asti Dewi Putri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm374

Abstract

Classification is the process of grouping objects based on their characteristics. Various classification methods have been employed, ranging from manual grouping to using technology as an aid in the process. One commonly used classification method is the K-Nearest Neighbor (K-NN) algorithm. K-NN predicts the class of data based on the majority class of its nearest neighbors. The novelty of this research lies in using the K-NN method on the case of Lazada application user sentiment on the Google Playstore. In this study, the review classification used is positive and negative labels. Additionally, three accuracy comparisons between training and testing data were used: 80% : 20%, 70% : 30%, and 60% : 40%. Based on the research results from the classification process of Lazada application user reviews on the Google Playstore, an accuracy of 87.00% was obtained for the training and testing data comparison of 80% : 20%.
KLASIFIKASI CURAH HUJAN DI KOTA MAKASSAR MENGGUNAKAN GRADIENT BOOSTING MACHINE (GBM) Hafid, Hardianti; Rais, Zulkifli; Rezky, Akhmad Rezky Ramadhana T
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm386

Abstract

Rainfall is one of the important parameters in determining the climate of an area. Makassar, as one of the largest cities in Indonesia, has varying rainfall patterns throughout the year. This research aims to classify rainfall in Makassar City using the Gradient Boosting Machine (GBM) method. The secondary data used in this study were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG), with predictor variables including wind speed, humidity, and air temperature, and the target variable being rainfall category, consisting of no rain, very light rain, light rain, moderate rain, heavy rain, and very heavy rain. To address class imbalance in the data, this study uses the Random Undersampling (RUS) technique. The GBM model with optimal hyperparameter configuration (n_estimators, learning_rate, max_depth, subsample, min_samples_leaf, max_features) achieved a classification accuracy rate of 98.46%, precision of 93%, recall of 98%, and F1-score of 95% with a training and testing data split of 80:20. The research results show that the GBM method is able to classify rainfall very well and can be used as a tool to assist in disaster mitigation planning and water resource management in Makassar City. 95% pada proporsi data pelatihan dan pengujian 80:20. Hasil penelitian menunjukkan bahwa metode GBM mampu mengklasifikasikan curah hujan dengan sangat baik dan dapat digunakan sebagai alat bantu dalam perencanaan mitigasi bencana serta pengelolaan sumber daya air di Kota Makassar.
Penerapan Metode Kuadratik untuk Peramalan Banyaknya Penduduk Miskin di Sulawesi Selatan Tahun 2008-2025 Mangiri, Nalto Batty; Aidid, Muhammad Kasim; Ikhwana, Nur
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm436

Abstract

Masalah kemiskinan adalah masalah yang kompleks dan bersifat multidimensional atau saling berkaitan antara berbagai aspek diantaranya yaitu aspek sosial, ekonomi, dan budaya, serta aspek lainnya. Banyaknya penduduk miskin di Indonesia adalah 23,85 juta pada Maret 2025. Provinsi Sulawesi Selatan pada Maret 2025, terdapat kurang lebih 698,13 ribu penduduk miskin. Sebagai langkah pencegahan meningkatnya angka kemiskinan perlu dilakukan peramalan banyaknya penduduk miskin sehingga pemerintah dapat melakukan perencanaan kebijakan. Data yang digunakan pada penelitian ini adalah data tahun 2008-2025 yang bersumber dari Badan Pusat Statistik Provinsi Sulawesi Selatan. Penelitian ini menggunakan Analisis Trend Nonlinear khususnya Metode Kuadratik untuk melakukan peramalan banyaknya penduduk miskin. Metode ini cocok digunakan untuk data 10 periode atau lebih. Metode Kuadratik memiliki nilai R-Square sebesar 80,24% dan MAPE sebesar 3,28%. Hasil Peramalan selama 6 tahun menunjukkan banyaknya penduduk miskin di Provinsi Sulawesi Selatan mengalami peningkatan.
Penerapan Analisis Regresi Spatial Durbin Model Terhadap Penyakit Tuberkulosis Di Provinsi Sulawesi Selatan Tahun 2022 Hadi, Muhammad Akhyar; Aswi; Mar'ah, Zakiyah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm459

Abstract

By supplying geographical effects at several sites that serve as the centre of observation, the spatial regression analysis approach assesses the connection between a single variable and multiple other variables. The Spatial Durbin Model is one technique utilised in spatial regression analysis. A special instance of the spatial autoregressive model (SAR) is the spatial Durbin model, which incorporates a spatial lag into the model by adding a lag influence to the independent variables. The goal of this study is to develop a Spatial Durbin model and identify the variables that significantly affect tuberculosis (TBC) in the province of South Sulawesi. The results of this research obtained a Spatial Durbin Model regression model which was significant at a significant level of P-value <α=0.1) using variable influencing factors with a determination coefficient (R2) of 49.74%. Elements that possess a noteworthy impact on the number of Tuberculosis (TB) diseases in South Sulawesi Province are per capita income.
PENERAPAN METODE HYBRID SSA-ARIMA PADA PERAMALAN INDEKS HARGA KEBUTUHAN PERTANIAN YANG DIBAYAR PETANI DI PROVINSI SULAWESI SELATAN: indonesia Fahmuddin S, Muhammad; Ruliana; Muh. Imam Shadiq
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm317

Abstract

This study aims to determine the results and accuracy of forecasting the farmer's price index (IHDP) in South Sulawesi Province using Hybrid SSA-ARIMA. Hybrid SSA-ARIMA is a combination of two good time series methods to improve forecasting accuracy, especially for IHDP data that contains trend and seasonal elements. The data used is the South Sulawesi IHDP data from January 2019 to June 2024 which is sourced from the official website of the Central Statistics Agency. The results of the IHDP forecast in South Sulawesi for the next 12 months from July 2023 to June 2024 tend to increase with the largest increase in September 2024 of 1.184 with a forecast accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.59%. This shows that Hybrid SSA-ARIMA has very good forecasting capabilities
Analisis Sentimen Ulasan Game Simulator Indonesia di Google Play Store Menggunakan Algoritma Naive Bayes Meliyana R, sitti Masyitah; Sudarmin; Sabrina Effendy, Yuni
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm336

Abstract

Sentiment analysis is the process of text data to understand the opinion contained in a sentence. The commonly used algorithm in this analysis is the Naïve Bayes Classifier. Naive Bayes Classifier (NBC) is a classification that uses statistical and probabilistic methods to group texts into several categories of sentiments such as positive and negative. The Indonesian simulator game analyzed is Angkot d Game. This algorithm is used to understand users' perceptions of the game and to identify the factors that affect user sentiment. The results show that the Naive Bayes Classifier has a high level of accuracy in classifying the sentiments of simulator game reviews. The findings of this analysis are also expected to provide insights to game developers about user preferences and complaints, allowing them to adjust features or aspects of the game to better meet user needs. This enables game developers to make significant changes to the games they develop, potentially increasing revenue in the Indonesian gaming industry and focusing more on games created by local developers. Keywords: Simulator Game, Sentiment analysis, Naive Bayes Classifier.
Penerapan Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Standar Pencemar Udara (ISPU) di Kota Makassar Sudarmin; Ruliana; Sasa Arisa, Sasa Arisa
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm400

Abstract

Air quality is a comprehensive indicator that reflects air pollution. The air quality index is defined as a description or value of the transformation of individual parameters of interrelated air pollution, such as PM10, SO2, CO, O3, NO2 into one value or a set of values ​​so that it is easy to understand for the general public. Therefore, predictions of the Air Pollutant Standard Index in the future are needed for future policy decision making. SVR is a development of Support Vector Machine (SVM) for regression cases. The aim of this study is to predict the Air Pollutant Standard Index (ISPU) in the future using the SVR method. In the SVR method, the best kernel is used as an aid in solving non-linear problems, the Min-Max Normalization method for data normalization, division of training and testing data, selection of the best model with Grid Search Optimization. The best prediction results were obtained using a radial kernel with values with parameters ​​ε = 0.1, C = 10, and γ = 1 with the smallest error of 0.0086, with an RMSE of 0.0894. The RMSE value indicates that the model's ability to follow data patterns well. From the model on the radial kernel, the predicted results of the Air Pollution Standard Index from January 1, 2025 to July 31, 2025 were obtained which were not constant or fluctuating with an interval range of 41,14 – 58,69 and based on the Air Pollution Standard Index category, it was in the fairly good category index.
APPLICATION OF TIME SERIES REGRESSION (TSR) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) IN RICE PRODUCTION FORECASTING IN INDONESIA Fahmuddin S, Muhammad; Ruliana; Fahmi, Nurul
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm412

Abstract

Rice production plays a crucial role in supporting food security in Indonesia. The annual fluctuations in rice yield necessitate accurate forecasting methods to support agricultural planning. This study aims to forecast rice production in Indonesia using two time series forecasting approaches: Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). The data used consist of monthly rice production from January 2020 to December 2024. The analysis results show that both methods are capable of modeling the data well, with high forecasting accuracy based on the Mean Absolute Percentage Error (MAPE). The TSR model yielded a MAPE of 13.838%, while the ARIMA(2,1,0)(0,1,0)12model achieved a lower MAPE of 13.1439%, indicating that the ARIMA model provides more accurate forecasting results. This study is expected to serve as a reference for policy-making and strategic planning in rice production management in the future.
Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.

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