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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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Pengendalian Kualitas Statistika Pada Pupuk ZA III di PT Petrokimia Gresik Menggunakan Maximum Half Normal Multivariate Control Chart (Max-Half-Mchart) Salsabila, Nadia; Wibawati
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/variansiunm210

Abstract

Max-Half-Mchart is one of the multivariate control diagrams that can monitor the average and variability of processes simultaneously or simultaneously. This diagram is a development of the Max-Mchat diagram with a half-normal distribution approach. This diagram is able to detect out of control signals well when there is a process shift. The data used in this study is data on the characteristics of ZA III product quality at PT Petrokimia Gresik. The quality control of ZA III Fertilizer at PT Petrokimia Gresik using Max-Half-Mchart shows that there is a shift in variability and average caused by the consistency of ( ) which is different in each production process, resulting in less than optimal products. Meanwhile, the overall production in the time period from October 2022 to September 2023 has been capable.
PERBANDINGAN EFEKTIVITAS DIAGRAM KONTROL DECISION ON BELIEF DAN DIAGRAM KONTROL P PADA PENGENDALIAN KUALITAS PRODUK BATA RINGAN DI PT. BUMI SARANA BETON Asmi, Nia Nurul; Sudarmin, Sudarmin; Rais, Zulkifli
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/variansiunm159

Abstract

Statistical quality control is a useful effort to monitor, control, analyze, manage, and improve products and processes using statistical methods. One of the tools used in statistical quality control is a control chart, which is a graph to show whether the performance of a process can maintain an acceptable level of quality with the aim of monitoring process shifts. P control charts and DOB control charts are diagrams used for attribute data. The DOB control chart is a new method with a Bayesian approach. Therefore, a comparison of the two control charts was carried out to determine which one had a better level of effectiveness in controlling the quality of light brick production at PT. Bumi Sarana Beton. The data used in this research is daily data on the production of light brick defects during May 2023. The results obtained are the production of light bricks at PT. Bumi Sarana Beton has not been statistically controlled using p control chart because there are four points out of control. Meanwhile, by using the DOB control chart, light brick production at PT. Bumi Sarana Beton has been statistically controlled because it did not occur out of control. Hence, the p control chart it can be said to have better effectiveness because it can detect more sensitively at 13.34% of points out of control compared to the DOB control chart on quality control of light brick products at PT. Bumi Sarana Beton
PEMODELAN GENERALIZED POISSON REGRESSION PADA KASUS AIDS DI PROVINSI NUSA TENGGARA TIMUR TAHUN 2023 GUNTUR, Robertus; Dappa, Jelita Susanti Bili
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/variansiunm168

Abstract

East Nusa Tenggara (ENT) Province is one of the contributors to the high number of AIDS cases in Indonesia. The purpose of study is to analyse the factors affecting the number of AIDS cases in the Province using the Generalized Poisson Regression method. The variable used is the number AIDS cases as a response variable, and seven predictor variables that might be influence the response variable. The data obtained from the publications of the Central Statistics Agency of ENT Province in 2024. The study shows that the GPR model applying six predictors yields the best model with value of AIC is 419.93. Those variables are the number of health facilities , the number of poor people , the human development index , sexually transmitted infections , the number of adolescents aged 15-24 years who received reproductive health counselling , and the open unemployment rate
Peramalan Suhu Rata – Rata Kota Padang Panjang dengan Membandingkan Metode SARIMA dan Holt – Winter Additive Putri, Fadhira Vitasha; Ikhsan, Easbi; Fitri, Fadhilah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm237

Abstract

Padang Panjang City, situated at an altitude of 650 to 850 meters above sea level and surrounded by high mountains, experiences significant temperature changes that affect various aspects of life such as public health, agriculture, and tourism. This study aims to forecast the monthly average temperature of Padang Panjang City from January 2017 to December 2023 by comparing SARIMA and Holt-Winters Additive forecasting methods. The results show that the SARIMA method, with an MSD value of 0.2206, is more accurate compared to the Holt-Winters Additive method, which has an MSD value of 0.29821. With the SARIMA model as the best method, the forecast indicates that the highest average temperature in Padang Panjang City will reach 23.1418 degrees Celsius in May 2024. These results are expected to provide a strong basis for planning and decision-making related to the temperature changes occurring in Padang Panjang City.
Algoritma K-Prototype dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020 Rais, Zulkifli; Annas, Suwardi; Muhammad Refaldy
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm20

Abstract

Clustering is something that is used to analyze data both in machine learning, data mining, pattern engineering, image analysis and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that, cluster 1 is a cluster that has characteristics of people's welfare which is better than cluster 2.
Penerapan Extreme Learning Machine (ELM) untuk Meramalkan Laju Inflasi di Indonesia Fahmuddin S, Muhammad; Annas, Suwardi; nurismi, Nur ismi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm92

Abstract

Inflation is generally the tendency for the prices of goods and services to rise continuously. An artificial neural network (ANN) is an information processing model that closely resembles how an organism's memory system works, such as information transmission processes in the brain. Forecasting is the activity of determining future events based on past data. A time series is a set of observations that occur consecutively in the correct amount of time based on a time index. The data used in this study are Indonesian monthly inflation data. Extreme Learning Machine (ELM) is an artificial neural network approach that uses a single hidden layer feedforward neural network architecture (SLFN). The advantages of ELM over traditional learning algorithms are learning speed, improved generalization performance, and simplified implementation. An error value of RMSE of 0.1992215 was obtained based on the analysis performed using the Extreme Learning Machine (ELM) method.
Analisis Pertumbuhan Ekonomi di Nusa Tenggara Barat dengan Regresi Panel Dinamis Nurul Azmia Dwi Shandy; Astuti, Alfira Mulya; Mahfudy, Sofyan
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 01 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm297

Abstract

Keberhasilan perekonomian suatu negara atau wilayah dapat dinilai melalui pertumbuhan ekonomi berdasar pada produk domestik regional bruto (PDRB). Penelitian ini bertujuan untuk memodelkan pertumbuhan ekonomi di NTB dengan regresi panel dinamis. Metode estimasi yang digunakan adalah generalized method of moment (GMM), baik untuk first difference GMM maupun System GMM. Variabel independen yang digunakan adalah indeks pembangunan manusia, jumlah penduduk, jumlah penduduk miskin, pengeluaran perkapita disesuaikan, tingkat partisipasi angkatan kerja, dan tingkat pengangguran terbuka. Data bersumber dari BPS NTB dan berbentuk data panel dengan unit pengamatan terdiri dari 10 kabupaten/kota di provinsi NTB selama periode 2019-2023. Pemilihan model terbaik berdasarkan kriteria ketakbiasan, instrumen valid, dan konsisten. Hasil analisis menunjukkan bahwa system GMM menghasilkan model terbaik untuk memodelkan pertumbuhan ekonomi di Provinsi NTB. PDRB tahun sebelumnya dan tingkat pengangguran terbuka berpengaruh signifikan secara simultan terhadap pertumbuhan ekonomi di provinsi NTB.
PENERAPAN METODE DOUBLE EXPONENTIAL SMOOTHING BROWN UNTUK MERAMALKAN JUMLAH KEMATIAN HIV/AIDS DI INDONESIA Anggraini, Dewi; Suwindi, Akhmad; Azhar, Muhammad; Ma’rifah, Nurul; Khairani, Diva Ghefira Mahardika; Annisa, Selvi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 01 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm299

Abstract

Penelitian ini bertujuan untuk memprediksi jumlah kematian akibat HIV/AIDS di Indonesia menggunakan metode Double Exponential Smoothing Brown. Data yang digunakan merupakan data sekunder dari situs Vizhub, yang mencatat kematian tahunan akibat HIV di Indonesia dari tahun 1990 hingga 2021. Metode pemulusan eksponensial ganda dipilih karena adanya tren peningkatan jumlah kematian dari tahun ke tahun. Nilai parameter α terbaik ditentukan melalui metode trial and error untuk meminimalkan tingkat kesalahan peramalan. Hasil analisis menunjukkan bahwa model Double Exponential Smoothing Brown dengan nilai α=0.9 memberikan peramalan yang akurat. Berdasarkan model ini, diproyeksikan bahwa jumlah kematian akibat HIV di Indonesia akan terus meningkat hingga tahun 2030. Hasil penelitian ini diharapkan dapat menjadi dasar bagi pembuat kebijakan untuk mengambil langkah-langkah pencegahan yang lebih efektif.
Analisis Perbandingan Model Double Exponential Smoothing dan ARIMA untuk Prediksi Harga Beras di Indonesia Nurul Azizah Muzakir; Muh Zarkawi Yahya
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 01 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm349

Abstract

Rice prices in Indonesia tend to increase from year to year and are influenced by various factors, such as domestic production and seasonal factors. Therefore, rice price forecasting is an essential thing to do. This study aims to analyze and compare the performance of two forecasting models, namely Holt’s Double Exponential Smoothing (DES) and Autoregressive Integrated Moving Average (ARIMA), in predicting rice prices in Indonesia. The data used is secondary data of average wholesale rice prices from January 2021 to December 2024. The results show that the optimal parameters of the Holt’s DES model are alpha = 0,9 and beta = 0,1, while the best ARIMA model is ARIMA(2,2,1) . Both models have a high level of accuracy with a Mean Absolute Percentage Error (MAPE) value of less than 1%. However, the ARIMA(2,2,1) model has a lower MAPE value than Holt’s DES model. Hence, it is more accurate in modeling rice prices in Indonesia. The forecasting results show that Holt’s DES model tends to produce higher rice predictions than . This occurs because Holt’s DES model is more sensitive to increasing trends, while ARIMA tends to be more conservative in capturing patterns of price changes. Thus, the selection of a model for rice price forecasting should consider the characteristics of the trend that occurs in the market, whether it is experiencing a continuous increase or has a fluctuating pattern.
Analisis Faktor-Faktor Yang Mempengaruhi Pertumbuhan Produk Domestik Bruto Di Asia Menggunakan Model Regresi Dummy Ivanna Genardi, Angelina; Rahkmawati, Yeni; Muhajirin Farid, Fuad
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 01 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm352

Abstract

This study aims to analyze the factors influencing the Gross Domestic Product (GDP) of 45 Asian countries in 2021. Understanding these factors is crucial for formulating effective development policies and achieving the Sustainable Development Goals (SDGs). Data were sourced from Our World in Data and the World Bank. Dummy regression was employed to examine the effects of the Human Development Index (HDI), population size, and income classification on GDP. The results indicate that HDI and income classification significantly impact GDP, while population size does not. High-income countries have higher GDP compared to others. The regression model achieved an Adjusted R-Square value of 0.9058, explaining 90.58% of the variability in GDP. These findings highlight that enhancing human resource quality is essential for driving economic growth, particularly in low- and middle-income countries in Asia. This study offers valuable insights for policymakers in fostering sustainable development

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