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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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Pengaruh Game Online Terhadap Gangguan Depresif Pada Mahasiswa Esco Surabaya Arief Ibrahim, Caraka; Hapsery, Alfisyahrina
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/variansiunm353

Abstract

Online games are one of the most popular games nowadays. The negative impacts that may arise from playing online games are mental health disorders such as higher emotional distress, anxiety, depression, and antisocial behavior. The results of the SEM-PLS analysis show that the research model for activity factors in playing online games that influence depressive disorders in students in Surabaya has an R2 value of 56.7%, which is a good enough model to explain the influence of existing variables. Hypothesis testing shows that the latent variables of the influence of achievement and the influence of activity significantly influence the increase in the level of depressive symptoms of students in Surabaya with parameter values of 0.335 and 0.561, which proves that depressive symptoms can be influenced by activity factors in playing online games
Pengaruh Rezim Politik dan PDB per Kapita terhadap Indeks Persepsi Korupsi di Negara Asia Fauzan Adzim, Muhammad
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/variansiunm354

Abstract

Korupsi merupakan isu krusial yang memengaruhi perkembangan ekonomi dan sosial di kawasan ini, sering kali terkait dengan budaya suap dan nepotisme. Untuk mengeksplorasi hubungan ini, penelitian menggunakan Indeks Persepsi Korupsi (IPK) sebagai indikator utama tingkat korupsi dan Produk Domestik Bruto (PDB) per kapita sebagai ukuran kondisi ekonomi. Penelitian ini bertujuan untuk menganalisis dampak kebebasan rezim politik dan kondisi ekonomi nasional terhadap tingkat korupsi di negara-negara Asia pada tahun 2022. Metodologi yang diterapkan adalah regresi linear berganda yang melibatkan variabel dummy, dengan model regresi berbentuk Y = 24,2831 + 0,0005X + 7,785 + 16,3432 + ε. Dalam model ini, Y mewakili IPK, X adalah PDB per kapita, menunjukkan rezim politik sebagian bebas, dan menunjukkan rezim politik bebas, sementara ε adalah galat. Hasil analisis menunjukkan bahwa negara-negara dengan rezim politik yang lebih bebas cenderung memiliki tingkat korupsi yang lebih rendah, sedangkan negara dengan PDB per kapita yang lebih tinggi cenderung memiliki tingkat korupsi yang lebih tinggi. Setiap peningkatan satu dolar internasional dalam PDB per kapita berhubungan dengan peningkatan proporsional dalam IPK, dengan mempertimbangkan variabel rezim politik. Penelitian ini memberikan wawasan penting untuk pengembangan kebijakan antikorupsi yang lebih efektif di Asia dengan menekankan perlunya memperbaiki tata kelola yang bersih dan transparan. Kontribusi penelitian ini terletak pada identifikasi faktor-faktor yang mempengaruhi tingkat korupsi dan implikasinya untuk strategi pemberantasan korupsi di kawasan tersebut
Pemodelan GWR Menggunakan Fungsi Pembobot Adaptive Box-Car Pada Angka Kesakitan DBD di Pulau Kalimantan Tahun 2023 Candra Dewi, Ni Luh Ayu; Hayati, Memi Nor; Fauziyah, Meirinda
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/variansiunm355

Abstract

Demam Berdarah Dengue (DBD) merupakan penyakit yang disebabkan oleh penyebaran virus dengue yang berkaitan dengan karakteristik suatu wilayah yang berbeda-beda. GWR merupakan pemodelan yang mempertimbangkan adanya aspek lokasi yang berbeda-beda sehingga akan menghasilkan penduga parameter yang bersifat lokal di setiap lokasi pengamatan. Penelitian ini bertujuan untuk mendapatkan model GWR dan faktor-faktor yang berpengaruh signifikan terhadap angka kesakitan DBD di kabupaten/kota di Pulau Kalimantan Tahun 2023. Penaksiran parameter model GWR menggunakan metode Weighted Least Square (WLS) dengan fungsi kernel adaptive box-car sebagai pembobot spasial dan nilai bandwidth optimum ditentukan menggunakan kriteria Cross-Validation (CV). Hasil penelitian mendapatkan nilai koefisien determinasi model GWR sebesar 51,04%, yang nilai koefisien determinasinya lebih besar dibandingkan regresi linier berganda. Hasil estimasi parameter model GWR didapatkan model yang nilai koefisien determinasinya berbeda-beda di setiap lokasi pengamatan. Faktor-faktor yang berpengaruh signifikan adalah ketinggian di atas permukaan laut, ketidaktersediaan fasilitas buang air besar, dan jarak ke Ibu Kota Provinsi
Analisis Support Vector Regression untuk Meramalkan Saham Perusahaan Dss di Indonesia Mahgfirah, Aulya Atika; Hikmah, Hikmah; Rahayu, Putri Indi
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/variansiunm356

Abstract

Forecasting is the process of estimating future events based on past information. In this study, the Support Vector Regression (SVR) method with the grid search time series cross-validation algorithm was used to analyze time series data. SVR is an extension of Support Vector Machine (SVM) for regression. This research aims to obtain the best model for predicting and forecasting the daily stock time series data of DSS company in Indonesia. The study compares four types of kernels—linear, polynomial, RBF, and sigmoid—to determine the best model. Model accuracy evaluation was conducted using RMSE, MSE, MAPE, and R-squared, where the model with the lowest error value was considered the best. The results show that SVR with a linear kernel, parameter C = 100, and epsilon = 0.01 produced an RMSE of 0.0583, MSE of 0.0034, MAPE of 10.53%, and R-squared of 0.99. Based on the MAPE value, this model is considered suitable for forecasting DSS stock, showing a downward trend in predictions
Analisis Perbandingan Potensi Kerugian Saham Apple Inc dan Samsung Electronics Co., Ltd dengan Value at Risk (VaR) Menggunakan Pendekatan Simulasi Monte Carlo Pada Aset Tunggal Hidayat, Rahmat; Awaluddin, Awaluddin; Marua, Rivansyah Usman; Tatali, Adinda Nabila; Faqihsyah, Muhammad
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/variansiunm357

Abstract

Dalam melakukan investasi saham di pasar modal tentu ada yang namanya kerugian yang dialami oleh investor . Kerugian inilah yang disebut sebagai resiko investor. Salah satu analisis yang mengukur risiko investor adalah Value at Risk.(VaR). Penelitian ini bertujuan untuk mengetahui seberapa besar risiko investasi pada aset tunggal saham Apple Inc dan Samsung Electronics Co., Ltd. Metode yang digunakan dalam menghitung value at risk adalah metode simulasi Monte Carlo yang membuat simulasi dengan membangkitkan nilai acak. Data yang digunakan dalam penelitian ini adalah data sekunder yaitu data penutupan harian saham Apple Inc. dan Samsung Electronics Co., Ltd dari 1 Januari 2022 – 31 Desember 2023. Berdasarkan hasil penelitian dengan tingkat kepercayaan 99%, 95%, dan 90%, rentang waktu satu hari dan besar dana investasi awal sebesar $100.000.000 diperoleh kemungkinan kerugian pada saham Apple Inc beturut-turut sebesar $ 4.111.565, $ 2.886.832, dan $ 2.233.242. Kemudian pada saham Samsung Electronic Co., Ltd berturut-turut sebesar $ 3.245.607, $ 2.292.911, dan $ 1.785.032. Hasil penelitian menunjukkan bahwa saham Samsung Electronics Co.,Ltd memilki risiko kerugian terkecil dibandingkan dengan saham Apple Inc
ANALISIS BIBLIOMETRIK TERHADAP PENGGUNAAN ANALISIS VARIANS (ANAVA) Quraisy, Andi; Nursakiah, Nursakiah; Takdirmin; Mahmud, Randy Saputra
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/variansiunm358

Abstract

Analisis varian atau biasa disingkat ANAVA merupakan salah satu analisis yang biasa digunakan untuk menganalisis ragam antar beberapa populasi. Penelitian ini merupakan penelitian yang membahas tentang analisis bibliometrik dengan menggunakan kata kunci ANAVA sebagai sumber dari data analisis. Adapun tujuan penelitian ini adalah untuk mengidentifikasi penelitian atau publikasi yang berkaitan dengan analisis varian atau ANAVA dan mengkaji karakteristik dari publikasi tersebut. Dari hasil analisis diperioleh bahwa hasil analisis berdasarkan software diperoleh 1261 istilah dan 17 istilah paling mendekati analisis varian atau ANAVA dan dari 17 istilah tersebut dikelompokkan menjadi 4 kluster yang berbeda yang menandakan hubungan dari tiap kluster, dari penggunaan kata kunci bersama terlihat bahwa kata kunci ANAVA merupakan kata kunci yang paling banyak digunakan yang ditandai dengan ukuran lingkaran disetiap kluster. Begitupula dengan dentitas atau kerapatan dari kata kunci ANAVA yang menunjukkan warna paling terang yang berarti semakin terang warna dentitasnya maka semakin banyak penelitian yang telah dilakukan yang menggunakan kata kunci ANAVA.
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana; Mustika M, Sitti Sri
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/variansiunm373

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.
Peramalan Nilai Inflasi di Indonesia Menggunakan Double Exponential Smoothing dan Triple Exponential Smoothing Sulaiman, Hasma; Ekawati, Darma; Yanti, Reski Wahyu
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/variansiunm380

Abstract

Inflation is a situation where there is a tendency for the price of goods to increase in general over a long period of time in a country. Monitoring the value of future inflation is very important to know, because the inflation rate has a significant impact on economic growth. Therefore, accurate and fast forecasting is needed. There are several methods that can be used to forecast, including double exponential smoothing and triple exponential smoothing. Parameters with the double and triple exponential smoothing methods often experience exponential increases or decreases in predicted values. These values ​​are often much larger or smaller than the actual data. To overcome this, a parameter is added that can dampen exponential growth, namely using the damped parameter. The damped parameter value is added to each trend smoothing. This study uses inflation data in Indonesia from 2017-2023. The purpose of this study is to determine the results of forecasting inflation values ​​in Indonesia using double exponential smoothing and triple exponential smoothing with or without damped parameters and to determine the best method. The results of this study are that the double exponential smoothing parameter damped method is very good to use based on a comparison of the smallest MAPE value of all the methods used with a MAPE value of 9.63% and the forecast results for January 2024 of 0.0261
Pemodelan dan Prediksi Pola Musiman Menggunakan Holt-Winters Pangruruk, Thesya Atarezcha; Mangiri, Nalto Batty; Rombeallo, Esra; Nurmayanti, Wiwit Pura
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/variansiunm391

Abstract

Samarinda City, with its tropical climate, experiences significant variations in rainfall throughout the year. This instability has the potential to cause impacts such as flooding, disruptions in the agricultural sector, and damage to infrastructure. This study aims to analyze and forecast the seasonal rainfall patterns in Samarinda City by applying the Holt Winters Exponential Smoothing method based on a multiplicative model. Monthly rainfall data were analyzed to identify stationarity properties in both mean and variance. The results indicate that the data are stationary in the mean but not in the variance, thus justifying the use of the Holt-Winters Multiplicative Exponential Smoothing model. Parameter estimation yielded alpha , beta , and gamma values of 1 each, with a MAPE of 50%, indicating a moderate level of accuracy. Despite the relatively high error rate, the model remains effective in illustrating seasonal patterns, which can be useful for preliminary water resource management planning in the region
Pemodelan Distribusi Spasial Hotspot di Kabupaten Banjar Menggunakan Log-Gaussian Cox Process Prabowo, Sigit Dwi; Susanti, Dewi Sri; Asianingrum, Al Hujjah
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/variansiunm402

Abstract

Forest and land fires are recurring ecological and socio-economic disasters in Banjar Regency, South Kalimantan Province, with complex triggers. A deep understanding of the spatial distribution of fire risk is crucial for effective mitigation efforts. This study aims to model the spatial intensity of hotspots as a proxy for forest and land fires events in Banjar Regency and produce a fire risk surface map. The data used in this study are hotspot data from the siPongi website in Banjar Regency for the period 2013–2024, along with elevation data analyzed using the Log-Gaussian Cox Process (LGCP) spatial statistical model. The analysis results show that elevation has a negative but statistically insignificant effect on hotspot intensity, where fire risk tends to be higher at lower elevations. The LGCP model proved effective in capturing the complex spatial patterns of hotspot occurrences, separating trends driven by covariates and residual spatial clustering. The resulting risk intensity map successfully identified high-risk clusters, particularly concentrated in western districts dominated by peatlands and agricultural activities.

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