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Jihadil Qudsi
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ijasds@unram.ac.id
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Kota mataram,
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INDONESIA
IJASDS: Indonesian Journal of Applied Statistics and Data Science
Published by Universitas Mataram
ISSN : -     EISSN : 30898382     DOI : -
Core Subject : Economy, Health, Education, Social,
Economics, Econometrics & Finance Environmental Science Mathematics Public Health Social Sciences
Indonesian Journal of Applied Statistics and Data Science (IJASDS) merupakan jurnal yang diterbitkan oleh Program Studi Statistika Fakultas MIPA Univeritas Mataram, Nusa Tenggara Barat, Indonesia. IJASDS menerima makalah hasil riset di semua bidang Statistika Murni, Metodologi Statistik, Statistik Terapan, Data Science, dan Statistik Komputasi. Jurnal ini juga menerima makalah tentang survey literatur yang menstimulasi riset di bidang-bidang tersebut di atas.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 25 Documents
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Determinasi Faktor Psikososial terhadap Kesehatan Mental Mahasiswa Matematika Universitas Mataram Selama Penyusunan Skripsi Sahadatin, Aulia; Anggara, Rosa; Baskara, Zulhan Widya; Putri, Dina Eka
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.6831

Abstract

This study examines the relationship between undergraduate thesis preparation and the mental health of Mathematics students at Universitas Mataram, focusing on key factors that influence the thesis process. These factors include motivation to graduate on time, peer support/social environment, availability of learning resources, and supervisor support. The findings indicate that motivation to graduate on time (x1) has the strongest positive impact on mental health. Additionally, supervisor support (x4) and the availability of learning resources (x3)  positively impact students' mental health, while social support  (x2) was found to have a negative effect. This study highlights the importance of motivation and academic support in promoting the mental well-being of students during the undergraduate thesis process.
Perbandingan Regresi Ridge dan Partial Least Square Dalam Mengatasi Multikolinearitas Pada Faktor-faktor Yang Mempengaruhi Kemiskinan di Nusa Tenggara Barat Sari, Baiq Desi Nurma; Harsyiah, Lisa; Baskara, Zulhan Widya
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8051

Abstract

Poverty is one of the most serious problems and must be addressed immediately. One of the steps to overcome poverty is to identify the factors that influence it. One of the statistical techniques used to examine the relationship between predictor variables and the response variable is regression analysis. An important assumption that must be met in regression analysis is the absence of multicollinearity. Multicollinearity refers to a condition where two or more predictor variables are highly correlated, which can reduce the accuracy of the regression model. Therefore, addressing multicollinearity is essential to obtain a reliable and valid model. Inthis study, two methods were employed Ridge regression and Partial Least Square (PLS) with the aim of overcoming the multicollinearity problem. The  R2adj value was used as a comparison criterion to evaluate model performance. Both methods were applied to poverty-related data that exhibited signs of multicollinearity. The R2adj value obtained from the ridge regression model was  68.57%, while the PLS model yielded a higher  R2adj value of 75.1% . Based on this comparison, it can be concluded that the PLS model produced more optimal results than ridge regression in addressing multicollinearity in the context of modeling factors that influence poverty levels in West Nusa Tenggara Province.
Peramalan Jumlah Kasus Demam Berdarah Dengue di Pulau Lombok Menggunakan Model Space Time Autoregressive (STAR) Haryati, Haryati; Bahri, Syamsul; Purnamasari, Nur Asmita; Jurniati, Jurniati
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8170

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease with potential to cause outbreaks. It progresses and often proves fatal, with a high mortality rate frequently attributed to delayed treatment. According to data from the West Nusa Tenggara (NTB) Provincial Health Office, the incidence of DHF in the region has shown a consistent upward trend year over year, necessitating increased vigilance and preventative measures. This study aims to develop an accurate forecasting model to predict the number of DHF cases. The resulting model is intended to serve as tool for the community and policymakers to anticipate the spread of the disease, particularly on Lombok Island. The analytical method employed is the Space-Time Autoregressive (STAR) model, a time-series technique that incorporates interdependencies across both location (space) and time. The data analyzed consists of monthly DHF case counts on Lombok Island from January 2018 to December 2-22. The research results indicate that the best-perfoming model is STAR (3, 1). The forecasting accuracy of this optimal model, measured by the Mean Absolute Scaled Error (MASE), for Central Lombok and North Lombok Regencies was 0.87 and 0.59, respectively. These MASE values, being less than 1, indicate that the forecasting performance of the STAR model is superior to that of a simple naïve baseline model.
Meramal Produksi Padi Nasional: Pendekatan Moving Average dan Triple Exponential Smoothing Aisya, Hakiki Latifa; Apriliana, Baiq Nurul; Andriani, Helmina
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8494

Abstract

National rice production is a crucial indicator for maintaining food security in Indonesia. Seasonal fluctuations and annual trends in rice production require accurate forecasting methods to support strategic decision-making. This study aims to compare the forecasting accuracy of national rice production using the Moving Average and Triple Exponential Smoothing methods. Monthly rice production data from the 2020–2024 period were used as the basis of analysis. The forecasting results show that the Moving Average method tends to respond slowly to changes in actual production values, while the Triple Exponential Smoothing method is more responsive in capturing seasonal patterns and trends. Accuracy measurements indicate that Moving Average produced MAPE of 41.39%, MAD of 1,828,830 tons, and MSE of 6.24 , while the Triple Exponential Smoothing method provided better results with MAPE of 18.05%, MAD of 814,216 tons, and MSE of 1.13 . Based on these findings, the Triple Exponential Smoothing method is recommended as a more suitable and effective forecasting technique for national rice production data characterized by seasonal patterns.
Analisis Faktor-Faktor Yang Mempengaruhi Besar Biaya Pengeluaran Belanja Online Mahasiswa Fmipa Universitas Mataram Menggunakan Regresi Tobit Widiawati, Tari Utami; Alfian, Muhammad Rijal; Baskara, Zulhan Widya
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8975

Abstract

Kegiatan konsumsi mahasiswa disamping untuk keperluan kuliah, kegiatan konsumsi juga dilakukan untuk menunjang penampilan dengan membeli barang secara online. Dalam berbelanja online, besar biaya pengeluaran mahasiswa dapat dipengaruhi oleh berbagai fakor. Tujuan dari penelitian ini untuk mengetahui model dan menentukan faktor-faktor yang mempengaruhi besar biaya pengeluaran belanja online mahasiswa menggunakan regresi tobit dimana variabel terikatnya mempunyai pengamatan tidak lengkap atau terdapat data yang hilang. Data yang digunakan merupakan data biaya pengeluaran belanja online mahasiswa FMIPA Universitas Mataram dan menggunakan 8 variabel bebas. Berdasarkan hasil penelitian terdapat 7 variabel bebas yang dinyatakan dapat berpengaruh secara positif dan satu variabel bebas berpengaruh negatif terhadap biaya pengeluaran belanja online mahasiswa FMIPA. Namun dari model yang dihasilkan diperoleh nilai R2 yang sangat kecil yaitu 0,113. Artinya, hanya 11,3% variasi pengeluaran belanja online dapat dijelaskan oleh delapan variabel bebas yang digunakan dalam penelitian ini, sementara 88,7% sisanya dipengaruhi oleh faktor lain di luar model. Hal ini juga dapat dilihat dari sedikitnya variabel yang signifikan secara individu.

Page 3 of 3 | Total Record : 25

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