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Jihadil Qudsi
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ijasds@unram.ac.id
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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 10 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2025): Mei" : 10 Documents clear
Peramalan Jumlah Kedatangan Penumpang Domestik di Bandara APT Pranoto Samarinda Menggunakan Maximal Overlap Discrete Wavelet Transform dengan Model Multiresolution Autoregressive Thifan Octavianto; Meiliyani Siringoringo; Ika Purnamasari
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5796

Abstract

The problem of forecasting domestic passenger arrivals has become increasingly important due to frequent fluctuations and seasonal patterns, as observed at APT Pranoto Airport in Samarinda. Such data requires an approach capable of capturing both long-term trends and rapid changes. This study employs the Maximal Overlap Discrete Wavelet Transform (MODWT), a modified version of the Discrete Wavelet Transform (DWT), which can be applied to data of any size. MODWT decomposes the data into wavelet coefficients and scaling coefficients, which are then used to construct a Multiresolution Autoregressive (MAR) model at each level of Daubechies wavelets. This method is used as a preprocessing step to improve forecasting accuracy. The best model is selected based on the smallest Mean Absolute Percentage Error (MAPE). The analysis results show that the best forecasting model is the one using Daubechies 6 wavelets, with an in-sample MAPE of 13.758% and an out-of-sample MAPE of 9.525%. The forecast of domestic passenger arrivals at APT Pranoto Airport for the period from October 2024 to December 2024 follows a trending pattern.
Regresi Komponen Utama dalam Mengatasi Multikolinieritas pada Faktor-Faktor yang Mempengaruhi Inflasi di Indonesia Salsabila Hadi Putri Ningrum; Khairatun Hisan; Triana Putri Ramdhani; Luzianawati Luzianawati; M. Daffa Rizki Zindawi; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5827

Abstract

Inflation is a significant concern for a developing country like Indonesia. To effectively anticipate inflationary trends, it is essential to conduct statistical analysis to determine what factors can influence inflation. This study utilized Principal Component Regression (PCR) to address multicollinearity in the regression model linking inflation to various factors. The results revealed that transportation, food, electricity and household fuel factors positively correlate with inflation, while health, education and clothing show negative correlations. However, the resulting regression model proved to be inadequate, as evidenced by a very low R-square value. This highlights the necessity for further refinement of the model to provide better information in the context of inflation management in Indonesia.
Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network Putri Aulia Rahmah; Memi Nor Hayati; Ariyanti Cahyaningsih
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5855

Abstract

The farmer exchange rate (NTP) is a significant indicator for measuring the purchasing power of Indonesian farmers, who are the main actors in the agricultural sector. This is because the agricultural sector is one of the main sectors in Indonesia, one of which is in East Kalimantan Province. This study aims to predict and forecast the NTP of East Kalimantan Province using the Neural Network (NN) method with the backpropagation algorithm. The data used is the NTP data of East Kalimantan Province for the period January 2020 to September 2024 obtained from the BPS of East Kalimantan Province. This study tested 5 NN architecture models with different numbers of layers in the hidden layer, namely 1, 2, 3, 4, and 5 layers in the hidden layer. The study was conducted using 1 input variable, a learning rate of 0.01, a maximum of 10,000 iterations, and a threshold of 0.5. Based on the training process that has been carried out, it was concluded that the best NN architecture that can be used to forecast the NTP of East Kalimantan Province is NN with 5 layers in the hidden layer with a MAPE of 2.087%.
Analisis Tren Sosial di Indonesia dengan Peta Kendali CUSUM (Studi Kasus: Perceraian, Kemiskinan, Pernikahan Dini, dan Tingkat Pendidikan) Navisah Navisah; Mawaddatul Fariha; Ketrin Jupina Ranti; Lita Astuti; Suwindah Puji Yarti; Lisa Harsyiah; Jihadil Qudsi
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.6909

Abstract

Social changes in Indonesia, in the last ten years, have attracted the attention of researchers, especially related to the problems of divorce, early marriage, education levels, and poverty. For example, early marriage is still a major problem in some places. BPS, in 2022, reported that the rate of early marriage in Indonesia was very high, from 16.23% in 2022 to 17.32% in 2023. Several studies have shown a correlation between poverty levels, education levels, and early marriage rates. One effective statistical approach to monitoring changes in trends in time data is the Cumulative Sum Control Chart (CUSUM). The CUSUM control chart method, social data trends can be analyzed longitudinally, detecting significant changes, and mapping the time and magnitude of the shifts that occur. A total of 36 data from 4 variables in the 2022-2024 range were processed using the R application to obtain the CUSUM control chart. The results obtained showed that the variables of education level and early marriage showed more data that was within the limits of the CUSUM constraint map, while the variables of divorce rate and poverty rate had a lot of data that was out of control, which occurred a lot in the months of 2023.
Faktor-Faktor yang Memengaruhi Minat Belanja Mahasiswa Kota Mataram pada Live Produk di Tiktok dan Shopee Zulhan Widya Baskara; Syifa Salsabila Satya Graha; Nisa Ul Istiqomah; Ika Wulandari; Ismi Asmawati; Zulhan Widya Baskara; Dina Eka Putri
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.6913

Abstract

The development of live shopping on the Shopee and TikTok platforms has changed consumer shopping behavior, including students in Mataram City. This study has two main objectives. The first objective is to identify eight independent variables that influence college students' shopping interest when Live shopping on the two platforms, which are analyzed using multiple linear regression. The second objective was to examine the relationship between shopping decisions and shopping interest using correlation analysis, which focused specifically on these two variables due to their significant relationship in the context of consumer action. Data was collected through a questionnaire that was tested for validity and reliability, with a Cronbach's Alpha value of 0.95 which indicates a high level of consistency. The results of the classical assumption test show that the model meets the assumption of multicollinearity, but does not meet the assumptions of normality and homogeneity. Multiple linear regression shows an R value of 0.75, which indicates a strong relationship between the independent variables and the shopping interest of respondents. Substantial factors that influence shopping interest include interaction and engagement, product quality and variety, and shopping satisfaction when Live. Meanwhile, price, influencer participation, time constraints, gender, and platform did not show a substantial influence.
Peramalan Jumlah Kedatangan Penumpang Domestik di Bandara APT Pranoto Samarinda Menggunakan Maximal Overlap Discrete Wavelet Transform dengan Model Multiresolution Autoregressive Octavianto, Thifan; Siringoringo, Meiliyani; Purnamasari, Ika
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5796

Abstract

The problem of forecasting domestic passenger arrivals has become increasingly important due to frequent fluctuations and seasonal patterns, as observed at APT Pranoto Airport in Samarinda. Such data requires an approach capable of capturing both long-term trends and rapid changes. This study employs the Maximal Overlap Discrete Wavelet Transform (MODWT), a modified version of the Discrete Wavelet Transform (DWT), which can be applied to data of any size. MODWT decomposes the data into wavelet coefficients and scaling coefficients, which are then used to construct a Multiresolution Autoregressive (MAR) model at each level of Daubechies wavelets. This method is used as a preprocessing step to improve forecasting accuracy. The best model is selected based on the smallest Mean Absolute Percentage Error (MAPE). The analysis results show that the best forecasting model is the one using Daubechies 6 wavelets, with an in-sample MAPE of 13.758% and an out-of-sample MAPE of 9.525%. The forecast of domestic passenger arrivals at APT Pranoto Airport for the period from October 2024 to December 2024 follows a trending pattern.
Regresi Komponen Utama dalam Mengatasi Multikolinieritas pada Faktor-Faktor yang Mempengaruhi Inflasi di Indonesia Ningrum, Salsabila Hadi Putri; Hisan, Khairatun; Ramdhani, Triana Putri; Luzianawati, Luzianawati; Zindawi, M. Daffa Rizki; Harsyiah, Lisa
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5827

Abstract

Inflation is a significant concern for a developing country like Indonesia. To effectively anticipate inflationary trends, it is essential to conduct statistical analysis to determine what factors can influence inflation. This study utilized Principal Component Regression (PCR) to address multicollinearity in the regression model linking inflation to various factors. The results revealed that transportation, food, electricity and household fuel factors positively correlate with inflation, while health, education and clothing show negative correlations. However, the resulting regression model proved to be inadequate, as evidenced by a very low R-square value. This highlights the necessity for further refinement of the model to provide better information in the context of inflation management in Indonesia.
Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network Rahmah, Putri Aulia; Hayati, Memi Nor; Cahyaningsih, Ariyanti
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5855

Abstract

The farmer exchange rate (NTP) is a significant indicator for measuring the purchasing power of Indonesian farmers, who are the main actors in the agricultural sector. This is because the agricultural sector is one of the main sectors in Indonesia, one of which is in East Kalimantan Province. This study aims to predict and forecast the NTP of East Kalimantan Province using the Neural Network (NN) method with the backpropagation algorithm. The data used is the NTP data of East Kalimantan Province for the period January 2020 to September 2024 obtained from the BPS of East Kalimantan Province. This study tested 5 NN architecture models with different numbers of layers in the hidden layer, namely 1, 2, 3, 4, and 5 layers in the hidden layer. The study was conducted using 1 input variable, a learning rate of 0.01, a maximum of 10,000 iterations, and a threshold of 0.5. Based on the training process that has been carried out, it was concluded that the best NN architecture that can be used to forecast the NTP of East Kalimantan Province is NN with 5 layers in the hidden layer with a MAPE of 2.087%.
Analisis Tren Sosial di Indonesia dengan Peta Kendali CUSUM (Studi Kasus: Perceraian, Kemiskinan, Pernikahan Dini, dan Tingkat Pendidikan) Navisah, Navisah; Fariha, Mawaddatul; Ranti, Ketrin Jupina; Astuti, Lita; Yarti, Suwindah Puji; Harsyiah, Lisa; Qudsi, Jihadil
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.6909

Abstract

Social changes in Indonesia, in the last ten years, have attracted the attention of researchers, especially related to the problems of divorce, early marriage, education levels, and poverty. For example, early marriage is still a major problem in some places. BPS, in 2022, reported that the rate of early marriage in Indonesia was very high, from 16.23% in 2022 to 17.32% in 2023. Several studies have shown a correlation between poverty levels, education levels, and early marriage rates. One effective statistical approach to monitoring changes in trends in time data is the Cumulative Sum Control Chart (CUSUM). The CUSUM control chart method, social data trends can be analyzed longitudinally, detecting significant changes, and mapping the time and magnitude of the shifts that occur. A total of 36 data from 4 variables in the 2022-2024 range were processed using the R application to obtain the CUSUM control chart. The results obtained showed that the variables of education level and early marriage showed more data that was within the limits of the CUSUM constraint map, while the variables of divorce rate and poverty rate had a lot of data that was out of control, which occurred a lot in the months of 2023.
Faktor-Faktor yang Memengaruhi Minat Belanja Mahasiswa Kota Mataram pada Live Produk di Tiktok dan Shopee Zulhan Widya Baskara; Graha, Syifa Salsabila Satya; Istiqomah, Nisa Ul; Wulandari, Ika; Asmawati, Ismi; Baskara, Zulhan Widya; Putri, Dina Eka
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.6913

Abstract

The development of live shopping on the Shopee and TikTok platforms has changed consumer shopping behavior, including students in Mataram City. This study has two main objectives. The first objective is to identify eight independent variables that influence college students' shopping interest when Live shopping on the two platforms, which are analyzed using multiple linear regression. The second objective was to examine the relationship between shopping decisions and shopping interest using correlation analysis, which focused specifically on these two variables due to their significant relationship in the context of consumer action. Data was collected through a questionnaire that was tested for validity and reliability, with a Cronbach's Alpha value of 0.95 which indicates a high level of consistency. The results of the classical assumption test show that the model meets the assumption of multicollinearity, but does not meet the assumptions of normality and homogeneity. Multiple linear regression shows an R value of 0.75, which indicates a strong relationship between the independent variables and the shopping interest of respondents. Substantial factors that influence shopping interest include interaction and engagement, product quality and variety, and shopping satisfaction when Live. Meanwhile, price, influencer participation, time constraints, gender, and platform did not show a substantial influence.

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