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Jihadil Qudsi
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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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Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means Sabna Zulfaa Sabina; Dzaki Ade Alfarez; Syifa Salsabila Satya Graha; Muhammad Yuzaul Auladi; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5415

Abstract

Poverty is a very serious problem for countries in the world, especially for developing countries like Indonesia. Poverty will have a big impact if it occurs in the long term with different factors. One province that is still in the spotlight for high levels of poverty is West Nusa Tenggara Province. Even though the number of poor people in West Nusa Tenggara Province has decreased, conditions of the ground show that there are still many people whose lives are far from decent. Therefore, the government must immediately find a solution to overcome the problem of poverty. To overcome cases of poverty in a region, we can group the characteristics of these regions based on poverty indicators into several clusters. Grouping in this case is carried out with data that will be analyzed using the K-Means cluster analysis method. So the results obtained by analysis using the K-Means cluster method for grouping 10 regencies/cities in West Nusa Tenggara Province based on poverty in 2022 formed 3 clusters, namely cluster 1 consisting of West Sumbawa Regency, Bima City and Mataram City, cluster 2 consisting of Bima Regency, Dompu Regency, West Lombok Regency, Central Lombok Regency, East Lombok Regency, and Sumbawa Regency, and cluster 3 consists of North Lombok Regency. Apart from that, the characteristics of each cluster were also obtained, namely cluster 1 containing the districts/cities with the highest PPM values. While RLS, AHH, and TPT have very high numbers in 2022, cluster 2 contains districts/cities that have quite low PPM, RLS, AHH, and TPT numbers in 2022, and cluster 3 contains a group of districts/cities with RLS, AHH, and TPT has quite low numbers compared to the high PPM in 2022.
Analisis Cluster Untuk Pengelompokan Provinsi Di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Metode Average Linkage Dede Saputra; Azrianti Ardania; Syaftirridho Putri; Adis Tia Juli Agil Asri; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5446

Abstract

Poverty is a major economic and social issue in Indonesia because it is a serious problem that can affect social welfare. Poverty is influenced by many factors including school enrollment rate, life expectancy, gross regional domestic product, human development index and open unemployment rate. Cluster analysis is a technique in multivariate statistics where objects are grouped based on proximity or similarity of properties so that objects that have close proximity (similar properties) will be in the same group (cluster). The purpose of this study is to cluster provinces in Indonesia based on poverty levels using the average linkage method. The results of this study obtained 5 clusters, where cluster 1 consists of Nanggroe Aceh Darussalam, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Central Java, East Java, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, North Kalimantan, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku and West Papua. Cluster 2 consists of Riau Islands, West Java, Banten and North Sulawesi. Cluster 3 consists of DKI Jakarta and East Kalimantan. Cluster 4 consists of DI Yogyakarta and the last cluster consists of Papua.
Perbandingan Regresi Nonparametrik Kernel dan Spline pada Pemodelan Hubungan antara Rata-Rata Lama Sekolah dan Pengeluaran per Kapita di Indonesia Zulhan Widya Baskara; Muhammad Syahrul; Humami Syifa Amanda; Indi Rizqy Fahrani; Yasmin Yasmin; Nur Asmita Purnamasari; Zulhan Widya Baskara
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5725

Abstract

Poverty remains a major issue in developing countries, including Indonesia. In 2021, Indonesia’s poverty rate reached 10.14%, or approximately 27.5 million people (BPS). Poverty alleviation is a primary goal within the Sustainable Development Goals (SDGs). Two important indicators for measuring poverty are per capita expenditure and average years of schooling, which can aid in formulating policies to reduce poverty. This study analyzes the relationship between average years of schooling and per capita expenditure in 2023 using nonparametric regression methods, specifically kernel and spline regression. The kernel regression analysis yielded an optimal bandwidth of 0.860 and a minimum GCV of 0.574. However, the truncated spline method, with one optimal knot, a minimum GCV of 0.5263514 at the 3rd order, and the smallest MSE of 0.4097892, proved to be more accurate in describing the relationship between the two variables. The study concludes that the truncated spline method is superior in modeling the relationship between per capita expenditure and average years of schooling, providing valuable insights for policy formulation aimed at poverty alleviation in Indonesia.
Pengaruh Kepariwisataan Terhadap Inflasi di Kota Mataram Baiq Wira Hartati; Lilik Hidayati; Valian Yoga Pudya Ardhana
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5783

Abstract

Inflation is part of macroeconomic implementation and is an important variable in developing a fiscal approach. Inflation is the rate of change in cost levels at a certain time compared to cost levels in the previous period. Inflation experiences fluctuations in inflation levels, which can affect people's daily lives. This research aims to determine the level of inflation in the tourism sector in Mataram City for the 2022-2024 period based on the number of guests staying at star hotels. The tourism sector is one of the important pillars of the Indonesian economy, especially in areas that have large tourism potential, namely West Nusa Tenggara (NTB). The data used in this research is secondary data obtained from the Central Statistics Agency (BPS) data on tourism and inflation for the City of Mataram. The analytical method used in this research is simple regression analysis, which is a parametric statistical method to determine the level of relationship between two variables. The results of the analysis show that inflation and tourism have a strong relationship of 0.871 based on the significance level.
Perbandingan Peramalan Jumlah Produksi Air Bersih PT. Air Minum Giri Menang dengan Metode Double Exponential Smoothing dari Holt dan Brown menggunakan Optimasi Algoritma Kuadratik Zulhan Widya Baskara; Era Pazira; Zulhan Widya Baskara; Qurratul Aini
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5793

Abstract

Regional Water Supply Companies (PDAM) play a crucial role in ensuring the availability of clean and consumable water. This study aims to compare the Double Exponential Smoothing (DES) methods—Brown’s one-parameter and Holt’s two-parameter—for forecasting the clean water production of PT. Air Minum Giri Menang (Perseroda), emphasizing parameter optimization using a quadratic algorithm. The algorithm efficiently determines the optimal smoothing parameters to minimize forecasting errors measured by the Mean Absolute Percentage Error (MAPE). The results indicate that Brown’s DES method, with a MAPE of 3.29%, outperforms Holt’s DES method, which has a MAPE of 3.96%. While both methods are highly accurate for forecasting (MAPE ≤ 10%), the quadratic algorithm optimization makes Brown’s DES method the preferred choice for planning clean water production for the January–June 2023 period.
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.

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