Indonesian Journal of Applied Statistics and Data Science
type: channel
description: 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. Indonesian Journal of Applied Statistics and Data Science terbit dua kali dalam setahun, pada bulan Mei dan November. IJASDS terbit pertama kali (Vol. 1 No. 1) pada November 2024. Alamat redaksi Indonesian Journal of Applied Statistics and Data Science, Program Studi Statistika FMIPA-UNRAM, email: ijasds@unram.ac.id Indonesian Journal of Applied Statistics and Data Science telah melakukan backup dan atau pengarsipan secara elektronik melalui PKP Preservation Network (PKP PN) dan keterlibatan di sistem pengarsipan LOCKSS, ini bisa di lihat di link Publisher Manifest
publisher: Universitas Mataram
language: en
publication name: Indonesian Journal of Applied Statistics and Data Science
issn: 3089-8382
items: An Analysis of the Determinants of Online Shopping Expenditure Levels Among FMIPA Students at the University of Mataram Using the Tobit Regression Model
items: Meramal Produksi Padi Nasional: Pendekatan Moving Average dan Triple Exponential Smoothing
items: Peramalan Jumlah Kasus Demam Berdarah Dengue di Pulau Lombok Menggunakan Model Space Time Autoregressive (STAR)
items: Perbandingan Regresi Ridge dan Partial Least Square Dalam Mengatasi Multikolinearitas Pada Faktor-faktor Yang Mempengaruhi Kemiskinan di Nusa Tenggara Barat
items: Faktor-Faktor yang Memengaruhi Minat Belanja Mahasiswa Kota Mataram pada Live Produk di Tiktok dan Shopee
items: Analisis Tren Sosial di Indonesia dengan Peta Kendali CUSUM (Studi Kasus: Perceraian, Kemiskinan, Pernikahan Dini, dan Tingkat Pendidikan)
items: Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network
items: Determinasi Faktor Psikososial terhadap Kesehatan Mental Mahasiswa Matematika Universitas Mataram Selama Penyusunan Skripsi
items: Forecasting the Number of Domestic Passenger Arrivals at APT Pranoto Samarinda Airport Using Maximal Overlap Discrete Wavelet Transform with Multiresolution Autoregressive Model
items: Regresi Komponen Utama dalam Mengatasi Multikolinieritas pada Faktor-Faktor yang Mempengaruhi Inflasi di Indonesia
items: Perbandingan Peramalan Jumlah Produksi Air Bersih PT. Air Minum Giri Menang dengan Metode Double Exponential Smoothing dari Holt dan Brown menggunakan Optimasi Algoritma Kuadratik
items: Pengaruh Kepariwisataan Terhadap Inflasi di Kota Mataram
items: Perbandingan Regresi Nonparametrik Kernel dan Spline pada Pemodelan Hubungan antara Rata-Rata Lama Sekolah dan Pengeluaran per Kapita di Indonesia
items: Analisis Cluster untuk Pengelompokan Provinsi di Indonesia berdasarkan Tingkat Kemiskinan menggunakan Metode Average Linkage
items: Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means

An Analysis of the Determinants of Online Shopping Expenditure Levels Among FMIPA Students at the University of Mataram Using the Tobit Regression Model
type: item
description: Student consumption activities, aside from meeting academic needs, are also carried out to support personal appearance by purchasing goods online. In online shopping, the amount of expenditure may be influenced by various factors. The purpose of this study is to identify the appropriate model and determine the factors that affect the magnitude of online shopping expenditures among students using Tobit regression, in which the dependent variable contains incomplete or censored observations. The data used consist of online shopping expenditures of FMIPA students at the University of Mataram, involving eight independent variables. Based on the results, seven independent variables were found to have a positive effect, while one variable had a negative effect on students’ online shopping expenditures. However, the resulting model produced a very small R² value of 0.113. This indicates that only 11.3% of the variation in online shopping expenditures can be explained by the eight independent variables used in the study, while the remaining 88.7% is influenced by other factors outside the model. This condition is also reflected in the limited number of variables that are individually significant.
subject: Articles
  topic: section
subject: Online Shopping
  topic: keywords
subject: Student
  topic: keywords
subject: Tobit Regression
  topic: keywords
creator: Tari Utami Widiawati
creator: Muhammad Rijal Alfian
creator: Zulhan Widya Baskara
rights: Copyright (c) 2025 Tari Utami Widiawati, Muhammad Rijal Alfian, Zulhan Widya Baskara https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 104
ending page: 112
doi: 10.29303/ijasds.v2i2.8975

Meramal Produksi Padi Nasional: Pendekatan Moving Average dan Triple Exponential Smoothing
type: item
description: 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 <!-- [if gte msEquation 12]><m:oMath><m:r>×</m:r><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r>10</m:r></m:e><m:sup><m:r>12</m:r></m:sup></m:sSup></m:oMath><![endif]--> , while the Triple Exponential Smoothing method provided better results with MAPE of 18.05%, MAD of 814,216 tons, and MSE of 1.13 <!-- [if gte msEquation 12]><m:oMath><m:r>×</m:r><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r>10</m:r></m:e><m:sup><m:r>12</m:r></m:sup></m:sSup></m:oMath><![endif]--> . 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.
subject: Articles
  topic: section
subject: Forecasting
  topic: keywords
subject: Moving Average
  topic: keywords
subject: Rice Production
  topic: keywords
subject: Triple Exponential Smoothing
  topic: keywords
creator: Hakiki Latifa Aisya
creator: Baiq Nurul Apriliana
creator: Helmina Andriani
rights: Copyright (c) 2025 Hakiki Latifa Aisya, Baiq Nurul Apriliana, Helmina Andriani https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 92
ending page: 103
doi: 10.29303/ijasds.v2i2.8494

Peramalan Jumlah Kasus Demam Berdarah Dengue di Pulau Lombok Menggunakan Model Space Time Autoregressive (STAR)
type: item
description: 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.
subject: Articles
  topic: section
subject: DHF Data
  topic: keywords
subject: Forecasting
  topic: keywords
subject: MASE
  topic: keywords
subject: STAR
  topic: keywords
creator: Haryati Haryati
creator: Syamsul Bahri
creator: Nur Asmita Purnamasari
creator: Jurniati Jurniati
rights: Copyright (c) 2025 Haryati Haryati, Syamsul Bahri, Nur Asmita Purnamasari, Jurniati Jurniati https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 70
ending page: 80
doi: 10.29303/ijasds.v2i2.8170

Perbandingan Regresi Ridge dan Partial Least Square Dalam Mengatasi Multikolinearitas Pada Faktor-faktor Yang Mempengaruhi Kemiskinan di Nusa Tenggara Barat
type: item
description: 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 <!-- [if gte msEquation 12]><m:oMath><m:sSubSup><m:sSubSupPr><m:ctrlPr></m:ctrlPr></m:sSubSupPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>R</m:r></m:e><m:sub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>adj</m:r></m:sub><m:sup><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>2</m:r></m:sup></m:sSubSup></m:oMath><![endif]--> 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 <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>68,57%</m:r></m:oMath><![endif]--> 68.57% , while the PLS model yielded a higher <!-- [if gte msEquation 12]><m:oMath><m:sSubSup><m:sSubSupPr><m:ctrlPr></m:ctrlPr></m:sSubSupPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>R</m:r></m:e><m:sub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>adj</m:r></m:sub><m:sup><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>2</m:r></m:sup></m:sSubSup></m:oMath><![endif]--> R2adj value of 75.1%<!-- [if gte ms…
subject: Articles
  topic: section
subject: Poverty
  topic: keywords
subject: Multicollinearity
  topic: keywords
subject: Ridge Regression
  topic: keywords
subject: Partial Least Square
  topic: keywords
creator: Baiq Desi Nurma Sari
creator: Lisa Harsyiah
creator: Zulhan Widya Baskara
rights: Copyright (c) 2025 Baiq Desi Nurma Sari, Lisa Harsyiah, Zulhan Widya Baskara https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 59
ending page: 69
doi: 10.29303/ijasds.v2i2.8051

Faktor-Faktor yang Memengaruhi Minat Belanja Mahasiswa Kota Mataram pada Live Produk di Tiktok dan Shopee
type: item
description: 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.
subject: Articles
  topic: section
subject: Live Shopping
  topic: keywords
subject: Shopee
  topic: keywords
subject: Tiktok
  topic: keywords
subject: Shopping Intention
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
creator: Syifa Salsabila Satya Graha
creator: Nisa Ul Istiqomah
creator: Ika Wulandari
creator: Ismi Asmawati
creator: Zulhan Widya Baskara
creator: Dina Eka Putri
rights: Copyright (c) 2025 Syifa Salsabila Satya Graha, Nisa Ul Istiqomah, Ika Wulandari, Ismi Asmawati, Zulhan Widya Baskara, Dina Eka Putri https://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 20
ending page: 33
doi: 10.29303/ijasds.v2i1.6913

Analisis Tren Sosial di Indonesia dengan Peta Kendali CUSUM (Studi Kasus: Perceraian, Kemiskinan, Pernikahan Dini, dan Tingkat Pendidikan)
type: item
description: 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.
subject: Articles
  topic: section
subject: Divorce
  topic: keywords
subject: Poverty
  topic: keywords
subject: Social
  topic: keywords
subject: CUSUM
  topic: keywords
creator: Navisah Navisah
creator: Mawaddatul Fariha
creator: Ketrin Jupina Ranti
creator: Lita Astuti
creator: Suwindah Puji Yarti
creator: Lisa Harsyiah
creator: Jihadil Qudsi
rights: Copyright (c) 2025 Navisah Navisah, Mawaddatul Fariha, Ketrin Jupina Ranti, Lita Astuti, Suwindah Puji Yarti, Lisa Harsyiah, Jihadil Qudsi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 12
ending page: 19
doi: 10.29303/ijasds.v2i1.6909

Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network
type: item
description: 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%.
subject: Articles
  topic: section
subject: Farmer Exchange Rate
  topic: keywords
subject: Forecast
  topic: keywords
subject: Neural Network
  topic: keywords
creator: Putri Aulia Rahmah
creator: Memi Nor Hayati
creator: Ariyanti Cahyaningsih
rights: Copyright (c) 2025 Putri Aulia Rahmah, Memi Nor Hayati, Ariyanti Cahyaningsih https://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 1
ending page: 11
doi: 10.29303/ijasds.v2i1.5855

Determinasi Faktor Psikososial terhadap Kesehatan Mental Mahasiswa Matematika Universitas Mataram Selama Penyusunan Skripsi
type: item
description: 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)<!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>x</m:r></m:e><m:sub><m:r>1</m:r></m:sub></m:sSub><m:r>)</m:r></m:oMath><![endif]--> has the strongest positive impact on mental health. Additionally, supervisor support (x4)<!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>x</m:r></m:e><m:sub><m:r>4</m:r></m:sub></m:sSub><m:r>)</m:r></m:oMath><![endif]--> and the availability of learning resources (x3)<!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>x</m:r></m:e><m:sub><m:r>3</m:r></m:sub></m:sSub><m:r>)</m:r></m:oMath><![endif]--> positively impact students' mental health, while social support <!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>x</m:r></m:e><m:sub><m:r>2</m:r></m:sub></m:sSub><m:r>)</m:r></m:oMath><![endif]--> (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.
subject: Articles
  topic: section
subject: Mental Health
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
subject: Undergraduate Thesis
  topic: keywords
creator: Aulia Sahadatin
creator: Rosa Anggara
creator: Zulhan Widya Baskara
creator: Dina Eka Putri
rights: Copyright (c) 2025 Aulia Sahadatin, Rosa Anggara, Zulhan Widya Baskara, Dina Eka Putri https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 81
ending page: 91
doi: 10.29303/ijasds.v2i2.6831

Forecasting the Number of Domestic Passenger Arrivals at APT Pranoto Samarinda Airport Using Maximal Overlap Discrete Wavelet Transform with Multiresolution Autoregressive Model
type: item
description: 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.
subject: Articles
  topic: section
subject: Daubechies
  topic: keywords
subject: MAR
  topic: keywords
subject: MODWT
  topic: keywords
subject: Passengers
  topic: keywords
subject: Wavelet
  topic: keywords
creator: Thifan Octavianto
creator: Meiliyani Siringoringo
creator: Ika Purnamasari
rights: Copyright (c) 2025 Thifan Octavianto, Meiliyani Siringoringo, Ika Purnamasari https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-11
publication date: 2025-06-11
starting page: 44
ending page: 58
doi: 10.29303/ijasds.v2i1.5796

Regresi Komponen Utama dalam Mengatasi Multikolinieritas pada Faktor-Faktor yang Mempengaruhi Inflasi di Indonesia
type: item
description: 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.
subject: Articles
  topic: section
subject: Inflation
  topic: keywords
subject: Multicollinearity
  topic: keywords
subject: Principal Component Regression
  topic: keywords
creator: Salsabila Hadi Putri Ningrum
creator: Khairatun Hisan
creator: Triana Putri Ramdhani
creator: Luzianawati Luzianawati
creator: M. Daffa Rizki Zindawi
creator: Lisa Harsyiah
rights: Copyright (c) 2025 Salsabila Hadi Putri Ningrum, Khairatun Hisan, Triana Putri Ramdhani, Luzianawati Luzianawati, M. Daffa Rizki Zindawi, Lisa Harsyiah https://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-31
publication date: 2025-05-31
starting page: 34
ending page: 43
doi: 10.29303/ijasds.v2i1.5827

Perbandingan Peramalan Jumlah Produksi Air Bersih PT. Air Minum Giri Menang dengan Metode Double Exponential Smoothing dari Holt dan Brown menggunakan Optimasi Algoritma Kuadratik
type: item
description: 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.
subject: Articles
  topic: section
subject: Brown
  topic: keywords
subject: Forecasting
  topic: keywords
subject: Double Exponential Smoothing
  topic: keywords
subject: Holt
  topic: keywords
subject: Quadratic Algorithm
  topic: keywords
creator: Era Pazira
creator: Zulhan Widya Baskara
creator: Qurratul Aini
rights: Copyright (c) 2024 Era Pazira, Zulhan Widya Baskara, Qurratul Aini https://creativecommons.org/licenses/by-sa/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 37
ending page: 47
doi: 10.29303/ijasds.v1i1.5793

Pengaruh Kepariwisataan Terhadap Inflasi di Kota Mataram
type: item
description: 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.
subject: Articles
  topic: section
subject: Inflation
  topic: keywords
subject: Hotel Guests
  topic: keywords
subject: Simple Regression
  topic: keywords
creator: Baiq Wira Hartati
creator: Lilik Hidayati
creator: Valian Yoga Pudya Ardhana
rights: Copyright (c) 2024 Baiq Wira Hartati, Lilik Hidayati, Valian Yoga Pudya Ardhana https://creativecommons.org/licenses/by-sa/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 31
ending page: 36
doi: 10.29303/ijasds.v1i1.5783

Perbandingan Regresi Nonparametrik Kernel dan Spline pada Pemodelan Hubungan antara Rata-Rata Lama Sekolah dan Pengeluaran per Kapita di Indonesia
type: item
description: 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.
subject: Articles
  topic: section
subject: Poverty
  topic: keywords
subject: Kernel Regression
  topic: keywords
subject: Spline Regression
  topic: keywords
creator: Muhammad Syahrul
creator: Humami Syifa Amanda
creator: Indi Rizqy Fahrani
creator: Yasmin Yasmin
creator: Nur Asmita Purnamasari
creator: Zulhan Widya Baskara
rights: Copyright (c) 2024 Muhammad Syahrul, Humami Syifa Amanda, Indi Rizqy Fahrani, Yasmin Yasmin, Nur Asmita Purnamasari, Zulhan Widya Baskara https://creativecommons.org/licenses/by-sa/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 11
ending page: 19
doi: 10.29303/ijasds.v1i1.5725

Analisis Cluster untuk Pengelompokan Provinsi di Indonesia berdasarkan Tingkat Kemiskinan menggunakan Metode Average Linkage
type: item
description: 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.
subject: Articles
  topic: section
subject: Poverty
  topic: keywords
subject: Cluster
  topic: keywords
subject: Hierarchy
  topic: keywords
subject: Average Linkage
  topic: keywords
creator: Dede Saputra
creator: Azrianti Ardania
creator: Syaftirridho Putri
creator: Adis Tia Juli Agil Asri
creator: Lisa Harsyiah
rights: Copyright (c) 2024 Dede Saputra, Azrianti Ardania, Syaftirridho Putri, Adis Tia Juli Agil Asri, Lisa Harsyiah https://creativecommons.org/licenses/by-sa/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 20
ending page: 30
doi: 10.29303/ijasds.v1i1.5446

Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means
type: item
description: 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.
subject: Articles
  topic: section
subject: Poverty
  topic: keywords
subject: Cluster Analysis
  topic: keywords
subject: K-Means Method
  topic: keywords
creator: Sabna Zulfaa Sabina
creator: Dzaki Ade Alfarez
creator: Syifa Salsabila Satya Graha
creator: Muhammad Yuzaul Auladi
creator: Lisa Harsyiah
rights: Copyright (c) 2024 Indonesian Journal of Applied Statistics and Data Science https://creativecommons.org/licenses/by-sa/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 1
ending page: 10
doi: 10.29303/ijasds.v1i1.5415