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INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 8 Documents
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Search results for , issue "Vol 16 No 2 (2024): Jurnal Aplikasi Statistika " : 8 Documents clear
Patterns, Determinants, and Elasticity of Household Food Consumption in Indonesia (Period 2021-2022) Aulia, Wifa Darma; Yuliana, Rita
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.652

Abstract

Introduction/Main Objectives: The increase in strategic food commodity prices significantly contributed to inflation, with the food, beverage, and tobacco category reaching 3.59% in March 2022. This price hike reduced household purchasing power, affecting welfare. This research examines how rising food prices impact household food consumption patterns in Indonesia. Background Problems: This study explores the effects of rising food prices on household consumption patterns. It addresses two key questions: What are household food consumption patterns, and what factors influence them? What is the elasticity of food demand in Indonesia between March 2021 and March 2022? Novelty: The study’s novelty lies in calculating food demand elasticity using demand theory assumptions, ensuring reliable results—unlike many studies that overlook these assumptions. Research Methods: The study uses the Linear Approximated-Almost Ideal Demand System (LA-AIDS) model with the Seemingly Unrelated Regression (SUR) method. Findings/Results: The results show that rising food prices in March 2022 changed household food consumption patterns. Own-price elasticity was negative, indicating reduced demand. Cross-price elasticity varied, with some food groups showing negative and others positive effects. All food groups were classified as normal goods based on expenditure elasticity.
The Utilization of Model Output Statistic (MOS) in Improving Weather Prediction Model Accuracy of Integrated Forecasting System (IFS) Isnaini Anjelina Ramadhan; Deni Septiadi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.732

Abstract

Introduction/Main Objectives: Integrated Forecasting System (IFS) is one of the most accurate numerical weather prediction (NWP) model for Indonesia region. Background Problems: However, in fact, each model always has bias potential against observation which causes inaccuracy in weather prediction. Novelty: This research intends to overcome this problem by building a weather prediction model based on Model Output Statistic (MOS) to minimize bias and improve NWP accuracy. Research Methods: Provide an outline of the research method(s) and data used in this paper. Explain how did you go about doing this research. Again, avoid unnecessary content and do not make any speculation(s). Finding/Results: Analysis result states that compared to IFS, MOS fluctuation pattern is more relevant to observation. MOS has higher correlation to observation and lower error. However, the variance of observation value tends to be better represented by IFS. The test result of heavy rain cases prove that the application of MOS is able to provide fairly accurate prediction. This weather prediction will be able to be the basis for decision-making and preventive measure in dealing with extreme condition that may occur.
Development of a Hybrid Fuzzy Geographically Weighted K-Prototype Clustering and Genetic Algorithm for Enhanced Spatial Analysis: Application to Rural Development Mapping Santoso, Agung Budi; Candra, Arya Candra; Nooraeni, Rani
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.789

Abstract

Introduction/Main Objectives: Clustering methods are crucial for geodemographic analysis (GDA) as they enable a more accurate and distinct characterization of a region. This process facilitates the creation of socio-economic policies and contributes to the overall advancement of the region. Background Problems: The fuzzy geographically weighted clustering (FGWC) method, which is a GDA technique, primarily handles numerical data and is prone to being stuck in local optima. Novelty: This study proposed two novel clustering methodologies: fuzzy geographically weighted k-prototypes (FKP-GW) and its hybrid clustering model, which combines genetic algorithm-based optimization (GA-FKP-GW). Research Methods: This research conduct simulation study comparing two of the proposed clustering method. For the empirical application, this study applied clustering technique using the official Village Potential Survey of Temanggung, Indonesia. Finding/Results: The evaluation results of experiments conducted on simulated data and study cases indicate that the proposed method yields distinct clustering results compared to the previous method while being comparably efficient. The empirical application identifies four distinct groups from the clustered villages, each displaying unique characteristics. The results of our research have the potential to benefit the development of the GDA method and assist the local government in formulating more effective development policies.
Aspect-Based Sentiment Analysis of Transportation Electrification Opinions on YouTube Comment Data Adilla, Rahmi Elfa; Huda, Muhammad; Aziz, Muhammad; Suadaa, Lya Hulliyyatus
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.790

Abstract

Introduction/Main Objectives: This research aims to conduct an aspect-based sentiment analysis of transportation electrification opinions on YouTube comment data. Background Problems: It is difficult to summarize the sentiment of many YouTube user comments related to electric vehicles (EVs) based on their aspects; therefore, aspect-based sentiment analysis is needed to conduct further analysis. Novelty: This study identifies five aspects of EV and their sentiments at the same time. The aspects are usefulness, ease of use, comfort, cost, and incentive policies. One of this study’s methods is the transfer learning model. This model can be a solution to overcome the shortcomings of deep learning in classifying aspect-based sentiment classification on small datasets. Research Methods: The sentiment classification model used is a machine learning model, namely support vector machine (SVM) and transfer learning models from pre-trained IndoBERT and mBERT. Finding/Results: Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 89.17% and 52.66%, respectively. Furthermore, the best IndoBERT model was developed with input in the form of a combination of aspects and comment sentences. Experimental results show that there is an improvement in performance with accuracy and F1-Score of 90% and 60.70%, respectively.
The Impact of ICT on Regional Economy in Indonesia Through MSEs as Mediators: Application of Causal Mediation Analysis in Instrumental-variable Regressions Luthfio Febri Trihandika; Ribut Nurul Tri Wahyuni; Maghfiroh, Meilinda Fitriani Nur
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.791

Abstract

Introduction/Main Objectives: The development of information and communication technology (ICT) and small and micro enterprises (SMEs) can encourage regional economic growth. Background Problems: Studies on the impact of ICT on the rural economy at the village level are very limited. Furthermore, the Indonesian study neglects to tackle the endogeneity issue associated with this variable and the indirect effects of ICT on the regional economy. Novelty: Using SMEs as a mediator, this study examines the impact of ICT (internet signal strength) on the village's local economy (nighttime light), both directly and indirectly. ICT is considered to be endogenous. Research Methods: This study employs causal mediation analysis in instrumental-variable (IV) regressions at the village level in 2018 and 2021, using lightning strike intensity as IV. Finding/Results: Internet signal strength can increase the number of SMEs, and this increase can positively and significantly impact the local economy. In addition, the direct impact of internet signal strength on the local economy is significantly negative. However, the total impact of internet signal strength is significantly positive.
Implementation of a RESTful API-Based Evolutionary Algorithm in a Microservices Architecture for Course Timetabling Zuhdi Ali Hisyam; Ridho, Farid; Setiyawan, Arbi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.796

Abstract

Introduction/Main Objectives: Implement an evolutionary algorithm within a RESTful API for a course timetabling system that employs a microservices architecture. Background Problems: The current course timetabling at Politeknik Statistika STIS uses the third-party application (aSc Timetables), which lacks a generator as a service, resulting in its inefficiency due to the lack of integration with SIPADU NG. Novelty: The evolutionary algorithm is built as a service (RESTful API) within a microservices architecture and supports custom constraints for timetables. Research Methods: One of the evolutionary algorithm families, the (1+1) evolutionary strategy, is implemented and used to create a course timetable 1000 times. Each course timetable created will have its cost calculated to assess the goodness of the algorithm implementation. The developed RESTful API is also evaluated through black box testing. Finding/Results: For the odd semester data, 40.5% of the trials yielded a cost value between 4 and 5, while for the even semester, all trials produced a cost value below 1. The resulting cost value is close to 0, which indicates that the timetable created has minimal violations.  Additionally, black box testing concluded that the service operates as expected, delivering the anticipated output.
Small Area Estimation Approaches Using Satellite Imageries Auxiliary Data for Estimating Per Capita Expenditure in West Java, Indonesia Feriyanto, Muhamad; Arie Wahyu Wijayanto; Ika Yuni Wulansari; Parwanto, Novia Budi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.799

Abstract

Introduction/Main Objectives: The economy of a country can determine the welfare of its people. One of the economic indicators in Indonesia is per capita expenditure, which has the lowest estimation at the district level. Background Problems: Sub-district level estimates provide detailed information on inequality that cannot be explained at the district level. Unfortunately, sub-district level estimates of per capita expenditure in Indonesia have poor Relative Standard Error (RSE) values. Research Method: The Small Area Estimation (SAE) method can improve estimator accuracy on small samples by using auxiliary variable information. Novelty: The existence of big geospatial data such as remote sensing provides an advantage in the efficient use of auxiliary variables. Finding Result: The Empirical Best Linear Unbiased Prediction (EBLUP) model using Nighttime Light Intensity (NTL) as an auxiliary variable provides the best results of the five proposed models. Remote sensing data can potentially be used in SAE auxiliary variables. 
Spatial Dependencies in Environmental Quality: Identifying Key Determinants Samosir, Omas Bulan; Karim, Rafidah Abd; Fauzi, M. Irfan; Berliana, Sarni Maniar
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.802

Abstract

Introduction/Main Objectives: Environmental quality is essential to human development because it reflects the condition of our natural surroundings. Background Problems: Understanding the determinants of environmental quality is crucial for Indonesia as it helps identify the key factors influencing environmental quality. Novelty: This study seeks to identify the determinants of environmental quality in regencies and municipalities on Java Island, incorporating spatial effects into the analysis. Research Methods: The dependent variable is environmental quality index. The independent variables are GRDP in industrial sector, GRDP in agricultural sector, urban population rate, population density, and poverty rate. We applied spatially lag regression model using contiguity spatial weight matrix. Finding/Results: This study shows the spatially lag regression model outperforms the OLS model. GRDP in the industrial sector, GRDP in the agricultural sector, urban population rate, and population density have negative effects, suggesting the increases in these variables were associated with lower environmental quality.

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