Jurnal Aplikasi Statistika & Komputasi Statistik
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.
Articles
143 Documents
<![CDATA[Development of a Hybrid Fuzzy Geographically Weighted K-Prototype Clustering and Genetic Algorithm for Enhanced Spatial Analysis: Application to Rural Development Mapping ]]>
<![CDATA[Santoso, Agung Budi]]>;
<![CDATA[Candra, Arya Candra]]>;
<![CDATA[Nooraeni, Rani]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.789
<![CDATA[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.]]>
<![CDATA[Aspect-Based Sentiment Analysis of Transportation Electrification Opinions on YouTube Comment Data ]]>
<![CDATA[Adilla, Rahmi Elfa]]>;
<![CDATA[Huda, Muhammad]]>;
<![CDATA[Aziz, Muhammad]]>;
<![CDATA[Suadaa, Lya Hulliyyatus]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.790
<![CDATA[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.]]>
<![CDATA[The Impact of ICT on Regional Economy in Indonesia Through MSEs as Mediators: Application of Causal Mediation Analysis in Instrumental-variable Regressions ]]>
<![CDATA[Luthfio Febri Trihandika]]>;
<![CDATA[Ribut Nurul Tri Wahyuni]]>;
<![CDATA[Maghfiroh, Meilinda Fitriani Nur]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.791
<![CDATA[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.]]>
<![CDATA[Implementation of a RESTful API-Based Evolutionary Algorithm in a Microservices Architecture for Course Timetabling ]]>
<![CDATA[Zuhdi Ali Hisyam]]>;
<![CDATA[Ridho, Farid]]>;
<![CDATA[Setiyawan, Arbi]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.796
<![CDATA[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.]]>
<![CDATA[Small Area Estimation Approaches Using Satellite Imageries Auxiliary Data for Estimating Per Capita Expenditure in West Java, Indonesia ]]>
<![CDATA[Feriyanto, Muhamad]]>;
<![CDATA[Arie Wahyu Wijayanto]]>;
<![CDATA[Ika Yuni Wulansari]]>;
<![CDATA[Parwanto, Novia Budi]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.799
<![CDATA[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. ]]>
<![CDATA[Spatial Dependencies in Environmental Quality: Identifying Key Determinants ]]>
<![CDATA[Samosir, Omas Bulan]]>;
<![CDATA[Karim, Rafidah Abd]]>;
<![CDATA[Fauzi, M. Irfan]]>;
<![CDATA[Berliana, Sarni Maniar]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v16i2.802
<![CDATA[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.]]>
<![CDATA[Separated Couples during the COVID-19 Outbreak: A Survival Support Vector Machine Analysis ]]>
<![CDATA[Setiarno Putera, Muhammad Luthfi]]>;
<![CDATA[Rafik Patrajaya]]>;
<![CDATA[Setiarno]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v17i1.739
<![CDATA[Introduction/Main Objectives: The separation between spouses has been rising noticeably in recent years in Palangka Raya, particularly during the COVID-19 outbreak. Background Problems: An analysis of time-to-event on those separations will be undertaken quantitatively using survival analysis by comparing the results yielded by Cox proportional hazards (PH) regression and non-parametric Survival Support Vector Machine (SUR-SVM). Novelty: This work suggests a feature selection method that looks for influencing elements related to the c-index by employing backward elimination. Research Methods: This study's data came from Indonesia's Supreme Court webpage, including a database of separation verdicts from the Palangka Raya Religious Court, spanning from April 2020 to March 2021. The response variables were the time-to-separation (marriage length until separation) (t) and the censored state of the occurrence (?). Finding/Results: Based on SUR-SVM, the factors contributing the most to the separation are the absence of children, unsteady employment of appellants, and finance motive as the primary reason. In terms of concordance index and Akaike Information Criterion (AIC), the SUR-SVM outperformed the Cox proportional hazard model. These values of SUR-SVM were 59.24 and 1899.78, respectively. SUR-SVM correctly classified 59.24% of separations based on the chronological order of events.]]>
<![CDATA[Early Study of LLM Implementation in Survey Interviews ]]>
<![CDATA[Lailatul Hasanah]]>;
<![CDATA[Yuniarto, Budi]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v17i1.792
<![CDATA[Introduction/Main Objectives: This research aims to conduct a preliminary study into the use of LLMs for extracting information to fill out questionnaires in survey interviews. Background Problems: BPS-Statistics Indonesia used paper-based questionnaires for interviews and is recently utilizing the Computer Assisted Personal Interviewing (CAPI) method. However, the CAPI method has some drawbacks. Enumerators must input data into the device, which can be burdensome and prone to errors. Novelty: This study uses a large language model (LLM) to extract information from survey interviews. Research Methods: This study utilizes a text-to-speech application to translate interview results into text. Translation accuracy is measured by the Word Error Rate (WER). Then the text was extracted using the ChatGPT 3.5 Turbo model. GPT-3.5 Turbo is part of the GPT family of algorithms developed by OpenAI. Finding/Results: The extraction results are formatted into a JSON file, which is intended to be used for automatic filling into the database and then evaluated using precision, recall, and F1-score. Based on research conducted by utilizing the Speech Recognition API by Google and the ChatGPT 3.5 Turbo model, an average WER of 10% was obtained in speech recognition and an average accuracy of 76.16% in automatic data extraction.]]>
<![CDATA[Quantile Regression with Constrained B-Splines for Modelling Average Years of Schooling and Household Expenditure ]]>
<![CDATA[Sasmita, Yoga]]>;
<![CDATA[Budiman Johra, Muhammad]]>;
<![CDATA[Jatmiko, Yogo Aryo]]>;
<![CDATA[Lubis, Deltha A.]]>;
<![CDATA[Rahmad, Rizal]]>;
<![CDATA[Sohibien, Gama Putra Danu]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v17i1.793
<![CDATA[Introduction/Main Objectives: Education serves as a driving force for the transformation of society to break the cycle of poverty. This study examines the relationship between average years of schooling and per capita household expenditure in Kalimantan Tengah Province in 2020. Background Problems: The method of estimating a regression model that is assumed to follow a certain form of regression equation such as linear, quadratic and others is called parametric regression. However, researchers often encounter difficulties in determining the model specification through data distribution, so the method used is nonparametric regression. Novelty: This research uses a quantile-based approach to explore how the impact of education on per capita expenditure varies across different levels of household education. This provides a more nuanced understanding of the relationship, showing not just whether education matters, but how its influence changes at different levels of educational attainment. Research Methods: The relationship between average years of schooling and per capita household expenditure is modeled using a quantile regression model with the constrained B-Splines method. Finding/Results: Based on the established classification, it can be concluded that an increase in the average years of schooling among household members tends to have a greater impact on raising per capita expenditure.]]>
<![CDATA[Comparison of Binary and Traditional Partial Least Squares Structural Equation Modeling: A Study on The Role of Multidimensional Poverty Dimension to Social Protection in Java Island ]]>
<![CDATA[Bhakti, Diana]]>;
<![CDATA[Adji, Ardi]]>;
<![CDATA[Mubarok, Endang Saefuddin]]>;
<![CDATA[Sukmono, Renny]]>;
<![CDATA[Salam, Rudi]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v17i1.795
<![CDATA[Introduction/Main Objectives: The traditional Partial Least Squares Structural Equation Modeling (PLS-SEM) method uses an ordinary least squares regression approach that assumes that indicators must have a continuous scale. When the indicators are categorical, the use of traditional PLS-SEM becomes less appropriate. Background Problems: Multidimensional poverty consists of dimensions that are measured by a binary scale. The use of binary PLS-SEM is better than traditional PLS-SEM in modeling the effect of dimensions on social protection on Java Island. Novelty: The use of binary PLS-SEM with factor scores from the item response theory model applied to the role of dimensions of multidimensional poverty to social protection has not been carried out yet. Research Methods: This study introduces binary PLS-SEM, which is modified from traditional PLS-SEM by changing the data input using a tetrachoric correlation matrix. Finding/Results: Empirical results show that the binary PLS-SEM measurement model is better than traditional PLS-SEM. Evaluation of the structural model shows that the path coefficients of binary PLS-SEM are better than traditional PLS-SEM. Both approaches have an overall model fit. The order of multidimensional poverty dimensions that affect social protection are education, living standard, and health.]]>
