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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Aplikasi Statistika & Komputasi Statistik Prosiding Seminar Nasional Official Statistics
Choir, Achmad Syahrul
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

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Continuance Usage of Collaboration Tools after Social Distancing and The Influential Factors Salsabila, Aulia Rido; Wilantika, Nori; Santoso, Ibnu; Choir, Achmad Syahrul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Working from home (WFH) during the COVID-19 pandemic has challenges in terms of communication and coordination among employees due to the distance. Therefore, collaboration tools were needed during the COVID-19 pandemic. As we recover from the pandemic, the government revoked the social distancing policy restricting people's activities. The revoke is assumed to influence the continued use of collaboration tools. This study aims to understand the continuance usage of collaboration tools after no more social distancing. This study also seeks to identify the factors influencing the ongoing use of collaboration tools by integrating the Technology Acceptance Model (TAM) and Expectation Confirmatory Model (ECM). The method of data analysis employed was the partial least squares structural equation model (PLS-SEM). The findings indicated that most of 437 respondents kept using collaboration tools after no more social distancing. However, there was a decrease in the frequency of use. Our study findings have also proved that Actual Continued Usage is influenced by Continuance Intention by 43%. Furthermore, a factor that influences continuance intention the most is the attitude toward using collaboration tools. The results of this study also support the integration of TAM and ECM to examine user intentions and behavior regarding the continuance use of a technology.
Nowcasting Produk Domestik Bruto Atas Dasar Harga Konstan Triwulanan Indonesia Kurniawan, Taufiq Agung; Choir, Achmad Syahrul
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2185

Abstract

As a component of macroeconomic assumptions, economic growth is used in the state budget drafting. However, the nominal GDP data used to calculate the economic growth value has a release lag in the first week of the second month after the quarter ends. This research aims to find the best nowcasting model for MIDAS regression, RFR, LightGBM, SVR, MLP, and ensemble methods. Then, based on the best model, the nominal GDP value in the 2023 fourth quarter and the 2024 first quarter are predicted. Overall, the MLP model with variable selection using SHAP values has the best evaluation indicators, therefore this model is used to predict the nominal GDP in both quarters. Using the MLP model, the predicted value of nominal GDP in both quarters is quite accurate when compared to nominal GDP data that have been released by BPS-Statistics Indonesia.
Modeling the Human Development Index of the West Nusa Tenggara Province using Panel Data Regression Astuti, Alfira Mulya; Islamiyah, Pizatul; Choir, Achmad Syahrul; Setambah, Mohd Afifi Bahurudin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31055

Abstract

The human development index is the primary indicator used to measure the level of success of human development. It is important to study because the human development index can provide a more comprehensive picture of a region or country's progress in improving its people's quality of life and guide the government in designing more effective development policies, identifying social gaps, and directing efforts to improve the quality of life of society as a whole. This research aims to identify the most significant component of the HDI calculation through the application of standardized coefficients and to analyze the influence of the number of poor people on the human development index in West Nusa Tenggara (NTB) province during 2010-2023 period. This research is quantitative in essence. The independent variables were life expectancy at birth, expected years of schooling, mean years of schooling, adjusted per capita expenditure, and number of poor people. The individual observation units in this study were 10 districts/cities in the NTB province. Data were sourced from Badan Pusat Statistik (BPS) NTB Province and analyzed using the panel regression method. The results of model selection show that the Fixed Effect Model is the best model for modeling the human development index in NTB province. The adjusted per capita expenditure had the greatest impact on the human development index of NTB Province in 2010–2023. The expected years of schooling was the variable that contributed the least to the entire components of the HDI in NTB province. The number of poor people had a significant effect on the human development index of NTB province from 2010 to 2023.  
Post-Pandemic Usage of Collaboration Tools: Influencing Factors Salsabila, Aulia Rido; Wilantika, Nori; Santoso, Ibnu; Choir, Achmad Syahrul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2295

Abstract

The distance existed due to the COVID-19 pandemic drove people to utilize collaboration tools to continue communication, coordination, and collaboration. The increasing use of collaboration tools during WFH impact on our way of working. The collaboration tools also offer efficiencies, allow workers to break silos, and increase the quality of communication at the company-wide level. As we recover from the pandemic, the government revoked the social distancing policy and it is assumed to influence the continued use of collaboration tools as people fully carry out their activities face-to-face again. This study aims to understand the continuance usage of collaboration tools after no more social distancing. This study also seeks to identify the factors influencing the ongoing use of collaboration tools by integrating the Technology Acceptance Model (TAM) and Expectation Confirmatory Model (ECM). The method of data analysis employed was the partial least squares structural equation model (PLS-SEM). The findings indicated that most of 437 respondents kept using collaboration tools after no more social distancing. However, there was a decrease in the frequency of use. Our study findings have also proved that Actual Continued Usage is influenced by Continuance Intention by 43%. Factor that influences continuance intention the most is the attitude toward using collaboration tools, which is influenced by users’ perceived usefulness of the collaboration tools. The results of this study also support the integration of TAM and ECM to examine user intentions and behavior regarding the continuance use of a technology.
Adding MSNBURR-IIa Distribution to MultiBUGS Ramadani , Eliana Putri; Choir, Achmad Syahrul; Pravitasari , Anindya Apriliyanti; Paraguison, Joynabel
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.804

Abstract

Introduction/Main Objectives: The MSNBurr-IIa distribution is a neo-normal distribution designed to fit right-skewed data better. This article aims to integrate the MSNBurr-IIa distribution into MultiBUGS, thereby enabling Bayesian estimation of its parameters. Background Problems: Markov Chain Monte Carlo (MCMC) is a popular method for Bayesian computations, although its implementation is frequently challenging. MultiBUGS, a statistical tool that uses the BUGS language, is used to make this easier. Novelty: This paper details integrating the MSNBurr-IIa distribution into MultiBUGS, allowing for estimating its parameters. The module's effectiveness is demonstrated through its application on both simulated data and regional economic growth data of Indonesian districts/cities in 2021. Research Methods: The MSNBurr-IIa module was developed using five steps: requirement, design, development, testing, and implementation in simulation and real-world data. It was built with Blackbox Component Builder, an integrated development environment (IDE) for the Component Pascal programming language. Finding/Results: The findings confirm that MultiBUGS, with the MSNBurr-IIa module, successfully estimates the distribution’s parameters across various datasets.
Pembangunan Dataset Sintetis Klasifikasi Baku Lapangan Usaha Indonesia 2020 dengan Generative Artificial Intelligence Silmi Kaffah, M. Ihsan; Rahman, Dimas Haafizh; Amnur, Muh. Alfian; Montolalu, Cloudya Qashwah; Siregar, Amir Mumtaz; Sinulingga, Geraldo Benedictus; Ayu Alistin, Zharifah Dhiya; Raihannur, Cut Indah; Putri Arivia, Anggi Marya; Rahmawati, Arih; Nauli Sihombing, Fiona Audia; Salsabiela, Rahmadika Kemala; Bahy, Sabastian Alfons; Suadaa, Lya Hulliyyatus; Choir, Achmad Syahrul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2581

Abstract

The limited quality datasets is a fundamental challenge in developing automatic classification of business description into the Indonesia Standard Industrial Classification (KBLI) using machine learning models. This research aims to develop a synthetic KBLI dataset using Generative AI via ChatGPT chatbot with a one-shot prompting technique. This technique is employed to generate business descriptions based on five-digit KBLI codes in order to address the limitations of labeled data and the variability of existing business descriptions. The dataset generated through prompt engineering and manual validation shows that 93,25% of the business descriptions align with the established KBLI standards. The average number of business descriptions per category demonstrates a fairly uniform distribution, ensuring sufficient representation for each five-digit code. This research makes a significant contribution in providing a dataset for training machine learning models in the automatic classification of business descriptions into the five-digit KBLI categories.
Co-Authors Alfira Mulya Astuti, Alfira Mulya Amnur, Muh. Alfian Ayu Alistin, Zharifah Dhiya Bahy, Sabastian Alfons Ibnu Santoso Islamiyah, Pizatul Kurniawan, Taufiq Agung Montolalu, Cloudya Qashwah Nauli Sihombing, Fiona Audia Paraguison, Joynabel Pravitasari , Anindya Apriliyanti Putri Arivia, Anggi Marya Rahman, Dimas Haafizh Rahmawati, Arih Raihannur, Cut Indah Ramadani , Eliana Putri Salsabiela, Rahmadika Kemala Salsabila, Aulia Rido SETAMBAH, MOHD AFIFI BAHURUDIN Silmi Kaffah, M. Ihsan Sinulingga, Geraldo Benedictus Siregar, Amir Mumtaz Suadaa, Lya Hulliyyatus Wilantika, Nori