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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Advances in Data and Information Systems Prosiding Seminar Nasional Official Statistics
Wilantika, Nori
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering 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.
Kajian Penerapan Machine Learning untuk Sistem Rekomendasi Mitra Statistik BPS Septianugraha, Damar; Wilantika, Nori; Suadaa, Lya Hulliyyatus; Prasetyo, Rindang Bangun; Huraira, Sabit
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.v2024i1.2211

Abstract

BPS routinely conducts censuses and surveys involving BPS partners in data collection and processing. Ensuring these partners exhibit good performance is crucial to minimize the risk of moral hazard, which can negatively impact stakeholders. This research aims to implement machine learning into an information system to recommend statistical partners based on classification results. The best model identified is XGBoost, which is integrated into the system for generating recommendations. System testing using black-box methods confirmed compliance in 41 scenarios. Additionally, the System Usability Scale (SUS) questionnaire yielded an average score of 65.5, indicating the system's potential and suitability for further development. The findings offer insights into utilizing partner characteristics data and evaluation in BPS's censuses and surveys, particularly for selecting assigned partners.
Pengembangan Aplikasi Chatbot dengan Large Language Model untuk Text-to-SQL Generation Nugraha, Gede Putra; Suadaa, Lya Hulliyyatus; Wilantika, Nori; Maghfiroh, Lutfi Rahmatuti
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.v2024i1.2252

Abstract

The agricultural census query builder system has two modes: a query builder mode with an interface that facilitates the selection of tables, columns, and query criteria, and an SQL programming mode for executing SQL queries. The system provides a list of queries for basic anomaly checking nationwide, but advanced and unique anomaly checking for each work unit requires writing SQL queries from scratch, which is inefficient. This research developed a chatbot application that translates user queries into SQL queries for data anomaly checking. This chatbot uses the Large Language Model (LLM) GPT-4o. The chatbot application development uses the Rapid Application Development (RAD) model for rapid system development. Black Box Test and System Usability Test with System Usability Scale (SUS) show the results as expected by the user, with an average SUS score of 84.17 which indicates the chatbot application is acceptable.
Analisis Keberlanjutan Penggunaan Platform Belanja Online Setelah Pandemi Covid-19 Nafi, Nazzala Qinthara; Wilantika, Nori; Gandhi, Arfive
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5499

Abstract

Sebagai upaya pengendalian Covid-19, pemerintah memberlakukan aturan ketat mengenai pembatasan mobilitas penduduk. Salah satu dampak adanya pembatasan mobilitas penduduk tersebut adalah meningkatnya penggunaan platform belanja online. Namun, setelah kondisi pandemi membaik, mobilitas penduduk sudah tidak dibatasi, sehingga muncul pertanyaan bagaimana kondisi pengguna platform belanja online setelah pandemi Covid-19. Atas dasar hal tersebut, penelitian ini bertujuan untuk mengetahui keberlanjutan penggunaan platform belanja online di Indonesia setelah tidak ada lagi pandemic Covid-19 dan mengetahui faktor-faktor yang mempengaruhinya. Penelitian ini menggunakan kerangka teoritis utama TAM dengan menambahkan variabel customer satisfaction, trust, dan system quality. Analisis data dilakukan menggunakan SEM PLS dengan jumlah responden sebanyak 443 orang. Dari hasil penelitian didapatkan bahwa mayoritas tetap akan menggunakan platform belanja online meski kondisi mobilitas tidak dibatasi dan didapati bahwa repurchase intention berpengaruh positif pada actual repurchase.
Deep Learning and Remote Sensing for Agricultural Land Use Monitoring: A Spatio-Multitemporal Analysis of Rice Field Conversion using Optical Satellite Images Wijayanto, Arie Wahyu; Zalukhu, Bill Van Ricardo; Putri, Salwa Rizqina; Wilantika, Nori; Yuniarto, Budi; Kurniawan, Robert; Pratama, Ahmad R.
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1385

Abstract

Rice is a staple food for over half of the global population, making its production crucial for food security, especially in Indonesia, the world's third-largest rice consumer. Population growth and urban expansion have led to agricultural land conversion, necessitating efficient monitoring methods. Traditional approaches, such as area sample frameworks and tile surveys, are costly and time-consuming, prompting the need for remote sensing and deep learning solutions. This study utilizes medium-resolution Sentinel-1, Sentinel-2, and Landsat-8 optical satellite imagery from 2013 and 2021 to analyze land cover changes in West Bandung and Purwakarta Regencies, key agricultural regions in Indonesia. A deep learning model is developed to classify land cover, validated through ground-truth evaluation, and applied to assess spatio-multitemporal land use conversion, paddy field estimation, and conversion rates. Results show that deep learning models effectively classify land cover with high accuracy, revealing significant agricultural land loss due to urban expansion. This research contributes to artificial intelligence (AI)-driven land monitoring, particularly in tropical regions, and supports policymakers in sustainable food agriculture land management. The findings highlight the potential of integrating remote sensing and deep learning for cost-effective agricultural monitoring, ensuring food security and sustainable land use. Future research should explore higher-resolution imagery and advanced AI techniques to enhance predictive accuracy and decision-making.
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.
Co-Authors Arfive Gandhi Arie Wahyu Wijayanto Choir, Achmad Syahrul Huraira, Sabit Ibnu Santoso Maghfiroh, Lutfi Rahmatuti Nafi, Nazzala Qinthara Nugraha, Gede Putra Pratama, Ahmad R. Putri, Salwa Rizqina Rindang Bangun Prasetyo Robert Kurniawan, Robert Salsabila, Aulia Rido Septianugraha, Damar Suadaa, Lya Hulliyyatus Yuniarto, Budi Zalukhu, Bill Van Ricardo