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All Journal Jurnal Pendidikan Sosiologi dan Humaniora Jurnal CoreIT JURNAL ILMIAH MATEMATIKA DAN TERAPAN JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL EDUCATION AND DEVELOPMENT JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Kepariwisataan: Jurnal Ilmiah International Journal of Economics (IJEC) International Journal of Educational Research Excellence (IJERE) Innovative: Journal Of Social Science Research Journal Emerging Technologies in Education Journal of Rural Tourism (JoRT) Jurnal Teknologi Cerdas Ulil Albab Jurnal Teknokes Journal of Computer Science Advancements Journal of Social Entrepreneurship and Creative Technology Socious Journal
Nugroho, Agung Yuliyanto
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Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia Nugroho, Agung Yuliyanto; Prasetio, Rachmat; Wong, Lucas; Rao, Ananya
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2251

Abstract

The rapid growth of digital technology in Indonesia has fostered the expansion of e-business systems, which in turn has generated vast volumes of data. However, many e-business platforms still face challenges in utilizing this data effectively to improve operational efficiency and decision-making. This research was conducted to explore the utilization of big data in enhancing the efficiency of e-business systems in Indonesia. The main objective of the study is to analyze how the integration of big data analytics contributes to optimizing business processes, customer engagement, and overall system performance in the Indonesian digital commerce ecosystem. A mixed-method approach was employed, combining quantitative surveys of 120 e-business practitioners with qualitative interviews involving 15 data analysts and IT managers from various sectors such as retail, fintech, and logistics. Data were analyzed using statistical tools and thematic coding to derive patterns and insights. The findings indicate that e-businesses implementing big data strategies reported a significant improvement in system responsiveness, personalized customer services, and data-driven decision-making. Moreover, big data utilization has been linked to enhanced supply chain management and real-time monitoring capabilities. Despite these benefits, challenges such as data privacy concerns, lack of skilled personnel, and high infrastructure costs remain significant barriers. In conclusion, the study confirms that the effective use of big data plays a crucial role in improving the efficiency and competitiveness of e-business systems in Indonesia. Future initiatives should focus on strengthening data governance and investing in human capital to maximize big data’s potential.
Penerapan Metode Double Moving Average Untuk Memprediksi Penjualan Tiket Bus Sinar Jaya Po Tambun Tundo, Tundo; Nugroho, Agung Yuliyanto; Saidah, Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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

Abstract

The Sinar Jaya Autobus Company (PO) is one of the buses engaged in the tourism business that sells and provides community needs such as bus tickets. This PO requires forecasting in data processing to produce accurate reports. The reason for this is because PO Bus Sinar Jaya in determining the demand for bus tickets cannot predict availability. Based on these reasons, the design of this system uses the Double Moving Average (DMA) forecasting method for the forecasting process in determining the amount and type of availability that will be sold for the following month. By using this calculation method it is hoped that the owner of PO Sinar Jaya will further optimize the things that can be detrimental to this PO in operating. If sales increase each month, using the DMA method, sales predictions for the next three months can be determined, the higher the number of ticket requests on the PO Sinar Jaya Bus, so that the forecasting results can help the PO to avoid running out of tickets according to consumer demand. Based on the research that has been carried out, it can be concluded that the Sinar Jaya PO Tambun bus ticket sales forecast using the Double Moving Average (DMA) method obtained the smallest MAPE value calculation results in order 2, namely 0.004599299 and the smallest MAPE value in order 3, namely 0.000614191. Comparison of the results of MAPE value calculations to determine the accuracy of forecasting results carried out with order 2 and order 3, it is proven that order 3 is more accurate for determining the error percentage results in this study.
Social Media Sentiment Analysis to Predict Market Trends in the Creative Industry Purwati, Purwati; Nugroho, Agung Yuliyanto; Hamka, Hamka; Sukoco, Hendro
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i1.2052

Abstract

The rise of social media has transformed how information spreads, creating an invaluable resource for analyzing market trends. In the creative industry, where consumer preferences shift rapidly, understanding social media sentiment is critical for businesses aiming to stay ahead of trends. Previous research on sentiment analysis has shown its potential in various fields, but its specific application in the creative industry remains underexplored. This research aims to investigate how social media sentiment analysis can predict market trends in the creative industry. By analyzing social media posts, reviews, and discussions, the study seeks to explore how positive, negative, and neutral sentiments influence market behavior and creative products’ success. The study employs a combination of data mining and sentiment analysis techniques to analyze social media content related to key creative products. Using machine learning algorithms, the research categorizes posts into sentiment categories and correlates them with market trends, such as sales and consumer behavior. A dataset consisting of social media content from multiple platforms over the past year was utilized for analysis. The results show that positive social media sentiment correlates with increased consumer engagement and sales in the creative industry, while negative sentiment predicts a decline in product success.
Health Information Technology Management Model to Improve User Performance and Satisfaction  yuniati, Nining; Nugroho, Agung Yuliyanto
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.123

Abstract

The rapid development of digital technology has reshaped the way healthcare institutions manage information, deliver services, and support clinical decisions. Despite these advances, many hospitals still struggle with inefficiencies resulting from weak Health Information Technology (HIT) governance and limited user skills. Most existing approaches prioritize technical deployment while paying less attention to managerial, organizational, and behavioral factors that are essential for sustainable success. To overcome these limitations, this study introduces and empirically evaluates a comprehensive Health Information Technology Management (HITM) model that combines strategic IT governance, system quality, and user dimensions to improve satisfaction and performance among healthcare professionals. The research examines how governance mechanisms, system quality, and user capabilities affect satisfaction and performance. The specific objectives are to identify the key drivers of system quality, evaluate the relationship between system quality and user satisfaction, and examine how satisfaction impacts user performance. The study contributes theoretically by presenting a more integrated framework that unites concepts from IT governance, Information Systems Success Theory, and Technology Acceptance Theory. It also offers empirical evidence of the importance of managerial structures in driving successful digital transformation in healthcare settings. A survey involving healthcare personnel from three public hospitals in Indonesia was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate strong model validity, accounting for 65% of the variance in user satisfaction and 59% of the variance in performance, with predictive relevance (Q²) values of 0.47 and 0.52, respectively. These outcomes demonstrate that mature governance, leadership support, cross-unit collaboration, and systematic user training enhance system quality, satisfaction, and ultimately performance. Future studies should expand testing in broader healthcare contexts with different resource conditions.
Evaluasi Kepuasan Mahasiswa terhadap Fasilitas Kampus Menggunakan Metode Simple Additive Weighting (SAW) Nugroho, Agung Yuliyanto; Tundo, Tundo; Saidah, Andi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i2.5324

Abstract

Evaluation of student satisfaction with campus facilities is one important aspect in improving the quality of college services and infrastructure. However, this satisfaction evaluation process has a gap between student expectations of the facilities provided by the campus. This research aims to design a Decision Support System (DSS) that can help the campus in assessing student satisfaction levels using the Simple Additive Weighting (SAW) method. This method was chosen because of its ability to conduct an assessment based on criteria that have certain weights so as to produce the best alternative ranking. The criteria used in this study include cleanliness, maintenance, comfort, completeness, condition, service, and level of satisfaction. Data was obtained through questionnaires distributed to students in various study programs, focusing on various aspects of campus facilities such as classrooms, laboratories, libraries, hall areas, and podcast studios. It was then processed using SAW steps, including matrix normalization and final score calculation. The result showed that the final results of the evaluation of student satisfaction with campus facilitiesf from 100 respodents obtained a fairly high score of 44 which was said to be quite good. This SAW method is able to provide a clear ranking of the level of student satisfaction, as well as identify areas that require improvement or enchancement at the college.
Spatial Statistical Analysis for Poverty Mapping Using Machine Learning: Spatial Statistical Analysis for Poverty Mapping Using Machine Learning Nugroho, Agung Yuliyanto; Puji Sarwono
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17883

Abstract

Poverty is a multidimensional problem influenced not only by economic factors but also by spatial dimensions such as geographic location, accessibility, and environmental characteristics. This study aims to analyze spatial patterns of poverty and develop a poverty prediction model using a geospatial data-based machine learning approach. The data used comes from a combination of open sources such as the Central Statistics Agency (BPS), Landsat satellite imagery, and regional infrastructure data. The methods used include spatial autocorrelation analysis (Moran's I) to identify poverty clustering patterns, Local Indicators of Spatial Association (LISA) to detect poverty hotspots, and Random Forest and Gradient Boosting models to predict poverty levels based on environmental, social, and economic variables. The results show that poverty has a significant spatial pattern, where areas with high poverty rates tend to cluster in areas with low infrastructure access and high population density. The machine learning model demonstrated better prediction accuracy than the traditional linear regression approach, with an R² value reaching 0.87 and a lower prediction error rate (RMSE). These findings emphasize the importance of integrating spatial analysis and machine learning technology in understanding the dynamics of poverty geographically. This research contributes to the development of spatial data analysis methods in the context of public policy, particularly in supporting more targeted poverty alleviation intervention planning. The mapping results can serve as a basis for local governments in identifying priority areas, allocating resources, and designing data-driven development policies. Thus, this approach offers an innovative solution towards more efficient and evidence-based decision-making in poverty alleviation in Indonesia.
Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis: Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis Nugroho, Agung Yuliyanto
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17884

Abstract

In the era of digital transformation, e-commerce platforms have become a major driver of economic activity, generating vast amounts of transaction data every day. Analyzing these data can provide valuable insights into customer behavior, purchasing trends, and business performance. This study aims to apply the K-Means clustering algorithm to identify and analyze transaction patterns in e-commerce systems. The research focuses on developing an efficient data-driven approach to segment customers based on their transactional attributes, such as purchase frequency, transaction value, and product category preferences. The methodology involves several stages: data preprocessing, including cleaning and normalization; feature selection based on relevant transactional indicators; and the application of the K-Means clustering algorithm to group customers into clusters with similar characteristics. The Elbow Method was used to determine the optimal number of clusters. Data were processed using the Python programming language and libraries such as Scikit-learn and Pandas. The results reveal that K-Means effectively segments e-commerce customers into distinct groups that reflect their purchasing patterns—ranging from high-value loyal customers to occasional buyers. Each cluster presents unique behavioral profiles that can be interpreted for targeted marketing strategies. The clustering outcome provides useful insights for customer relationship management (CRM), inventory optimization, and personalized product recommendations. In conclusion, the application of the K-Means algorithm demonstrates significant potential in uncovering hidden patterns within large-scale e-commerce transaction data. The findings support the use of mathematical and computational models in improving decision-making processes in digital commerce. Future research is recommended to enhance cluster accuracy by integrating hybrid algorithms or deep learning-based segmentation approaches.
PERBANDINGAN DAYA TARIK VISUAL DAN RASA ANTARA PASTRY ARTISAN DAN PASTRY KOMERSIAL Suswanto, Suswanto; Nugroho, Agung Yuliyanto
Jurnal Education and Development Vol 14 No 1 (2026): Vol 14 No 1 Januari 2026
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v14i1.7641

Abstract

Perkembangan industri kuliner menunjukkan meningkatnya perhatian konsumen terhadap kualitas rasa dan daya tarik visual produk pastry. Pastry artisan, yang dibuat secara tradisional dengan sentuhan kreatif, dianggap memiliki keunggulan estetika dan cita rasa dibanding pastry komersial yang diproduksi massal. Penelitian ini bertujuan untuk membandingkan daya tarik visual dan rasa antara pastry artisan dan pastry komersial serta mengevaluasi preferensi konsumen terhadap kedua jenis produk. Metode yang digunakan adalah uji sensori dengan 50 panelis terlatih menilai aspek visual (warna, bentuk, dekorasi, penataan) dan aspek rasa (manis, gurih, tekstur, aroma, aftertaste) menggunakan skala Likert 1–5. Data dianalisis secara deskriptif dan uji t untuk mengetahui perbedaan signifikan antara kedua jenis pastry. Hasil penelitian menunjukkan bahwa pastry artisan memperoleh nilai visual rata-rata 4,6 dan rasa 4,4, sedangkan pastry komersial masing-masing 3,8 dan 3,9. Analisis uji t menunjukkan perbedaan signifikan pada kedua aspek (p < 0,05), menegaskan bahwa konsumen lebih menyukai pastry artisan. Pembahasan mengungkapkan bahwa keunggulan pastry artisan terkait dengan perhatian pada detail, kreativitas dekorasi, dan penggunaan bahan berkualitas tinggi, sementara pastry komersial cenderung fokus pada produksi massal dengan standarisasi yang membatasi variasi rasa dan estetika. Kesimpulan penelitian ini menegaskan bahwa pastry artisan lebih disukai konsumen baik dari segi visual maupun rasa. Temuan ini memiliki implikasi penting bagi produsen bakery dalam strategi pengembangan produk, diferensiasi, dan inovasi untuk memenuhi preferensi konsumen modern.
POTENSI DAN STRATEGI PENGEMBANGAN WISATA ALAM PUNCAK DARMA, PELABUHAN RATU, SUKABUMI JAWA BARAT Triyono, Joko; Nugroho, Agung Yuliyanto; Nugroho, Dwi Yoso
Jurnal Education and Development Vol 14 No 1 (2026): Vol 14 No 1 Januari 2026
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v14i1.7688

Abstract

Penelitian ini bertujuan untuk mengkaji potensi wisata alam Puncak Darma di Pelabuhan Ratu, Sukabumi, serta merumuskan strategi pengembangannya dalam kerangka pariwisata berkelanjutan. Puncak Darma dikenal sebagai salah satu destinasi unggulan dengan daya tarik panorama laut, tebing, dan keindahan alam pegunungan yang memiliki nilai strategis dalam mendukung sektor pariwisata daerah. Metode penelitian menggunakan pendekatan kualitatif dengan teknik pengumpulan data melalui observasi lapangan, wawancara dengan pemangku kepentingan lokal, serta studi dokumentasi. Hasil penelitian menunjukkan bahwa Puncak Darma memiliki potensi besar untuk dikembangkan sebagai destinasi wisata alam berbasis minat khusus, terutama untuk aktivitas fotografi, olahraga ekstrem, dan wisata edukasi lingkungan. Namun demikian, pengembangan destinasi ini masih menghadapi kendala pada aspek aksesibilitas, fasilitas pendukung, promosi, serta keterlibatan masyarakat lokal. Strategi pengembangan yang direkomendasikan mencakup peningkatan infrastruktur dan akses jalan, penguatan promosi berbasis digital, kolaborasi pemerintah–swasta–masyarakat, serta penerapan prinsip ekowisata agar potensi wisata dapat terkelola secara berkelanjutan. Dengan strategi yang tepat, Puncak Darma berpeluang menjadi ikon pariwisata alam Kabupaten Sukabumi yang mampu mendukung pertumbuhan ekonomi lokal sekaligus menjaga kelestarian lingkungan.
The Utilization of Social Media As a Means of Public Education in Digital-Based PKM Programs Larisu, Zulfiah; Nasim, Abu Sahman; Nugroho, Agung Yuliyanto; Raule, Jean Henry
Socious Journal Vol. 2 No. 2 (2025): Socious Journal - April
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/k9c23w02

Abstract

The development of social media as part of digital transformation has opened up new opportunities in community education efforts, especially through the Student Creativity Program (PKM). This study aims to examine the strategy of utilizing social media as an educational tool in digital-based PKM activities. Social media such as TikTok, Instagram, and Facebook are used strategically to reach various segments of society, taking into account generational characteristics, information needs, and socio-cultural contexts. A descriptive qualitative approach was used in this study, with data collection techniques through in-depth interviews with implementing students, supervising lecturers, and beneficiary communities. Data analysis was carried out thematically with source triangulation to maintain validity. The results of the study indicate that the integration of social media in PKM not only increases access to educational information but also strengthens community participation in the learning process. The success of the program is highly dependent on the design of content that is visually attractive, substantively contextual, and participatory in its implementation. However, challenges related to digital literacy, information credibility, and community involvement still need to be addressed through sustainable strategies. Thus, social media has the potential to be an effective educational platform if utilized critically, creatively, and collaboratively within the framework of digital-based community service.
Co-Authors Abdul Malik Akbar, Riolandi Albadri, Albadri Anshoriyah, Sofiyatul Apriyanto, Apriyanto Aurelius Fredimento Betty Yel, Mesra Christine Widilestari Dina Destari Firdaus, M. Surya Maulana Flora Grace Putrianti Hamka Hamka, Hamka Herryani, Herra Hidayat, Noura Rizqyaannisa Ignatius Septo Pramesworo Joko Triyono Kristanto, Ferat Larisu, Zulfiah Liyan, Sur masriah masriah, masriah Musa Hefer Smas Nasim, Abu Sahman Nugroho, Dwi Yoso Nur Rohman Prasetio, Rachmat Puji Sarwono Purwati Purwati Rao, Ananya Raule, Jean Henry Rosalina Andriyani Ebang Pareira Saidah, Andi Sanjaya, Lalu Rudi Shimbun, Annisa Fikria Sisca Cletus Lamatokan Sitti Hasnah Sri Yulianto Fajar Pradapa Sudiro, Syawal Sukoco, Hendro Suswanto Suswanto, Suswanto Sutopo, Joko Syamsu, Moch. Nur Titik Haryanti Tundo, Tundo Waahib, Achmad Nashrul Wajnah, Wajnah Widiyanto, Nur Wong, Lucas Wulandari, Lastiani Warih Yulianto, Dwi Hery Yuniati, Nining