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All Journal POSITIF InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan SISFOTENIKA Technomedia Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) CCIT (Creative Communication and Innovative Technology) Journal ADI Journal on Recent Innovation (AJRI) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics International Journal of Marine Engineering Innovation and Research ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Journal of Engineering, Science and Information Technology International Journal of Cyber and IT Service Management (IJCITSM) Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Marviola Hardini
Universitas Raharja
Religion Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior Sunarya, Po Abas; Rahardja, Untung; Chen, Shih Chih; Lic, Yung-Ming; Hardini, Marviola
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.155

Abstract

This study compares Logistic Regression and Random Forest in predicting e-commerce customer churn. Utilizing the E-commerce Customer dataset, it navigates the complexities of customer interactions and behaviors, offering a rich context for analysis. The methodology focuses on meticulous data preprocessing to ensure data integrity, setting the stage for applying and evaluating Logistic Regression and Random Forest. Both models were assessed using accuracy, precision, recall, F1-Score, and AUC-ROC. Logistic Regression showed an accuracy of 90%, precision of 91% for class 0 and 82% for class 1, recall of 98% for class 0 and 50% for class 1, F1-Score of 94% for class 0 and 62% for class 1, and AUC-ROC of 0.88. Random Forest, with its ability to handle complex patterns, demonstrated higher overall performance with an accuracy of 95%, precision of 95% for class 0 and 93% for class 1, recall of 99% for class 0 and 74% for class 1, F1-Score of 97% for class 0 and 82% for class 1, and an AUC-ROC of 0.97. This comparative analysis offers insights into each model's strengths and suitability for predicting customer churn. The findings contribute to a deeper understanding of machine learning applications in e-commerce, guiding stakeholders in enhancing customer retention strategies. This research provides a foundation for further exploration into the digital social dynamics that shape customer behavior in the evolving digital marketplace.
Understanding Consumer Acceptance of AI in the Leisure Economy: A Structural Equation Modeling Approach Susilawati; Juliastuti, Dyah; Hardini, Marviola
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2348

Abstract

This research examines the determinants of consumer acceptance of artificial intelligence (AI) in the leisure economy, using a structural equation model to analyze responses from 560 participants. The study focuses on several psychological factors: Perceived Ease of Use (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Value (PV), and Habit (HB), and their impact on Behavioral Intention (BI) to adopt AI technologies. Results indicate significant influence of six constructs (PE, FC, SI, PV, HM, HB) on BI, with the exception of one hypothesis. The research also assesses the role of Personal Innovativeness in enhancing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model's predictive accuracy. This study contributes to understanding AI adoption in leisure, offering valuable insights for AI application development and marketing strategies in this sector.
Interior Design Alphabet Incubator 3.0 Based on Planner 5D Mertayasa, I Komang; Rahardja, Untung; Hardini, Marviola; Harahap, Eka Purnama
Technomedia Journal Vol 8 No 1 Juni (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v8i1.1982

Abstract

Membuat desain interior ruangan 3D membutuhkan proses yang panjang. Proses desain memainkan peran vital dalam menciptakan desain interior 3D yang cukup bagus. Dalam pengerjaan desain interior, permasalahan yang dihadapi adalah kapasitas ruangan dan tata letak objek yang akan ditempatkan. Maka dari itu, desain interior 3D dapat dilihat dari kualitasnya dan menarik bagi yang melihatnya. Terdapat permasalahan pada desain interior 3D yaitu objek terkunci, perlu beralih ke akun prabayar atau premium, tekstur yang terbatas saat menambahkan gambar dari perangkat, kapasitas ruang, dan tata letak objek. Penelitian ini membahas tentang bagaimana penggunaan aplikasi 5D planner dalam desain interior 3D yang berkaitan dengan prinsip dasar interior sederhana, sehingga peneliti mengolah aplikasi Planner 5D dalam desain interior 3D dan metode menggambar konvensional di ruang inkubator alfabet. Namun, penggambaran dilakukan dalam tiga tahap dalam pembuatan desain interior ini. Yaitu penataan 3D yang dibagi menjadi tiga area yaitu bidang objek dan pengaturan sudut pandang menggunakan aplikasi 5D planner.
Viewboard Effectiveness on Raharja Internet Cafe Website as Sales Information Submission Media Nurhaeni, Tuti; Karts, Kawl W; Hardini, Marviola
Aptisi Transactions On Technopreneurship (ATT) Vol 1 No 1 (2019): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v1i1.6

Abstract

Information is a key to success in communicating. Communication will not be possible without information. But if the information we convey is wrong it will cause a problem. At Raharja University there is a facility that provides needs services to students such as searching for material, printing, scans, and binding documents, there are also accessories and service places if there are students who have problems with the equipment given for study, Raharja Internet Cafe. Raharja Internet Cafe has a website to be used by students who want to order their needs. However, the admin who maintains Raharja Internet Cafe cannot monitor what products or goods are the best-selling or the most sold because they are not well monitored. With no monitoring of the admin sales process or the guard cannot report the goods to the University, or the conventional data recording process. These problems are certainly considered ineffective because Raharja University is engaged in technology in each of its lecture activities. Creating a viewboard on the Raharja Internet Cafe website as a medium for delivering sales information is the solution. The method that I use is a method of data collection namely observation and literature study and a qualitative approach. The results of this study are that the presence of a viewboard on the Raharja Internet Cafe website can facilitate the admin or guard of Raharja Internet Cafe to make a report to the Raharja University.
Convolutional Neural Networks in Medical Image Understanding Upreti, Megha; Pandey, Chitra; Bist, Ankur Singh; Rawat, Buphest; Hardini, Marviola
Aptisi Transactions On Technopreneurship (ATT) Vol 3 No 2 (2021): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v3i2.188

Abstract

In the era of social media images/pictures play a vital role. Facebook, whatsapp, instagram everywhere we see a lot of pictures nowadays. Along with social media, the pictures play a very important role in medical science. Medical Image can help in diagnosis, clinical treatment and teaching tasks. Traditional classification of images has reached an end because of its time taking nature and efforts made to extract, select and classify . This problem is solved with the help of CNN(Convolutional neural network).In medical science we have treatment for body anomalies that were not there before .Using the deep learning models of CNN we can detect the disease like Cancer ,Lung Infection and treat it. This article aims to provide a comprehensive survey of applications of CNNs in medical image understanding.
Pemanfaatan AI untuk Kesehatan Publik: Pengalaman dan Harapan Pengguna Sistem Kualitas Udara dengan UTAUT2 Aini, Qurotul; Santoso, Nuke Puji Lestari; Hardini, Marviola; Faturahman, Adam; Maulana, Sabda; Apriliasari, Dwi
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3233

Abstract

Pencemaran udara telah terbukti ada di seluruh dunia. Ini menunjukkan banyak bukti yang mempengaruhi efek buruk terkait kesehatan yang menyebabkan penyakit dan bahkan kematian, dan perkembangan teknologi telah membantu memantau paparan orang terhadap pencemaran udara. Penelitian ini menganalisis faktor-faktor yang mempengaruhi kegunaan yang dirasakan dari deteksi pencemaran udara pada aplikasi AIKU berdasarkan model Teori Penerimaan dan Penggunaan Teknologi yang Terpadu (UTAUT2) dan juga Teori Motivasi Perlindungan Diri (TMPD). Sebanyak 371 peserta dengan sukarela menjawab survei mandiri yang terdiri dari konstruk yang diadaptasi yang mencakup faktor-faktor seperti, Harapan Kinerja (PE), Harapan Upaya (EE), Pengaruh Sosial (SI), Kondisi yang Memfasilitasi (FC), Motivasi Hedonis (HM), Nilai Harga (PV), Niat menggunakan (IU), Meningkatkan Kesehatan Masyarakat (EPH). Metode Persamaan Struktural (SEM) digunakan untuk menentukan faktor-faktor yang mempengaruhi kegunaan yang dirasakan dari aplikasi AIKU. Hasil penelitian menunjukkan bahwa PE adalah faktor utama yang menyebabkan IU yang sangat tinggi di kalangan pengguna sehingga dapat meningkatkan EPH, selain itu FC terbukti menjadi faktor paling signifikan kedua yang mempengaruhi IU, diikuti oleh HM, PV, SI, EE. Studi ini mempertimbangkan evaluasi kegunaan di antara aplikasi seluler terkait kesehatan yang mencakup pencemaran udara. Hasil kerangka kerja dalam model ini diterapkan untuk mengevaluasi faktor dan aplikasi lain yang terkait dengan kesehatan masyarakat
Digital Onboarding in Agricultural Platforms and its Impact on Agricultural Productivity Sunarjo, Richard Andre; Pujiati, Tri; Apriliasari, Dwi; Hardini, Marviola
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 6 No 2 (2025): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v6i2.688

Abstract

This study explores the transformative role of digital onboarding in the agricultural sector, emphasizing its potential to bridge the digital divide, enhance productivity, and promote sustainability. By integrating advanced technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT), digital onboarding provides a pathway for stakeholders to adopt precision agriculture. It also helps overcome key obstacles such as limited digital skills and unequal access to tools or infrastructure. The research extends digital ecosystem theory by framing onboarding as a practical pathway for building inclusive and resilient farming systems. It highlights the importance of integrating technological innovations with policy frameworks and capacity building initiatives to ensure equitable benefits for smallholder farmers and marginalized groups. A qualitative and analytical approach was employed, synthesizing insights from scientific literature, case studies, and expert reviews. The study examines global agricultural digital platforms such as Estagrx to evaluate the effectiveness of digital onboarding strategies in diverse geographical and socio- economic contexts. Findings reveal that digital onboarding strengthens agricultural system robustness by enhancing stakeholder engagement, improving supply chain transparency, and optimizing resource utilization. Additionally, it supports sustainable agricultural practices through precision techniques that reduce wastage and conserve resources. The study concludes that addressing barriers such as cultural resistance and socioeconomic disparities requires collaborative efforts among governments, private sectors, and international organizations to scale digital solutions for agriculture effectively.
Application of Database Normalization in Increasing Data Storage Efficiency Hardini, Marviola; Agarwal, Vertika; Apriani, Desy; Widjaya, Irene Apriani; Setiawaty, Elika; Nurasiah, Nurasiah
International Transactions on Artificial Intelligence Vol. 3 No. 2 (2025): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i2.799

Abstract

Database normalization is a key process in relational database design that reduces redundancy and ensures data integrity. As data volumes increase, maintaining efficient and consistent storage becomes critical. This study investigates the application of normalization techniques from First Normal Form (1NF) to Third Normal Form (3NF) on a sample inventory database to evaluate their impact on storage efficiency. The process focuses on eliminating data repetition and optimizing table structures to enhance performance. Experimental results show that normalization reduces database size by approximately 30%, significantly minimizing redundancy. Smaller, more organized tables improve storage utilization, especially in large-scale systems. However, normalization can introduce query complexity due to increased joins, potentially affecting execution time. Despite this, the trade-off is considered acceptable given the gains in data integrity and storage optimization. This research emphasizes the value of normalization for scalable and maintainable systems. It also aligns with Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) and Goal 12 (Responsible Consumption and Production), by promoting efficient digital infrastructure and responsible data management practices. These improvements contribute to more sustainable, cost-effective systems in industries relying on large-scale data, such as e-commerce, healthcare, and finance. In conclusion, normalization is an essential tool for optimizing storage and ensuring data consistency in relational databases. Although performance trade-offs exist, they can be mitigated through indexing and query optimization. The study offers insights for database designers seeking to balance efficiency and system performance in data-intensive environments.
Management of Utilizing Data Analysis and Hypothesis Testing in Improving the Quality of Research Reports Yusup, Muhamad; Syauqi Naufal, Romzi; Hardini, Marviola
APTISI Transactions on Management (ATM) Vol 2 No 2 (2018): ATM (APTISI Transactions on Management)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.306 KB) | DOI: 10.33050/atm.v2i2.789

Abstract

Data analysis and mathematical techniques play a central role in quantitative data processing. Quantitative researchers estimate (strength) the strength of the relationship of variables, and test hypotheses statistically. Unlike the case with qualitative research. Although qualitative researchers might test a hypothesis in the analysis process, they do not estimate or test hypotheses about the relationship of variables statistically. Through tests or statistical tests can be used as the main means for interpreting the results of research data. It is through this statistical test that we as researchers can compare which data groups and what can be used to determine probabilities or possibilities that distinguish between groups based on an opportunity. Thus, it can provide evidence to determine the validity of a hypothesis or conclusion. In this study, we will discuss the preparation of data for analysis such as editing data, coding, categorizing, and entering data. As well as discussing the differences in data analysis for descriptive statistics and inferential statistics, differences in data analysis for parametric and non-parametric statistics in research, explanations of multivariate data analysis procedures, and also forms of research hypotheses.
Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns Rahardja, Untung; Dewanto, Ignatius Joko; Djajadi, Arko; Candra, Ariya Panndhitthana; Hardini, Marviola
APTISI Transactions on Management (ATM) Vol 6 No 1 (2022): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v6i1.1768

Abstract

This research method uses CRISP-DM with emerging pattern supervision modeling and EPM Algorithm. The contribution of the research is to assist the Government in overcoming the problem of the spread of the COVID-19 cluster in several regions in Indonesia. The research aims to implement information on the COVID-19 data mining pattern in the DKI Jakarta area. The problems faced are the difficulty of identifying the pattern of COVID-19 data in one area, it is difficult to dig up data on the http://corona.jakarta.go.id website. It is not easy to decide on the handling of COVID-19. The output of the research results in a cluster of information on COVID-19 in the DKI Jakarta area based on Significance level depends on the Covid Map In terms of Region, Status, Gender, & age And Signification can be the basis for determining covid OTG, DTG, and Positive. The theoretical and practical implications can be stated as follows: The use of supervised emerging pattern methods can affect the processing of COVID-19 data. For 5 Regions in DKI Jakarta and distribution to determine covid OTG, DTG, and Positive. The result of the development of this data mining system is to produce pattern reports to produce Supervised Emerging Patterns technology for decision making at the COVID-19 Task Force in DKI Jakarta.
Co-Authors Abigail Leffia Aditya Mardiansyah Agarwal, Vertika Alfri Adiwijaya Ali Djamhuri Amitkumar Dudhat Anandha Fitriani Andriyansah . Ani Wulandari Ankur Singh Apriliasari, Dwi Ariya Panndhitthana Candra Arko Djajadi Bist, Ankur Singh Chandra Abianto Chen, Shih Chih Cristiano Gatot Wagner Dedeh Supriyanti Desy Apriani Dewi Immaniar Dwi Andayani Dwi Julianingsih Dwi Julianingsih Dwi Nur Ramadhan Eka Dian Astuti Elisa Ananda Natalia Faturahman, Adam Fhia Amelia Fitra Putri Oganda Fitra Putri Oganda Fitra Putri Oganda Green, Thomas Harahap, Eka Purnama Hetilaniar Hetilaniar Hikam, Ihsan Nuril Hiroshi Kenta Husni Teja Sukmana Ignatius Joko Dewanto, Ignatius Joko Ihsan Nuril Hikam Ilham, Muhammad Ghifari Isabella Maria Juliastuti, Dyah Karts, Kawl W Kristina Vaher Lena Magdalena Lic, Yung-Ming Lusyani Sunarya Mardiana Mardiana Marta Rodriguez Maulana Abbas Maulana Arif Komara Maulana Faqih Farail Maulana, Sabda Megha Upreti Mertayasa, I Komang Mey Anggraeni Mochamad Heru Riza Chakim Muhamad Yusup Muhammad Rehan Anwar Nanda Septiani Nasrul Hidayat Ninda Lutfiani Novalita Savitri, Agnes Nuke Puji Lestari Santoso Nur Azizah NURAENI, RANI Nurasiah Nurasiah Nurhaeni, Tuti Pandey, Chitra Parker, Jonathan Po Abas Sunarya Pujiati, Tri Qurotul Aini Qurotul Aini Qurotul Aini Rafika, Ageng Setiani Ramiro Santiago Ikhsan Rawat, Buphest Richard Andre Sunarjo Rissa Rahmania Santoso, Nuke Puji Lestari Semaria Eva Elita Girsang Setiawaty, Elika Sheila Aulia Anjani Sheila Aulia Anjani Sherli Triandari Siti Nurindah Sari Sondang Visiana Sihotang Souza Nurafrianto Windiartono Putra Suca Rusdian Sudaryono Sudaryono Sunarjo, Richard Andre Suryari Purnama Syauqi Naufal, Romzi Tarisya Ramadhan Tuti Nurhaeni Untung Rahardja Upreti, Megha Widjaya, Irene Apriani Widya Febriani Yao, Goh Yusuf Tojiri