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All Journal Startupreneur Business Digital (SABDA Journal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Madani, Muchlishina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Foundations for AI Driven Communication Models Qualitative Analysis of Indonesian Language Adaptation E-Commerce Sunarya, Po Abas; Prabowo, Dimas Aditya; Angel, Mary; Fitriawati, Nora; Fernando, Erick; Susetyono, Eko; Madani, Muchlishina
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.v3i2.798

Abstract

In the rapidly evolving digital economy, effective communication between sellers and customers on e-commerce platforms plays a pivotal role in shaping user experience and satisfaction. This study explores the adaptation of the Indonesian language within these interactions, focusing on the linguistic styles, usage patterns, and communication challenges faced by sellers and customers. Employing a qualitative descriptive approach, data were collected from direct conversations and product descriptions on leading Indonesian e-commerce platforms. Findings reveal a dominant use of semi-formal and informal language styles, enhanced by abbreviations, emojis, and popular digital jargon, which collectively foster a sense of familiarity and responsiveness. However, balancing language standardization with the demands for fast and engaging communication remains a significant challenge. The results underline the critical need for communication models that can adapt to the dynamic nature of digital discourse while maintaining clarity and politeness. This research lays the groundwork for developing intelligent communication systems powered by artificial intelligence, which can effectively interpret and generate contextually appropriate language in e-commerce settings. The insights gained here offer valuable foundations for future work in creating AI-driven tools aimed at enhancing digital customer engagement and satisfaction through culturally and linguistically aware communication strategies.
Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications Andayani, Dwi; Madani, Muchlishina; Agustian, Harry; Septiani, Nanda; Wei Ming, LI
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.29

Abstract

In the dynamic realm of digital marketing, the convergence of Big Data and machine learning has ushered in transformative changes, reshaping strategies through advanced data analytics and predictive modeling. This paper examines the pivotal role of these technologies in enhancing marketing practices, focusing on their impact on consumer targeting, engagement, and overall campaign effectiveness. By harnessing vast datasets and applying sophisticated machine learning algorithms, marketers can now predict consumer behavior with unprecedented accuracy, personalize marketing messages, and optimize operational strategies to maximize engagement and return on investment. Despite the profound advantages, the integration of these technologies raises substantial challenges, including data privacy concerns and the need for specialized skills. Through a mixed-methods approach combining quantitative data analysis and qualitative interviews, this study not only demonstrates the improved predictive accuracy and segmentation capabilities afforded by these technologies but also discusses the barriers to their full potential realization. The findings highlight a clear trajectory towards more data-driven, responsive marketing paradigms, suggesting a future where digital marketing strategies are increasingly informed by insights derived from Big Data and machine learning. This paper aims to provide a comprehensive overview of the current landscape and future potential of these transformative technologies in digital marketing.
Digital Innovation in Smart Waste Sorting Using Renewable Energy for Sustainable Startups Rahardja, Untung; Santoso, Nuke Puji Lestari; Oganda, Fitra Putri; Madani, Muchlishina; Saputra, Muhamad Stabil Tanwin
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 1 (2026): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i1.1063

Abstract

The accumulation of inorganic waste in urban environments requires inno- vative technological solutions that support the Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production). Smart waste management systems integrating Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as promising digital innovations to improve waste sorting efficiency. This study presents the development of a smart waste sorting system called Orange Box, designed to support sustainable startup initiatives in environmental technology. A major challenge in deploying IoT-based devices in outdoor public areas is the limited availability of conventional electrical infrastructure, where reliance on extension cables is inefficient and potentially unsafe. Therefore, this research aims to design and evaluate an independent off-grid electrical system based on renewable energy to ensure continuous operation of the device. The proposed system utilizes solar panels as the primary energy source, with energy conversion and distribution managed through a 500W inverter and a 20A Power Supply Unit (PSU) that supplies power to a Raspberry Pi 5–based control system. Experimental measurements indicate that the system operates with an average power consumption of approximately 10–12W and reaches a peak load of 17.17W during active waste sorting operations. The estimated daily energy consumption ranges from 288Wh to 338Wh when considering inverter efficiency. These findings demonstrate that integrating renewable energy infrastructure with IoT-based smart waste sorting technology represents a viable digital innovation to support sustainable startups while contributing to SDG 7 (Affordable and Clean Energy).
Sharia-Guided Artificial Intelligence for Ethical Transformation in Modern Education Madani, Muchlishina; Agustian, Harry; Faturahman, Adam; Sutarman, Asep; Ikhsan, Ramiro Santiago
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.975

Abstract

The rapid advancement of digital technologies has accelerated the integration of Artificial Intelligence (AI) into educational systems worldwide. Despite its benefits, concerns about ethics, algorithmic bias, transparency, and value alignment remain significant, particularly in faith-based educational institutions that emphasize moral and spiritual development. This study aims to propose a Sharia-Guided Artificial Intelligence (SGAI) model that supports ethical, accountable, and value-oriented transformation in modern education. The research employs a mixed-method approach by combining qualitative analysis of Islamic educational principles and ethical frameworks with quantitative evaluation of AI-supported governance practices in educational institutions. Data were collected from educators, administrators, and students within Islamic educational environments to assess the feasibility, acceptance, and integrity of AI implementation within a Sharia-compliant framework. The results indicate that AI systems guided by Sharia principles can improve curriculum personalization, administrative efficiency, transparency, and institutional accountability while reducing algorithmic bias and maintaining ethical values in educational processes. Furthermore, the proposed model encourages responsible data governance, ethical decision-making, and inclusive learning environments supported by intelligent technologies. The integration of intellectual, ethical, social, and spiritual dimensions also strengthens holistic educational development. In conclusion, embedding faith-based ethical intelligence into AI-driven educational management can foster sustainable, trustworthy, and socially responsible innovation while providing a practical framework for value-aligned AI adoption in education.
Co-Authors Agustian, Harry Angel, Mary Dwi Andayani, Dwi Erick Fernando B311087192 Faturahman, Adam Fitra Putri Oganda Fitriawati, Nora Ikhsan, Ramiro Santiago Po Abas Sunarya Prabowo, Dimas Aditya Santoso, Nuke Puji Lestari Saputra, Muhamad Stabil Tanwin Septiani, Nanda Susetyono, Eko Sutarman, Asep Untung Rahardja Wei Ming, LI