Publication Search

This literature review analyzes the impact of data privacy issues on consumer usage intention in Indonesian e-commerce platforms. The study aims to map privacy threats and understand the phenomenon of the privacy paradox in the context of digital retail. Using a literature review method, this study synthesized findings from international journals published in the last five years from Scopus, Web of Science, and Google Scholar databases. The results show that excessive data collection, unauthorized secondary use, and data breaches significantly increase consumer privacy concerns. However, the research also confirms a privacy paradox, where consumers willingly share personal data to gain shopping convenience and promotions. The findings highlight that strong brand trust and a seamless user experience can effectively mitigate these privacy fears. Practically, this study recommends that e-commerce companies implement the Privacy by Design framework, utilize dynamic passwords (OTP), and provide transparent privacy settings as a competitive marketing advantage. Future research should explore specific regional demographics and the direct impact of new technologies like AI chatbots on consumer security perceptions.

The development of the digital economy has driven increased customer interaction through online chat services, making customer satisfaction a key factor in business success. Response speed and chat service quality are two important aspects in shaping the customer experience, but previous research has tended to examine them separately. This study aims to analyze the influence of online chat services and response speed on customer satisfaction partially and simultaneously. The method used is a qualitative approach with a literature review of 12 scientific articles from 2020–2025 obtained from academic databases such as Google Scholar and SINTA. The analysis technique used is descriptive-critical through the identification, comparison, and synthesis of previous research findings. The results show that online chat services have a positive effect on customer satisfaction, primarily through interaction quality such as information accuracy, ease of use, and problem-solving ability. Response speed has also proven to be an important determinant, where a fast response significantly increases customer satisfaction. However, speed without quality has the potential to decrease satisfaction. The discussion shows that the two variables have a complementary and inseparable relationship. Online chat services function as a medium for interaction, while response speed is a quality attribute that determines the effectiveness of the service. Therefore, the integration of both in one model is the main contribution of this research in filling the literature gap, especially in the context of e-commerce in Indonesia.

The advancement of Artificial Intelligence (AI) has brought significant changes to various aspects of life, including the field of education, particularly Islamic Education. This study aims to explore various AI-based learning strategies that can be implemented to create a more effective, interactive, and contemporary learning process. The method employed in this study is a literature review by examining various scholarly sources, such as journals, books, and academic articles discussing the implementation of AI in education. The findings indicate that the integration of AI can be realized through personalized learning tailored to students’ needs, the use of chatbots as learning assistants, the analysis of students’ progress and learning outcomes, as well as the utilization of technology-based learning media. These strategies are not only capable of improving students’ cognitive aspects but can also strengthen the internalization of Islamic values when applied appropriately and systematically. Furthermore, this study emphasizes that teachers’ technological competence, infrastructure readiness, and the development of an adaptive curriculum are crucial factors in the successful implementation of AI-based learning within the context of Islamic Education.

Mental health issues among university students have become a growing concern, driven by academic pressures, career uncertainties, and complex social transitions. However, a large proportion of students remains reluctant to seek professional psychological support due to social stigma and limited access to institutional counseling services. This study proposes the design and implementation of an Android-based chatbot application that integrates mood tracking and sentiment analysis to continuously monitor the emotional states of university students. The system employs a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) model trained on the EmoContext conversational dataset (SemEval-2019 Task 3), comprising 30,160 labeled three-turn dialogue instances across four emotion classes: angry, happy, sad, and others. The model was fine-tuned for four epochs using the AdamW optimizer with a learning rate of 2e-5 and a maximum sequence length of 128 tokens. Evaluation on a held-out validation set of 6,032 samples yielded an overall accuracy of 88.28%, a macro-average F1-score of 0.87, and a weighted-average F1-score of 0.88. Per-class F1-scores were 0.89 (angry), 0.83 (happy), 0.91 (others), and 0.86 (sad). The classified emotion is transmitted in real time to the chatbot response logic, which generates empathetic replies and personalized relaxation recommendations based on the detected mood. Primary data collection through questionnaires and interviews with 62 and 19 university students respectively confirmed the need for accessible digital mental health support. The results demonstrate that RoBERTa-based fine-tuning on conversational data provides a reliable foundation for real-time emotion-aware mental health chatbot systems.

This study aims to analyze the implementation of Artificial Intelligence (AI)-based learning media and its implications for student learning motivation in Islamic Religious Education (PAI). This study uses a systematic literature review approach by examining various reputable scientific articles discussing the integration of AI in education and the dynamics of learning motivation in the context of PAI. The results of the study indicate that the use of AI through adaptive learning systems, educational chatbots, gamification, and learning analytics can increase the effectiveness, personalization, and interactivity of learning. This implementation has a positive impact on cognitive motivation through increased conceptual understanding, affective motivation through active participation and emotional engagement, and spiritual motivation through strengthening reflection and internalization of Islamic values. However, ethical challenges, the risk of depersonalization of the teacher's role, and inequality in digital access are crucial issues that require policy attention and human-centered pedagogical design. Theoretically, this study offers an integrative conceptual framework that combines technological innovation with Islamic educational epistemology. Practically, the results of this study provide recommendations for teachers, schools, and policymakers to develop AI-based PAI learning models that are adaptive, ethical, and oriented towards character building.

This research aims to develop a customized chatbot based on a local large language model (LLM) using Ollama Anything as a form of psychosocial support for Pencak Silat athletes. Mental toughness is a critical factor for Pencak Silat athletes, particularly when coping with competitive failure or sports-related injuries. Injuries sustained in Pencak Silat competitions often involve psychological consequences, including trauma, fear, anxiety, and disturbances in self-identity. To address these challenges, the proposed chatbot functions as a screen-integrated psychosocial support system for athletes. This research used an experimental method combined with Natural Language Processing (NLP) techniques was employed to construct a digital twin chatbot capable of simulating athlete-centered conversations. The Pencak Silat Athlete Chatbot is designed to assist athletes by providing responsive support when they experience defeat or performance setbacks during competitions. The research findings indicate that, although the chatbot is functional, its conversational responses remain relatively rigid, access times are prolonged, and further testing with Pencak Silat athletes in controlled settings is required. Overall, the development of the Pencak Silat Athlete Digital Twin Chatbot represents an ongoing effort to advance digital innovation and strengthen the ecosystem of sports achivements development in Indonesia.

The development of digital technology has driven transformations in public services, including the judicial sector. Legal Aid Posts (Posbakum) in Religious Courts play a strategic role in ensuring access to justice, particularly for vulnerable and economically disadvantaged communities. Although Posbakum services have been implemented in accordance with Supreme Court Regulation (PERMA) Number 1 of 2014, challenges persist at the pre-litigation stage, especially regarding the public’s initial legal information readiness and administrative preparedness. Field data from the Posbakum of the Slawi Religious Court for the period January-August 2025 show that out of 1,337 registered cases, approximately 600 applicants arrived with administratively incomplete documents at their initial visit. These shortcomings were generally technical and could be completed within the same service flow. This condition indicates the need to strengthen access to early legal information that is clear, understandable, and easily accessible. This study aims to analyze community needs for digital-based Posbakum services at the pre-litigation stage and to formulate a conceptual chatbot model as an alternative service enhancement. The research employs a descriptive qualitative approach through interviews, observations, and document analysis. The findings indicate that the community requires a legal information system that is simple, interactive, and accessible via mobile devices. Based on these findings, this study proposes the SI-SATSET chatbot model (Simple, Accountable, Targeted, Efficient, and Affordable Information System) as a digital pre-litigation assistant. This model is expected to support Posbakum performance, strengthen community legal empowerment, and improve access to justice in Religious Courts.  

The rapid advancement of Information and Communication Technology (ICT) has accelerated the digital transformation of public services, including land administration. However, public complaint services at the Land Office of Southwest Sumba Regency still encounter challenges such as unstructured complaint procedures, manual data processing, risk of data loss, and limited public access to clear information. These issues highlight the need for an innovative and accessible complaint information system. This study aims to design and implement a chatbot-based public complaint service information system to enhance accessibility, transparency, and service effectiveness. A qualitative research method with a system development approach was applied. Data were obtained through interviews, observations, and documentation. The system was developed using a rule-based approach with a Finite State Machine (FSM) algorithm and implemented through the Typebot.io platform. The findings indicate that the chatbot provides structured, consistent, and user-friendly information, reduces manual workload, and improves public readiness before submitting complaints directly, while supporting future integration and system enhancement.

The application of Large Language Models (LLM) in public services encourages government agencies to adopt Retrieval Augmented Generation (RAG)-based chatbots as interfaces for regulatory knowledge and official documents. Although RAG is designed to increase the supportability of answers to authoritative sources, various studies show that this system is still vulnerable to hallucinations, which have the potential to reduce public trust and pose legal risks. This article presents a Systematic Literature Review (SLR) on the use of RAG in government chatbots with a focus on the definition, mitigation strategies, and evaluation of groundedness. The literature search was conducted in the period 2021–2025 through the SpringerLink, Scopus, and Taylor & Francis databases, resulting in 7,947 articles filtered using the PRISMA framework to obtain 100 articles Q1–Q2. Based on eight research questions, this study maps publication trends, document domains, RAG architecture, retrieval strategies, definitions of groundedness, and evaluation metrics used. The SLR results indicate conceptual fragmentation in the definition and measurement of groundedness, with the dominance of text-similarity-based metrics that are inadequate for regulatory contexts. As a conceptual contribution, this article formulates the Semantic Alignment Score (SAS) as a groundedness metric based on semantic alignment, evidence coverage, and entailment relationships, positioned to support the evaluation and auditing of government document chatbots.

In this modern era, governments are required to provided faster, more efficient services that can be accessed anytime and anywhere. Digital transformation encourages governments to introduce service innovations, one of which is through the use of chatbots. Therefore, this study uses a Grounded Theory-based Systematic Literature Review (SLR) approach to map and analyze 100 research related to use of chatbots in the context of government administration, considering the year of publication, type of publication, sector domain, purpose of chatbot implementation, and user satisfaction level. The assessment results show that research related tor chatbots has increased sharply since 2020, with the greatest focus on the administration sector, e-government, and policies or regulations that emphasize the advantages of chatbots in providing fast, responsive, and efficient services. There are few researches that examine in depth other aspects such as the quality and accuracy of information, data security, and the level os user trust. Accordingly, subsequent researchers should focus on strengthening the quality of information, improving security and data protection, and expanding studies in sectors that have been under-researched so that the use of chatbots in government administration can develop more evenly, effectively, and sustainably.

Fenomena chatbot dalam budaya populer mengalami perkembangan pesat seiring majunya teknologi Artificial Intelligence (AI) dan Natural Language Processing (NLP). Melalui pendekatan Systematic Literature Review (SLR) terhadap sejumlah publikasi ilmiah periode 2017–2025, penelitian ini menganalisis tren, peran, dan dampak sosial-budaya dari penggunaan chatbot dalam berbagai domain seperti pendidikan, musik, anime, fandom, dan interaksi manusia–komputer. Hasil kajian menunjukkan adanya peningkatan signifikan jumlah publikasi, terutama sejak 2020, yang menandai semakin kuatnya integrasi AI dalam praktik budaya populer. Analisis tematik berbasis Grounded Theory mengungkap bahwa chatbot tidak hanya berfungsi sebagai alat bantu percakapan, tetapi juga sebagai agen sosial, representasi budaya, dan medium pembentukan identitas digital. Di sisi lain, fenomena ini memunculkan isu etika seperti otentisitas budaya dan privasi data pengguna. Penelitian ini menegaskan perlunya pendekatan multidisipliner dalam pengembangan chatbot budaya populer agar dapat menghadirkan inovasi yang cerdas, etis, serta berkesadaran budaya.

Perkembangan teknologi kecerdasan buatan mendorong pemanfaatan chatbot sebagai media interaksi digital di berbagai bidang. Namun, rendahnya keterlibatan pengguna masih menjadi tantangan utama dalam penggunaan chatbot. Gamifikasi hadir sebagai pendekatan yang berpotensi meningkatkan engagement dengan mengintegrasikan elemen permainan seperti poin, lencana, level, tantangan, dan penghargaan ke dalam sistem chatbot. Penelitian ini bertujuan untuk mengkaji secara sistematis integrasi chatbot dan gamifikasi melalui tinjauan literatur terhadap publikasi ilmiah periode 2020–2025. Metode yang digunakan adalah Systematic Literature Review (SLR) berbasis Grounded Theory dengan tahapan define, search, select, dan analysis. Proses seleksi menghasilkan 100 artikel yang dianalisis untuk mengidentifikasi tren penelitian, domain penerapan, target pengguna, serta peran dan efektivitas elemen gamifikasi. Hasil kajian menunjukkan bahwa bidang pendidikan menjadi domain dominan dalam penerapan chatbot gamifikasi, diikuti oleh sektor bisnis dan hiburan. Elemen gamifikasi terbukti mampu meningkatkan keterlibatan, motivasi, dan pengalaman pengguna, khususnya pada konteks pembelajaran. Namun demikian, sebagian besar penelitian masih menerapkan mekanisme gamifikasi yang bersifat sederhana dan berfokus pada evaluasi jangka pendek. Selain itu, aspek etika, privasi, dan personalisasi pengguna masih kurang mendapat perhatian. Penelitian ini menyimpulkan bahwa integrasi chatbot dan gamifikasi memiliki potensi besar dalam meningkatkan engagement pengguna, serta membuka peluang penelitian lanjutan pada pengembangan mekanisme adaptif, studi longitudinal, dan penerapan lintas domain di masa depan.

Perkembangan teknologi kecerdasan buatan telah memberikan dampak signifikan pada berbagai sektor, termasuk sektor pendidikan. Salah satu implementasi kecerdasan buatan yang banyak digunakan adalah chatbot, program perangkat lunak yang dirancang untuk mensimulasikan percakapan melalui teks atau suara. Dalam pendidikan, chatbot berfungsi sebagai jembatan komunikasi antara manusia dan sistem digital, yang sangat populer berkat kemajuan dalam kecerdasan buatan, pembelajaran mesin, jaringan saraf, dan pemrosesan bahasa alami. Kondisi ini menunjukkan bahwa chatbot telah menjadi bagian penting dari transformasi pendidikan digital. Penelitian ini bertujuan untuk mengidentifikasi tren, topik, dan peran chatbot dalam pendidikan digital melalui pendekatan kajian literatur. Metode penelitian yang digunakan adalah Grounded Theory Literature Review. Pengumpulan data dilakukan dengan mengumpulkan dan menyeleksi artikel ilmiah berdasarkan kata kunci terkait chatbot dan pendidikan, kesesuaian tahun publikasi, topik, dan kelayakan sumber. Setiap artikel diekstraksi informasinya untuk memperoleh gambaran mengenai distribusi artikel sesuai tahun publikasi, metode penelitian yang digunakan, fokus tingkat pendidikan, perkembangan topik penelitian, serta peran chatbot dalam proses belajar. Hasil penelitian menunjukkan adanya peningkatan minat terhadap penggunaan chatbot di berbagai jenjang pendidikan, terutama terkait pengetahuan umum, pembelajaran bahasa, pembelajaran daring, dan keterampilan kognitif. Temuan ini memberikan kontribusi dalam memahami perkembangan chatbot sebagai bagian dari pendidikan digital dan memberikan implikasi bagi pengembangan teknologi pembelajaran yang lebih adaptif dan responsif.

Perkembangan teknologi kecerdasan buatan (AI) telah menciptakan peluang baru dalam layanan kesehatan mental, khususnya melalui chatbot berbasis teks. Chatbot berkembang sebagai alat yang skalabel dan mudah diakses untuk dukungan emosional instan. Namun, di balik popularitasnya, pemahaman menyeluruh mengenai validitas klinis dan tantangan etisnya masih terfragmentasi. Penelitian ini bertujuan meninjau literatur secara kritis untuk memetakan perkembangan, mengevaluasi efektivitas, dan mengidentifikasi tantangan utama chatbot. Menggunakan metodologi Grounded Theory Literature Review (GTLR), sebanyak 111 artikel peer-reviewed yang terbit antara 2020 dan 2025 dianalisis. Sintesis data mengidentifikasi sebuah kategori inti yang disebut "Paradoks Aksesibilitas Semu". Temuan menunjukkan bahwa meskipun chatbot berhasil mendemokratisasi akses layanan secara kuantitas, efektivitasnya sering kali bias oleh dominasi demografi pengguna muda (digital natives) dan mekanisme "Digital Placebo", bukan karena kedalaman intervensi klinis yang substantif. Lebih jauh, analisis mengungkap kelemahan sistemik berupa "Jalan Buntu Digital", di mana chatbot sering gagal menangani situasi krisis berisiko tinggi, serta adanya isu komodifikasi data emosional yang mengancam privasi pengguna. Penelitian ini menyimpulkan bahwa narasi chatbot sebagai pengganti terapis adalah prematur. Peran yang paling etis dan efektif untuk teknologi ini adalah sebagai "Asisten Triase" yang terintegrasi dalam model perawatan berjenjang, di bawah pengawasan ketat prinsip bioetika primum non nocere. Pendekatan multidisiplin yang kuat antara psikologi, teknis, dan etika diperlukan untuk memastikan intervensi ini aman.

The development of Artificial Intelligence (AI) in the 21st century has brought significant transformation to the field of education, including Islamic Religious Education (PAI). One of the most practical implementations of this technology is the chatbot an automated conversational system capable of providing quick and contextual responses to user queries. This study aims to analyze the utilization of AI-based chatbots in addressing Islamic-related questions among students of the Islamic Education Department at IAIN Kerinci. The research employs a descriptive qualitative approach, with data collected through interviews, observations, and documentation. The findings reveal that students use chatbots as an initial source of information to understand Islamic concepts such as fiqh, tafsir, and hadith. Chatbots serve as learning aids that promote active learning and enhance students’ digital religious literacy. However, the study also identifies limitations related to the accuracy and validity of the sources used by the system, indicating that students still need verification from lecturers and authoritative Islamic literature. Overall, AI-based chatbots hold great potential to support interactive and contextual Islamic learning, provided their use is guided by academic supervision rooted in Islamic values.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

The development of Artificial Intelligence (AI) technology has provided new opportunities to enhance the quality of learning, particularly in elementary science education. One form of AI utilization is chatbots, which function as interactive learning media capable of encouraging students’ active participation. In science learning, questioning behavior is an important indicator of students’ cognitive engagement; however, many elementary school students tend to be passive in asking questions. This study aimed to examine the effect of using AI chatbots in science learning on students’ questioning behavior in Grade V of SDN Plalangan 03 Gunungpati, Semarang City. This study employed a quantitative approach with a quasi-experimental design. Data were collected through observation, questionnaires, and documentation. The data were analyzed to identify changes in students’ questioning behavior before and after the implementation of AI chatbots in science learning. The results indicated that the use of AI chatbots improved students’ questioning behavior in terms of frequency, confidence, and activeness in asking questions. AI chatbots provided a comfortable and flexible interaction space for students, which helped reduce psychological barriers in questioning. It can be concluded that the use of AI chatbots has a positive effect on students’ questioning behavior in elementary school science learning.

This paper reviews the role of Artificial Intelligence (AI) in Islamic education within secondary schools, emphasizing both its practical uses and the ethical challenges it presents. The review looks into the current trends, tools, and the impact of AI on the learning experience, as well as its ethical implications from an Islamic perspective. The study follows a systematic literature review (SLR) approach based on the PRISMA guidelines and includes research from 2022 to 2025, sourced from platforms like Google Scholar. After a thorough selection process, 15 articles were included in the review, offering valuable insights into the technological and ethical aspects of AI in Islamic secondary education. The use of AI has notably enhanced learning outcomes in Islamic education by allowing personalized learning, boosting student engagement, and streamlining feedback mechanisms. Tools like intelligent tutoring systems and educational chatbots have been widely adopted. However, challenges around data privacy, algorithmic bias, and technology access persist. Additionally, incorporating Islamic ethical values into AI-driven educational platforms presents both opportunities and challenges. Addressing these ethical implications is vital, requiring frameworks that align with Islamic principles such as maṣlaḥa (public welfare), justice, and human dignity. Education policies and teacher training programs should concentrate on promoting the responsible use of AI, ensuring it improves educational experiences while preserving ethical and cultural integrity.

In their growth and development, fish can be affected by various types of diseases, one of which is caused by bacteria. FisherBot is an innovative solution in the form of an integrated Internet of Things (IoT)-based system that combines ozonation technology to prevent fish disease outbreaks, and is equipped with a beginner-friendly artificial intelligence (AI)-based chatbot feature. The method used in this research is the ADDIE development model, which includes Analysis, Design, Development, Implementation, and Evaluation. FisherBot consists of several sensors, namely DHT-11, pH, TDS-40, and turbidity, to monitor pond water quality in real-time, and an ozonizer that injects 0.5 ppm/hour, functioning as a sterilizer for pond water. The results obtained show that the FisherBot system is able to accurately monitor water quality parameters and significantly reduce total bacteria. The test results show that all sensors are able to work with good accuracy, with an average temperature measurement of 28,9℃, pH of 6.94, TDS-40 of 869 ppm, and turbidity of 27,4 NTU. Using the Total Plate Count (TPC) method, total bacterial testing showed that the bacterial population in the pond decreased to an average of 3,200 CFU/ml compared to the control without ozone, which was 5,777.78 CFU/ml. FisherBot is expected to increase productivity, reduce fish mortality, and sustainably enhance the contribution of the aquaculture sector to national income.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered