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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Pendidikan (Teori dan Praktik) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Ilmiah Flash Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) ILKOM Jurnal Ilmiah SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal ilmiah teknologi informasi Asia Advance Sustainable Science, Engineering and Technology (ASSET) Systematic Literature Review Journal
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Pengembangan e-LKPD Interaktif Berbasis Liveworksheets sebagai Sarana Pembiasaan Berpikir Kritis Peserta Didik Moh. Robieth Alfan Alhamid; Triyanna Widiyaningtyas; Satria Putra Pratama
Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Vol. 5 No. 3 (2025): Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI), 2025 (3)
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/jppi.v5i3.1794

Abstract

Perkembangan teknologi memberikan peluang besar untuk meningkatkan kualitas pembelajaran, seperti penggunaan media digital. Penelitian ini bertujuan untuk mengembangkan e-LKPD interaktif berbasis Liveworksheets yang berfungsi sebagai sarana untuk pembiasaan peserta didik berpikir kritis dalam proses pembelajaran. Penelitian menerapkan metode Research and Development (R&D), menggunakan model pengembangan 4D (Define, Design, Develop, Disseminate). Data dikumpulkan melalui observasi, tes diagnostik, wawancara, validasi ahli, serta angket respon peserta didik. Perolehan hasil penelitian berupa data kuantitatif dan kualitatif. Hasil validasi ahli media didapatkan persentase kelayakan 85% dan ahli materi 82%, keduanya termasuk sangat layak. Pengujian kelompok kecil diperoleh persentase 88,4% dan pada kelompok besar diperoleh 83%, keduanya menunjukkan bahwa pembiasaan berpikir kritis peserta didik dapat dilatih menggunakan e-LKPD yang dikembangkan. e-LKPD ini mengintegrasikan pendekatan Culturally Responsive Teaching (CRT) dan Teaching at the Right Level (TaRL) untuk menyesuaikan materi dengan karakteristik peserta didik. Melalui penelitian ini disimpulkan bahwa pemanfaatan Liveworksheets untuk mengembangkan e-LKPD dapat menjadi alternatif dalam membiasakan peserta didik mampu berpikir kritis pada konteks pembelajaran informatika di SMP.
Educational Data Mining: Multiple Choice Question Classification in Vocational School Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3499

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification
Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency Riska, Suastika Yulia; Widiyaningtyas, Triyanna; Elmunsyah, Hakkun; Sendari, Siti
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.1227

Abstract

The swift advancements in Artificial Intelligence and Machine Learning have rendered datasets essential; nonetheless, their heightened utilization has engendered intricate ethical dilemmas that are frequently neglected. This study seeks to delineate and highlight ethical concerns associated with the collection of primary data and the reutilization of secondary datasets in computer science research. We employed a Systematic Literature Review (SLR) methodology in accordance with the PRISMA 2020 guidelines, examining 72 publications sourced from five esteemed academic databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar) published from 2021 to 2025. The study results indicate that ethical difficulties emerge uniformly in both primary and secondary datasets. Primary datasets primarily face challenges related to privacy threats, anonymization, and Informed Consent, whereas secondary datasets are more susceptible to licensing infringements, dataset repurposing, and insufficient preparation transparency. The three domains that predominantly encountered these challenges were Machine Learning, Computer Vision, and Natural Language Processing. Moreover, practices of data manipulation, including cherry-picking and concealed preparation, were identified as detrimental to scientific integrity. This study's findings underscore the need for enhanced ethical standards for datasets and greater transparency in preparation documentation to ensure the repeatability of data-driven research.
EVALUATION OF CITIZEN SCIENCE PARTICIPANTS' SATISFACTION WITH GEOSPATIAL TECHNOLOGY IN SMART GOVERNANCE Sari, Heni Vidia; Arief, M. Habibullah; Widiyaningtyas, Triyanna
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9232

Abstract

IT productivity paradox phenomenon shows that increasing investment in information technology is not always followed by increasing productivity or user satisfaction. In the context of smart governance, citizen science becomes a strategic approach to evaluate the effectiveness of geospatial technology. This study explores the relationship between self-efficacy, spatial abilities, and user knowledge of system quality and information quality and their impact on user satisfaction. The case study was conducted on the Sistem Informasi Penataan Ruang (Si-Petarung) of Malang City. The results showed that self-efficacy has the most dominant influence on system quality with a dominance value of 40.5%, and user knowledge has the greatest impact on information quality with a dominance value of 32.29%. Information quality is also proven to be the most significant factor in increasing user satisfaction with a dominance value of 47.93%. These findings emphasize the importance of individual abilities, system quality, and information quality in supporting the successful implementation of geospatial technology and provide a basis for developing a system that is more inclusive and responsive to user needs.
Comparative Study of Random Forest and Ordinal Regression in Concept Map Quality Assessment: The Role of TF-IDF, BERT, and SMOTE-based Balancing Rismayanti, Nurul; Prasetya, Didik Dwi; Widiyaningtyas, Triyanna; Hirashima, Tsukasa
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2906.336-345

Abstract

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics
Pengembangan Self-Management melalui PjBL untuk Meningkatkan Kemampuan Computational Thinking pada Pembelajaran Informatika Fase D: Penelitian Natasya Titania Ramadhanti; Triyanna Widiyaningtyas; Satria Putra Pratama
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 April - Juni
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

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

Abstract

Purpose – This Classroom Action Research (CAR) aims to develop students’ self-management and enchance their computational thinking skills through the implementation of the Project-based Learning (PjBL) model in Informatics learning for Phase D. The study focuses on how PjBL can be used not only to improve cognitive skills, but also to promote personal responsibilities and autonomy in students’ learning. Methods – The study was conducted in two cycles, each comprising the stages of planning, action, observation, and reflection stages. The research subjects were seventh-grade students at a junior high school in Malang. Data were collected through multiple methods, including observation, interviews, and documentation, to ensure a comprehensive understanding of the learning process and outcomes. Findings – The findings indicate that the application of PjBL effectively improves students’ independence in managing time, tasks, and their own learning processes. Moreover, students demonstrated significant improvement in computational thinking skills. Research Implications – Based on the findings, PjBL has been proven to be an effective approach for enhancing meaningful learning experiences, foster active student engagement, and support the development of 21st-century skills.
Literature Review: Ethical Perspectives in the Development of Artificial Intelligence and Recommender Systems Utami, Sri Farida; Elmunsyah, Hakkun; Widiyaningtyas, Triyanna
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.4364

Abstract

The development of Artificial Intelligence (AI) and recommendation systems has had a major impact on various digital service sectors, including social media, e-commerce, and healthcare. Although this technology offers efficiency and personalised services, its implementation also raises complex ethical challenges, such as privacy protection, algorithmic bias, and system accountability. This study aims to analyse ethical perspectives in the development of AI and recommendation systems through a literature review approach. The methods used include analysis of various scientific articles classified into several main ethical dimensions, namely data privacy and security, algorithmic fairness, social media and recommendation systems, digital communication, and educational and publication ethics. The results of the study show that issues of privacy, transparency, and the potential for user behaviour manipulation are dominant issues. Ethics in AI no longer functions solely as an individual value, but as the foundation of responsible and sustainable institutional governance.
FTFPOS-IDF: A Fuzzy Rule-Based Thematic Term Weighting Scheme for Bloom's Taxonomy Question Classification Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2957

Abstract

The increasing adoption of Artificial Intelligence (AI) in education has created a growing demand for automated and reliable assessment systems. Existing Bloom’s Taxonomy (BT) question classification approaches commonly rely on TF-IDF-based weighting schemes, which assign static term weights and often fail to capture the varying thematic importance of terms across cognitive levels. To address this limitation, this study proposes a novel Fuzzy Thematic Feature and Part-of-Speech Inverse Document Frequency (FTFPOS-IDF) weighting scheme that integrates fuzzy rule-based reasoning with Natural Language Processing (NLP) to dynamically assign thematic weights according to Bloom’s Taxonomy relevance. The proposed framework combines Machine Learning (ML) and Deep Learning (DL) classifiers with Chi-Square feature selection to reduce irrelevant features and improve classification performance. Experimental results demonstrate that FTFPOS-IDF consistently outperforms conventional TF-IDF variants across multiple classification models. The highest performance was achieved by the Multilayer Perceptron (MLP) classifier with an accuracy of 86.7%. These findings indicate that fuzzy rule-based thematic weighting can effectively enhance Bloom’s Taxonomy question classification and support scalable, reliable, and sustainable digital assessment systems in educational environments.
Optimized BiLSTM and GRU Models Using QHBM for Forex Price Prediction Febrianto Alqodri; Triyanna Widiyaningtyas; Didik Dwi Prasetya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6226

Abstract

The foreign exchange market is highly volatile and complex, making accurate price prediction challenging. This study aims to develop an optimized deep learning framework for predicting daily closing prices of seven major currency pairs (AUDUSD, EURUSD, GBPUSD, USDCAD, USDCHF, USDCNY, and USDJPY) by integrating Bidirectional Long Short-Term Memory (BiLSTM) and GatedRecurrent Unit (GRU) models with optimization strategies. Historical data from the Federal Reserve Economic Data were evaluated using Fixed Date Split and Walk Forward Validation (WFV), where WFV consistently achieved better performance than the fixed date. To enhance model performance, hyperparameter optimization was conducted using the Queen Honey Bee Migration (QHBM) algorithm, a metaheuristic approach inspired by the migration behavior of queen bees, divided into two characteristics: high learning rate and low learning rate. The optimized models achieved performance improvements of approximately 10-70% in MAPE and RMSE compared to the baseline models, while maintaining high R2 values. The results indicate that optimal configurations are pair-specific, wheremost currency pairs perform best with a high learning rate and high unit settings, while AUDUSD achieves superior performance with a low learning rate and low unit configuration. This study contributes a novel integration of WFV and QHBM-based optimization. Adaptive deep learning models with proper validation significantly improve forecasting accuracy, robustness, and generalization forfinancial decision-making and algorithmic trading applications.
Groundedness in Government Document Chatbots: A Systematic Literature Review and Metric Oriented Analysis Frendy Rumambi; Didik Dwi Prasetya; Triyanna Widiyaningtyas; Abdul Karim
Systematic Literature Review Journal Vol. 2 No. 1 (2026): January : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v2i1.277

Abstract

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.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Abdul Karim Abdullah Iskandar Syah Adam Ramadhani P Adiba Qonita Adrian Juhari Ahmad Farobi Ahmad Fuadi Ahmad Reza Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed Alvin Bintang Abitara Annas Gading Pertiwi Ardiansyah, Jevri Tri Arif Mudi Priyatno Aya Shofia Mufti Azlan Mohd Zain Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Darwis, Herdianti Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Fadli Hidayat, M. Noer Falah, Moh Zainul Febrianto Alqodri Fitriyah Fitriyah Fitriyah Fitriyah Frendy Rumambi Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Hakkun Elmunsyah Haviluddin Haviluddin Hazizah, Chalista Yulia Heni Vidia Sari Heru Wahyu Herwanto Hirashima, Tsukasa I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Habibullah Arief M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Maskur Maskur Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Ilham Ramdhani Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nabila Salsabila Nafalski, Andrew Natasya Titania Ramadhanti Nazhiroh Tahta Arsyillah Nurhidayati Okazaki Yasuhisa, Okazaki Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rendy Yani Susanto Rhomdani, Rohmad Wahid Rismayanti, Nurul Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Sari, Heni Vidia Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Farida Utami Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma