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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pendas : Jurnah Ilmiah Pendidikan Dasar Cetta: Jurnal Ilmu Pendidikan ILKOM Jurnal Ilmiah at-tamkin: Jurnal Pengabdian kepada Masyarakat SENTIA 2016 SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Edunesia : jurnal Ilmiah Pendidikan Letters in Information Technology Education (LITE) Ideguru: Jurnal Karya Ilmiah Guru Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Decode: Jurnal Pendidikan Teknologi Informasi Emerging Information Science and Technology Bulletin of Community Engagement Journal of Education Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Journal of Health and Nutrition Research JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Journal of Embedded Systems, Security and Intelligent Systems
Didik Dwi Prasetya
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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PENGEMBANGAN ALAT ASESMEN AWAL MELALUI INSTASTORY GUNA MENENTUKAN GAYA BELAJAR SISWA KELAS XI SMA Muhammad Zidni Ridlo; Didik Dwi Prasetya; Ahmad Yusuf Setiawan; Nadiah Alma Ratnaduhita; Adi Wahyu Wardani
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 02 (2025): Volume 10, Nomor 02 Juni 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i02.25940

Abstract

Effective learning requires a way to understand the characteristics of students well, one of which is by understanding their learning styles. Therefore, the purpose of this study is to develop an initial assessment tool through instastory to determine the learning styles of students in grade XI SMA. the procedure used in this study is the ADDIE model development procedure this procedure consists of several steps namely analysis, design, development, implementation, and evaluation. In this study, data collection instruments through expert validation sheets. After going through the validation process, the next step is to analyze the data. In the media expert validation test, it obtained an average value of 92% with a statement that the media was valid / feasible to use, and the results of the material validation test obtained an average of 88% which stated that the material was valid / feasible to use. Then it was tested on students with an average result of 88% with a fairly valid category. Therefore, this initial assessment tool through instastory can be used as an interesting and solutive initial assessment tool in determining students' learning styles.
Pengaruh Few-shot Learning pada Kinerja LLM untuk Ekstraksi Entitas Iklan Lowongan Kerja Shafelbilyunazra, Alvalen; Prasetya, Didik Dwi
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1069

Abstract

Ekstraksi informasi dari teks tidak terstruktur, seperti iklan lowongan kerja, merupakan tantangan besar. Pendekatan tradisional berbasis fine-tuning membutuhkan dataset berlabel masif dan sumber daya komputasi tinggi. Sebagai alternatif, Large Language Model (LLM) dengan In-Context Learning (ICL) menawarkan efisiensi. Penelitian ini menginvestigasi pengaruh few-shot learning, khususnya variasi jumlah contoh (k), terhadap akurasi LLM dalam ekstraksi entitas dari iklan lowongan kerja berbahasa Indonesia. Menggunakan model Gemini, eksperimen dilakukan dengan skenario zero-shot (k=0) hingga few-shot (k=1, 3, 5, 10, 20). Setiap skenario dievaluasi lima kali menggunakan Monte Carlo Cross-Validation, dengan metrik Presisi, Recall, dan F1-Score. Hasil menunjukkan korelasi positif antara jumlah contoh dan akurasi, namun dengan point of diminishing returns. Peningkatan kinerja drastis terjadi pada 1-5 shot, dan performa mencapai kejenuhan setelah 10 shot. Model cenderung memiliki Presisi lebih tinggi daripada Recall, memprioritaskan kebenaran ekstrak. Studi ini menyimpulkan bahwa strategi prompting optimal memerlukan keseimbangan akurasi dan efisiensi, merekomendasikan 5-10 contoh untuk sebagian besar aplikasi. Temuan ini memberikan panduan praktis untuk optimalisasi penggunaan LLM dalam ekstraksi informasi.
Abstract Syntax Tree Model for Minimizing False Negative in Semantic Evaluation of Python Fill-in-the-Blank Nurhasan, Usman; Prasetya, Didik Dwi; Patmanthara, Syaad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11090

Abstract

This study develops and evaluates an automated assessment model using Abstract Syntax Trees (AST) with a view to overcoming the limitations of string-matching techniques in the assessment of Fill-in-the-Blank (FIB) programming answers. Traditional string-matching techniques have a relatively high False Negative Rate (FNR) of 21.5% within the context of detecting semantic equivalence. The current model uses semantic structural triangulation to ascertain the semantic similarity of student answers. Technical assessment shows that the AST approach markedly reduces the FNR to 4.5%. The model demonstrates high reliability (ϰ = 0.83) with high classification accuracy (F1 Score = 0.966) which attests to its inferential validity. From a pedagogical perspective, system implementation leads to substantial learning gains, evidenced by a large effect size (Cohen’s d = 1.82) and a high normalized gain (Normalized Gain = 0.90). Multiple regression analysis confirms that semantic accuracy is the primary causal factor driving improved student comprehension. Ontologically, while AST is valid as a partial representation, its limitations—particularly tree isomorphism in recursive structures—highlight the need for further exploration of graph isomorphism approaches. Control Flow Graphs (CFG) and Data Flow Graphs (DFG) offer more expressive relational models for capturing control and data dependencies. The model demonstrates functional feasibility with a System Usability Scale (SUS) score of 76.47. Overall, the AST Triangulation Model is validated as pedagogically effective, inferentially robust, and supportive of evaluative transparency. Future research recommends validating the model on more complex tasks and releasing it as open-source to support reproducibility.
Comparison of Text Representation for Clustering Student Concept Maps Reni Fatrisna Salsabila; Didik Dwi Prasetya; Triyanna Widyaningtyas; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4598

Abstract

This research aims to address the critical challenge of selecting a text representation method that effectively captures students’ conceptual understanding for clustering purposes. Traditional methods, such as Term Frequency-Inverse Document Frequency (TF-IDF), often fail to capture semantic relationships, limiting their effectiveness in clustering complex datasets. This study compares TF-IDF with the advanced Bidirectional Encoder Representations from Transformers (BERT) to determine their suitability in clustering student concept maps for two learning topics: Databases and Cyber Security. The method used applies two clustering algorithms: K-Means and its improved variant, K-Means++, which enhances centroid initialization for better stability and clustering quality. The datasets consist of concept maps from 27 students for each topic, including 1,206 concepts and 616 propositions for Databases, as well as 2,564 concepts and 1,282 propositions for Cyber Security. Evaluation is conducted using two metrics Davies-Bouldin Index (DBI) and Silhouette Score, to assess the compactness and separability of the clusters. The result of this study is that BERT consistently outperforms TF-IDF, producing lower DBI values and higher Silhouette Scores across all clusters (k= 2 - k=10). Combining BERT with K-Means++ yields the most compact and well-separated clusters, while TF-IDF results in overlapping and less-defined clusters. The research concludes that BERT is a superior text representation method for clustering, offering significant advantages in capturing semantic context and enabling educators to identify student misconceptions and improve learning strategies.
Revealing Interaction Patterns in Concept Map Construction Using Deep Learning and Machine Learning Models F.ti Ayyu Sayyidul Laily; Didik Dwi Prasetya; Anik Nur Handayani; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4641

Abstract

Concept maps are educational tools for organizing and representing knowledge, enhancing comprehension, and memory retention. In concept map construction, much knowledge can be utilized. Still, concept map construction is complex, involving actions that reflect a user’s thinking and problemsolving strategies. Traditional methods struggle to analyze large datasets and capture temporal dependencies in these actions. To address this, the study applies deep learning and machine learning techniques. This research aims to evaluate and compare the performance of Long Short-Term Memory (LSTM), K-Nearest Neighbors (K-NN), and Random Forest algorithms in predicting user actions and uncovering user interaction patterns in concept map construction. This research method collects and analyzes interaction logs data from concept map activities, using these three models for evaluation and comparison. The results of this research are that LSTM achieved the highest accuracy (83.91%) due to its capacity to model temporal dependencies. Random Forest accuracy (80.53%), excelling in structured data scenarios. K-NN offered the fastest performance due to its simplicity, though its reliance on distance-based metrics limited accuracy (70.53%). In conclusion, these findings underscore the practical considerations in selecting models for concept map applications; LSTM demonstrates effectiveness in predicting user actions and excels for temporal tasks, while Random Forest and K-NN offer more efficient alternatives in computational.
Enhancing Semantic Similarity in Concept Maps Using LargeLanguage Models Muhammad Zaki Wiryawan; Didik Dwi Prasetya; Anik Nur Handayani; Tsukasa Hirashima; Wahyu Styo Pratama; Lalu Ganda Rady Putra
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4727

Abstract

This research uses advanced models, Generative Pre-trained Transformer-4 and Bidirectional Encoder Representations from Transformers, to generate embeddings that analyze semantic relationships in open-ended concept maps. The problem addressed is the challenge of accurately capturing complex relationships between concepts in concept maps, commonly used in educational settings, especially in relational database learning. These maps, created by students, involve numerous interconnected concepts, making them difficult for traditional models to analyze effectively. In this study, we compare two variants of the Artificial Intelligence model to evaluate their ability to generate semanticembeddings for a dataset consisting of 1,206 student-generated concepts and 616 link nodes (Mean Concept = 4, Standard Deviation = 4.73). These student-generated maps are compared with a reference map created by a teacher containing 50 concepts and 25 link nodes. The goal is to assess the models’ performance in capturing the relationships between concepts in an open-ended learning environment. The results show that demonstrate that Generative Pretrained Transformers outperform other models in generating more accurate semantic embeddings. Specifically, Generative Pre-trained Transformer achieves 92% accuracy, 96% precision, 96% recall, and 96% F1-score. This highlights the Generative Pretrained Transformer’s ability to handle the complexity of large, student-generatedconcept maps while avoiding overfitting, an issue observed with the Bidirectional Encoder Representationsfrom Transformer models. The key contribution of this research is the ability of two complex models and multi-faceted relationships among concepts with high precision. This makes it particularly valuable in educational environments, where precise semantic analysis of open-ended data is crucial, offering potential for enhancing concept map-based learning with scalable and accurate solutions.
Performance Evaluation of Artificial Intelligence Models for Classification in Concept Map Quality Assessment Wahyu Styo Pratama; Didik Dwi Prasetya; Triyanna Widyaningtyas; Muhammad Zaki Wiryawan; Lalu Ganda Rady Putra; Tsukasa Hirashima
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4729

Abstract

Open-ended concept maps generated by students give better flexibility and present a complex analysis process for teachers. We investigate the application of classification algorithms in assessing openended concept maps, with the purpose of providing assistance for teachers in evaluating student comprehension. The method used in this study is experimental methods, which consists of data collection, preprocessing, representation generation, and modelling with Feedforward Neural Network, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Our dataset, derived from concept maps, consists of 3,759 words forming 690 propositions, scored carefully by experts to ensure high accuracy in the evaluation process. Results of this study indicate that K-NN outperformed all other models, achieving the highest accuracy and Receiver Operating Characteristic-Area Under the Curve scores, demonstrating its robustness in distinguishing between classes. Support Vector Machine excelled in precision, effectively minimizing false positives, while Random Forest showcased a balanced performance through its ensemble learning approach. Decision Tree and Linear Regression showed limitations in handling complex data patterns. FeedforwardNeural Network can model intricate relationships, but needs further optimization. This research concluded that Artificial Intelligence classification enables a better assessment for teachers, enables the path for personalized learning strategies in learning.
Assessing the Semantic Alignment in Multilingual Student-Teacher Concept Maps Using mBERT Nadindra Dwi Ariyanta; Didik Dwi Prasetya; Ilham Ari Elbaith Zaeni; Tsukasa Hirashima; Reo Wicaksono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5046

Abstract

This study examines the effectiveness of mBERT (Multilingual Bidirectional Encoder Representations from Transformers) in assessing semantic alignment between student and teacher concept maps in multilingual educational contexts, comparing its performance with TF-IDF. Using datasets in both Indonesian and English, the study demonstrates that mBERT outperforms TF-IDF in capturing complexsemantic relationships, achieving 96% accuracy, 96% precision, 100% recall, and a 98% F1 score in the Indonesian dataset. In contrast, TF-IDF achieved higher precision (73%) and accuracy (79%) in the English dataset, where mBERT recorded 54% accuracy, 47% precision, but 90% recall. Semantic alignment was measured using cosine similarity to calculate the cosine of the angle between vectorsrepresenting textual embeddings generated by both models. This method facilitates cross-linguistic semantic comparison, overcoming challenges related to word frequency and syntactic variations. While mBERT’s computational demands and the study’s limited linguistic scope suggest room for improvement, the findings highlight the potential for hybrid models and emphasize the transformative impact of AI-driven tools, such as mBERT, in fostering inclusive and effective multilingual education.
Stochastic Optimization for Hostage Rescue Using Internet of Things and Queen Honey Bee Algorithm Achmad Afif Irwansyah; Aripriharta Aripriharta; Didik Dwi Prasetya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5065

Abstract

This study proposes a stochastic optimization model to enhance the efficiency of hostage rescue operations using Internet of Things technology and the Queen Honey Bee Migration algorithm. The model aims to reduce response time and energy consumption by leveraging real-time data from IoT sensors to adapt dynamically to field conditions. Simulation tests conducted in a multi-story building environment demonstrated a 40% improvement in response time and a 35% reduction in energy consumption compared to conventional methods. The system also achieved up to 94.8% positioning accuracy using RSSI analysis and demonstrated consistent performance across floors. The results indicate that integrating QHBM and IoT provides a scalable and adaptive solution for mission-critical operations, with potential applications in real-world tactical planning.
Inovasi Articulate Storyline: Tingkatkan Keterampilan UI/UX Figma dan Pembelajaran Kreatif Muttaqiyah, Khusnul; Herwanto, Heru Wahyu; Prasetya, Didik Dwi; Rofiudin, Amir
Ideguru: Jurnal Karya Ilmiah Guru Vol. 10 No. 3 (2025): September 2025 Edition
Publisher : Dinas Pendidikan, Pemuda dan Olahraga Daerah Istimewa Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51169/ideguru.v10i3.2030

Abstract

This study aims to develop interactive learning media based on Project-Based Learning (PjBL) using Articulate Storyline to enhance students’ learning outcomes and creativity in the UI/UX Graphic Design subject using Figma at Islamic boarding school–based vocational schools. The research employed a Research and Development (R&D) method using the ADDIE model, which includes systematic stages of analysis, design, development, implementation, and evaluation. Validation by material and media experts indicated high validity, confirming that the developed media met the standards of content feasibility, visual design, navigation, and pedagogical effectiveness. The trial was conducted in three vocational schools over two learning cycles involving 169 students. The results revealed a significant improvement in learning achievement, with an average N-Gain score of 0.69 (moderate–high category). Moreover, 82.84% of students were categorized as creative and highly creative, based on fluency, flexibility, originality, and elaboration aspects. These findings confirm that integrating interactive digital media with a project-based approach effectively enhances students’ competencies, creativity, and mastery of digital design skills in vocational learning contexts.
Co-Authors Abdul Wafi Achmad Afif Irwansyah Adi Wahyu Wardani Ahmad Fajruddin Syauqi Ahmad Yusuf Setiawan Ainun Nur Baiti Aji P Wibawa Aji Prasetya Wibawa Akbar, Asna Isyarotul Andi Baso Kaswar Andi Baso Kaswar Andika Dwiyanto, Felix Andrew Nafalski Anik Nur Handayani Anjar Dwi Rahmawati Arifiati Fitri Anggraini Aripriharta - Aryo Pinandito Ashar, Muhammad Azhar Ahmad Smaragdina Bagaskoro Biantoro, Yudhi Bintang Romadhon Cakir, Gulsun Kurubacak Denis Eka Cahyani Dwi Widiyasari Dyah Ayu Langening Tyas Ella Amelia Widodo F.ti Ayyu Sayyidul Laily Fadhli Almu’iini Ahda Fadli Hidayat, M. Noer Fatrisna Salsabila, Reni Firdaus, Nabilah Zakiyah Salmaa Gradiyanto Radityo Kusumo Hafid, Ahmad Hairani Hairani Hakkun Elmunsyah Hanifah Muslimah Az-Zahra, Hanifah Muslimah Haq, Salsabila Thifal Nabil Hariyanto Hariyanto Hayashi, Yusuke Heru Wahyu Herwanto Hirashima, Tsukasa I Nyoman Gede Arya Astawa Ibrahim, Firmansyah Ilham Ari Elbaith Zaeni Intan Sulistyaningrum Sakkinah Iskandar Syah, Abdullah Kalifatullah, M. Ajie KHOIRUL ANWAR Khoirul Anwar Kusumo, Gradiyanto Radityo Laily, F.ti Ayyu Sayyidul Lalu Ganda Rady Putra Langlang Gumilar Lismi Animatul Chisbiyah Luqman Affandi Lutfi Budi Ilmawan, Lutfi Budi M. Ajie Kalifatullah Marsono Marsono Marsono Marsono Maskur Maskur Mayadi, Mayadi Mega Oktaviana Moh. Nur Zamzami Moh. Zainul Falah Muhammad Arief Nugroho Muhammad Aris Ichwanto muhammad hafiizh, muhammad Muhammad Jauharul Fuady Muhammad Mushawwir Muhammad Zaki Wiryawan Muhammad Zidni Ridlo Mukhamad Angga Gumilang Muladi Muttaqiyah, Khusnul Nadiah Alma Ratnaduhita Nadindra Dwi Ariyanta Nafalski, Andrew Nanscy Evi Wardani Natalina Wahyu Siswijayanti Nena Erviana Nunung Nurjanah Nur Hidayat, Wahyu Nuryakin, Mokhamad Perkasa, Gigih Prasetya, Luhur Adi Prasetyo, Muchamad Wahyu Pratiwi, Hardyanti Prihandicha, Adiftya Bayu Putro, Maulana Nur Antoro Ratnaduhita, Nadiah Alma Reni Fatrisna Salsabila Reo Wicaksono Ridlo, Muhammad Zidni Rofiudin, Amir Ryan Kurniawan Samodra, Joko Setiadi Cahyono Putro Setiawan, Ahmad Yusuf Setyani, Ida Agus Shafelbilyunazra, Alvalen Sigit Perdana Siti Sendari Sofiya Anggraini Sri Sumanti, Endang Sucipto Sucipto Sucipto Sucipto Sulistyo, Danang Arbian Syaad Patmanthara Syaichul Fitrian Akbar Syamsul Arifin Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Tsukasa Hirashima Tsukasa Hirashima Tsukasa Hirashima Tuwoso Usman Nurhasan Usman Nurhasan Utomo Pujianto Wahfi, Muhammad Fikri Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wahyudi, Erlik Prasetyo Wardani, Adi Wahyu Wibawa, Aji P Wibisono Sukmo Wardhono, Wibisono Sukmo Widiyanti Widiyanti, Widiyanti Yana Andayani Yusril Imamuddin Zainul Falah, Moh.