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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro PEMBELAJAR: Jurnal Ilmu Pendidikan, Keguruan, dan Pembelajaran Pendas : Jurnah Ilmiah Pendidikan Dasar ILKOM Jurnal Ilmiah SENTIA 2016 Buletin Ilmiah Sarjana Teknik Elektro SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Ideguru: Jurnal Karya Ilmiah Guru Didaktika: Jurnal Kependidikan Science in Information Technology Letters International Journal of Engineering, Science and Information Technology EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Bulletin of Community Engagement Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Integrasi dan Harmoni Inovatif Ilmu-ilmu Sosial Jurnal Inovasi Teknologi dan Edukasi Teknik ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Abdimas Pedagogi: Jurnal Ilmiah Pengabdian kepada Masyarakat
Wahyu Sakti Gunawan Irianto
Universitas Negeri Malang
Religion Arts 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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Analyzing event relationships in Andersen's Fairy Tales with BERT and Graph Convolutional Network (GCN) Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Ghosh, Anusua; Hernandez, Leonel
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1810

Abstract

This study explores the narrative structures of Hans Christian Andersen's fairy tales by analyzing event relationships using a combination of BERT (Bidirectional Encoder Representations from Transformers) and Graph Convolutional Networks (GCN). The research begins with the extraction of key events from the tales using BERT, leveraging its advanced contextual understanding to accurately identify and classify events. These events are then modeled as nodes in a graph, with their relationships represented as edges, using GCNs to capture complex interactions and dependencies. The resulting event relationship graph provides a comprehensive visualization of the narrative structure, revealing causal chains, thematic connections, and non-linear relationships. Quantitative metrics, including event extraction accuracy (92.5%), relationship precision (89.3%), and F1 score (90.8%), demonstrate the effectiveness of the proposed methodology. The analysis uncovers recurring patterns in Andersen's storytelling, such as linear event progressions, thematic contrasts, and intricate character interactions. These findings not only enhance our understanding of Andersen's narrative techniques but also showcase the potential of combining BERT and GCN for literary analysis. This research bridges the gap between computational linguistics and literary studies, offering a data-driven approach to narrative analysis. The methodology developed here can be extended to other genres and domains, paving the way for further interdisciplinary research. By integrating state-of-the-art NLP models with graph-based machine learning techniques, this study advances our ability to analyze and interpret complex textual data, providing new insights into the art of storytelling
CHATGPT DALAM PRAKTIK PPG: STRATEGI BARU TINGKATKAN HASIL BELAJAR SISWA SMP NEGERI 7 MALANG Hasriani; Irianto, Wahyu Sakti Gunawan; Wardhana, Nyoman Dedi Kusuma; Hermansyah; Abbar, Habib Muhammad; Ulum, Khoirul
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5752

Abstract

Perkembangan teknologi kecerdasan buatan (AI) telah membuka peluang baru dalam dunia pendidikan, salah satunya melalui penggunaan ChatGPT. Artikel ini bertujuan untuk mengkaji penerapan ChatGPT sebagai strategi pembelajaran inovatif dalam Praktik Pengalaman Lapangan (PPL) mahasiswa Pendidikan Profesi Guru (PPG) di SMP Negeri 7 Malang. Penelitian ini menggunakan pendekatan Penelitian Tindakan Kelas (PTK) model Kemmis dan McTaggart, yang dilaksanakan dalam dua siklus pada siswa kelas VII A sebanyak 30 orang. Fokus pembelajaran adalah pengenalan konsep dasar pemrograman dengan media Scratch yang dipadukan dengan ChatGPT sebagai pendamping interaktif. Pada Siklus I, pembelajaran diarahkan untuk memperkuat pemahaman siswa terhadap konsep-konsep dasar seperti variabel dan perulangan. Sedangkan pada Siklus II, siswa menerapkan konsep-konsep tersebut dalam proyek animasi menggunakan Scratch. Hasil penelitian menunjukkan bahwa penggunaan ChatGPT membantu siswa memahami materi dengan lebih mudah, meningkatkan motivasi belajar, serta mendorong partisipasi aktif dalam proses pembelajaran. Dengan demikian, ChatGPT terbukti menjadi strategi baru yang efektif dalam meningkatkan hasil belajar siswa dan relevan dengan kebutuhan pendidikan abad ke-21.
Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems Hayati, Lilis Nur; Handayani, Anik Nur; Gunawan Irianto, Wahyu Sakti; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.948

Abstract

This research explores the optimization of YOLO-based computer vision algorithms for real-time recognition of Indonesian Sign Language (BISINDO) letters under diverse environmental conditions. Motivated by the communication barriers faced by the deaf and hearing communities due to limited sign language literacy, the study aims to enhance inclusivity through advanced visual detection technologies. By implementing the YOLOv5s model, the system is trained to detect and classify correct and incorrect BISINDO hand signs across 52 classes (26 correct and 26 incorrect letters), utilizing a dataset of 3,900 images augmented to 10,920 samples. Performance evaluation employs k-fold cross-validation (k=10) and confusion matrix analysis across varied lighting and background scenarios, both indoor and outdoor. The model achieves a high average precision of 0.9901 and recall of 0.9999, with robust results in indoor settings and slight degradation observed under certain outdoor conditions. These findings demonstrate the potential of YOLOv5 in facilitating real-time, accurate sign language recognition, contributing toward more accessible human-computer interaction systems for the deaf community.
Penerapan Modul Sensor dan Arduino terhadap Minat, Motivasi, dan Hasil Belajar Siswa Kelas 10 TITL SMK (STM) Turen Putra, Adhi Pramana Estiawanda; Herwanto, Heru Wahyu; Irianto, Wahyu Sakti Gunawan; Soraya, Dila Umnia
TEKNO: Jurnal Teknologi Elektro dan Kejuruan Vol 34, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v34i2p119-127

Abstract

Capaian daya serap materi pelajaran masih menjadi permasalahan di Indonesia. Peningkatan sarana prasarana berupa bahan ajar menjadi upaya untuk meningkatkan kualitas pendidikan. Modul sebagai salah satu bahan ajar dapat membantu siswa memahami, mempelajari dan menerapkan pembelajaran sesuai dengan kebutuhan serta memberikan motivasi belajar pada siswa. Kegairahan, lingkungan, dan keinginan belajar memengaruhi minat dan motivasi siswa dalam pembelajaran. Kurangnya motivasi dan minat belajar terjadi di SMK (STM) Turen, karena itu diperlukan inovasi kegiatan pembelajaran menarik minat, motivasi serta efektif dan efisien. Penelitian ini menggunakan disain quasi experiment dengan pendekatan kuantitatif. Variabel yang digunakan yaitu satu variabel independen yaitu penerapan modul sensor dan arduino (X1) dan 3 variabel dependen yaitu minat belajar (Y1), motivasi belajar (Y2) dan hasil belajar (Y3). Metode pengumpulan data penelitian ini yaitu observasi, non tes (angket/kuesioner), dokumentasi (nilai ulangan harian), dan tes (post-test). Nilai rata-rata Ulangan Harian kelas eksperimen sebesar 35,21 dan kelas kontrol sebesar 39,58. Rata-rata nilai post-test kelas eksperimen 76,95 dan kelas kontrol 61,25. Minat belajar pada indikator keterlibatan, ketertarikan dan perasan senang siswa kelas eksperimen dan kontrol termasuk kategori tinggi. Motivasi belajar pada indikator dorongan kegiatan belajar, kegiatan pembelajaran menarik kelas eksperimen dan kontrol termasuk kategori tinggi, sedangkan pada indikator kemauan belajar kelas eksperimen termasuk kategori sangat tinggi dan kelas kontrol tinggi. Penerapan modul trainer sensor arduino memiliki dampak terhadap minat, motivasi, dan hasil belajar siswa kelas 10 SMK (STM) Turen.
The Effect of Pedagogical Competence and Professional Competence of Students After Teaching Assistance on Interest in Becoming ICT Teachers and Teaching Readiness of Informatics Engineering Education Students, Universitas Negeri Malang Herwanto, Heru Wahyu; Irianto, Wahyu Sakti Gunawan
TEKNO: Jurnal Teknologi Elektro dan Kejuruan Vol 34, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v34i2p99-108

Abstract

Survey findings indicate that some Informatics Engineering Education students show low interest in pursuing a teaching career, leading to reduced motivation, enthusiasm, and teaching readiness. This lack of interest negatively affects their pedagogical and professional competence. The Teaching Assistance Program was created for educational students to hone their teaching skills and develop their capabilities through curriculum development and implementation. This study aims to:(1) reveal the effect of pedagogical competence on interest in becoming an ICT teacher and teaching readiness; (2) reveal the effect of professional competence on interest in becoming an ICT teacher and teaching readiness; (3) reveal the effect of interest in becoming an ICT teacher on teaching readiness; (4) reveal the effect of pedagogic competence and professional competence on teaching readiness through the intervening variable interest in becoming a teacher. The study found that professional competence significantly influences students’ interest in becoming ICT teachers, while pedagogical competence significantly affects teaching readiness. Interest in becoming an ICT teacher also has a strong positive effect on teaching readiness. Additionally, professional competence indirectly enhances teaching readiness through increased career interest, whereas pedagogical competence shows no significant indirect effect through interest.
Few-Shot-BERT-RNN Narrative Structure Analysis for Andersen's Stories Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3932

Abstract

Event Extraction (EE) is a pivotal task for NLP, where important events in the narrative text need to be detected and recognized. We present an alternative method for extracting events from Hans Christian Andersen's fairy tales, utilizing Few-Shot Learning with BERT (Bidirectional Encoder Representations from Transformers) and RNN (Recurrent Neural Network) in this paper. We selected Andersen's fairy tales because they are characterized by rich narratives and symbolic language, which also often prevents automatic event extraction. To reduce reliance on labeled samples, we utilize the Few-Shot Learning method, which enables the model to learn from a small number of labeled event examples trivially. The BERT model is used to generate deep representations by modeling the context between words and sentences. RNN is essential to capture the sequence of events in the story, which determines the structure of the narrative. The findings demonstrate that the proposed framework significantly improves event extraction, with high values of evaluation metrics such as in accuracy, precision, recall, and F1-score. The proposed method is also effective in extracting non-explicit events while keeping the narrative context. Despite the challenges posed by metaphorical language and subjective events, this work demonstrates that Few-Shot Learning, BERT, and RNNs offer a promising solution to the task of event extraction from complex narratives.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Bi-LSTM and Attention-based Approach for Lip-To-Speech Synthesis in Low-Resource Languages: A Case Study on Bahasa Indonesia Setyaningsih, Eka Rahayu; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Kristian, Yosi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14310

Abstract

Lip-to-speech synthesis enables the transformation of visual information, particularly lip movements, into intelligible speech. This technology has gained increasing attention due to its potential in assistive communication for individuals with speech impairments, audio restoration in cases of missing or corrupted speech signals, and enhancement of communication quality in noisy or bandwidth-limited environments. However, research on low-resource languages, such as Bahasa Indonesia, remains limited, primarily due to the absence of suitable corpora and the unique phonetic structures of the language. To address this challenge, this study employs the LUMINA dataset, a purpose-built Indonesian audio-visual corpus comprising 14 speakers with diverse syllabic coverage. The main contribution of this work is the design and evaluation of an Attention-Augmented Bi-LSTM Multimodal Autoencoder, implemented as a two-stage parallel pipeline: (1) an audio autoencoder trained to learn compact latent representations from Mel-spectrograms, and (2) a visual encoder based on EfficientNetV2-S integrated with Bi-LSTM and multi-head attention to predict these latent features from silent video sequences. The experimental evaluation yields promising yet constrained results. Objective metrics yielded maximum scores of PESQ 1.465, STOI 0.7445, and ESTOI 0.5099, which are considerably lower than those of state-of-the-art English systems (PESQ > 2.5, STOI > 0.85), indicating that intelligibility remains a challenge. However, subjective evaluation using Mean Opinion Score (MOS) demonstrates consistent improvements: while baseline LSTM models achieve only 1.7–2.5, the Bi-LSTM with 8-head attention attains 3.3–4.0, with the highest ratings observed in female multi-speaker scenarios. These findings confirm that Bi-LSTM with attention improves over conventional baselines and generalizes better in multi-speaker contexts. The study establishes a first baseline for lip-to-speech synthesis in Bahasa Indonesia and underscores the importance of larger datasets and advanced modeling strategies to further enhance intelligibility and robustness in low-resource language settings.
PENINGKATAN HASIL BELAJAR INFORMATIKA MELALUI MODEL PEMBELAJARAN WINDOWS SHOPPING PADA MATERI APLIKASI PERKANTORAN DI KELAS VIII C SMP NEGERI 7 MALANG Al-Jabbar, Habib Muhammad; Irianto, Wahyu Sakti Gunawan; Wardhana, Nyoman Dedi Kusuma; Hermansyah
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 2 (2025): Volume 10 Nomor2, 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.v10i2.25838

Abstract

This Classroom Action Research was conducted as a solution to apply a learning model proven effective in improving Informatics learning outcomes through the implementation of the Windows Shopping learning model. The research was divided into two cycles and carried out in class VIII C of SMP Negeri 7 Malang, involving 32 students. Each cycle consisted of four stages: planning, implementation, observation, and reflection. Data were collected using observation, tests, and documentation techniques. The data were then analyzed quantitatively using descriptive statistics by calculating the N-gain score. The results of this classroom action research show an improvement in student learning outcomes in each cycle. In the first cycle, the average pretest score increased from 54.06 to 73.75 in the posttest, with an average N-gain of 0.43 (medium category). In the second cycle, the average pretest score rose from 63.28 to 87.81 in the posttest, with an average N-gain of 0.67 (medium to high category). The implementation of the Windows Shopping model improved conceptual understanding, active engagement, and collaboration among students. These findings indicate that the Windows Shopping learning model is effective and has a positive impact on project-based Informatics learning in the context of the Merdeka Curriculum.
PENGARUH PENERAPAN MODEL PEMBELAJARAN TEAMS GAMES TOURNAMENT (TGT) DENGAN MEDIA QUIZIZ TERHADAP MOTIVASI BELAJAR DAN HASIL BELAJAR KOGNITIF MATA PELAJARAN INFORMATIKA SISWA KELAS VII SMP NEGERI 7 MALANG Hermansyah; Irianto, Wahyu Sakti Gunawan; Wardhana, Nyoman Dedi Kusuma; Al-Jabbar, Habib Muhammad
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 2 (2025): Volume 10 Nomor2, 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.v10i2.25839

Abstract

This classroom action research aimed to examine the effectiveness of implementing the Team Game Tournament (TGT) cooperative learning model integrated with the Quizizz game-based platform in enhancing students’ learning motivation and achievement. The research subjects consisted of 24 students from Class VII H at SMP Negeri 7 Malang. One of the key elements in the implementation of the TGT model is the reward system, which is believed to increase students' interest and motivation to positively engage in competition. With the support of Quizizz as a learning medium, students were encouraged to develop a sense of responsibility in answering questions, both individually and collaboratively. This study employed a quantitative experimental approach using a pre-test and post-test control group design. The research was conducted in two cycles, each consisting of the stages of planning, implementation, observation, and reflection. Data collected included students’ learning activity and motivation. Learning activity was measured using an observation sheet on instructional implementation, assessed by a single observer. Meanwhile, learning motivation was measured using a questionnaire comprising 20 items, covering indicators such as attention, relevance, satisfaction, and confidence. The data were analyzed descriptively. The results of the study indicate that: 1.In Cycle I, students appeared less active and showed little initiative in taking notes on key points of the material. However, in Cycle II, students demonstrated increased engagement, actively responded to questions from the teacher and peers, and showed willingness to ask questions. 2. Students' learning motivation also improved: in Cycle I, 60% of students were categorized as highly motivated, 6.66% as moderately motivated, and 33% as poorly motivated. In Cycle II, 90% of students fell into the highly motivated category, while 10% were moderately motivated. These findings indicate that the application of the TGT learning model supported by the Quizizz platform is effective in enhancing students' classroom activity and learning motivation.
Co-Authors Abbar, Habib Muhammad Abdul Hadi, Afif Abdullah Iskandar Syah Agro Lukman Putra Aguwin Ardi Pranata Ahmad Faiz Risvan Haqiqi Ahmad Fuadi Ahsan, Muhammad Zamani Aji Prasetya Wibawa Al-Jabbar, Habib Muhammad Amir Rofiudin Amrullah, Muhammad Shalahuddin Ananda Putri Syaviri Andrew Nafalski Anik Nur Handayani Anusua Ghosh, Anusua Artina Tri Wistiawati Asmoro, Achmad Shoddiq Bayu Budi Wibowotomo Damanhuri, Nor Salwa Didik Dwi Prasetya Dila Umnia Soraya Dimas Jundan Ashshiddiqi Dolly Indra Eka Rahayu Setyaningsih Erma Widayanti Erna Daniati Erna Daniati Ernis Hidayati Evania Kurniawati Falah, Moh. Zainul Febri Handoyo Fildzah Zata Izzati Fitri, Anisa Hilya Hakkun Elmunsyah Hasriani Hermansyah Heru Wahyu Herwanto I Made Wirawan Isnandar Isyatul Karimah Khamdan, Candra Wahyu Nur Kirana, Karika Candra Leonel Hernandez, Leonel lilis nurhayati Luhur Adi Prasetya M. Aris Ichwanto M. Dimas Aviv Fahreza M. Rodhi Faiz Milenia, Herpri Mohammad Husein An Nabawi Muhammad Afnan Habibi Muhammad Jauharul Fuady Mukhammad Riyadi Erwanenda Mungalim, Rifqy Nafalski, Andrew Nor Salwa Damanhuri Novita Tri Indrasari Nugroho, Andhik Catur Nugroho, Bagas Nur Laila Indra Sapitra Wahyu Ilyasari Putra, Adhi Pramana Estiawanda Ria Febrianti Rian Syahmulloh Hendranawan Rina Dewi Indahsari Rizka Afdalia Rosa Andrie Asmara Saifuddin, Farikh Salsabila Thifal Nabil Haq Samodra, Joko Saputra, Ismed Eko Hadi Sari, Gilang Rafiqa Setiadi Cahyono Putro Setyaningsih, Eka Rahayu Shandy Krisnawan Siti Salina Mustakim Siti Sendari Slamet Wibawanto Soraya Norma Mustika Sujito Sujito Syaad Patmanthara Triyanna Widiyaningtyas Tuwoso Tuwoso Ulum, Khoirul Utomo Pujianto Veithzal Rivai Zainal Wardhana, Nyoman Dedi Kusuma Widiyanti Wistiawati, Artina Tri Yosi Kristian Yoto Yoto Yuni Rahmawati