Article Per Year (5 Year)
p-Index From 2021 - 2026
3.137
P-Index
This Author published in this journals
All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Riset dan Kajian Pendidikan Fisika Jurnal Pendidikan Fisika Indonesia Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Scientific Journal of Informatics Jurnal Spektra Educational Management Innovative Journal of Curriculum and Educational Technology The Journal of Educational Development JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Jurnal Pendidikan Matematika (JUDIKA EDUCATION) UPEJ Unnes Physics Education Journal Journal of Innovative Science Education Journal of Primary Education Physics Education Research Journal Jurnal Bakti Masyarakat Indonesia Physics Communication Jurnal Penelitian Tindakan Sekolah dan Kepengawasan Jurnal Riptek Prosiding Seminar Nasional Pascasarjana Proceeding of International Conference on Science, Education, and Technology Jurnal Meteorologi dan Geofisika Scientific Journal of Informatics Physics Communication
Wahyu Hardyanto
Unknown Affiliation
Agriculture, Biological Sciences & Forestry 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 Mathematics Physics Social Sciences Transportation Other

Published : 78 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 78 Documents
Search

 4   5   6   7   8 
Integrating C4.5 and K-Nearest Neighbor Imputation with Relief Feature Selection for Enhancing Breast Cancer Diagnosis Purwinarko, Aji; Budiman, Kholiq; Widiyatmoko, Arif; Sasi, Fitri Arum; Hardyanto, Wahyu
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21673

Abstract

Purpose: Breast cancer remains a significant cause of mortality among women, requiring accurate diagnostic methods. Traditional classification models often face accuracy challenges due to missing values and irrelevant features. This investigation advances the classification of breast cancer through the amalgamation of the C4.5 algorithm with K-Nearest Neighbor (KNN) imputation and Relief feature selection methodologies, thereby augmenting data integrity and enhancing classification efficacy. Methods: The Wisconsin Breast Cancer Database (WBCD) was the core reference for evaluating the proposed methodology. KNN imputation addressed missing values, while Relief selected the most relevant features. The C4.5 algorithm executed training by utilizing data segregations in the corresponding proportions of 70:30, 80:20, and 90:10, with its efficiency gauged through a range of metrics, particularly accuracy, precision, recall, and F1-score. Result: This innovative methodology achieved the highest classification accuracy of 98.57%, surpassing several existing models. Particularly noteworthy, the strategy being analyzed exhibited remarkable success relative to PSO-C4.5 (96.49%), EBL-RBFNN (98.40%), Gaussian Naïve Bayes (97.50%), and t-SNE (98.20%), demonstrating associated advancements of 2.08%, 0.17%, 1.07%, and 0.37%. These results confirm its effectiveness in handling missing values and selecting relevant features. Novelty: Unlike prior studies that addressed missing values and feature selection separately, this research integrates both techniques, enhancing classification accuracy and computational efficiency. The findings suggest that this approach provides a reliable breast cancer diagnosis method. Future work could explore deep learning integration and validation on larger datasets to improve generalizability.
Principal's Leadership Style in Improving Teacher Performance at Muhammadiyah Plus Junior High School of Salatiga Cahyono, Guntur; Kardoyo, Kardoyo; Hardyanto, Wahyu; Mahmud, Amir
Proceedings of International Conference on Science, Education, and Technology Vol. 10 (2024)
Publisher : Universitas Negeri Semarang

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

Abstract

This study holds significant importance as it is based on the ongoing improvements and achievements received by the students, teachers, and Muhammadiyah Plus Junior High School of Salatiga institution. The school principal's leadership style, a crucial factor in these achievements, is one of the attempts to develop the charity work of Muhammadiyah in the education field. This study aims to identify the leadership styles adopted in the school to enhance the teachers' performances. It applied a qualitative approach by employing interviews and other relevant resources to this study. The collected data revealed that the transformative leadership style is identified as the leadership style that was adopted by the Muhammadiyah Plus Middle School principal. The applied leadership style has impacted the well-established teacher performance, which is proven by the huge number of achievements received by the students, teachers, and school institutions. Thus, the leadership style that has been adopted by the school principal has impacted optimal teacher performance in the teaching-learning process and improved achievements through the supporting facilities that the school continues to strive for. Teacher performance continues to improve by building a common vision between the foundation, school principals, and teachers.
PENGEMBANGAN DAN UJI COBA ALAT PRAKTIKUM DIGITAL INOVATIF BERBASIS SMARTPHONE Wahyuni, Siti; Hardyanto, Wahyu; Darsono, Teguh; Sulhadi; Akhlis, Isa
Jurnal Bakti Masyarakat Indonesia Vol. 8 No. 2 (2025): Jurnal Bakti Masyarakat Indonesia
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jbmi.v8i2.35280

Abstract

Adanya tuntutan pelibatan teknologi dalam proses pembelajaran menjadi tantangan yang harus dihadapi guru. Penyelidikan ilmiah dalam pembelajaran fisika menjadi sangat penting, namun sayangnya tidak semua sekolah memiliki fasilitas laboratorium yang memadai. Agar pembelajaran tetap terlaksana dengan baik, keadaan tersebut memaksa guru untuk aktif dan kreatif menciptakan praktikum yang inovatif dengan sumber daya yang ada. Keadaan ini dapat diatasi dengan praktikum digital, yang salah satunya dapat dilakukan dengan memanfaatkan smartphone masing-masing peserta didik. Praktikum fisika dengan smartphone dapat menjadi inovasi praktikum digital karena guru dapat merancang sendiri jenis percobaannya. Kegiatan pengabdian kepada masyarakat ini dilaksanakan dengan metode Community Based Participatory Research (CBPR) pada MGMP Fisika SMA/MA Kabupaten Semarang Jawa Tengah. Kegiatan berupa pelatihan terkait pengenalan set alat praktikum digital, membaca data, mengolah, memvisualisasikan dalam grafik, dan menginterpretasikannya. Aplikasi digital yang digunakan adalah Arduino Science Journal yang dapat dioperasikan dengan memanfaatkan sensor yang telah tersedia pada smartphone android. Alat praktikum yang dihasilkan berupa satu set alat papan luncur yang dapat divariasikan sudut kemiringannya, dilengkapi dengan alat peluncur berupa mobil mainan yang dimodifikasi dengan holder HP. Alat ini dapat digunakan untuk mengukur percepatan, kemudian data digital dapat diolah ke dalam besaran fisis lain, seperti kecepatan dan posisi setiap saat. Pada akhir kegiatan, set alat praktikum disumbangkan kepada MGMP sebagai percontohan agar dapat diadopsi oleh pihak sekolah untuk memenuhi kebutuhan fasilitas laboratorium.
Development of Website-Based Teaching Materials with a Multi-Representation Approach Assisted by Scratch Simulation in Circular Motion Yulita Nurbaiti; Wahyu Hardyanto; Suharto Linuwih
Physics Communication Vol. 9 No. 1 (2025): February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/pc.v9i1.8268

Abstract

This study is an R&D research that aims to describe the feasibility and characteristics of website-based teaching materials with a multi-representation approach assisted by Scratch simulation in a circular motion. The development model used in this study was a 4-D development model, (define, design, develop, disseminate). The define stage consists of the draft instrument, learning objectives flow (ATP), analysis and learning modules, and analysis journal. The design stage consists of teaching materials design and instrument expert validation design for evaluating the teaching materials. The development stage consists of developing instruments for assessing the digital teaching, teaching materials prototype, and expert validation and revisions based on the results of expert validation. The dissemination stage consists of the final validation consisting of the results of the final validation of the product and the dissemination of teaching materials through the publication of scientific papers in journals.  Based on the results of expert validation, the percentage of average of score on the material aspect is 91.25% with a very feasible category. The percentage of an average score on the media aspect is 92.82% with the very feasible category. The readability percentage of teaching materials has an average value of 87.59% in the easy-to-understand category. The percentage of teacher and student responses was 97.50% and 88.61% in the very good category response.
Simulation of Volcanic Ash Dispersion From Mount Ruang Using the Puff Model (April 29–30, 2024): English Hidayanti, Arifatul; Hardyanto, Wahyu; Munandar, Arif; Wiujianna, Atri; Meinofelia, Erika
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1082

Abstract

This study aims to simulate and predict volcanic ash dispersion from the 29 April 2024 eruption of Mount Ruang by coupling the PUFF model with RGB analysis of Himawari-8 imagery. Driven by meteorological fields from the NOAA Global Forecast System, the PUFF model provides 24-hour forecasts of ash transport by integrating Lagrangian and Eulerian representations of particle motion. For validation, Himawari-8 satellite imagery was processed using the RGB method with IR1, IR2, and IR4 channels to visually detect the spatial distribution of ash clouds, enabling effective differentiation between volcanic ash and meteorological clouds and improving detection accuracy. The model forecasts closely match the timing and distribution patterns observed in the satellite imagery, indicating strong agreement between numerical simulation and remote sensing analysis. Overall, the results demonstrate that the PUFF model delivers reliable short-term guidance on ash dispersion, and the integration of numerical modeling and satellite-based analysis confirms its effectiveness in supporting early-warning capabilities, aviation safety, and volcanic hazard risk mitigation.
Evaluasi Kinerja Algoritma Random Forest dan KNN untuk Prediksi Cuaca di Jakarta Wibowo, Nurdin; Hardyanto, Wahyu; Sepriando, Alpon; Djuniadi, Djuniadi; Nurmayati, Tri Nurmayati; Alam, Fakhrul
Jurnal Pendidikan Matematika : Judika Education Vol. 9 No. 3 (2026): Jurnal Pendidikan Matematika:Judika Education
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/wzj0r898

Abstract

This study aims to compare the performance of Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying daily weather categories at Kemayoran Meteorological Station, Jakarta. The data used were BMKG observation data from 2017 to 2023, with classification targets consisting of No Significant Weather, RA, TS, and TS,RA. The variables included temperature, rainfall, air pressure, humidity, wind speed, sunshine duration, wind direction, and month. The data were preprocessed and divided into training and testing sets using an 80:20 ratio. The results showed that Random Forest achieved an accuracy of 78% with a weighted average F1-score of 0.75, while KNN achieved an accuracy of 65% with a weighted average F1-score of 0.59. Random Forest performed better in classifying dominant weather categories, although both algorithms still had limitations in identifying minority categories, particularly TS. These findings indicate that Random Forest is more suitable for daily weather classification based on BMKG observation data in urban areas.   Keywords: K-Nearest Neighbors; Random Forest; Weather Classification
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch Wahyu Hardyanto; Nila Prasetya Aryani; Defin Andestian; Sugiyanto; Wahyu Setyaningrum; M Fadil Mardiansyah; Muhamad Anbiya Nur Islam; Aji Purwinarko
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35918

Abstract

Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Prediksi Fine Fuel Moisture Code Menggunakan Algoritma Random Forest untuk Sistem Peringatan Dini Kebakaran Hutan Mulyaningsih, Sri; Hardyanto, Wahyu; Sepriando, Alpon; Djuniadi, Djuniadi
Jurnal Pendidikan Matematika : Judika Education Vol. 8 No. 6 (2025): Jurnal Pendidikan Matematika:Judika Education
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/xy82p541

Abstract

This study aims to predict the Fine Fuel Moisture Code (FFMC) as an indicator of the ignitability level of natural fuels using the Random Forest algorithm. FFMC is an essential component of fire danger rating systems, used to describe the dryness level of fine fuels; therefore, accurate prediction is crucial for forest and land fire mitigation efforts. The research utilizes meteorological variables—including maximum temperature, relative humidity, wind speed, and rainfall—as input parameters. The model implementation is carried out in Google Colab using the Python programming language, accompanied by hyperparameter tuning through GridSearchCV to obtain the most optimal model configuration.The results indicate that relative humidity and rainfall are the most influential meteorological features in predicting FFMC. The developed Random Forest model demonstrates excellent performance, as reflected by a Mean Squared Error of 8,875 and an R-squared value of 0,974, which signifies a very high predictive capability. These findings confirm that the machine-learning approach based on Random Forest is able to produce precise FFMC estimations, thereby giving the model strong potential for integration into early warning systems for forest and land fires. Such integration would support decision-making processes, enhance the effectiveness of mitigation strategies, and strengthen preparedness in managing fire-related risks.   Keywords: Fine Fuel Moisture Code, Fire Danger Prediction, Meteorological Features, Machine Learning, Random Forest.
Co-Authors - Kardoyo -, Murwatiningsih A.A. Ketut Agung Cahyawan W Abdur Rasyid Achmad Slamet Achmad Sopyan, Achmad Agus Hermanto Agus Priyono Aji Purwinarko, Aji Al Asy'Ari, Hasan Alam, Fakhrul Ali Djamhuri Amir Mahmud Amir Yusuf Andi Mardiana Paduppai Ani Rusilowati Arif Munandar Arif Widiyatmoko, Arif Arif Widyatmoko Arik Pujiyanti Aris Budiyono Aryono Adhi, Aryono Azwar Anas Budi Naini Mindyarto, Budi Naini Budiman, Kholiq Cheristiyanto Cheristiyanto, Cheristiyanto Darsono, Teguh Dedi Prestiadi Defin Andestian DEWI RAHMAWATI Djuniadi Djuniadi Durhan, Ferdinandus Durhan, Ferdinandus Dyah Maya Nihayah Ellianawati, Ellianawati Ernawati, Turini Ernawati, Turini Fitri Arum Sasi, Fitri Arum Gita Ayu Permatasari Guntur Cahyono, Guntur Hari Wibawanto Harjito Kusmanto Haryono Haryono Hidayanti, Arifatul I Gusti Agung Ketut Yoga, I Gusti Agung Ketut Indah Kusumaningrum, Wiwik Intana, Nila Muna Irfana, Shiva Isa Akhlis Isnayanti, Riska Sukma Kosam, Darussalam Kosam, Darussalam Lestari, Putri Yunia M Fadil Mardiansyah Made Sudana, I Made Sudana, I Maman Rachman Maria, Ulfa Martuti, Martuti Martuti, Martuti Masrukan Masrukan Masrukhi Masrukhi Masturi Masturi Meinofelia, Erika Milah, Isna Lukluil Mubasir, Yazid Muhamad Anbiya Nur Islam Mujiono - Mukarromah, Syarifatul Muslimah Susilayati Muslimah Susilayati Nila Prasetya Aryani Noor, Muhammad Elfin Noor, Muhammad Elfin Nurfiyani, Novi Tri Nurhasan Ropii Nurmayati, Tri Nurmayati Parmin Parmin Phany Ineke Putri, Phany Ineke Pranoto, Alfian Setya Putut Marwoto Retno Sri Iswari Sadimin Sadimin Saiful Ridlo Samsu, Ridwan Samsu, Ridwan Sepriando, Alpon Siswoyo, Rasdi Eko Siswoyo, Rasdi Eko Siti Wahyuni Siti Wahyuni Sri Mulyaningsih Sri Wardhani Sucihatiningsih DWP Sugianto Sugianto Sugiyanto Sugiyo Sugiyo, Sugiyo Sugiyono Sugiyono Suharto Linuwih Sulhadi Suprihatini, Noerhidayah Suprihatini, Noerhidayah Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Sutikno Sutikno Suwito Eko Pramono Titi Prihatin Tri Joko Raharjo Ummi Fauziyah Veronika Ndapaloka, Veronika Wahyu Setyaningrum Wibowo, Nurdin Widya Nugraheni Widiningrum Wijanayu, Adiratna Wiji Wahyudi, Urip Muhayat Wiji Wahyudi, Urip Muhayat Winarto, Anton Windrajaya, Eko Rudy Wiujianna, Atri Wiwi Isnaeni Woro Sumarni Yeni Rima Liana Yozi Aulia Rahman, Yozi Aulia Yulita Nurbaiti