Article Per Year (5 Year)
p-Index From 2020 - 2025
2.93
P-Index
This Author published in this journals
All Journal Statistika Jurnal Peluang Proceedings of Annual International Conference Syiah Kuala University - Social Sciences Chapter Jurnal Gaussian Jurnal Pengajaran MIPA Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Didaktik Matematika Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam Jurnal Riset Pendidikan Matematika Jurnal Elemen Eduma : Mathematics Education Learning and Teaching Jurnal Informatika Upgris Jurnal Penelitian Pendidikan IPA (JPPIPA) Numeracy : Jurnal Ilmiah Pendidikan Matematika International Journal for Educational and Vocational Studies JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) ESTIMASI: Journal of Statistics and Its Application Journal of Applied Data Sciences Narra J International Journal of Education and Digital Learning (IJEDL) Jurnal Natural Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Heca Journal of Applied Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal
Muhammad Subianto
Department Of Statistics, Faculty Of Mathematics And Natural Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy 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 Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Design and Development of Website and Android-Based Aceh Biodiversity Applications Subianto, Muhammad; Harnelly, Essy; Misbullah, Alim; Afidh, Razief Perucha Fauzie; Akhyar, Fikrul; Irfan, Muhammad; Dharma, Wira; Nazaruddin; Zulfan
Jurnal Penelitian Pendidikan IPA Vol 11 No 4 (2025): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i4.10535

Abstract

Aceh is an exceptional region in Indonesia characterized by significant biodiversity and abundant natural wealth. Despite these numerous benefits, there is limited information regarding the potential of biodiversity in this region. Therefore, this research aimed to design and develop Website and Android-based applications to facilitate access to information regarding biodiversity in Aceh. In the development process, the Waterfall method was used, and the applications consisted of two main components, namely the back-end and the front-end. The users were divided into three categories, including contributors, verificators, and admins. After the development was complete, functionality testing was conducted using the black box method, and usability was examined using the Post-Study System Usability Questionnaire (PSSUQ). The results of functionality testing showed that both applications operate efficiently, thereby providing users with a satisfying experience when accessing information about Aceh biodiversity. The usability score was approximately 7 for Website-based and 6 for Android-based applications, showing a high level of usability.
Mathematical Connection Ability through the Application of the AIR (Auditory Intellectualy Repetition) Learning Model Assisted by Geogebra Software Mardhiah, Ainal; Ikhsan, M.; Subianto, Muhammad
International Journal of Education and Digital Learning (IJEDL) Vol. 1 No. 4 (2023): International Journal of Education and Digital Learning (IJEDL)
Publisher : Lafadz Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47353/ijedl.v1i4.21

Abstract

expected model scan fosters students' mathematical connection skills, namely through the application of the AIR (Auditory Intellectual Repetition) model assisted by geogebra software. The purpose of this study was to determine differences in students' mathematical connection abilities after obtaining learning by applying the AIR (Auditory Intellectual Repetition) model assisted by geogebra software. This study used a quantitative approach with a pre-test and post-test control group design. The population of this study was class IX students of SMPN 1 Peukan Pidie by taking samples of two classes consisting of an experimental class and a control class. The sample selection was done by random sampling. The instrument used is a mathematical connection ability test. The data analysis technique uses the ANOVA test. Based on the results of the study, it was found that there were differences in the mathematical connection abilities of students who were taught through the application of the AIR (Auditory Intellectual Repetition) model assisted by geogebra software with conventional learning. Furthermore, the results of this study also identified that there was no interaction between learning and student level on students' mathematical connection abilities.
Hybrid Ensemble Learning with SMOTEENN and Soft Voting for Stunting Risk Prediction: A SHAP-Based Explainable Approach Furqany, Nuwairy El; Subianto, Muhammad; Rusyana, Asep
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.829

Abstract

Stunting remains a critical public health concern in Indonesia, with long-term consequences for physical growth, cognitive development, and human capital. This study introduces a hybrid machine learning framework to predict household-level stunting risk by integrating Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN), soft voting ensemble, and SHapley Additive exPlanations (SHAP). The objective is to enhance both predictive accuracy and interpretability in identifying high-risk households. A dataset of 115,579 household records from West Sumatra, comprising 20 demographic, socioeconomic, health, and housing predictors, was utilized. Preprocessing steps included handling missing values, categorical encoding, and applying SMOTEENN exclusively on the training set to mitigate class imbalance. The baseline models demonstrated limited sensitivity, with XGBoost performing best at 74.56% accuracy and 71.08% F1-score on imbalanced data. After applying SMOTEENN, performance improved substantially, with XGBoost achieving 91.82% accuracy and 91.74% F1-score. Further improvements were obtained through hybridization, where the Random Forest and XGBoost soft voting ensemble reached 91.95% accuracy and 92.46% F1-score, representing a notable gain over individual classifiers. SHAP analysis added interpretability by identifying family members, education level, diverse food consumption, occupation, and drinking water source as dominant predictors of stunting risk. The novelty of this study lies in the integration of SMOTEENN with ensemble learning and SHAP, providing not only robust performance but also transparency in feature contributions. The findings demonstrate that the proposed framework improves sensitivity to minority classes, delivers superior predictive accuracy compared to baseline models, and offers interpretable insights to guide targeted interventions. By combining methodological rigor with explainability, this research contributes a practical decision-support tool for policymakers, supporting early detection of at-risk households and accelerating stunting reduction efforts in Indonesia.
A Convolutional Neural Network Model for Mushroom Toxicity Recognition Irvanizam, Irvanizam; Subianto, Muhammad; Jamil, Muhammad Salsabila
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.359

Abstract

Mushroom poisoning remains a public health concern, often caused by misidentifying toxic species that visually resemble edible ones. This study investigates the feasibility of using a Convolutional Neural Network (CNN) to classify five mushroom species, Amanita caesarea, Amanita phalloides, Cantharellus cibarius, Omphalotus olearius, and Volvariella volvacea into toxic and non-toxic categories based on image data. A dataset of 137 images was collected and preprocessed through resizing, normalization, and data augmentation. A modified AlexNet-based CNN was trained and evaluated using accuracy, precision, recall, and F1-score. The best-performing model achieved a validation accuracy of 0.40, indicating limited discriminative capability. These findings highlight that the dataset size is insufficient for training a CNN from scratch and that the model cannot reliably distinguish species with subtle morphological differences. The study concludes that larger datasets, improved image quality, and transfer learning approaches are essential for achieving practical and deployable mushroom classification performance.
Co-Authors . Zulfan Afidh, Razief Perucha Fauzie Ahmad, Noor Atinah Ainal Mardhiah Akhyar, Fikrul ALFIAN FUTUHUL HADI Almunir Sihotang Asep Rusyana Azzahra, Syarifah Fathimah Bagus Sartono Cut Morina Zubainur Cut Mulyawati Cut Rina Rossalina Dwi Fadhiliani Earlia, Nanda Essy Harnelly EVI RAMADHANI Farsiah, Laina Fitriana AR Furqany, Nuwairy El Ghazi Mauer Idroes Hijriyana P., Meildha Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. INA YATUL ULYA Indah Manfaati Nur Irnanda , Irnanda, Irnanda Irvanizam, Irvanizam Jamil, Muhammad Salsabila Kairupan, Tara S. Kurniadinur, Kurniadinur M. Ikhsan M. Ikhsan M. Ikhsan Maulana, Aga Miftahuddin Miftahuddin Miftahuddin Miftahuddin Miftahuddin Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Al Agani Muhammad Iqbal Muhammad Irfan Mukhamad Najib Mursyida, Waliam Nazaruddin Niode, Nurdjannah Jane Nisya Fajri Noviandy, Teuku R. Nurbaiti Nurbaiti Nurdjannah J. Niode Nurjani Nurjani Nurjannah Nurjannah Nurleila, Nurleila Prakoeswa, Cita RS. Purnama Mulia Farib Rahmah Johar Razief Perucha Fauzie Afidh Reza Wafdan Rika Fitriani Rika Siviani Rinaldi Idroes Rizal Munadi RR. Ella Evrita Hestiandari S.Pd. M Kes I Ketut Sudiana . Salmawaty Salmawati Salmawaty Salmawaty Sasmita, Novi Reandy sufriani, sufriani Sugara, Dimas Rendy Suhartono Suhendra, Rivansyah Suryadi Suryadi Teuku Rizky Noviandy Tuti Asmiati Vivi Dina Melani Vivi Dina Melani Vivi Dina Melani Widya Sari Wira Dharma Wisnu Ananta Kusuma Yusrizal Yusrizal Zahriah, Zahriah Zainal Abidin Zainal Abidin Zhilalmuhana, Teuku Zulfan