Article Per Year (5 Year)
p-Index From 2021 - 2026
2.81
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 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 Jurnal Didaktik Matematika
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 : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
Evaluation of atopic dermatitis severity using artificial intelligence Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Bulqiah, Mikyal; Idroes, Ghazi M.; Niode, Nurdjannah J.; Sofyan, Hizir; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.511

Abstract

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils Kurniadinur, Kurniadinur; Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Ahmad, Noor Atinah; Irvanizam, Irvanizam; Subianto, Muhammad; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.220

Abstract

The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.
Psoriasis severity assessment: Optimizing diagnostic models with deep learning Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Prakoeswa, Cita RS.; Kairupan, Tara S.; Idroes, Ghifari M.; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 4 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i3.1512

Abstract

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.
PERBANDINGAN MODEL KALIBRASI BERBASIS PLASMA-ACTIVATED WATER MENGGUNAKAN PRINCIPAL COMPONENT REGRESSION DAN PARTIAL LEAST SQUARE REGRESSION DALAM R Suhartono, Suhartono; Subianto, Muhammad
JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) Vol 11, No 1 (2025)
Publisher : Universitas Bhinneka PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jp2m.v11i1.6869

Abstract

To ensure that the plasma reactor tool can simulate the Plasma Activated Water (PAW) liquid accurately. To ensure the quality of the PAW liquid according to the plasma reactor design, it is necessary to create a calibration model that can ensure that the model matches the observation data and improve the predictive ability of the model. The purpose of this article is to build a calibration model based on the quality of PAW liquid produced from a plasma reactor. This study used the Principal Component Regression (PCR) method and the Partial Least Square Regression (PLS-R) method. The advantage of the PCR method is that it reduces data based on correlation values. While the PLS-R method reduces data based on the most relevant factors in interpreting the data. Based on the experiments conducted, it was concluded that to build a calibration model based on plasma reactor data and PAW liquid data, the PCR method is better than the PLS-R method. This is shown based on the RMSEP and R2 values in the PCR method of 0.09625571 and 93.04699% while in the PLS-R method of 0.09873341 and 92.84436%. For the R2 value in the PCR method is greater which indicates that the data variant value is more acceptable to the calibration model than in the PLS-R method, then the RMSEP value in the PCR method is smaller which indicates that the statistical error value is more acceptable than PLS-R.
Unpacking research on computational thinking in mathematics education: A systematic literature review Cut Morina Zubainur; Cut Rina Rossalina; Muhammad Subianto; Dwi Fadhiliani
Jurnal Elemen Vol 11 No 2 (2025): April
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jel.v11i2.29183

Abstract

Computational Thinking (CT) is an essential 21st-century skill to prepare students for higher education and future careers. However, comprehensive insights into how CT is effectively implemented in mathematics learning regarding strategies, suitable topics, and integration trends are still limited. This systematic review explores empirical studies on CT in mathematics education from December 2019 to November 2024, sourced from Emerald, EBSCO, and ProQuest databases. Following PRISMA guidelines, 22 articles were selected from an initial 8,518 based on defined inclusion and exclusion criteria. The findings show that CT strongly supports students’ problem-solving skills, particularly through Project-Based Learning (PjBL), which fosters engagement, collaboration, and algorithmic thinking. Geometry and statistics emerged as the most effective topics for developing CT, as they promote decomposition, pattern recognition, and abstraction skills aligned with junior high school cognitive development. Although CT-related research varies in focus, integrating CT into mathematics remains vital, especially with the rise of digital tools and interdisciplinary learning. This review provides insight into current research trends, key strategies, and appropriate mathematical content for CT development. Recommendations include providing CT training for teachers, embedding CT into the curriculum, and encouraging interdisciplinary collaboration to equip students with the digital-age competencies needed for real-world problem-solving and conceptual understanding.
Effectiveness of Tangent Equation Curve Learning through GeoGebra Software Assisted Module to Improve Students Creative Mathematical Thinking Skills Nurjannah, Nurjannah; Subianto, Muhammad; Abidin, Zainal
International Journal for Educational and Vocational Studies Vol. 1 No. 8 (2019): December 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/ijevs.v1i8.1805

Abstract

The Curriculum of 2013 mandates that learning carried out by teachers in the classroom can foster a high-level thinking process including the ability to think creatively. Modules that can help creative students have been developed but not as good as the help of the GeoGebra Software module that can promote students creative thinking. This research focuses on the effectiveness of the tangent equation curve module GeoGebra Software assisted which aimed to make the students have the ability to think creatively. The subjects of the research were students of class XI of SMA 3 Banda Aceh. The instruments carried out in this research were creative thinking questions and observation sheets. Data analysis was performed by using descriptive analysis techniques. This research showed that the students' mathematical creative thinking ability was 85.98% which included in very good and good category, and overall student activity reached 91.1%. it means that the activities of students have run well. In conclusion, the teachers and students can use this module to improve mathematical creative thinking skills and the interest of students in learning mathematics assisted by GeoGebra software.
Penerapan Aplikasi-Aplikasi Microsoft Office dan Google Docs dalam Upaya Peningkatan Media Pembelajaran di Madrasah Aliyah Negeri 5 Bireuen Irvanizam, Irvanizam; Misbullah, Alim; Zulfan, Zulfan; Farsiah, Laina; Subianto, Muhammad
PESARE: Jurnal Pengabdian Sains dan Rekayasa Vol 1, No 1 (2023): Oktober 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/pesare.v1i1.33833

Abstract

This community service activity aims to introduce Microsoft Office and Google Docs applications to teachers and students at Madrasah Aliyah Negeri (MAN) 5 Bireuen as an online teaching media in performing teaching and learning processes during the COVID-19 pandemic. This activity was carried out by a community service team of lecturers from the Department of Informatics, Universitas Syiah Kuala. The activity was held for three days from 2 until 4 April 2021 and consisted of two sessions. The first session introduced Microsoft Office applications for learning at the high school level. The second session demonstrates Google Docs applications for providing teaching materials. The activity participants were very enthusiastic about participating in this activity by asking lots of questions and being explained by the community service team. The result of this activity is that teachers find it very easy and quick to understand how to use these applications for their teaching and learning activities. They hope that online learning activities using the website-based Content Management System method will continue to be carried out as future works.
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.
Co-Authors . Zulfan Ahmad, Noor Atinah Ainal Mardhiah ALFIAN FUTUHUL HADI Alim Misbullah Almunir Sihotang Asep Rusyana Baehaqi Bagus Sartono Cut Morina Zubainur Cut Mulyawati Cut Rina Rossalina Dwi Fadhiliani Earlia, Nanda Essy Harnelly Essy Harnelly EVI RAMADHANI Farsiah, Laina Fikrul Akhyar 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 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 Syarifah Fathimah Azzahra 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