Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Fourier AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) Computer Science and Information Technologies
Aris Thobirin
Universitas Ahmad Dahlan
Aerospace Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 30 Documents
Search

 1   2   3 
THE RELATIONSHIP BETWEEN LEARNING HABITS, LEARNING ENVIRONMENT AT HOME AND NUMERICAL ABILITY WITH MATHEMATICS LEARNING OUTCOMES Anis Ni'matus Sholihah; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 4, No 12: Desember 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v4i12.17221

Abstract

The results of students mathematics learning associated with many factors. Learning Habits, Learning Environment at Home and Numerical Ability are some of the factors that related to students mathematics learning outcomes. This research aims to determine the presence or absence of a positive and significant relationship between learning habits, learning environment at home and numerical ability with mathematics learning outcomes in students class VIII of even semester in SMP Muhammadiyah 2 Depok Sleman regency academic year 2016/2017. The population in this research was all students class VIII of even semester in SMP Muhammadiyah 2 Depok Sleman regency academic year 2016/2017. Samples were taken by random sampling technique to the classes derived class VIIIC as a class sample consisting of 32 students. The data collection techniques such as questionnaires techniques and test techniques. Data analysis used correlation analysis and multiple linear regression analysis. The results showed that there were positive and significant relationship between (1) learning habits with mathematics learning outcomes, with ; (2) learning environment at home with mathematics learning outcomes, with ; (3) Numerical Ability with mathematics learning outcomes, with ; (4) learning habits and learning environment at home with mathematics learning outcomes, with ; (5) learning habits and numerical ability with mathematics learning outcomes, with ; (6) learning environment at home and numerical ability with mathematics learning outcomes, with ; (7) learning habits, learning environment at home and numerical ability with mathematics learning outcomes, with multiple correlation coefficient  with linear regression equation  relatively large contribution  and  with double determination coefficient  and effective large contribution  and .
ENHANCEMENT OF MATHEMATICS LEARNING USING RECIPROCAL TEACHING APPROACH FOR CLASS X STUDENTS OF VOCATIONAL SCHOOL OF MUHAMMADIYAH KRETEK Istiqomah Istiqomah; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 4, No 5: Mei 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v4i5.16364

Abstract

This research was conducted because the independence of learning mathematics in class X AK 1 even semester of SMK Muhammadiyah Kretek Bantul in the 2015/2016 academic year was still lacking. The purpose of this study is to improve the independence of learning mathematics using a reciprocal teaching approach to class X AK 1 even semester of SMK Muhammadiyah Kretek Bantul in the academic year 2015/2016. This research is a type of classroom action research. Subjects in this study were students of class X AK 1 even semester of SMK Muhammadiyah Kretek Bantul in the 2015/2016 academic year. While the object studied in this study is the reciprocal teaching approach as an effort to improve mathematics learning independence of students of class X AK 1 even semester of SMK Muhammadiyah Kretek Bantul in the academic year 2015/2016. The study was conducted in 2 cycles, namely, cycle I and cycle II, each cycle consisting of 2 meetings. Cycle I and cycle II use the Reciprocal Teaching Approach. Data collection techniques in this study, namely observation, interviews, tests, and documentation. Analysis of the data used is descriptive qualitative. The results showed that learning using the Reciprocal Teaching Approach could improve mathematics learning independence of students of class X AK 1 even semester of SMK Muhammadiyah Kretek Bantul in the academic year 2015/2016. This is evident from the results of observations of students 'mathematics learning independence in each cycle has increased, namely the average percentage of students' mathematics learning independence in cycle I amounted to 48.5119% who achieved enough criteria, and in the second cycle increased to 64.88095% which achieved good criteria. The results of interviews with students showed a positive response to the independence of students learning mathematics.
IMPROVING THE STUDENT'S ACTIVENESS IN MATHEMATICS LEARNING THROUGH COOPERATIVE LEARNING MODEL OF ASSISTED INDIVIDUALIZATION TEAM Annisa Nurohmawati; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 6, No 9: September 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v6i9.19331

Abstract

This research background is active less of VIII grade SMP Muhammadiyah 2 Kalasan Sleman on the semester academic year 2016/2017 in the learning mathematics. This research aims to increase students' activeness in the learning of mathematics using cooperative learning model type Team assisted individualization (TAI) class VIII Junior High School (SMP) Muhammadiyah 2 Kalasan, Sleman semester academic even year 2016/2017. This research is a class act. The setting used is a class VIII SMP Muhammadiyah 2 Kalasan, district Sleman totaling 28 students. The study was conducted in two cycles of the first cycle and the second cycle, each cycle consisting of three meetings. The first cycle and the second cycle using cooperative learning model type TAI. Data collected by observation using observation sheet student learning activeness, and interviews. Analysis instrument using content validity. Analysis of the data used is descriptive qualitative. The result showed that using cooperative learning model type TAI can increase students' activeness in mathematics learning in class VIII student of SMP Muhammadiyah 2 Kalasan the school year 2016/2017. This is evident from the observation of students' learning activities in each cycle. The average percentage of student activity observation on the first cycle, 54,16%, reached sufficient criteria. On the second cycle, it increased to 63,77% that reached both criteria. From interviews with students showed a positive response to the students learning activeness.
DEVELOPMENT LEARNING MEDIA BASED ON MACROMEDIA FLASH ON MATRIX MATERIAL Age Samanta Putra; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 6, No 5: Mei 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v6i5.19265

Abstract

Mathematics is a compulsory subject at the secondary school level, especially on matrix material, sometimes students are still difficult to distinguish rows and columns. It is not easy to do multiplications on matrices. Learning media are expected to be a facility that supports learning. This study aims to develop learning tools based on Macromedia flash subject matter matrix class XI. In developing this learning media based on using Research and Development with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). This research was conducted at Islamic Senior High School (MA) Muallimin Yogyakarta. The research instrument used was a questionnaire. The quantitative data analysis technique calculates the score of the developed learning media's feasibility test results. The results of research on the development of interactive learning media mathematics based on Macromedia flash subject matter matrix XI based on the quality of each indicator in terms of material experts in the excellent category with an average percentage score of 86.84%, in terms of media experts in the category are very feasible with a percentage score an average of 88.33%, and student responses in the exciting category with an average percentage score of 77.37. The learning media based on Macromedia flash subject matter matrix XI class material is very feasible in the learning process.
DEVELOPMENT OF ELECTRONIC MODULE OF MATHEMATICS ON SEQUENCE SUBJECT MATTER FOR CLASS XI Sonya Madyo Ratri; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 6, No 9: September 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v6i9.19327

Abstract

This research is motivated by the lack of teaching materials in electronic modules to help students learn independently. This development research aims to develop, find out the feasibility, and know the students' response to the mathematics e-module subject matter for high school class XI students based on the curriculum 2013 using assessment from material experts, media Experts student responses. This research is development research with ADDIE (Analysis, Design, Development, Implementation, and Evaluation) models that produce products in the form of mathematics e-module subject matter sequence for high school students of class XI curriculum 2013 using Kvisoft Flipbook Maker Pro Software. Subjects in this study were material experts, media experts, and class XI students at SMA Negeri 1 Sewon Bantul and State Senior High School (SMA Negeri) 2 Ngaglik Sleman in the 2017/2018 Academic Year. The object of this study is the product of e-modules developed. This data collection technique is in the form of a questionnaire technique. The research data were obtained from questionnaires for media experts, material experts, and student responses. The data in this study were analyzed quantitatively and qualitatively to determine the feasibility of the e-module developed and to find out the students' response to the e-modules developed. This study succeeded in developing Mathematical e-module subject matter for high school students based on the curriculum 2013. From the expert material assessment results, an average score of 92.3 was obtained with eligible criteria. The media expert assessment obtained an average score of 104.3 with very feasible criteria. The assessment of student responses obtained an average score of 74.4 with good criteria. These results indicate that the mathematics e-module subject matter sequence for high school students in class XI based on the curriculum 2013 is appropriate for the classroom's learning process.
RELATIONSHIP BETWEEN LEARNING MOTIVATION, LEARNING INDEPENDENCE AND STUDENT LEARNING ENVIRONMENT AT HOME WITH MATHEMATICS LEARNING RESULTS OF CLASS VIII STUDENTS OF SMP NEGERI 1 IMOGIRI Khafid Baihaqi Iskak; Aris Thobirin
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol 4, No 8: Agustus 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedust.v4i8.17168

Abstract

In this study low mathematics learning outcome was associated with many factors. Learning motivation learning self-reliance, and learning environment of students at home were some of the factors that might be linked to learning outcomes. This study aimed at identifying the presence or absence of a positive and significant relationship between student’s learning motivation, learning Self-Reliance,  learning environment at home with mathematics learning outcomes on grade VIII Junior High School I Imogiri in odd semester 2016/2017. The study population was all students in grade VIII of SMP Negeri 1 Imogiri, Bantul, Odd Semester in the academic year 2016/2017 consisting of seven classes with a total of 216 students. Samples were students of class VIII A as a class sample of 32 students using random sampling techniques to the class of techniques of data collection using questionnaires to determine learning motivation, learning self-reliance, and the learning environment of students at home, the test method was used to determine students' mathematics learning outcomes. the data analysis for hypothesis testing used simple linear regression analysis and multiple linear regression. The results showed that there was a positive and significant relationship between 1) learning motivation and the mathematics learning outcomes, with r = 0.4051991131;2) learning self-reliance and  mathematics learning outcomes, with r = 0.3976856431; 3) learning environment of students at home and mathematics learning outcomes, with r = 0.2964336693; 4) learning motivation and learning self-reliance with mathematics learning outcomes, with r = 0.4547066454; 5) learning motivation and learning environment of student at home with mathematics learning outcomes, with r = 0.4364494415; 6) learning self-reliance and the learning environment of students at home with mathematics learning outcomes, with r = 0.5279264426; 7) Student’s learning Motivation, learning Self-Reliance, and learning environment of Student at home with mathematics learning outcomes, with  = 3.639515644 > = 2.95, multiple correlation coefficient r = 0.29668461 with linear regression equation Y = -45.92818127 + 0.126289034 X1+0.68816534 X2+0,467791704 X3, SR = 8.943701276 %, SR = 57.00738487 % and SR  = 34.04891385 % and   SE  = 2.509143579 %, SE = 15.99334652 % and SE = 9.552377804 %.
PERFORMANCE EVALUATION OF TRANSFER LEARNING MODELS BASED ON OPTIMIZATION IN AGRICULTURAL PEST CLASSIFICATION Attarik Mohammad; Sugiyarto Surono; Aris Thobirin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7800

Abstract

Pests in agriculture lower crop yields and jeopardize the world’s food security. Thus, quick and precise pest identification is crucial for successful pest management. Convolutional Neural Networks (CNN) and other deep learning techniques have made it possible to automatically classify pests thanks to developments in digital image processing and artificial intelligence (AI). Using three optimization algorithms, Adam, RMSprop, and SGD, this study assesses three transfer learning architectures, ResNet50V2, Xception, and EfficientNetB0. This study’s primary contribution is a comparative analysis of CNN architectures and optimization techniques to determine the best configuration for classifying agricultural pests. The dataset, which includes 5494 pest photos from 12 classes, was acquired via Kaggle. A ratio of  80%, 10%, and 10% was used to separate the data into training, validation, and testing sets. The performance of feature extraction and classification was enhanced by applying transfer learning with fine-tuning. According to findings, Xception with Adam and RMSprop has the highest accuracy of 94%. Adam and EfficientNetB0 both achieved competitive results with the same precision. These results suggest that the performance of agricultural pest classification models is influenced by both optimizer and architecture choices.
Analysis of Color Space Transformations on MobileNetV2 Performance for Image Classification Sherlyn Vironica; Sugiyarto Surono; Aris Thobirin
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.41353

Abstract

This study analyzes the effect of color space transformation on the performance of MobileNetV2 for rice leaf disease classification using RGB, HSV, CIELab, and their combinations. The RGB color space is used as the baseline representation, while HSV and CIELab are applied to provide alternative representations of color information. In addition, a dual-stream architecture is employed to combine different color spaces for feature extraction. The results show that the choice of color space influences classification performance. In the single color-space scenario, RGB achieves the highest accuracy of 91.42%, while in the combined scenario, the RGB+CIELab model achieves the best performance with an accuracy of 97.00%. These findings suggest that the use of multiple color spaces can provide richer feature representations and may improve classification performance. Furthermore, the results indicate that optimizing input representation plays an important role in improving model performance, particularly when using lightweight architectures such as MobileNetV2. This study shows that color space transformation can improve classification performance in the rice leaf disease dataset used in this study.
Hybrid ResNet50 with Convolutional Block Attention Module (CBAM) for Image Classification using Fine-Tuning Aulya Rachma Dewi; Aris Thobirin; Sugiyarto Surono
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2089

Abstract

Image classification is a crucial area in digital image processing that requires models capable of robust and stable feature representation. The main challenges in this study include variations between visual classes, di-verse image quality, and limited labeled data, which often hinder the model’s ability to generalize optimally. This study proposes a hybrid ResNet50-CBAM approach, which integrates the strengths of the ResNet50 archi-tecture in deep feature extraction with the Convolutional Block Attention Module (CBAM) attention mecha-nism to improve the model’s focus on the most informative areas of the image. The training process was carried out in two phases, namely transfer learning to utilize the initial representation from the ImageNet dataset, fol-lowed by fine-tuning to adjust the network weights to the image characteristics of the research dataset. The datasets were reorganized and split into 70% training, 15% validation, and 15% testing subsets to ensure a balanced distribution of samples. In addition, various augmentation techniques were applied to increase data diversity and improve the model’s generalization capability. The evaluation results showed that this hybrid approach achieved an overall accuracy of 99%, indicating very high and consistent performance across the entire dataset. The integration of CBAM into the ResNet50 architecture was proven to strengthen the feature extraction process by highlighting the most relevant areas, resulting in a more accurate, stable, and effective image classification model for a wide range of artificial intelligence image processing applications.
Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization Khusnul Khotimah; Sugiyarto Surono; Aris Thobirin
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p112-121

Abstract

The advancement of deep learning in computer vision has result in substantial progress, particularly in image classification tasks. However, challenges arise when the model is applied to small and unbalanced datasets, such as X-ray data in medical applications. This study aims to improve the classification performance of fracture X-ray images using the EfficientNet architecture optimized with grey wolf optimization (GWO). EfficientNet was chosen for its efficiency in handling small datasets, while GWO was applied to optimize hyperparameters, including learning rate, weight decay, and dropout to improve model accuracy. Random cropping, rotation, flipping, color jittering, and random erasing, were used to expand the diversity of the dataset, and class weighting is applied to overcome class imbalance. The evaluation uses accuracy, precision, recall, and F1-score metrics. The combination of EfficientNetB0 and GWO resulted in an average 4.5% improvement in model performance over baseline methods. This approach provides benefits in developing deep learning methods for medical image classification, especially in dealing with small and imbalanced datasets.
Co-Authors Adi Setya Priambudi Age Samanta Putra Anindita Lusiana Anis Ni'matus Sholihah Annisa Mutiara Sukma Annisa Nurohmawati Aprilian Dwi Putranto Ardina Dwi Ervinawati Attarik Mohammad Aulya Rachma Dewi Cindy Ainun Majid Dody Okkosila Guntur Prabowo Hanifah Hanifah Istiqomah Istiqomah Khafid Baihaqi Iskak Khusnul Khotimah Kurnia Prantauwati Luthfie Ghany Yahya Moh Rizki Yuniardi Basuni Muhammad Fahrul Styawan Muhammad Raezia Nano Gunawan Nely Ulfatullatifah Nova Risky Amalia Novitasari Hariyanti Nurul Amalina Kurniasih Puguh Wahyu Prasetyo, Puguh Sherlyn Vironica Shoffi Arba Sari Sonya Madyo Ratri Sugiyarto Surono Tina Puji Rahayu Wijayanti, Dian Eka