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All Journal Media Statistika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Berdaya Mandiri International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Matematika International Journal of Engagement and Empowerment (IJE2) Bulletin of Applied Mathematics and Mathematics Education
Sugiyarto Surono, Sugiyarto
Department of Mathematics, Faculty of Mathematics and Science Ahmad Dahlan University, Yogyakarta
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Perbandingan 5 Jarak K-Nearest Neighbor pada Analisis Sentimen Mujhid, Almuzhidul; Thobirin, Aris; Firdausy, Salma Nadya; Surono, Sugiyarto; Rahmadani, Lanova Ade
Jurnal Ilmiah Matematika Vol 8, No 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v0i0.23170

Abstract

K-Nearest Neighbor (KNN) merupakan algoritma yang biasa digunakan untuk klasifikasi. Penelitian ini menggunakan ulasan aplikasi Maxim di Google Play Store. Pengguna yang sudah mengunduh aplikasi Maxim berhak memberikan ulasan di Google Play Store guna berbagi informasi untuk pengguna lain. Implementasi K-Nearest Neighbor (KNN) terhadap Sentiment Analysis ulasan aplikasi Maxim dapat digunakan untuk menentukan kelas ulasan bernilai positif, neutral, atau negatif. Peneliti melakukan perbandingan 5 jarak yang berbeda untuk metode KNN yaitu jarak Euclidean, Manhattan, Minkowski, Chebyshev dan Canberra. Pengujian yang telah dilakukan memberikan hasil akurasi pada klasifikasi KNN dengan jarak yang berbeda, memberikan hasil akurasi yang berbeda-beda, yaitu jarak Euclidean  84 persen, jarak Manhattan  79 persen, jarak Minkowski 84 persen, jarak Chebyshev  7 persen dan jarak Canberra =44 persen.
Fuzzy Support Vector Machine Using Function Linear Membership and Exponential with Mahanalobis Distance Sukeiti, Wiwi Widia; Surono, Sugiyarto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 2 (2022): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i2.6912

Abstract

Support vector machine (SVM) is one of effective biner classification technic with structural risk minimization (SRM) principle. SVM method is known as one of successful method in classification technic. But the real-life data problem lies in the occurrence of noise and outlier. Noise will create confusion for the SVM when the data is being processed. On this research, SVM is being developed by adding its fuzzy membership function to lessen the noise and outlier effect in data when trying to figure out the hyperplane solution. Distance calculation is also being considered while determining fuzzy value because it is a basic thing in determining the proximity between data elements, which in general is built depending on the distance between the point into the real class mass center. Fuzzy support vector machine (FSVM) uses Mahalanobis distances with the goal of finding the best hyperplane by separating data between defined classes. The data used will be going over trial for several dividing partition percentage transforming into training set and testing set. Although theoretically FSVM is able to overcome noise and outliers, the results show that the accuracy of FSVM, namely 0.017170689 and 0.018668421, is lower than the accuracy of the classical SVM method, which is 0.018838348. The existence of fuzzy membership function is extremely influential in deciding the best hyperplane. Based on that, determining the correct fuzzy membership is critical in FSVM problem.
Comparative study of unsupervised anomaly detection methods on imbalanced time series data Hanifa, Riza Aulia; Thobirin, Aris; Surono, Sugiyarto
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.431

Abstract

Anomaly detection in time series data is essential, especially when dealing with imbalanced datasets such as air quality records. This study addresses the challenge of identifying point anomalies rare and extreme pollution levels within a highly imbalanced dataset. Failing to detect such anomalies may lead to delayed environmental interventions and poor public health responses. To solve this, we propose a comparative analysis of three unsupervised learning methods: K-means clustering, Isolation Forest (IForest), and Autoencoder (AE), including its LSTM variant. These algorithms are applied to monthly air quality data collected in 2023 from 2,110 cities across Asia. The models are evaluated using Area Under the Curve (AUC), Precision, Recall, and F1-score to assess their effectiveness in detecting anomalies. Results indicate that the Autoencoder and Autoencoder LSTM outperform the others with an AUC of 98.23%, followed by K-means (97.78%) and IForest (96.01%). The Autoencoder’s reconstruction capability makes it highly effective for capturing complex temporal patterns. K-means and IForest also show strong results, offering efficient and interpretable solutions for structured data. This research highlights the potential of unsupervised anomaly detection techniques for environmental monitoring and provides practical insights into handling imbalanced time series data.
SUSTAINABLE MATERIALS NATURAL COLORS THE SUNGGING WAYANG PROCESS AS A REBRANDING OF THE GENDENG BANGUNJIWO WAYANG ARTISAN COMMUNITY IN BANTUL, YOGYAKARTA Susanto, Moh. Rusnoto; Mariah, Siti; Lukitaningsih, Ambar; Surono, Sugiyarto; Kinanti, Marlita Diyah Wening; Idam, Gabriela; Azis, Septiyan Ibnu; Prasetyo, Haryanto Nur; Damayanti, Fanita; Maulida, Tasya
International Journal of Engagement and Empowerment (IJE2) Vol. 5 No. 3 (2025): International Journal of Engagement and Empowerment
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ije2.v5i3.239

Abstract

This community service project aims to develop natural dyes (sustainability materials) to support the wayang puppet painting process as a rebranding strategy for the artisan community in Gendeng Hamlet, Bangunjiwo, Bantul, Yogyakarta. The implementation method involves a series of activities carried out through a Participatory Action Research (PAR) approach, involving artisans, academics, and facilitators. The stages include training, mentoring, exploration of local materials (such as Indigofera leaves, tingi bark, and turmeric), and applied trials on wayang puppets. The results of the activities showed an increase in the artisans' capacity in natural dye extraction and application techniques, more aesthetically pleasing and environmentally friendly visual quality of wayang, and the formation of a new community identity with the branding “Wayang Warna Alam” (Natural Color Wayang). This innovation not only increases the added value of wayang products but also strengthens cultural and ecological sustainability and opens up market opportunities in the context of the creative economy.
Algoritma Support Vector Regression dan Analisis Long Short-Term Memory sebagai Penanganan Missing Data Parinzka, Zellya; Surono, Sugiyarto; Thobirin, Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Time series multivariat adalah jenis data yang sering digunakan di berbagai bidang seperti keuangan, statistik, dan kesehatan karena dapat menjelaskan hubungan kompleks antar variabel. Namun, sering kali terdapat masalah seperti missing data yang dapat menjadi tantangan signifikan dalam proses analisis, mengurangi kualitas data dan akurasi model prediksi. Penelitian ini bertujuan untuk mengatasi masalah missing data time series multivariat dengan menggunakan teknik Support Vector Regression (SVR) untuk imputasi missing data dan Long Short-Term Memory (LSTM) sebagai analisis prediktif. SVR diterapkan untuk memprediksi missing data berdasarkan hubungan antar variabelnya, sementara LSTM digunakan untuk memodelkan pola temporal dalam data yang telah diimputasi. Evaluasi kinerja menunjukkan bahwa metode ini dapat meningkatkan kualitas data dan akurasi prediksi secara signifikan. Dengan menghasilkan metrik evaluasi RMSE 0.16, MSE 0.03, dan MAE 0.13, metode integratif ini tidak hanya menawarkan solusi yang efektif untuk menangani missing data, tetapi juga membantu memperkuat penerapan machine learning dalam analisis data time series multivariat. Selain itu, penelitian ini menunjukkan relevansi praktis dari integritas metode imputasi berbasi SVR dan analisis prediktif dengan LSTM, yang mampu dalam meningkatkan integritas data serta menghasilkan model prediksi yang akurat, sehingga berpotensi mendukung pengambilan keputusan berbasis data dalam berbagai bidang yang lebih luas dan realistis, khususnya pada analisis indikator kesehatan seperti Life Expectancy.   Abstract Multivariate time series is a type of data often used in various fields such as finance, statistics, and health because it can explain complex relationships between variables. However, there are often issues like missing data that can pose significant challenges in the analysis process, reducing data quality and model prediction accuracy. This research aims to address the missing data problem in multivariate time series by using Support Vector Regression (SVR) for imputing missing data and Long Short-Term Memory (LSTM) for predictive analysis. SVR is applied to predict missing data based on the relationships between the variables, while LSTM is used to model temporal patterns in the imputed data. Performance evaluation shows that this method can significantly improve data quality and prediction accuracy. With evaluation metrics of RMSE 0.16, MSE 0.03, and MAE 0.13, this integrative method not only offers an effective solution for handling missing data but also helps strengthen the application of machine learning in multivariate time series data analysis. Furthermore, this research demonstrates the practical relevance of the integrity of SVR-based imputation methods and predictive analysis with LSTM, which can enhance data integrity and produce accurate predictive models, thus potentially supporting data-driven decision-making in broader and more realistic fields, particularly in the analysis of health indicators such as Life Expectancy.
Performance Evaluation of AdamW, RMSProp, and Nadam Optimizers on EfficientNetB2 Model for Image Data Classification Damayanti, Fanita; Surono, Sugiyarto; Thobirin, Aris
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1482

Abstract

This study examines the effect of different optimization algorithms on the performance of the EfficientNetB2 model in classifying lung and colon histopathology images. Three commonly used optimizers AdamW, RMSprop, and Nadam were analyzed to compare their influence on convergence trends, classification accuracy, and overall learning consistency. Using a five-class dataset covering benign and malignant tissue samples, the experimental results show that all three optimizers are able to deliver reliable predictions, although with varying performance characteristics. RMSprop emerges as the most effective optimizer, achieving the highest accuracy across all evaluation stages, with 99.05% during training, 99.16% on validation, and 98.72% on testing, along with the lowest loss values. This indicates that RMSprop facilitates faster and more stable convergence compared to the other two methods. AdamW also demonstrates strong predictive performance but shows limitations when distinguishing cancer types with closely similar morphological structures. Nadam attains high accuracy in early stages yet exhibits lower initial stability than RMSprop. Overall, pairing EfficientNetB2 with RMSprop provides the most optimal configuration for this classification task. These results offer valuable insights for designing better training strategies and strengthening the effectiveness of medical imaging based computer aided diagnostic systems.
Application of EfficientNetV2-S Architecture with Focal Loss to Overcome Class Imbalances in Skin Cancer Classification Wati, Marfungah; Thobirin, Aris; Surono, Sugiyarto
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1524

Abstract

Imbalanced class distributions in skin lesion image datasets can reduce the effectiveness of multiclass classification models. This research proposes a classification model based on the EfficientNetV2-S architecture with the application of two-stage training and loss functions that emphasize learning in classes with limited data. The models were trained using on-the-fly image augmentation and evaluated to assess generalization capabilities to the test data. In the initial stage, the model is trained by freezing the backbone and only updating the classifier layer. Next, fine-tuning was carried out on part of the backbone layer to adjust the representation of features to the image characteristics of the skin lesion. Evaluation is conducted through multiple training times with different random initializations to ensure consistency of results. The test results showed that the model experienced an improvement in performance after the fine-tuning process, with an accuracy of about 88% as well as an increase in F1-score values in some classes. Overall, the results indicate that the proposed approach may help improve classification performance when dealing with imbalanced skin cancer image data.
Deep Convolutional Generative Adversarial Network-Enhanced Data Augmentation for Imbalance Facial Acne Severity Classification Using a Fine-Tuned EfficientNet-B1 Nisya, Khoirun; Surono, Sugiyarto; Thobirin, Aris
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5548

Abstract

Imbalanced datasets often hinder the generalization capability of Convolutional Neural Networks (CNNs) in medical image classification, leading to overfitting and reduced performance on minority classes. This study aims to develop an acne severity classification model using EfficientNet-B1 combined with geometric and photometric  augmentation, as well as  and Deep Convolutional Generative Adversarial Network (DCGAN)-based augmentation to address class imbalance. The dataset consists of 1,380 facial images categorized into four acne severity levels: Normal, Level 0, Level 1, and Level 2. Preprocessing includes RGB conversion, bilinear resizing, and center cropping. The data are split into training (80%), validation (10%), and testing (10%) sets. Geometric and photometric augmentation applies horizontal flipping, 45° rotation, color jittering, and random resized cropping, while DCGAN generates synthetic samples to balance minority classes. The EfficientNet-B1 model is fine-tuned using compound scaling, MBConv blocks, Swish activation, Batch Normalization, Cross-Entropy loss, and AdamW optimizer, with 5-fold cross-validation for robustness. Experimental results demonstrate that DCGAN-based augmentation achieves superior performance, with a test accuracy of 94% and an average F1-score of 0.93, outperforming geometric and photometric data augmentation (90% accuracy and 0.88 F1-score). DCGAN augmentation also significantly reduces misclassification between visually similar acne severity levels, particularly Level 0 and Level 1. These findings indicate that integrating DCGAN with EfficientNet-B1 effectively enhances generalization on imbalanced medical image datasets, providing a robust and replicable framework for acne severity classification and related medical imaging applications.
Co-Authors Afriyani, Sintia Ambar Lukitaningsih Aris Thobirin, Aris Azis, Septiyan Ibnu Azizan, Ahamad Tarmizi Bilqisa, Nazla H. Damayanti, Fanita Dewi, Deshinta Arrova Dina Arumsari, Mela Eliyanto, Joko Endang Darmawan Fahrian Hipmi, Ahmad Firdausy, Salma Nadya Hanifa, Riza Aulia Hariadi Wijaya, Made Haryati, Anisa Eka Haryati, Annisa Eka Idam, Gabriela Irsalinda, Nursyiva Khoirun Nisya Kinanti, Marlita Diyah Wening Kusnul Khotimah Laely, Dera Kurnia Lianti, Desi Maulida, Tasya Maulidya, Thasya Alwa Muhamad Safiih Lola Mujhid, Almuzhidul Nur Rahmatiya Abas, Siti Nurwijayanti Pangestu, Raka Parinzka, Zellya Prasetyo, Haryanto Nur Puguh Wahyu Prasetyo, Puguh Rahmadani, Lanova Ade Rismawati, Dewi Saidon, Hasnul Jamal Siti Mariah, Siti Solihin, Iwan Mahmud Sukeiti, Wiwi Widia Susanto, Moh. Rusnoto Umi Mahmudah Wardani, Rahmania Ayu Wati, Marfungah Wen, Goh Kang Wijayanti, Dian Eka Wiwit, Ilma Woro Supadmi Yudi Ari Adi, Yudi Ari