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All Journal Media Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi 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) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Berdaya Mandiri International Journal of Advances in Data and Information Systems Computer Science and Information Technologies 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

Author-ID : 522902

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PENGEMBANGAN PLATFORM PROMOSI UMKM DALAM RANGKA MENDUKUNG KEGIATAN KOTAGEDE SMART DISTRICT Surono, Sugiyarto; Adi, Yudi Ari; Irsalinda, Nursyiva
Jurnal Berdaya Mandiri Vol 3, No 1 (2021): Jurnal Berdaya Mandiri (JBM)
Publisher : Universitas PGRI Yogyakarta

Usaha mikro kecil dan menengah (UMKM) merupakan salah satu penggerak roda perekonomian di masyarakat. Kotagede merupakan salah satu kecamatan di kota Yogyakarta yang memiliki UMKM dengan jumlah 497 usaha. Dalam upaya mempromosikan dan mempublikasikan produk-produk yang ada di Kecamatan Kotagede dan menunjang program Kecamatan Kotagede menuju Smart District maka diperlukan analisis khusus UMKM Kecamatan Kotagede untuk menciptakan suatu Platform yang memuat informasi UMKM baik pelaku maupun produk yang dihasilkan serta fasilitas lain yang dibutuhkan oleh pelaku UMKM maupun masyarakat. Hasil analisis data UMKM yang telah dilakukan menunjukkan bahwa UMKM dengan modal dibawah Rp. 250.000.000 sebesar 80%. Oleh karena itu, dalam rangka mengembangkan UMKM diwilayahnya pihak kecamatan sebaiknya membuat platform perizinan untuk mempermudah proses perizinan UMKM.

COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING Haryati, Anisa Eka; Surono, Sugiyarto
MEDIA STATISTIKA Vol 14, No 2 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.2.137-145

Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.

Optimization of feature selection on semi-supervised data Wijayanti, Dian Eka; Afriyani, Sintia; Surono, Sugiyarto; Dewi, Deshinta Arrova
Bulletin of Applied Mathematics and Mathematics Education Vol. 4 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v4i1.11104

This research explores feature selection optimization in semi-supervised text data by utilizing the technique of dividing data into training and testing sets and implementing pseudo-labeling. Proportions of data division, namely 70:30, 80:20, and 90:10, were used as experiments, employing TF-IDF weighting and PSO feature selection. Pseudo-labeling was applied by assigning positive, negative, and neutral labels to the training data to enrich information in the classification model during the testing phase. The research results indicate that the linear SVM model achieved the highest accuracy with a 90:10 data division proportion with a value of 0.9051, followed by Random Forest, which had an accuracy of 0.9254. Although RBF SVM and Poly SVM yielded good results, KNN showed lower performance. These findings emphasize the importance of feature selection strategies and the use of pseudo-labeling to enhance the performance of classification models in semi-supervised text data, offering potential applications across various domains that rely on semi-supervised text analysis.

THE ART OF TATAH SUNGGING WAYANG AS A STRENGTHENING BRANDING COMMUNITY OF WAYANG PURWA CRAFTSMEN ASSOCIATION IN WAYANG GENDENG HAMLET, BANGUNJIWO BANTUL-YOGYAKARTA Susanto, Moh. Rusnoto; Mariah, Siti; Lukitaningsih, Ambar; Surono, Sugiyarto; Saidon, Hasnul Jamal; Azizan, Ahamad Tarmizi; Dina Arumsari, Mela; Ahmad, Fandy; Wiwit, Ilma; Lianti, Desi; Pangestu, Raka; Bilqisa, Nazla H.; Maulidya, Thasya Alwa; Rismawati, Dewi; Khotimah, Kusnul
International Journal of Engagement and Empowerment (IJE2) Vol. 4 No. 3 (2024): 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.v4i3.172

Gendeng Hamlet in Bangunjiwo Bantul is a hamlet where puppet craftsmen grow as one of the oldest craft centers in Bangunjiwo. Gendeng Hamlet, as a puppet craftsman, once experienced a heyday in the era of President Soeharto and continues to survive amid the times. The puppet artisans of Gendeng Bangunjiwo Hamlet over time, with assistance from akadfemisi and various servants from 1various agencies and ministries until now. The artisans continue to survive and consistently maintain the tradition of Javanese puppet inlay and try to restore the golden age by supporting the existence of the Gendeng Leather Puppet Craft Center with improved product quality. The method determined to implement PM UPUD community service is collaborative and participatory. The stages of implementation are as follows: stages or steps in implementing the solutions offered to overcome the problems of target partners. Describe the method of implementing community service each year, at least containing the following: Socialization, Training, Technology implementation, Mentoring and evaluation, and Sustainability Program. The program outputs obtained include: (1) Increasing the capacity of superior products, up-skilling creative human resources, and planning transfers. The flagship product of the Tatah Sungging Gendeng Craft Center is shadow puppets. More specifically, Yogyakarta style wayang kulit. Puppet artisans in Gendeng hamlet can make high-quality works—Puppet Patterning Application Technology & Application of Machine Learning, and Strengthening Branding. (2) Gendeng's leather puppets could penetrate foreign markets during its heyday. In the archipelago itself, especially Yogya and surrounding areas, gender leather puppets dominate the market. The wayang production of Gendeng has yet to be able to repeat its former glory period

Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization Khotimah, Khusnul; Surono, Sugiyarto; Thobirin, Aris
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

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.

ANALYSIS OF TUBERCULOSIS PATIENT CHARACTERISTICS OF GORONTALO CITY HOSPITAL USING K-MEANS CLUSTERING METHOD: Analisis Karakteristik Pasien Tuberkulosis Rumah Sakit Kota Gorontalo Menggunakan Metode K-Means Clustering Hariadi Wijaya, Made; Nur Rahmatiya Abas, Siti; Fahrian Hipmi, Ahmad; Darmawan, Endang; Supadmi, Woro; Surono, Sugiyarto
Jurnal Berkala Epidemiologi Vol. 13 No. 2 (2025): Jurnal Berkala Epidemiologi (Periodic Epidemiology Journal)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbe.V13I22025.147-155

Background: Tuberculosis (TBC) is a major health problem in Indonesia, especially in Gorontalo, with high spread due to poor ventilation, overcrowding, and unhealthy lifestyles. Purpose: To analyze the characteristics of TB patients in one of Gorontalo City's hospitals using K-Means Clustering. Methods: Data including age, gender, TBC history, HIV status, diabetes history, hypertension, drug resistance, drug side effects, and treatment results were analyzed for the number of clusters using the K-Means method because it is effective in grouping data based on similarity, easy to implement, and works well on large datasets. Results: The analysis resulted in three clusters. Cluster 0 (219 individuals): majority female (63.50%), mean age 45.37 years, low address score (0.49), low resistance and therapy (6.40%), no comorbidities, all experienced side effects (100%), and survival rate 4.10%. Cluster 1 (150 individuals): mean age 52.21 years, higher address score (0.77), resistance 7.30%, therapy 5.30%, comorbidities 100%, all experienced adverse events, and survival rate 4.70%. Cluster 2 (98 individuals): mean age 48.58 years, address score 0.65, very low resistance and therapy (2%), no side effects, 42.90% had comorbidities, and the highest survival rate (12.20%). Conclusion: Three clusters were obtained from the analysis using K-Means. Clustering supports specific interventions such as comorbidity management or intensive surveillance, improving TB control programs in Gorontalo.

Machine Learning-Based Early Breast Cancer Detection Through Temperature and Color Skin with Non-Invasive Smart Device Salsabila, Sona Regina; Surono, Sugiyarto; Ibad, Irsyadul; Prasetyo, Eko; Subrata, Arsyad Cahya; Thobirin, Aris
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30340

Breast cancer remains a significant global health issue, affecting millions of women and often leading to late-stage diagnoses. Traditional diagnostic methods, such as mammograms, ultrasounds, and biopsies, are effective but can be costly, invasive, and not widely accessible, causing delays in detection and treatment. This research highlights the potential of using machine learning models with physiological data for early breast cancer detection. By capturing subtle physiological variations from a smart bra, the device allows real-time, non-invasive monitoring, offering a preventive solution that reduces the need for frequent clinical visits. The data were collected from a modified mannequin designed to simulate conditions related to breast cancer. To classify cancerous conditions based on temperature and color data, three machine learning models were evaluated. The Random Forest (RF) model proved to be the most effective, achieving 89% accuracy, 86.11% precision, 88.57% recall, and an F1-score of 87.33%, demonstrating strong performance in identifying complex patterns. The Support Vector Machine (SVM) achieved an accuracy of 81.25%, precision of 85.7%, recall of 80%, and an F1-score of 82.64%. The Multilayer Perceptron (MLP) exhibited an accuracy of 72%, precision of 69.69%, recall of 65.71%, and an F1-score of 67.52%, suggesting potential but requiring further optimization. These models serve as valuable tools to assist medical professionals in early screening efforts. Future research should aim to improve the models’ generalizability by expanding the dataset, utilizing data augmentation, applying transfer learning, and incorporating additional variables. Clinical validation and human trials are essential next steps to evaluate the system's effectiveness.

COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS Mahmudah, Umi; Surono, Sugiyarto; Prasetyo, Puguh Wahyu; Lola, Muhamad Safiih; Haryati, Annisa Eka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

One of the most widely used methods of survival analysis is Cox proportional hazard regression. It is a semiparametric regression used to investigate the effects of a number of variables on the dependent variable based on survival time. Using the Cox proportional hazard regression method, this study aims to estimate the factors that influence the survival of patients with type 2 diabetes mellitus. The estimated parameter values, as well as the Cox Regression equation model, were also investigated. A total of 1293 diabetic patients with type 2 diabetes were studied, with data taken from medical records at PKU Muhammadiyah Hospital in Yogyakarta, Indonesia. These variables have regression coefficients of 1.36, 1.59, -0.63, 0.11, and 0.51, respectively. Furthermore, the results showed the hazard ratio for female patients was 1.16 times male patients. Patients on insulin treatment had a 4.92-fold higher risk of death than those on other therapy profiles. Patients with normal blood sugar levels (GDS 140 mg/dl) had a 1.12 times higher risk of death than those with other blood glucose levels. Type 2 diabetes mellitus is a challenge for many Indonesians, in addition to being a deadly condition that was initially difficult to diagnose. As a result, patient survival analysis is needed to reduce the patient's risk of death.

OPTIMIZATION OF PORTFOLIO USING FUZZY SELECTION Wardani, Rahmania Ayu; Surono, Sugiyarto; Wen, Goh Kang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The problem of portfolio optimization concerns the allocation of the investor’s wealth between several security alternatives so that the maximum profit can be obtained. One of the methods used is Fuzzy Portfolio Selection to understand it better. This method separates the objective function of return and the objective function of risk to determine the limit of the membership function that will be used. The goal of this study is to understand the application of the Fuzzy Portfolio Selection method over shares that have been chosen on a portfolio optimization problem, understand return and risk, and understand the budget proportion of each claim. The subject of this study is the shares of 20 companies included in Bursa Efek Indonesia from 1 January 2021 until 1 January 2022. The result of this study shows that from 20 shares, there are 10 shares that is suitable in the forming of optimal portfolio, those are ADRO (0%), ANTM (43.3%), ASII (0%), BBCA (0%), BBRI (0%), BBTN (0%), BRPT (0%), BSDE (0%), ERAA (16%), and INCO (40.7%). The expected return from the portfolio is 0.0878895207 or 8.8% for the return and 0.0226022117 or 2.3% for the risk.

FUZZY TIME SERIES BASED ON THE HYBRID OF FCM WITH CMBO OPTIMIZATION TECHNIQUE FOR HIGH WATER PREDICTION Irsalinda, Nursyiva; Laely, Dera Kurnia; Surono, Sugiyarto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1245-1256

Time series data represents measurements taken over a specific period and is often employed for forecasting purposes. The typical approach in forecasting involves the analysis of relationships among estimated variables.In this study, we apply Fuzzy Time Series (FTS) to water level data collected every 10 minutes at the Irish Achill Island Observation Station. The FTS, which is based on Fuzzy C-Means (FCM), is hybridized with the Cat and Mouse Based Optimizer (CMBO). This hybridization of FCM with the CMBO optimizer aims to address weaknesses inherent in FTS, particularly concerning the determination of interval lengths, with the ultimate goal of enhancing prediction accuracy.Before conducting forecasts, we execute the FCM-CMBO process to determine the optimal centroid used for defining interval lengths within the FTS framework. Our study utilizes a dataset comprising 52,562 data points, obtained from the official Kaggle website. Subsequently, we assess forecasting accuracy using the Mean Absolute Percent Error (MAPE), where a smaller percentage indicates superior performance. Our proposed methodology effectively mitigates the limitations associated with interval length determination and significantly improves forecasting accuracy. Specifically, the MAPE percentage for FTS-FCM before optimization is 20.180%, while that of FCM-CMBO is notably lower at 18.265%. These results highlight the superior performance of the FCM-CMBO hybrid approach, which achieves a forecasting accuracy of 81.735% when compared to actual data.

Co-Authors Afriyani, Sintia Ambar Lukitaningsih Andhika, Reynaldi Ikbar Surya Ariani, Nabilah Evi Aris Thobirin, Aris Audina, Maretta Mia Azis, Septiyan Ibnu Azizan, Ahamad Tarmizi Bidul, Winarsi J. Bilqisa, Nazla H. Damayanti, Fanita Dewi, Deshinta Arrova Dina Arumsari, Mela Eko Prasetyo Eliyanto, Joko Endang Darmawan Fahrian Hipmi, Ahmad Fatma Indriani Firdausy, Salma Nadya Goh Khang Wen Hanifa, Riza Aulia Hariadi Wijaya, Made Haryati, Anisa Eka Haryati, Annisa Eka Ibad, Irsyadul Idam, Gabriela Irsalinda, Nursyiva Khoirun Nisya khusnul khotimah Kinanti, Marlita Diyah Wening Kurniawan, Tri Basuki Kusnul Khotimah Laely, Dera Kurnia Lianti, Desi Mahmudah, Kunti 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 rusmining, rusmining Saidon, Hasnul Jamal Salsabila, Sona Regina Siti Mariah, Siti Solihin, Iwan Mahmud Subrata, Arsyad Cahya Sukeiti, Wiwi Widia Susanto, Moh. Rusnoto Umi Mahmudah Vironica, Sherlyn Wardani, Rahmania Ayu Wati, Marfungah Wen, Goh Kang Wijayanti, Dian Eka Wiwit, Ilma Woro Supadmi Yudi Ari Adi, Yudi Ari

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