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All Journal EKSAKTA: Journal of Sciences and Data Analysis AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan Media Farmasi : Jurnal Ilmu Farmasi (Journal Of Pharmaceutical Science) Jurnal Fourier Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Delta: Jurnal Ilmiah Pendidikan Matematika Jurnal Berdaya Mandiri Jurnal Sains dan Kesehatan

Sugiyarto Surono

universitas Ahmad Dahlan

Author-ID : 2552132

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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IMPLEMENTASI ALGORITMA SPECTRAL CLUSTERING UNTUK ANALISIS SENTIMEN Qonitat Rohmah Hidayati; Sugiyarto Surono
Delta: Jurnal Ilmiah Pendidikan Matematika Vol 9, No 1 (2021): Delta : Jurnal Ilmiah Pendidikan Matematika
Publisher : Universitas Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31941/delta.v9i1.1229

Data mining is a study that collects, cleans, processes, analyzes and benefits from data. One of the techniques known in data mining is the Spectral Clustering technique. Spectral clustering is a technique that follows the Connectivity approach, where this method classifies points that are connected or directly adjacent. The purpose of this study is to determine the level of public sentiment towards the 2017 Jakarta Pilkada using the Spectral Clustering method. The test data was obtained from the scraping process on Twitter from October 1, 2016 to April 20, 2017. In this study, input data consisting of tweet data and output data were used in the form of sentiments that have been clustered into 3, namely positive, negative and neutral. Obtained 4571 negative data, 1899 neutral data and 1588 positive data. with the highest possible win rate in the first round on Ahok. In the second round with 2205 data, 604 positive tweets were obtained, 1123 neutral data, 479 negative data for negative tweets. In the second round Anies Baswedan received higher positive and lower negative responses than Candidate Ahok, so that the chances of winning against Anies Baswedan were higher than Ahok.

DISKRITISASI EQUAL-WIDTH INTERVAL PADA NAIVE BAYES (STUDI KASUS: KLASIFIKASI PASIEN TBC) Hariyani Hariyani; Sugiyarto Surono
AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan Vol 10, No 2: Desember 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/admathedu.v10i2.20129

Klasifikasi Naive Bayes merupakan teknik untuk memprediksi probabilitas keanggotaan suatu kelas dengan menerapkan teorema Bayes. Klasifikasi Naive Bayes akan lebih baik jika menggunakan data yang berbentuk kategorik, sehingga dalam penelitian ini digunakan diskritisasi equal-width interval pada Naive Bayes untuk mengubah data yang berbentuk numerik menjadi kategorik. Tujuan dari penelitian ini adalah untuk menerapkan metode Naive Bayes dengan diskritisasi equal-width interval dalam mengklasifikasi pasien TBC di Puskesmas Sewon 1. Hasil penelitian ini menunjukkan akurasi sebesar 100% dengan perbandingan data training dan data testing sebesar 80%:20% dan 90%:10%, sehingga klasifikasi Naive Bayes dapat dikategorikan baik dalam mengklasifikasi pasien TBC.

Rough Set Theory for Dimension Reduction On Machine Learning Algorithm Rani Nuraeni; Sugiyarto Surono
Jurnal Fourier Vol. 10 No. 1 (2021)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Dimension reduction is a method applied in machine learning sector to significantly improve the efficiency of computational process. The application of high number variables in certain dataset is expected to be able to provide more information to analyze. However, this application of high number of variables will impacted on the computational time and weight linearly. Dimension reduction method serves to transforming the high dimension data into much lower dimension without significantly reduce the initial information and characteristic provided by the initial data. Core and Reduct is a method acquired through the concept of Rough Set. Dataset functioning as the input and output on Machine Learning can be perceived as informational system. The objective of this research is to determine the impact of the dimension reduction application on machine learning algorithm on the reduction of computational time and weight. Core and Reduct will be applied in few popular machine learning method such as Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN). This research applied on 5 UCI machine learning dataset which are Iris, Seeds, Years, Sonar, and Hill-Valley. Furthermore, Machine learning metrics such as Accuracy, Recall, Precision, and F1-Score also observed and compared. This research resulted in the conclusion that Core and Reduct is able to decrease the computational time up to 80% and maintain the value of each evaluation model.

A Survival Analysis with Cox Regression Interaction Model of Type II Diabetes Mellitus in Indonesian Simeftiany Indrilemta Lomo; Sugiyarto Sugiyarto; Endang Darmawan
Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan Vol 15, No 1 (2021): Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan
Publisher : Fakultas Kedokteran UPN Veteran Jakarta Kerja Sama KNPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33533/jpm.v15i1.2942

Type II diabetes mellitus is a metabolic syndrome characterized by hyperglycemia. Diabetes is still one of the world's health threats where the number of people with disabilities and mortality rates continue to increase over time. This study aims to analyze the survival of patients with type II diabetes mellitus as well as factors that affect it using survival analysis. This study used medical record data of type II diabetes mellitus patients undergoing treatment for the period 2015-2019 at PKU Muhammadiyah Gamping Hospital and PKU Muhammadiyah Hospital Yogyakarta.

Sentiment analysis using fuzzy naïve bayes classifier on covid-19 Zhurwahayati Putri; Sugiyarto Sugiyarto; Salafudin Salafudin
Desimal: Jurnal Matematika Vol 4, No 2 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Fuzzy Naive Bayes Classifier method has been widely applied for classification. The Fuzzy Naive Bayes method which consists of a combination of two methods including fuzzy logic and Naive Bayes is used to create a new system that is expected to be better. This research aim to find out the society's sentiments about COVID-19 in Indonesia and the use of the results of the Fuzzy Naive Bayes Classifier. The data of this research is obtained by scraping on Twitter in the period from January 1, 2020 to April 30, 2020. The classification method used in this research is the Fuzzy Naive Bayes Classifier method by applying the fuzzy membership function. In this research, sentiment analysis uses input data whose source is taken from tweets and the output data consists of sentiment data which is classified into three classes, namely positive class, negative class, and neutral class. In the distribution of training and testing data of 70%: 30%, the accuracy of the classification model using the confusion matrix is 83.1% based on 1199 tweet data consisting of 360 testing data and 839 training data. Also the presentation of each sentiment class was obtained which was dominated by positive sentiments, namely the positive class by 36.7%, the negative class by 35.0%, and the neutral class by 28.3%. Based on the results of the presentation, it can be concluded that there are still many people who have positive opinions or give positive responses to the presence of COVID-19 in Indonesia.

COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS Umi Mahmudah; Sugiyarto Surono; Puguh Wahyu Prasetyo; Muhamad Safiih Lola; Annisa Eka Haryati
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.

K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province Ummu Wachidatul Latifah; Sugiyarto - Surono; Suparman suparman
Desimal: Jurnal Matematika Vol 5, No 2 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district. To facilitate data grouping based on the similarity of existing characteristics, it is necessary to have a data grouping method, namely the clustering technique. There are several algorithms that are often used in clustering techniques, namely K-Means and Fuzzy C-Means. Each algorithm has advantages and disadvantages. Therefore, in this research, the comparison of the best clustering algorithms will be discussed. The purpose of this research is to compare the K-Means and Fuzzy C-Means algorithms in the grouping of Regencies/Cities in Central Java Province in 2021 based on the Human Development Index (IPM) indicator. The method used is descriptive qualitative. The data used is obtained from the Central Java Statistics Agency, which is in the form of the Central Java Province HDI indicator in 2021 which consists of 35 Regency/City members and has 4 variables, namely Life Expectancy (AHH) (X1), Expectation of School Years (HLS) (X2), Average Length of School (RLS) (X3) and Expenditure Per Capita (X4). The results showed that after a comparative analysis with the standard deviation ratio method, the FCM clustering method was better than the K-Means method. The value of the FCM standard deviation ratio is 0.460093 and the K-Means standard deviation ratio is 0.473601.

PENGEMBANGAN PLATFORM PROMOSI UMKM DALAM RANGKA MENDUKUNG KEGIATAN KOTAGEDE SMART DISTRICT Sugiyarto Surono; Yudi Ari Adi; Nursyiva Irsalinda
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.

Penerapan K-Means untuk Clustering Berdasarkan Tingkat Keparahan COVID-19 di Rumah Sakit Swasta Indonesia: Penerapan K-Means untuk Clustering Berdasarkan Tingkat Keparahan COVID-19 Kathina Deswiaqsa; Endang Darmawan; Sugiyarto Sugiyarto
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art5

In December 2019, coronavirus (COVID-19) caused by SARS-CoV-2 was first discovered in Wuhan, China. This virus has a high transmission rate and can be transmitted through droplets, airborne, and aerosols. The clinical manifestations are very diverse ranging from mild, moderate, and severe. Therefore, this study aims to conduct a clustering of the spread of the Covid-19 pandemic to facilitate the identification and handling. The method of the K-Means algorithm can be used as a method to obtain the desired clustering. The implementation and evaluation were conducted using RapidMiner tools and Davies Bouldin Index (DBI) respectively. Furthermore, the data sources by Kangdra (2020) were used with a total sample of 110 for the period March-June 2020. The results showed that the optimal cluster is located at k: 2 with a DBI value: 0,094 as the lowest value. Therefore, the cluster is strong since a smaller DBI value gives a better cluster. The clustering obtained is Cluster 1 and 2 with mild and moderate severity. The results are expected to facilitate a better zone identification of the COVID-19 severity level and rising people awareness.

Efek Samping Hipoglikemi yang Dialami oleh Pasien Geriatri yang Berisiko Sindrom Metabolik: Side Effects of Hypoglycemic Experienced by Geriatric Patients Who are at Risk of Metabolic Syndrome Ditya Ayu Natalia; Sugiyarto Sugiyarto; Endang Darmawan
Jurnal Sains dan Kesehatan (J. Sains Kes.) Vol. 4 No. 4 (2022): Jurnal Sains dan Kesehatan (J. Sains Kes.)
Publisher : Fakultas Farmasi, Universitas Mulawarman, Samarinda, Indonesia

Metabolic syndrome is a collection of symptoms of metabolic problem of the body with a several diseases. This study was to observe the relationship of geriatric patients characteristics versus clinical conditions (blood pressure, blood glucose, cholesterol and triglyceride) and find out which drugs are most often used. This research was conducted in 3 hospitals in Yogyakarta retrospectively. The number of respondents to the study was 101 geriatric patients who were at risk of metabolic syndrome and experienced hypoglycemic side effects. The data was collected using medical record data. Data analysis of the characteristics of respondents and drug groups was carried out descriptively. The relationship of the subject characteristics and the clinical state of the patient was carried out by using Chi-square. The results saw that the majority of geriatric patients who are at risk of developing metabolic syndrome are women (52.38%) with a normal body mass index (71.43%). More common history of diseases experienced by geriatric patients at risk of metabolic syndrome are diabetes mellitus and hypertension (60,95%) and patients with no complications (67.62%). The history of the disease correlates with the clinical state of the patient in particular cholesterol and triglyceride levels (p<0,05). In addition, complications correlate also with blood pressure values (p<0,05). The most commonly used classes of drugs in geriatric patients at risk of metabolic syndrome are ARB antihypertensive (76.19%), insulin antidiabetics (50,48%), and statin antidislipidemia (41.90%).

Co-Authors Annisa Eka Haryati Ditya Ayu Natalia Endang Darmawan Endang Darmawan Endang Darmawan Endang Darmawan Hariyani Hariyani Kathina Deswiaqsa Muhamad Safiih Lola Nursyiva Irsalinda Puguh Wahyu Prasetyo, Puguh Qonitat Rohmah Hidayati Rani Nuraeni Salafudin Salafudin Simeftiany Indrilemta Lomo Suparman suparman Umi Mahmudah Ummu Wachidatul Latifah USWATUN HASANAH Yudi Ari Adi, Yudi Ari Zhurwahayati Putri

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