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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Komputer dan Informasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer UNEJ e-Proceeding Indonesian Journal of Applied Statistics Journal of Mathematics and Mathematics Education (JMME) International Journal of Computing Science and Applied Mathematics Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Pattimura Proceeding : Conference of Science and Technology Prosiding University Research Colloquium
Irwan Susanto
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Humanities 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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PENINGKATAN JIWA WIRAUSAHA SANTRI MELALUI PELATIHAN PEMANFAATAN SAMPAH PLASTIK MENJADI PRODUK BERNILAI JUAL Etik Zukhronah; Winita Sulandari; Isnandar Slamet; Sri Subanti; Sugiyanto Sugiyanto; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 9: February 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i9.4777

Abstract

Lack of public understanding about the proper handling of plastic waste can damage the environment. Based on the results of a survey conducted on students at the Darul Muttaqin Islamic Boarding School, Sragen, it can be seen that the waste management in the boarding school has not been carried out properly. In general, waste is directly disposed of in a landfill, without prior sorting between organic and inorganic waste. In this case, the residents of the cottage have not tried to process waste, especially plastic waste into useful products. For this reason, the service team for the Statistics Study Program FMIPA UNS held a socialization and training on the use of plastic waste into ornamental flower products. The purpose of this activity is to equip students with skills, as well as to foster an entrepreneurial spirit by marketing products from plastic waste to the general public. In the end, the success of product marketing will provide its own advantages as an alternative source of income for the students. In the future, the activities carried out consistently and sustainably will not only provide good benefits for the students but also the preservation of the surrounding environment.
Robust Regression Generalized Scale (GS) Estimation On Profit Data Of Poultry Farm Companies Safira Callisa; Yuliana Susanti; Irwan Susanto
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Abstract

Poultry farming is the business of cultivating poultry such as breeding chickens, laying hens, and broilers to obtain meat and eggs. Robust regression is a regression method that is used when some outlier data affect the model so that the distribution of the error is not normal. Estimates on robust regression that can overcome outliers such as Generalized Scale (GS) estimation, GS estimation is seen as an extension of S estimation. GS estimation is a solution for minimizing M estimation with paired scale error. This estimate is applied to poultry data companies in 2020 as an indicator to determine the robust regression model. It is concluded that the factors that affect the total profit of poultry farming companies in Indonesia in 2020 are wages for workers and electricity and water.
Retinopathy Classification using Convolutional Neural Network Method with Adaptive Momentum Optimization and Applied Batch Normalization Slamet, Isnandar; Susilotomoa, Dhestahendra Citra; Zukhronah, Etik; Subanti, Sri; Susanto, Irwan; Sulandari, Winita; Sugiyanto, Sugiyanto; Susanti, Yuliana
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.309

Abstract

Retinopathy is a common eye disease in Indonesia, ranking fourth after cataracts, glaucoma, and refractive errors. It can be overcome by early diagnosis with optical coherence tomography (OCT), but this imaging technique takes much time. In this research, retinal imaging was carried out using an expert system. The expert system in this study was formed using the convolutional neural network (CNN or ConvNet) method. CNN is an algorithm of deep learning that uses convolution operations to process two-dimensional data, such as images and sounds. This research consisted of 4 stages: data collection, preprocessing, model design, and model testing. A CNN model was formed with three arrangements, consisting of two convolutional layers and one pooling layer. The ReLU activation function, zero padding, and batch normalization were used in all three formats. Then, the flattening process was carried out, and the Softmax activation function was used at the end of the architecture. The model was built using eight epochs, and optimization of Adaptive Momentum resulted in a 98.96% test data accuracy value. The result was considered good so that CNN could be used as an alternative in retinopathy diagnosis. Further research is suggested to use other optimizations or other model architectures.
Implementation of Scale-Invariant Feature Transform Convolutional Neural Network for Detecting Distracted Driver Fhadilla, Nahdatul; Sulandari, Winita; Susanto, Irwan; Slamet, Isnandar; Sugiyanto, Sugiyanto; Subanti, Sri; Zukhronah, Etik; Pardede, Hilman Ferdinandus; Kadar, Jimmy Abdel
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.222

Abstract

A distraction while driving a vehicle may result in fatal consequences, namely accidents that may leave road users seriously injured or even dead. In order to mitigate this risk, it is imperative to establish a distracted driver detection system that is both precise and real-time. This research focuses on the application of artificial intelligence, with a particular emphasis on deep learning, which is achieved through the utilization of the Convolutional Neural Network (CNN) model. In order to enhance the detection of inattentive drivers and produce a more precise model, a scaleinvariant feature transform (SIFT)-CNN combination is proposed. The activities of the driver while operating a vehicle are categorized into ten categories in this study. One of these categories is considered a normal condition, while the remaining nine are classified as inattentive behaviors. This study implemented Adam optimization with 64 batches, a learning rate of 0.001, and epochs of 20, 25, 50, and 100. The proposed CNNSIFT model is capable of achieving superior performance in comparison to the solitary CNN model, as evidenced by the experimental results. The CNN-SIFT model has achieved 99% accuracy and a 0.05 loss when the hyperparameter configuration is optimized for 50 epochs. The analysis indicates that the accuracy of the features obtained from CNN-SIFT can be improved by approximately 1% compared with CNN to classify the type of driver distraction behavior. The model's reliability was further enhanced by its evaluation on test data, which resulted in high accuracy, precision, recall, and F1-score values. The model's ability to accurately identify driver behavior with a high degree of reliability is demonstrated by these results, which are a positive contribution to the improvement of road safety.
Implementasi High Order Intuitionistic Fuzzy Time Series Pada Peramalan Indeks Harga Saham Gabungan Nugraha, Titis Jati; Sulandari, Winita; Slamet, Isnandar; Subanti, Sri; Zukhronah, Etik; Sugianto, Sugianto; Susanto, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127363

Abstract

Indeks Harga Saham Gabungan (IHSG) adalah indeks yang mengukur kinerja harga semua saham yang terdaftar di Bursa Efek Indonesia (BEI). Pergerakan IHSG menjadi acuan para investor untuk menetapkan keputusan finansial yang berkaitan dengan untung rugi dalam berinvestasi. Oleh karenanya, informasi peramalan IHSG yang akurat sangat penting bagi para investor. Penelitian ini membahas penerapan metode High Order Intuitionistic Fuzzy Time Series (HOIFTS) dalam peramalan IHSG di BEI. Metode HOIFTS melibatkan tiga indikator, yaitu derajat keanggotaan, derajat non-keanggotaan, dan fungsi skor (indeks intutionistic) sehingga model yang dihasilkan mampu menangani ketidakpastian dalam data. Tahapan penting dalam pemodelan HOIFTS adalah pada intuitionistic fuzzification, penentuan relasi logika fuzzy intutionistic, dan proses intutionistic defuzzification order tinggi. Penelitian ini menetapkan metode Chen, baik order satu maupun order tinggi sebagai metode pembanding untuk melihat seberapa jauh keberhasilan metode HOIFTS dalam meramalkan data bulanan IHSG. Perbandingan nilai RMSE (root mean square error) dan MAPE (mean absolute percentage error) yang dihasilkan oleh model HOIFTS dan dua model benchmark, yaitu Chen order satu dan Chen order tinggi, menunjukkan bahwa metode HOIFTS memiliki nilai kesalahan yang paling kecil yakni nilai RMSE adalah sebesar 57,042 dan MAPE sebesar 0,837% pada data training, sedangkan pada data testing diperoleh nilai RMSE sebesar 38,466 dan MAPE sebesar 0,487%. Dengan demikian, metode HOIFTS lebih direkomendasikan dalam peramalan IHSG dibandingkan dua metode lain yang dibahas dalam penelitian ini.   Abstract The Composite Stock Price Index (CSPI) is an index that measures the price performance of all shares listed on the Indonesia Stock Exchange (ISE). CSPI is a reference for investors to determine financial decisions related to profit and loss in investing. Therefore, accurate CSPI forecasting information is very important for investors.  This research discusses the application of the HOIFTS method in forecasting CSPI on the ISE. The HOIFTS method involves three indicators, namely degree of membership, degree of non-membership, and a score function (intuitionistic index) so that the resulting model is able to handle uncertainty in the data. Important stages in HOIFTS modeling are intuitionistic fuzzification, determination of intuitionistic fuzzy logic relations, and the intuitionistic higher order defuzzification process. This research determines the Chen method, both first order and high order as a comparison method to see how successful the HOIFTS method is in predicting monthly CSPI data. The comparison results of the RMSE (root mean square error) and MAPE (mean absolute percentage error) values ​​produced by the HOIFTS and two benchmark models, i.e., the first order Chen’s and high-order Chen’s, show that the HOIFTS method yields the smallest error value, namely the RMSE value is 57.042 and the MAPE is 0.837% on the training data, whereas in testing data obtained an RMSE value of 38.466 and a MAPE of 0.487%. Thus, the HOIFTS method is more recommended in forecasting CSPI compared to the other two methods discussed in this research.    
Modeling Human Development Index of East Java Using Spatial Autoregressive and Spatial Error Ensemble Jelita, Nadia Aulia; Handajani, Sri Sulistijowati; Susanto, Irwan
PYTHAGORAS Jurnal Pendidikan Matematika Vol 19, No 2: December 2024
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v19i2.78621

Abstract

The human development index (HDI) is an indicator used to monitor the government's success in developing the quality of human life. East Java Province's HDI is the lowest compared to other provinces on Java Island. Therefore, it is necessary to improve human development in this province. Attention must be paid to all aspects of human development, including the relationship between neighboring regions. The spatial regression method is an analysis method that considers the spatial dependency of the data. Ensemble spatial regression combines several spatial models by adding noise to the response variable, which is expected to reduce the diversity in the data. This research aims to use ensemble spatial regression to examine the East Java HDI. East Java HDI has spatial lag and spatial error dependence, modeled with SAR and SEM. Queen contiguity is used as a spatial weight. The SEM model does not fulfill the homogeneity assumption, so it is continued with the ensemble method. The ensemble method is proven to reduce diversity, so  SEM Ensemble fulfills the assumption of homoscedasticity. After analysis using SAR and SEM Ensemble, the SAR model was chosen as the best model with the largest  and lowest AIC value. Significant variables on East Java HDI are life expectancy, expected years of schooling, average years of schooling, and expenditure per capita.
Combined Model of Markov Switching and Asymmetry of Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity for Early Detection of Financial Crisis in Hong Kong Sugiyanto, Sugiyanto; Subanti, Sri; Slamet, Isnandar; Zukhronah, Etik; Susanto, Irwan; Sulandari, Winita; Aprilia, Nabila Churin
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21943

Abstract

The financial crisis in Hong Kong occurred in 1997 and 2008. To prevent a crisis or reduce the impact of a crisis, action is needed through early detection of the crisis using export indicator. The combination of Markov Switching and Asymmetric Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity (MS-AGSARMACH) models explains the crisis well. The results show that the MSAGSARMACH(2,1,1) model can explain past and future crises well.
Forecasting of Indonesian Crude Prices using ARIMA and Hybrid TSR-ARIMA Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Slamet, Isnandar; Sugiyanto, Sugiyanto; Susanto, Irwan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 10, No 2 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v10i2.21946

Abstract

Forecasting of Indonesian crude prices (ICP) is crucial for the government and policymakers. It helps them develop appropriate economic policies, budget allocations, and energy strategies. Forecasting methods that can be used are Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). This study aims to forecast ICP using ARIMA and hybrid TSR-ARIMA models. The data used in this study is the ICP per month, from January 2017 to November 2022. The data is divided into two groups, the data from January 2017 to December 2020 is used as training data, and the data from January 2021 to November 2022 is used as testing data. The MAPE values for the testing data of the TSR-ARIMA(2,1,0) and ARIMA(2,1,0) models are 8.24% and 17.37% respectively. Based on this, it can be concluded that the TSR-ARIMA(2,1,0) model is better than the ARIMA(2,1,0) model for forecasting ICP.
Co-Authors Adella Okky Herashanti Anindya Apriliyanti Pravitasari Aprilia, Nabila Churin Brodjol Sutija Suprih Ulama Fhadilla, Nahdatul Fithriasari, Kartika Handajani, Sri Sulistijowati Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet, Isnandar Jelita, Nadia Aulia Kadar, Jimmy Abdel Kristoforus Exelsis Pratama Nugraha, Titis Jati Nur Iriawan Pardede, Hilman Ferdinandus Quraini Septy Wardhani Safira Callisa Shinta Budiati Sri Subanti Sri Sulistijowati Handajani Sri Sulistijowati Handajani Sugianto Sugianto Sugiyanto - Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Sulandari, Winita Supriyadi Wibowo Susilotomoa, Dhestahendra Citra Wahyuni Suryaningtyas, Wahyuni Winita Sulandari Winita Sulandari Winita Sulandari Yuliana Susanti Yuliana Susanti, Yuliana Zukhronah, Etik