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All Journal International Journal of Advances in Applied Sciences Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Dasar Kontinu: Jurnal Penelitian Didaktik Matematika Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Applied Statistics Journal of Mathematics and Mathematics Education (JMME) BAREKENG: Jurnal Ilmu Matematika dan Terapan Business Innovation and Entrepreneurship Journal TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Journal of Informatics Management and Information Technology Konstelasi: Konvergensi Teknologi dan Sistem Informasi Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Prosiding Seminar Nasional Official Statistics Prosiding Simposium Nasional Multidisiplin (SinaMu) International Journal of Computing Science and Applied Mathematics-IJCSAM
Zukhronah, Etik
Universitas Sebelas Maret
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Model Hibrida ARIMA-Neural Network untuk Peramalan Kasus Tuberkulosis Agung Setyabudi, Arriza; Etik Zukhronah; Isnandar Slamet
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.597

Abstract

Tuberculosis (TB) remains a significant public health challenge in Surakarta City, necessitating accurate forecasting methods to support effective and planned control strategies. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average-Neural Network (ARIMA-NN) hybrid model in forecasting monthly TB cases in the Surakarta region. The performance of this hybrid model is further compared with the ARIMA model. The research data used consists of monthly TB case data from January 2019 to September 2024 obtained from the Surakarta City Health Department. The data is divided into two sets: training data from January 2019 to December 2023 and testing data from January 2024 to September 2024. The ARIMA(0,1,1) model was identified as the best model for capturing the linear component of the data, yielding a Mean Absolute Percentage Error (MAPE) of 14.52% on the training data and 16.55% on the testing data. The residuals from the ARIMA(0,1,1) model were then further modeled using a Neural Network with 5 hidden neuron architecture, period lookback 6, and a learning rate of 0.1, to capture the remaining non-linear patterns. The developed ARIMA(0,1,1)-NN hybrid model showed better forecasting performance, with a MAPE value of 14.34% on the training data and 14.48% on the testing data. These results indicate that the ARIMA-NN hybrid approach offers the potential for improved accuracy compared to the ARIMA model in the context of TB case forecasting in Surakarta.
Peramalan Harga Penutupan Saham PT Bank Rakyat Indonesia Tbk Menggunakan Model Hibrida ARIMA - SVR Melati Anggiasari; Etik Zukhronah; Sri Sulistijowati Handajani
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 5 No. 2 (2025): Desember 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v5i2.11432

Abstract

PT Bank Rakyat Indonesia Tbk merupakan salah satu perusahaan bank milik pemerintah terbesar di Indonesia. Harga saham PT Bank Rakyat Indonesia Tbk cenderung fluktuatif, sehingga diperlukan model peramalan yang dapat membantu para investor dalam meramalkan pergerakan harga saham di masa mendatang. Penelitian ini bertujuan untuk menerapkan model hibrida ARIMA – SVR pada peramalan harga penutupan saham PT Bank Rakyat Indonesia Tbk. Data yang digunakan dalam penelitian ini adalah harga penutupan saham PT Bank Rakyat Indonesia Tbk periode 2 Januari 2023 hingga 29 Februari 2024. Data dibagi menjadi data training yang berjumlah 239 data dan data testing yang berjumlah 40 data. Data training dimodelkan menggunakan ARIMA, kemudian residu dari ARIMA dimodelkan menggunakan SVR. Hasil peramalan model ARIMA dan SVR dijumlahkan untuk mendapatkan model hibrida. Evaluasi model hibrida dilakukan menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model hibrida ARIMA (1,1,0) – SVR menggunakan kernel Radial Basis Function (RBF) dengan nilai hyperparameter C = 0,1, ε = 0,01, dan γ = 0,3 memiliki nilai MAPE data testing sebesar 1,193% yang lebih rendah dibandingkan dengan nilai MAPE pada model hibrida ARIMA (0,1,1) – SVR.
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; Sri Subanti; Isnandar Slamet; Etik Zukhronah; Irwan Susanto; Winita Sulandari; Nabila Churin Aprilia
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4541

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 Etik Zukhronah; Winita Sulandari; Sri Subanti; Isnandar Slamet; Sugiyanto Sugiyanto; Irwan Susanto
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4563

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.
Markov-switching and noise-to-signal ratio approach for early detection of currency crises Sugiyanto, Sugiyanto; Nirwana, Muhammad Bayu; Slamet, Isnandar; Zukhronah, Etik; Parahita, Syifa’ Salsabila Gita
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp42-54

Abstract

Economic instability can easily lead to a currency crisis. Therefore, observing a number of crisis indicators is crucial for building an early warning system (EWS). However, selecting the indicators most responsive to the crisis is the best choice. For this purpose, the noise-to-signal ratio (NSR) method was used. Monthly data from 1990-1925 were used in the autoregressive moving average (ARMA), generalized autoregressive moving average with generalized autoregressive conditional heteroscedasticity (GARMACH), and Markov-switching (MS)-GARMACH hybrid models to explain the crisis. Model interpretation indicates that there will be no crisis from May 2025-April 2026.
Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Extreme Learning Machine (ELM) pada Peramalan Peredaran Uang Kartal di Indonesia Pratiwi, Afita Ulya; Zukhronah, Etik; Slamet, Isnandar
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.83128

Abstract

Money is generally accepted as legal tender in fulfilling an obligation. Money circulation is very important to be considered and controlled, to have a positive impact on the economy. Control of money circulation is usually emphasized on the type of cash, which is in the form of paper and metals. One of the ways that can help in controlling cash is by forecasting. This study aims to compare the accuracy of forecasting results on cash circulation data using the SARIMA and ELM methods. The data used is the circulation cash from January 2011 to April 2022. The SARIMA method is a method for forecasting time series data containing seasonality, while the ELM method is a method on artificial neural networks that can do forecasting. The best SARIMA model obtained is SARIMA (1,1,0)(0,1,0)12. The best ELM architecture obtains 12 input layer neurons, 45 hidden layer neurons, and 1 output layer. The measure of forecasting error to determine the best model is using MAPE. The results show that the SARIMA method has a training data MAPE of 2,3270% and testing data of 2,2772%, while the ELM method has a training data MAPE of 4,2548% and testing data of 3,8615%. Therefore, the SARIMA method is better than the ELM method at forecasting the circulation of cash in Indonesia.Keywords: cash; extreme learning machine; seasonal autoregressive integrated moving average.  
Profile Of Tourist Visits In Sangiran Site Area, Sragen Regency Subanti, Sri; Slamet, Isnandar; Sulandari, Winita; Zukhronah, Etik; Sugiyanto, Sugiyanto; Susanto, Irwan
Journal of Mathematics and Mathematics Education Vol 11, No 1 (2021): Journal of Mathematics and Mathematics Education (JMME)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jmme.v11i1.52744

Abstract

Tourism activities are chain activities that involve various sectors and related institutions. Tourism is one of the fields in the lives of the people of Sragen Regency, which has become one of the priorities in development in recent years. This is based on the local government's awareness that tourism development can support regional income while at the same time increasing the standard of living of people living in tourist areas. For this reason, evaluating the impact of tourism in an area on the socioeconomic conditions of the community is an important thing to know. Sangiran is one of the most complete paleontological sites in Indonesia. Sangiran has also been designated as a cultural heritage by UNESCO on December 5, 1996, with the designation number C.593. The Sangiran site itself is located in Sragen Regency and Karanganyar Regency, Central Java Province. In general, the background of the population in the Sangiran Site area comes from the Javanese ethnic group, who in daily life communicate using the Javanese language. The Sangiran site has been known as an ancient human area from the Pleistocene. Not only storing archaeological wealth, but Sangiran is also very rich in artistic potential, both from prehistoric times and the present. Many things can be enjoyed in Sangiran. Apart from the museum that presents archaeological findings full of meaning, the public can also enjoy the local culture, including traditional arts, traditional ceremonies, local architecture, and folk crafts, adding value to the site. This study aims to determine the profile of tourist visits in the Sangiran Site Area. This study found that the factors that influence the number of visits to the Sangiran Site Area are travel costs, age, gender, and monthly income of respondents related to visiting the Sangiran Site Area. Furthermore, the factors that influence the respondents' willingness to accept ticket offers in the market hypothesis scenario in the Sangiran Site Area are the nominal price of the entrance ticket to a market hypothesis given to respondents, age, gender, monthly income of respondents, education level of respondents, and origin of the respondent.
Co-Authors Adi Wicaksono, Nanda Adigama Tri Nugraha Agung Setyabudi, Arriza Aji Susanto Aprilia, Nabila Churin Aryani, Fadhilatul Nida Atika Amalia Brilianto, Maximilianus Noel Dewi Retnosari Dina Rohmah Dyah Auliya Agustina Effendi, Arfiyansyah Rizki Fhadilla, Nahdatul Fransisca Trisnani Ardikha Putri Handajani, Sri Sulistijowati Hidayah, Nurrul Hizkia Yotant Pradana Husna Afanyn Khoirunissa Ilahi, Esa Permata Sari Putri Indra Raditya , Dionisius Irwan Susanto Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet, Isnandar Kadar, Jimmy Abdel Kusuma, Erin Jihan Wahyu Martina Hilda Melati Anggiasari Muhammad Zidni Subarkah Nabila Churin Aprilia Nafi'udin, Fauzi Nirwana, Muhammad Bayu Nugraha, Titis Jati Parahita, Syifa’ Salsabila Gita Pardede, Hilman Ferdinandus Pratiwi, Afita Ulya Pratiwi, Hasih pratiwi, hasih S Sugiyanto Solikhah Novita Intan Sri Subanti Sri Sulistijowati Handajani Sugianto Sugianto Sugiyanto - Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Sulandari, Winita Supriyadi Wibowo Susilotomoa, Dhestahendra Citra Tito Tatag Prakoso Winita Sulandari Winita Sulandari Yuliana Susanti Yuliana Susanti, Yuliana Yulvia Fitri Rahmawati