Article Per Year (5 Year)
p-Index From 2021 - 2026
2.978
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Natural A Jurnal Tata Kota dan Daerah International Journal of Advances in Intelligent Informatics Prosiding Seminar Matematika dan Pendidikan Matematik ELINVO (Electronics, Informatics, and Vocational Education) QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Belitung Nursing Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Islamic Monetary Economics and Finance JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) Musamus Journal of Physical Education and Sport (MJPES) Infotekmesin Inferensi Infotek : Jurnal Informatika dan Teknologi Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Proceeding ISETH (International Summit on Science, Technology, and Humanity) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Sewagati PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Riyahuna: Jurnal Pendidikan Bahasa Arab Jurnal Terapan Pendidikan Dasar dan Menengah Journal of Mathematics, Computation and Statistics (JMATHCOS)
Heri Kuswanto
Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 45 Documents
Search

 1   2   3   4   5 
Modeling Inflation Volatility Using ARIMAX-GARCH Aryani, Sri; Kuswanto, Heri; Suhartono, S
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2386

Abstract

Forecasting inflation is necessary as a basis for making decisions and high quality good planning in economic development in Indonesia particularly for the government and businessmen. The forecasting generally uses time series data. However, there is a time series data which is difficult to obtain stationary, i.e., the variance on financial time series data such as the stock price index, interest rates, inflation, exchange rates, and etc. It is mainly caused by the inconsistency of variance (heteroscedasticity). This study developed Autoregressive Integrated Moving Average (ARIMA) model using exogenous factors, namely the price of oil and outlier detection to forecast inflation. Another modeling which is expected to solve the problem of heteroscedasticity is a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. In this study, the asymmetric GARCH of Glosten Jagannathan Runkle-GARCH (GJR-GARCH) was carried out. This model could accommodate the volatility in the form of negative shocks that can leverage the effect. The data used in this study was the Inflation rate of Indonesia and world oil prices in January 1991 to December 2014 respectively. The results showed that ARIMAX-GJR GARCH is the best model to forecast national inflation volatility.
The Comparison of Classical and Bayesian Bivariate Binary Logistic Regression Prediction for Unbalanced Response (Case Study: Customers of Antivirus Software 'X' Company) Susila, Muktar Redy; Kuswanto, Heri; Fithriasari, Kartika
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2394

Abstract

The purpose of this study was to compare the performance of classical bivariate binary logistic regression and Bayesian bivariate binary logistic regression. The sizes of sample used in research were small and large sample. The size of the small sample was 200 and the large sample was 10000 samples. Parameter estimation method that often used in logistic regression modeling is maximum likelihood which is called the classical approach. However, using a maximum likelihood parameter estimation has several weaknesses. When the number of sample is small and the dependent variable is unbalanced, bias parameters are frequently obtained. Nevertheless, when the sample size is too large, it has propensity to reject H0. As the solution, the use of Bayesian approach to overcome the small sample size problem and unbalanced dependent variable is suggested. The case study carried out in this research was customer loyalty of 'X' Company. This study used two dependent variables, i.e. Customer Defections and Contract Answer. Initial information on the number of consumers who defected and not defected was unbalanced, likewise for the Contract Answers. Based on the comparison of classical and Bayesian bivariate binary logistic regression prediction, Bayesian method was evidenced to yield better performance compared to classical method.
Predictive Analytics for Property Valuation Using Random Forest in Malang City Noorihsan, Sandrian Yulian Firmansyah; Widhianingsih, Tintrim Dwi Ary; Kuswanto, Heri
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2411

Abstract

The property market in Malang City continues to expand alongside rising housing demand, yet limited price transparency still constrains informed decision-making for buyers, sellers, and developers. This study develops a data-driven property price prediction model using the Random Forest algorithm, selected for its robustness and ability to capture complex nonlinear relationships. An initial dataset of 4,358 property listings was collected through web scraping from Rumah123.com, and after thorough preprocessing including data cleaning, handling missing values, and feature refinement 1,573 valid observations remained for analysis. The model incorporates key property characteristics, covering temporal variables (month, year), physical attributes (land area, building area, number of bedrooms and bathrooms, electricity capacity, number of floors), property characteristics (certificate type, property type, property condition, furniture condition, hook position), and price information. Using optimally tuned hyperparameters, the final Random Forest model achieved an R² of 76.66% and a MAPE of 25.27%, indicating strong predictive performance relative to standard regression benchmarks. These findings offer managerial implications by providing objective, data-driven price estimates that can support developers, agents, and prospective buyers in pricing decisions, marketing strategies, and fair value assessments during negotiations.
Low birth weight among neonates in rural areas of Indonesia: A secondary data analysis Nelwati, Nelwati; Malini, Hema; Efendi, Ferry; Kuswanto, Heri; Has, Eka Misbahatul Mar’ah; Sampurna, Mahendra Tri Arif
Belitung Nursing Journal Vol. 12 No. 2 (2026): March - April
Publisher : Belitung Raya Publisher - Belitung Raya Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.4163

Abstract

Background: Low birth weight (LBW) is a major global health concern because of its strong association with infant mortality, morbidity, and impaired long-term development. The determinants of LBW among neonates in rural areas of Indonesia remain underexplored. Objective: To examine the prevalence of LBW and determine its associated factors among neonates in rural areas of Indonesia.   Methods: A cross-sectional study used secondary data sources from the 2017 Indonesian Demographic and Health Survey. A total of 6,701 mothers who lived in rural areas were included. Explanatory variables were maternal age, maternal education, smoking status, parity, birth interval, twin history, antenatal care (ANC), husband support, wealth quintile, region of residence, and complications during pregnancy. The outcome variable was the prevalence of LBW. Data were analyzed using bivariate analysis with a Chi-square test (χ2) and multivariable logistic regression. Results: The prevalence of LBW was 6.65%. First birth [AOR = 1.486; 95% CI: 1.126-1.959], twin history [AOR = 27.165; 95% CI = 13.006-56.738], fewer than four ANC visits [AOR = 2.193; 95% CI = 1.519-3.164], and complications during pregnancy [AOR = 1.890; 95% CI = 1.427-2.503] were significantly associated with the prevalence of LBW. Conclusion: This study revealed the prevalence of LBW among neonates in rural areas of Indonesia. First birth, twin history, ANC visits, and complications during pregnancy were significantly associated with LBW. It is suggested that health professionals should strengthen the quality of antenatal care and improve health promotion and education during pregnancy for rural mothers to reduce the prevalence of LBW.
Hybrid ARX–GARCH–LSTM Approach for Volatility Estimation of Indonesia’s Oil and Gas Sector Stocks: A Case Study of PGAS Ferigo Taufani Tri Hakiki; Irhamah Irhamah; Heri Kuswanto
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/rrv0zb05

Abstract

This study develops a hybrid ARX–GARCH–LSTM approach to estimate the volatility of PGAS stock during the 2010–2025 period. Stock volatility exhibits characteristics such as heteroskedasticity, volatility clustering, and nonlinear patterns, requiring an approach capable of capturing volatility dynamics more accurately. The proposed approach integrates the ARX model to capture the influence of external factors, the GARCH model to model time-varying volatility, and Long Short-Term Memory (LSTM) to learn nonlinear patterns from the residual/errors of the GARCH model. The modeling process begins by transforming stock prices into log returns, followed by ARX estimation to purify returns from the influence of exogenous variables. The ARX residuals are then modeled using GARCH(1,1), and the residual/errors generated by the GARCH model are subsequently used as input for the LSTM model to construct the hybrid ARX–GARCH–LSTM model. The results show that the hybrid ARX–GARCH–LSTM model outperforms the GARCH and baseline LSTM models with an RMSE value of 0.004250, an MAE value of 0.003077, and an R² value of 0.827293. Compared to the GARCH model, the hybrid approach reduces RMSE by 42.43% and MAE by 48.53%, while increasing the R² value by 72.83%. These findings indicate that the integration of statistical models and deep learning methods can improve the accuracy of stock volatility estimation and potentially support investment decision-making and financial risk management.
Co-Authors A.A. Ketut Agung Cahyawan W Adatul Mukarromah Ahmad Fathoni Ahmad Shofiyuddin Ichsan Ani Budi Astuti Ary Miftakhul Huda Bani Unggul, Didik Beben Sutara, Beben Dea Astri Titi Dedy Dwi Prastyo Dwi Rantini Effendi, Magdalena Eka Supriatna Ekarini, Fitria Faiqah, Elok Farida Ariany Fatul, Fitasari Ferigo Taufani Tri Hakiki Ferry Efendi Fithriasari, Kartika Fitri Ayu Kusumawati, Fitri Ayu Hady Suryono Hariadi Hariadi Has, Eka Misbahatul Mar’ah Hema Malini, Hema Hia, Supriadi Idrus Syahzaqi Indah Idamawarti, Itamara Irhamah Irhamah - Jamaluddin Jamaluddin Kinanti Hanugera Gusti Koesniawanto, Moch. Made Ayu Dwi Octavanny Marsono Marsono Moch. Koesniawanto Mohammad Hatta Rafsanjani Muhammad Hisyam Lee Munjahid Nelwati, Nelwati Nitivijaya, Maulidiah Noorihsan, Sandrian Yulian Firmansyah Novi Ajeng Salehah Nur Iriawan Paramita, NLP Satyaning Pradnya Pramono Dwi Utomo Puspita Kartikasari Qori’atunnadyah, Marita Raditya Sukmana Rahmawati, Hasri Rouhan, Asva Abadila Rumaisa Kruba S Suhartono, S Salafiyah Isnawati Sampurna, Mahendra Tri Arif Sediono, Sediono Setiawan Setiawan Sri Aryani, Sri Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suryansah Susila, Muktar Redy Sutikno Sutikno Ulfa Siti Nuraini Widhianingsih, Tintrim Dwi Ary Wiwiek Setya Winahju