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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Pangabdhi Jurnal Sains Matematika dan Statistika Syntax Literate: Jurnal Ilmiah Indonesia MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Jurnal Sosial Humaniora Mathvision : Jurnal Matematika Variance : Journal of Statistics and Its Applications G-Tech : Jurnal Teknologi Terapan Inferensi JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Journal of Mathematics Education and Science Berkala Penelitian Hayati Indonesian Community Journal Jurnal Statistika dan Komputasi (STATKOM) Economic Development Analysis Journal Limits: Journal of Mathematics and Its Applications
Sediono, Sediono
Department Of Mathematics, Faculty Of Science And Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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SEGMENTATION OF FRESH GRADUATES' JOB INTEREST AND MOTIVATION BASED ON FINITE MIXTURE PARTIAL LEAST SQUARES (FIMIX-PLS) Hermawan, Mohamad David; Kurniawan, Ardi; Mardianto, M. Fariz Fadillah; Sediono, Sediono
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page229-238

Abstract

Rapid technological advancements have significantly transformed the global labor market, impacting various industries and workers in Indonesia who may need to be adequately prepared to adapt. The current landscape demands individuals who can acquire knowledge quickly and adapt to modern technologies. The unemployment rate in Indonesia, especially among fresh graduates, is still a concern. Lack of motivation and interest in finding a job and high expectations of working conditions contribute to this problem. This research aims to address the gap in research by using the Finite Mixture Partial Least Squares (FIMIX-PLS) approach to examine the segmentation of fresh graduate characteristics about their interest and motivation in finding a job. Segmentation based on latent variable relationships in the structural model can be overcome with Finite Mixture Partial Least Square (FIMIX-PLS) to identify more homogeneous characteristics. This research analyzes explicitly the impact of compensation, work environment, and company reputation on the interest and motivation of fresh graduates in finding a job. This research resulted in the best segmentation of two segments: the 1st segment at 77.8% (265 samples) and the 2nd segment at 22.2% (75 samples).
Prediction Analysis of Jakarta Composite Index Movement Using Support Vector Regression Method Marcelena Vicky Galena; Sediono Sediono; M. Fariz Fadillah Mardianto; Elly Pusporani
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.5879

Abstract

The JCI is an important indicator that reflects the performance of the Indonesian stock market. In recent times, the JCI has faced significant fluctuations due to complex factors, including global economic conditions and market sentiment, which make predicting its movements challenging. Good prediction is needed to support market stability and sustainable economic development as per SDGs point 8. This study applies a modern nonparametric regression method, namely Support Vector Regression (SVR), to predict a dataset in the form of weekly JCI data from the period April 2022 to October 2024 obtained from the investing.com website. The analysis shows that the SVR model with RBF kernel function provides the best performance, with MAPE of 1.43%, RMSE of 121.6196, and MAE of 104.65. The findings also reveal that the fluctuation pattern of the JCI cannot be fully explained based solely on historical data. External variables, such as global economic conditions and market sentiment, have a significant influence on the prediction results. Therefore, the SVR method can be utilized to optimize portfolio allocation based on weekly JCI predictions. In addition, the results of this study provide guidance for policymakers in designing proactive economic policies to mitigate market volatility and increase investor confidence.
Analisis Keputusan Hedging pada Bank Non-Syariah di Indonesia Menggunakan Model Regresi Logit Biner Data Panel dengan Efek Acak Koesnadi, Grace Lucyana; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
Jurnal Sains Matematika dan Statistika Vol 11, No 1 (2025): JSMS Januari 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i1.33886

Abstract

Volatilitas pasar global yang semakin tinggi telah menjadi tantangan besar bagi sektor perbankan di Indonesia, khususnya dalam menghadapi fluktuasi nilai tukar rupiah. Dalam mengatasi risiko ini, strategi hedging menjadi langkah penting untuk menjaga stabilitas keuangan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi keputusan hedging pada bank non-syariah di Indonesia, seperti leverage, likuiditas, profitabilitas, ukuran perusahaan, dan peluang pertumbuhan. Dengan menggunakan regresi logit biner pada data panel dengan efek acak, penelitian ini memanfaatkan data sekunder dari laporan keuangan tahunan bank non-syariah yang terdaftar di Bursa Efek Indonesia (BEI) untuk periode 2020-2022. Hasil analisis menunjukkan bahwa leverage dan ukuran perusahaan memiliki pengaruh signifikan terhadap keputusan hedging, sedangkan likuiditas dan peluang pertumbuhan menunjukkan pengaruh yang bervariasi. Penelitian ini memberikan wawasan penting terkait pengelolaan risiko nilai tukar yang strategis untuk memperkuat stabilitas keuangan sektor perbankan non-syariah di Indonesia, serta mendukung pengambilan keputusan yang lebih akurat dalam mitigasi risiko keuangan.
MODELING AND FORECASTING THE TOTAL VOLUME OF GOODS TRANSPORTED BY RAIL IN INDONESIA USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa; Oktavia, Sabrina Salsa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp829-842

Abstract

Transportation has an important role in supporting the mobility of people in Indonesia. Trains are included in the most widely used transportation category because they are effective and efficient, not only transporting passengers, trains also have a role in the distribution of goods. This study aims to model and forecast total volume of goods transported through rail transportation in Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method because the data has seasonal trend. The data used comes from the Central Statistics Agency (BPS) from January 2013 to April 2024. The results were obtained that the SARIMA (0,1,1)(0,1,1)12 model is the best model with a MAPE value of 0.96% which is included in the category of accurate model. In addition to being an additional insight, this research can also be a reference in the management of railway transportation considering the number of uses both passengers, the distribution of goods that continue to increase, and can be recommendation for other research that same topic with it.
Forecasting Futures Gold Prices Using Pulse Function Intervention Analysis Approach Miranda, Ariadna Sopia; Andriani, Putu Eka; Sediono, Sediono; Syahzaqi, Idrus
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21979

Abstract

Gold is a precious metal that plays an important role in global trade and is often use as a financial standard in various countries. In 2024, gold prices surged sharply due to global macroeconomic factors, such as economic uncertainty, positioning gold as a safe haven for investors. Accurate predictions of future gold prices are crucial for helping investors make informed decisions and adapt to market changes. In line with Sustainable Development Goal (SDG) 8 on Decent Work and Economic Growth, this study uses the pulse function intervention analysis approach to predict gold prices by identifying patterns of changes in the pre-intervention and post-intervention periods. This study aims to make a significant contribution to the use of comprehensive and relevant predictive tools by considering the effects of interventions, supporting investor decision-making, and contributing to economic growth. The best model was obtained at ARIMA (0,2,1) with intervention parameters b=0, r=2, and s=0. The prediction results show a close alignment with actual data, yielding a MAPE value of 1.289%. Additionally, this model produces the smallest AIC value of 1125.1, an SBC value of 1135.86, and an MSE value of 1403.11, demonstrating excellent predictive capability.
PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA Syahzaqi, Idrus; Sediono, Sediono; Dyaksa, Mega Kurnia; Vionita, Anggi Triya; Ghasani, Anisah Nabilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2207-2218

Abstract

Climate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people.
Modeling Youth Development Index in Indonesia Using Panel Data Regression for Binary Response with Random Effect Widyangga, Pressylia Aluisina Putri; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21734

Abstract

Indonesia has the largest youth population in Southeast Asia, yet its Youth Development Index (YDI) ranks only fifth in the region. This study aims to fill the gap in empirical research by modeling the YDI in Indonesia using binary logit and binary probit regressions with random effects, based on panel data from 34 provinces during 2020–2022. The YDI categories are defined according to the national target of 57.67 set by the Ministry of Youth and Sports Affairs. The analysis reveals that the binary probit model performs better than the binary logit model, with a classification accuracy of 93.14% and a McFadden R-squared of 0.4064. Gender Inequality Index (GII) and Expected Years of Schooling (EYS) significantly affect the likelihood of achieving the YDI target. These results highlight the critical role of gender equality and education in advancing youth development in Indonesia. The binary probit model provides a practical tool for policymakers to predict and evaluate the effectiveness of development programs targeting youth outcomes. This research not only contributes methodologically to the study of youth development using advanced econometric models but also offers policy-relevant insights that support the strategic goals of Indonesia Emas 2045. By identifying key leverage points such as gender equity and education access, the findings reinforce the importance of inclusive and evidence-based planning to nurture a generation of resilient, empowered, and high-performing youth who can lead Indonesia toward a prosperous future.
A Comparison of Multivariate Adaptive Regression Spline and Spline Nonparametric Regression on Life Expectancy in Indonesia Pratama, Bagas Shata; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.29413

Abstract

Life expectancy is a key indicator of a population’s overall health and well-being. It also reflects the effectiveness of government efforts in improving public welfare. Despite various initiatives by both the government and society to improve life expectancy in Indonesia, significant disparities remain. This quantitative study aims to support these efforts by analyzing factors influencing life expectancy in Indonesia using data from the Indonesian Central Agency of Statistics (BPS) in 2023. A comparative analysis was conducted using two methods: Multivariate Adaptive Regression Spline (MARS) and Spline Nonparametric Regression. The results show that the MARS model outperforms the Spline model, achieving a lower Mean Squared Error (MSE) of 1.183 and a higher R-Square of 82.7%. Key variables significantly influencing life expectancy include access to decent housing, access to safe drinking water, per capita expenditure, and the Gini ratio. The findings not only confirm the presence of complex interactions among predictor variables effectively captured by the MARS method, but also contribute to the existing literature by emphasizing the importance of socioeconomic determinants in health outcomes. From a policy perspective, the results suggest that government strategies should prioritize improving access to basic needs and reducing inequality. These insights can guide targeted, data-driven interventions aimed at enhancing life expectancy in Indonesia.
Peramalan Jumlah Barang Kereta Api di Indonesia Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Oktavia, Sabrina Salsa; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa
Jurnal Statistika dan Komputasi Vol. 4 No. 1 (2025): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v4i1.4424

Abstract

Background: Freight transportation is an important part of the business run by PT Kereta Api Indonesia. To support effective strategic planning and infrastructure development, an accurate prediction of the amount of goods to be transported in the future is required. Therefore, historical data-based forecasting methods such as Seasonal Autoregressive Interated Moving Average (SARIMA) can be a relevant approach to predict the number of railway goods in Indonesia. Objective: Obtain a suitable model to forecast the number of goods transported by rail transportation in Indonesia, and to determine the results of the forecasting. Methods: This research uses the time series method with the Seasonal Autoregressive Integrated Moving Averang (SARIMA) model approach based on data characteristics that show seasonal patterns. SARIMA itself is able to integrate seasonal pattern components in the data and is able to effectively capture periodic and structural dynamics in seasonal data. Results: The best model obtained is probabilistic SARIMA(0,1,1)(0,1,1)12, using secondary data sourced from the Central Bureau of Statistics (BPS) in the range of January 2013 to March 2024. Forecasting for the next 12 months (April 2023 to March 2024) shows a Mean Absolute Percentage Error (MAPE) value of 8.03% which indicates that the level of forecasting accuracy is very good. Conclusion: The probabilistic ARIMA(0,1,1)(0,1,1)12 model can be used as a reliable reference in predicting the amount of goods transported through rail transportation in Indonesia.
World Gold Price Prediction After United State Election Using Pulse Function Intervention Analysis Sediono, Sediono; Vionita, Anggi Triya; Renianti, Fayza Shafira
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33706

Abstract

The United States (US) election in November 2024 had a significant impact on global economic conditions, especially world gold prices. A key effect was the strengthening of the US dollar, leading to a sharp drop in gold prices to 2,582.1 USD. This study aims to model and forecast gold prices using the pulse function intervention analysis method. The analysis uses weekly data, with the intervention point set in the second week of November 2024 (t = 101). The best pre-intervention model was identified as ARIMA(0,2,1), while the best intervention model had orders b = 1, r = 0, s = 0, based on analysis of the Cross Correlation Function (CCF). The resulting model shows significant parameters and strong performance, with a MAPE of 1.51\%, AIC of -530.394, SBC of -525.030, and MSE of 0.0002037. Forecasts indicate gold prices are likely to increase again through the end of July 2025. These findings show that the pulse intervention model effectively captures external shocks, such as post-election dollar appreciation. The study improves our understanding of the dynamics of global gold prices and offers insights that can help policymakers develop strategies to mitigate the risks caused by fluctuations in the external market.
Co-Authors Adinda Tries Melati Aditya, Josephin Viona Ailsa Shafa Salsabila Ainaya Zakiyah Alfredi Yoani Aliffia, Netha Ameliatul 'Iffah Ana, Elly Andriani, Putu Eka Anggakusuma, Aurellia Calista Antonio Nikolas Manuel Bonar Simamora Ardi Kurniawan Ardi Kurniawan Arum Eka Ismiranda Putri Aufa Muhammad Yogi Riyanto Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Azis, Aurelia Islami Azizah Dewi Ariyani Bagas Maulana Chaerobby Fakhri Fauzaan Purwoko Christopher Andreas Citra Imama Deby Victoria Dewanti, Maria Setya Dhyana Venosia Dhyana Venosia Dita Amelia Dita Amelia Dita Amelia, Dita Dyaksa, Mega Kurnia Effendi, Magdalena Elly Ana Elly Ana Elly Pusporani Eris Tri Kurniawati Faizun, Nurin Ferissa Maulida Ismi Ghasani, Anisah Nabilah Handoko Darmokoesoemo Hani Sudarmanto Hariawan Widi Nugroho Helda Urbhani Rosa Heri Kuswanto Hermawan, Mohamad David I Kadek Pasek Kusuma Adi Putra Idrus Syahzaqi Idrus Syahzaqi Ilma Amira Rahmayanti Jannah, Sa’idah Zahrotul Karima, Sasy Okti Khoirun Niswatin Koesnadi, Grace Lucyana Kresna Oktafianto M. Fariz Fadillah Mardianto Marcel Laverda Subiyanto Marcel Laverda Subiyanto Marcelena Vicky Galena Mardianto, M. Fariz Fadillah Mas Loegito Miranda, Ariadna Sopia Mochamad Rasyid Nabila, Ainaya Zakiyah Naufal Ramadhan Al Akhwal Siregar Nauvaldy, Muhammad Netha Aliffia Noviatus Sholihah Nugroho, Hariawan Widi Nur Chamidah Nurul Fajriah Deswani Sangadji Oktavia, Sabrina Salsa Pramesti, Helfira Lady Ari Pratama, Bagas Shata Previan, Anggara Teguh Putri Fardha Asa Oktavia Hans Putri Nur Farida Rahmanita, Tentri Ryan Rahmat Agung Ibrahim Rahmawati, Hasri Renianti, Fayza Shafira Salsabylla Nada Apsariny Sasy Okti Karima Sa’idah Zahrotul Jannah Siagian, Kimberly Maserati Siti Maghfirotul Ulyah Siti Maghfrotul Ulyah Steven Soewignjo Suliyanto Suliyanto Suliyanto Suliyanto, Suliyanto Toha Saifudin Toha Saifudin Trisnadi Widyaleksono Catur Putranto Vionita, Anggi Triya Widyangga, Pressylia Aluisina Putri Wieldyanisa, Ezha Easyfa Yoani, Alfredi