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Hasih Pratiwi
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 6 Documents
 1 
Search results for , issue "vol 9, no 1 (2026)" : 6 Documents clear
Bayesian Neural Network untuk Prediksi Diabetes: Uncertainty Quantification dalam Machine Learning Sabrina Adnin Kamila; Kusman Sadik; Cici Suhaeni; Agus Mohamad Soleh
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.103994

Abstract

Penelitian ini bertujuan mengevaluasi dan membandingkan kinerja tiga model machine learning, yaitu random forest (RF), feedforward neural network (FNN), dan bayesian neural network (BNN), dalam klasifikasi diabetes menggunakan Diabetes Health Indicators Dataset dari UCI Machine Learning Repository yang memiliki ketidakseimbangan kelas. Prapemrosesan data meliputi normalisasi fitur menggunakan StandardScaler dan penanganan ketidakseimbangan kelas dengan synthetic minority over-sampling technique (SMOTE). Evaluasi model dilakukan menggunakan metrik akurasi dan skor F1, yang didukung oleh classification report dan confusion matrix. Hasil evaluasi menunjukkan bahwa RF menghasilkan akurasi tinggi (0,8493) namun skor F1 yang rendah (0,3386), yang mengindikasikan rendahnya sensitivitas model terhadap kasus positif diabetes. FNN memberikan performa yang lebih seimbang dengan skor F1 sebesar 0,4490 setelah penyesuaian threshold optimal. Sementara itu, BNN mencapai akurasi 0,8498 dan skor F1 sebesar 0,4043, serta memiliki keunggulan tambahan berupa kemampuan mengukur ketidakpastian prediksi melalui pendekatan Monte Carlo Dropout. Dengan demikian, FNN lebih unggul dalam keseimbangan klasifikasi, sementara BNN lebih relevan untuk aplikasi medis yang membutuhkan informasi tingkat kepercayaan prediksi guna mendukung pengambilan keputusan klinis yang lebih andal.This study aims to evaluate and compare the performance of three machine learning models, namely random forest (RF), feedforward neural network (FNN), and bayesian neural network (BNN), for diabetes classification using the Diabetes Health Indicators Dataset from the UCI Machine Learning Repository, which exhibits significant class imbalance. Data preprocessing includes feature normalization using StandardScaler and class imbalance handling through synthetic minority over-sampling technique (SMOTE). Model performance is evaluated using accuracy and F1-score metrics, supported by classification report and confusion matrix analysis. The results show that RF achieves high accuracy (0.8493) but a low F1-score (0.3386), indicating poor sensitivity to positive diabetes cases. FNN provides more balanced performance with an F1-score of 0.4490 after optimal threshold adjustment. Meanwhile, BNN achieves an accuracy of 0.8498 and F1-score of 0.4043, while offering the additional advantage of uncertainty quantification through Monte Carlo Dropout. Therefore, FNN is more effective for balanced classification performance, while BNN is more suitable for medical applications that require prediction confidence information to support more reliable and informed clinical decision-making.Kata Kunci: Prediksi diabetes, kuantifikasi ketidakpastian, bayesian neural network, classification imbalance, machine learning.Keywords: Diabetes prediction, uncertainty quantification, bayesian neural network, classification imbalance, machine learning.
Value at Risk Estimation of Portfolio Affected by the BDS Movement: A Copula Approach Binarvian Sofawi; Dhoriva Urwatul Wutsqa
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.95849

Abstract

This study aims to estimate value at risk (VaR) as a measure of the maximum potential loss in an investment portfolio through the application of a Copula approach to stocks affected by the Boycott, Divestment, and Sanctions (BDS) Movement. The data used are the daily return data of MAPI from PT Mitra Adiperkasa, Tbk, FAST from PT Fast Food Indonesia, Tbk, and UNVR from PT Unilever Indonesia, Tbk obtained from the closing stock prices. The returns used are daily simple returns from March 1, 2019, to February 29, 2024, consisting of 1,130 days. The model used in this study is the ARMA-GARCH Copula model. Autoregressive moving average (ARMA) is used due to the involvement of time influence in estimation, while generalized autoregressive conditional heteroskedasticity (GARCH) is used to address the high volatility in stocks. The selection of the best copula model using maximum likelihood estimation (MLE) involves five copulas: gaussian copula, t-Student copula, Clayton copula, Frank copula, and Gumbel copula. The results of the analysis show that Clayton copula is the best model, with VaR of the portfolio of stocks affected by the Boycott, Divestment, and Sanctions (BDS) movement at the 99%, 95%, and 90% confidence levels are 3.45%, 2.11%, and 1.55%, respectively. These findings suggest that lower tail dependence plays an important role in portfolio risk, indicating the potential for simultaneous extreme losses. Therefore, investors are encouraged to consider copula-based risk measurement methods and diversification strategies to minimize potential portfolio losses.Keywords: ARMA, copula, GARCH, returns, value at risk
Forecasting the U.S. Treasury Yield Curve Using the Hybrid Dynamic Nelson-Siegel and Long Short-Term Memory (LSTM) Method Neni Alya Firdausanti; Alya Shafira
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.114966

Abstract

U.S. Treasury (UST) securities are widely regarded as safe-haven assets and serve as global financial benchmarks, making the U.S. Treasury yield curve a key indicator of market expectations and economic risks, including recession probabilities. For Indonesia, where foreign exchange reserves are partly allocated to UST securities, accurate yield curve forecasts are essential for effective reserve management and monetary policy formulation. This study proposes a hybrid forecasting framework that integrates the dynamic Nelson–Siegel (DNS) model with long short-term memory (LSTM) networks to improve the accuracy and stability of U.S. Treasury yield curve forecasts. The decay parameter in the DNS model is estimated using the Newton–Raphson method, while the remaining parameters are estimated using ordinary least squares (OLS). The resulting DNS latent factors are subsequently used as input features for the LSTM model under various hyperparameter configurations. Forecasting performance is evaluated using the root mean squared error (RMSE) and benchmarked against a DNS–ARIMA model. The empirical results demonstrate that the proposed DNS-LSTM approach consistently outperforms DNS-ARIMA across all maturities, yielding lower forecasting errors and greater flexibility in capturing yield curve dynamics, particularly during the post-pandemic period. Overall, the DNS-LSTM model offers a more robust and data-driven alternative to traditional yield curve forecasting methods. These findings have practical implications for foreign reserve management, exchange rate stabilization, and investment decision-making. Future research may extend this framework by incorporating macroeconomic variables and exploring longer forecast horizons.Keywords: Bonds, yield, dynamic Nelson-Siegel, long short-term memory, U.S. Treasury
Forecasting Clove Price in South, Central, and North Sulawesi Using Generalized Space Time Autoregressive and Vector Autoregressive Elly Pusporani; M. Fariz Fadillah Mardianto; Nike Meliana Rahmawati; Anggita Nariswari; Firqa Aqila Hizbullah
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.112470

Abstract

Cloves are a strategic plantation commodity in Indonesia with important economic and cultural value, and their price volatility directly affects farmers’ welfare, supply chain stability, and regional economic planning. Although previous studies have shown that the generalized space time autoregressive (GSTAR) model is more flexible than the space time autoregressive (STAR) model for heterogeneous locations, empirical studies comparing GSTAR and vector autoregressive (VAR) models for clove price forecasting across geographically interconnected provinces remain limited. This study addresses that gap by comparing the forecasting performance of GSTAR and VAR for monthly clove prices in North Sulawesi, Central Sulawesi, and South Sulawesi. The novelty of this study lies in the application of GSTAR with three spatial weighting schemes uniform, inverse distance, and cross-correlation normalization and its comparison with VAR in the context of clove price forecasting. Monthly data from January 2015 to December 2024 obtained from the Central Statistics Agency were analyzed using an 80:20 training-testing split. Stationarity testing showed that all series became stationary after first differencing, and lag selection based on the Akaike information criterion identified lag 1 as optimal for both models. The results indicate that the GSTAR(1)I(1) model with cross-correlation normalization weights provides the best forecasting performance, with an average MAPE of 3.18% and RMSE of 5,729.84, outperforming the VARI(1,1) model, which produced an average MAPE of 10.57% and RMSE of 15,214.11. These findings confirm that incorporating spatial dependence significantly improves forecasting accuracy and demonstrates that GSTAR is a more effective model for geographically interconnected commodity markets.Keywords: Love price, forecast, GSTAR, SDGs 8, decent work and economic growth, VAR.
Pemodelan Efek Transmisi Inflasi Minyak Goreng di Jawa Barat, Jawa Tengah, dan Jawa Timur dengan Pendekatan BEKK GARCH M. Hafidz Habibullah
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.100076

Abstract

Konsumsi minyak goreng di Indonesia menunjukkan kecenderungan meningkat dan berfluktuasi setiap tahunnya. Selain faktor internal, volatilitas inflasi minyak goreng juga dapat dipengaruhi oleh spillover effect dari wilayah lain. Dengan mengasumsikan adanya heteroskedastisitas pada model rata-rata, model Baba, Engel, Kroner-generalized auto regressive conditional heteroscedasticity (BEKK-GARCH) cocok digunakan untuk menganalisis volatilitas spillover effect dari inflasi minyak goreng di Jawa Barat, Jawa Tengah, dan Jawa Timur. Efek transmisi berita, yang ditandai dengan transmisi dua arah, terjadi dari inflasi minyak goreng di Jawa Barat ke Jawa Timur dan dari Jawa Tengah ke Jawa Timur, sedangkan transmisi satu arah diamati dari Jawa Timur ke Jawa Tengah. Untuk spillover effect , yang ditandai dengan transmisi volatilitas dua arah, terjadi antara Jawa Barat dan Jawa Timur, serta dari Jawa Tengah ke Jawa Timur, sementara transmisi satu arah terjadi dari Jawa Tengah ke Jawa Barat. Oleh karena itu, pengendalian inflasi harus difokuskan pada wilayah dengan tingkat konsumsi tertinggi.Cooking oil consumption in Indonesia shows a tendency to increase and fluctuate annually. Besides internal factors, the volatility of cooking oil inflation can also be influenced by spillover effects from other regions. Assuming heteroscedasticity in the average model, the Baba, Engel, Kroner-generalized auto regressive conditional heteroscedasticity (BEKK-GARCH) model is suitable for analyzing the volatility spillover effect of cooking oil inflation in West Java, Central Java, and East Java. The news transmission effect, marked by two-way transmission, occurs from cooking oil inflation in West Java to East Java and from Central Java to East Java, while one-way transmission is observed from East Java to Central Java. For the spillover effect, characterized by two-way volatility transmission, it takes place between West Java and East Java, as well as from Central Java to East Java, while one-way transmission occurs from Central Java to West Java. Consequently, controlling inflation should focus on regions with the highest consumption levels.Kata kunci: Inflasi minyak goreng, spillover effect, BEKK-GARCH.Keywords: Cooking oil inflation, spillover effect, BEKK-GARCH.
Analisis Unmet Need Pelayanan Kesehatan pada Lansia di Indonesia dengan Regresi Logistik Biner Tiga Level Iftina Ika Rahmawati; Christiana Anggraeni Putri
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.110631

Abstract

Indonesia memasuki masa ageing population dengan tantangan yang muncul akibat populasi semakin menua. Lansia merupakan kelompok yang rentan dalam hal kesehatan, tetapi di tahun 2024 unmet need pelayanan kesehatan pada lansia masih cukup tinggi dan selama sepuluh tahun terakhir tidak mengalami penurunan yang signifikan. Penelitian ini bertujuan untuk menganalisis variabel-variabel yang memengaruhi unmet need pelayanan kesehatan pada lansia di Indonesia tahun 2024 berdasarkan karakteristik individu dan kontekstual. Data yang digunakan meliputi Survei Sosial Ekonomi Nasional Maret, Potensi Desa, dan publikasi Badan Pusat Statistik. Metode analisis yang digunakan adalah regresi logistik biner tiga level dengan random intercept. Hasil analisis menunjukkan unmet need lebih banyak terjadi pada lansia tua dengan tingkat pendidikan tidak bersekolah, masih bekerja, merokok, tidak memiliki jaminan kesehatan, serta tidak ada caregiver. Variabel usia kategori lansia tua, tingkat pendidikan kategori tidak sekolah, status bekerja, status merokok, kepemilikan jaminan kesehatan, pengeluaran, keberadaan caregiver, persentase penduduk miskin, rasio fasilitas kesehatan, rasio dokter, serta rasio anggaran belanja fungsi kesehatan berpengaruh signifikan terhadap status unmet need pelayanan kesehatan pada lansia. Nilai random intercept yang signifikan menunjukkan adanya perbedaan antar rumah tangga (ICC= 0,1560) dan antar kabupaten/kota (ICC= 0,1042) dalam peluang terjadinya unmet need pelayanan kesehatan. Nilai tersebut mengindikasikan bahwa sebesar 15,60% dan 10,42% dari total varians unmet need pelayanan kesehatan disebabkan oleh faktor tingkat rumah tangga dan kabupaten/kota. Indonesia is moving toward an ageing population phase, accompanied by challenges arising from an increasingly older demographic. Older adults are a vulnerable group in terms of health; however, in 2024, the proportion of unmet healthcare needs among this group remains relatively high and has not shown a significant decline over the past decade. This study aims to analyze the factors associated with unmet healthcare needs among older adults in Indonesia in 2024 based on individual and contextual characteristics. The data used were drawn from the March National Socioeconomic Survey (Susenas), the Village Potential Survey (Podes), and publications by Statistics Indonesia (Badan Pusat Statistik). A three-level binary logistic regression model with a random intercept was applied. The results show that unmet healthcare needs are more prevalent among older adults of advanced age, those with no formal education, those who are still working, smokers, individuals without health insurance, and those without caregivers. Significant factors include age, education level, employment status, smoking status, health insurance ownership, expenditure, presence of a caregiver, poverty rate, health facility ratio, physician ratio, and health expenditure ratio. The significant random intercept indicates that differences exist between households (ICC = 0.1560) and regions (ICC = 0.1042) in the likelihood of experiencing unmet healthcare needs. These values suggest that 15.60% and 10.42% of the total variance in unmet healthcare needs are attributable to household-level and region-level factors, respectively.Kata Kunci: Lansia, unmet need pelayanan kesehatan, regresi logistik biner tiga level.Keywords: Older adult, unmet need for health services, three level binary logistic regression.

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