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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi REKAYASA Knowledge Engineering and Data Science Zero : Jurnal Sains, Matematika, dan Terapan Agroteknika Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Pengabdian Masyarakat Nusantara Bulletin of Information Technology (BIT) Jurnal Abdi Panca Marga Indonesian Business Review Journal of Applied Agricultural Science and Technology Abdimas Indonesian Journal Indonesian Journal of Applied Mathematics Indonesian Journal of Statistics and Its Applications
Nuramaliyah, Nuramaliyah
Universitas Terbuka
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Comparison of ARIMA, Random Forest, and Hybrid ARIMA-Random Forest Models in Forecasting Indonesian Crude Oil Prices Yeni Rahkmawati; Selvi Annisa; Hardianti Hafid; Nuramaliyah Nuramaliyah; Emeylia Safitri
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.36540

Abstract

The price of Indonesian crude oil (ICP) is highly volatile due to fluctuations in global demand, energy policies, and geopolitical tensions, making accurate forecasting challenging. This study compares three forecasting models: ARIMA, Random Forest, and Hybrid ARIMA–Random Forest. The models are evaluated using Time-Series Cross-Validation (TSCV) with MAPE, sMAPE, and RMSE as performance metrics. The results indicate that the Hybrid ARIMA–Random Forest model achieves the lowest MAPE and sMAPE, while Random Forest attains the lowest RMSE, and ARIMA exhibits the highest forecast errors. Diebold–Mariano (DM) tests confirm that ARIMA’s predictive accuracy is significantly lower than both machine-learning-based models, whereas no significant difference is found between Random Forest and the hybrid model. Out-of-sample forecasts for January–June 2026 show relatively stable price movements within 59–63 USD per barrel, with short-term fluctuations reflected in wide prediction intervals. These findings suggest that Indonesian crude oil prices contain both linear and non-linear components, which are effectively captured by the hybrid approach. Overall, the Hybrid ARIMA–Random Forest model provides the most accurate forecasts in percentage-based metrics, offering a robust and reliable tool for policymakers, investors, and market participants navigating volatile oil markets.
Analyzing COVID-19's Educational Impact in Indonesia: K-Means and Self-Organizing Map Approach Ika Nur Laily Fitriana; Emeylia Safitri; Ria Faulina; Nuramaliyah Nuramaliyah; Fonda Leviany
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2581

Abstract

The COVID-19 pandemic has affected the education sector. This research aimed to investigate the impact of COVID-19 on the education sector in Indonesia, especially on school participation indicators, using cluster analysis. We used fifteen factors related to the involvement indicators of students in elementary, junior secondary, and senior secondary education. The comparison of factors between 2019 and 2020 related to the effects of COVID-19, which began to proliferate in Indonesia in March 2020. Consequently, comparing those periods yields insights into the timeframe before and after the spread of COVID-19. To assess the pandemic's influence on the education sector, we performed an inferential statistical analysis using a nonparametric location test to identify significant changes between variables in 2019 and 2020. Subsequently, we performed cluster analysis using K-Means and Self-Organizing Map (SOM) approaches. The optimal cluster obtained for K-Means and SOM is three clusters. The results indicate that SOM and K-Means exhibit similar performances. Changes in cluster members in 2019 and 2020 indicate an enormous impact due to COVID-19. Cluster 3, which consists of DKI Jakarta, West Java, Central Java, East Java, and North Sumatra, is most affected by the pandemic from the educational sector.
Profil Sensori Abon Ikan Komersial dengan Metode Check-All-That-Apply (CATA) dan Ideal Profile Method (IPM) Ratna Nurmalita Sari; Nuramaliyah Nuramaliyah
Agroteknika Vol 9 No 2 (2026): Juni 2026
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/agroteknika.v9i2.754

Abstract

Abon ikan merupakan salah satu produk yang memiliki nilai tambah serta proses pembuatannya dapat memperpanjang umur simpan komoditas perikanan. Penelitian terkait profil sensori dari abon ikan komersial dengan menggunakan analisis sensori berbasis konsumen masih terbatas, sehingga penelitian ini bertujuan memetakan profil sensori guna membantu produsen menyusun strategi pengembangan produk. Metode Check-All-That Apply (CATA) dan metode profil ideal digunakan dalam penelitian ini, dengan pengolahan data menggunakan XLSTAT Sensory. Dari tiga produk abon yang diuji, terdapat perbedaan signifikan pada 7 dari 14 atribut sensori, yaitu pada atribut warna serta tingkat penyerapan minyak yang disebabkan oleh perbedaan formulasi bumbu rempah dan proses pemasakan. Produk A memiliki warna cokelat terang, produk B berwarna cokelat keemasan, dan produk C berwarna cokelat gelap. Tekstur produk C kering dan berminyak berbeda dari produk lainnya. Rasa manis signifikan ditemukan pada produk B. Dari ketiga produk yang diuji, belum terdapat produk yang memenuhi profil ideal konsumen, yang ditunjukkan oleh posisi kuadran yang berbeda. Hasil dari IPM menunjukkan adanya atribut rasa pahit dan pedas yang tidak diharapkan pada produk abon ikan.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
Comparative Analysis of Deep Learning Algorithms for Predicting ENSO Based on Non-sequential Sampling Procedure Algorithms Faulina, Ria; Hasanah, Siti Hadijah; Nuramaliyah, Nuramaliyah; Fitriana, Ika Nur Laily
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN and LSTM models in predictive accuracy and robustness. Specifically, the proposed model achieved the lowest Mean Absolute Error (MAE) of 13.97 and Root Mean Square Error (RMSE) of 15.76 across multiple test samples. These findings indicate that the integration of convolution-based feature extraction and sequential memory learning effectively captures complex ENSO temporal patterns. The proposed approach offers a reliable computational framework for climate forecasting and may contribute to improved anticipatory planning in climate-sensitive decision-making contexts.
Ensemble Anomaly Detection and SHAP-Based Attribution for Mapping Educational Inequality across Indonesian Provinces Emeylia Safitri; I Gusti Ngurah Sentana Putra; Yeni Rahkmawati; Ika Nur Laily Fitriana; Nuramaliyah Nuramaliyah; Ria Faulina
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.31161

Abstract

Educational inequality across Indonesia’s 38 provinces remains a challenge to equitable development. This study identifies anomalous provincial educational profiles by comparing Isolation Forest, Local Outlier Factor, and One-Class SVM across 50 parameter configurations in five analytical categories. The framework integrates ensemble majority voting, SHAP-based attribution, and leave-one-out robustness testing. Under the Silhouette-based selection criterion, Isolation Forest with contamination 0.05 ranked highest in four categories, with Silhouette Scores of 0.47-0.60 and Stability Scores of 1.0. However, sensitivity analysis showed that this criterion tends to favor configurations detecting fewer anomalies, so IF (0.05) should not be considered unambiguously superior. Highland Papua was consistently identified as anomalous across all categories. SHAP highlighted socioeconomic indicators, particularly rural child-labor participation, as important contributors to anomaly scores. Given the single-year design, 38 observations, and high feature-to-sample ratio, findings should be interpreted as exploratory descriptive mapping rather than causal or broadly generalizable inference.
Co-Authors Abda Abda Abdul Salam Agus Santoso Agus Santoso Akbar, Arya Zufikar Amry Purnama Mauladi ASEP SAEFUDDIN Baehaqi Diah Anggeraini Hasri Emeylia Safitri Emeylia Safitri Emeylia Safitri Fahlia Fahlia Fahlia, Fahlia Faulina, Ria Faulina, Ria Fendy Maradita Fitriana, Ika Nur Laily Fitriyantari Fonda Leviany Hanifa Sri Nuryani Hardiansyah, Rian Hardianti Hafid Hartini Hartini Hasanah, Siti Hadijah I Gusti Ngurah Sentana Putra Ika Nur Laily Fitriana Ika Nur Laily Fitriana Leviany, Fonda Lukmanul Hakim Lukmanul Hakim Maradita, Fendy Mika Alvionita Sitinjak Mikhratunnisa, Mikhratunnisa Muhammad Nur Aidi Oryza Safitri Ratna Nurmalita Sari Ria Faulina Safitri, Emeylia Safitri, Oryza Sari, Ratna Nurmalita Selvi Annisa Sri Nuryani, Hanifa Yeni Rahkmawati Yeni Rahkmawati