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All Journal Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA) Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Rakha Maheswara
Unknown Affiliation
Earth & Planetary Sciences Mathematics Social Sciences

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Pengelompokkan Faktor yang Memengaruhi Kemiskinan di Jawa Timur Tahun 2023 Menggunakan Analisis Cluster Abghaza Bayu Kusuma Wardhana; Rakha Maheswara; Sri Pingit Wulandari
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 2 No. 6 (2024): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v2i6.304

Abstract

Poverty means the inability to fulfill the basic needs of family members, both food and non-food. In this study, we will analyze several indicators that are assumed to be factors that influence poverty in East Java in 2023, including East Java in 2023, including the percentage of poor people, life expectancy, average years of schooling, and unemployment rate. life expectancy, average years of schooling, and open unemployment rate using cluster analysis to group kabupatens. cluster analysis to group districts/cities into clusters based on the factors that influence poverty. factors that influence poverty. The data used is secondary data obtained through the Central Bureau of Statistics (BPS) website as much as 38 data. Then the data obtained were analyzed for data characteristics, multivariate normal distribution assumption test, independent assumption test, and cluster analysis. assumption test, multivariate normal distribution, independent assumption test, cluster analysis hierarchical, and non-hierarchical cluster analysis, and selection of the best method to determine the optimum cluster. optimum cluster. So that the results obtained data characteristics tend not to be equal, fulfill the multivariate normal distribution assumption test, dependent data. At Hierarchical clustering results obtained the grouping of districts/cities in East Java based on the factors that influence poverty into 5 based on factors that influence poverty into 5 clusters, with 7 districts/municipalities in cluster 1, 16 districts/municipalities in cluster 2, 10 districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4. districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4, and 1 district/municipality in cluster 5. Based on these results, differences in characteristics between clusters indicates that there are significant variations in poverty factors in each region. The results of the non-hierarchical clustering resulted in the grouping of districts/municipalities in East Java based on the factors affecting poverty into 2 clusters, with 13 clusters. factors that influence poverty as many as 2 clusters, with 13 cluster 1, 25 districts/cities in cluster 2. Also, the results of the ANOVA test results obtained the results of all variables of the factors that influencing poverty in districts/municipalities in East Java Province significantly on poverty.
Kajian Penentuan Variabel dan Pendekatan Model Peramalan Harga Saham INCO Moch Abdillah Nafis; Brodjol Sutijo Suprih Ulama; Rakha Maheswara
Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora Vol. 9 No. 3 (2026): Kaganga: Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/tgvd6v19

Abstract

Nickel is a strategic commodity that plays an important role in the global industry, particularly as a key material for electric vehicle batteries. PT Vale Indonesia Tbk (INCO), a nickel mining company listed on the Indonesia Stock Exchange, experiences stock price fluctuations driven by technical and macroeconomic factors. This study aims to develop a forecasting model for INCO's stock price using Bidirectional Long Short-Term Memory (Bi-LSTM) optimized with a Genetic Algorithm (GA) and to identify the most influential predictor variables using Shapley Additive Explanations (SHAP). Monthly data from January 2007 to December 2025 include INCO's stock price, nickel price, exchange rate, inflation, and the BI-Rate. A forward selection procedure was applied to determine the best predictor combination, after which the model was trained using the Adam Optimizer with GA-optimized hyperparameters and evaluated using Mean Absolute Percentage Error (MAPE). The best model was obtained from the combination of historical stock price, nickel price, and the BI-Rate, with optimal hyperparameters of 150 epochs, a batch size of 20, 150 neurons, a learning rate of 0.002844, and a dropout of 0.0502, producing a MAPE of 8.751%. SHAP results indicate that historical stock price and nickel price contribute the most to the prediction, whereas the BI-Rate contributes relatively less. This study concludes that combining Bi-LSTM, GA, and predictor variable selection improves the forecasting accuracy of INCO's stock price and provides useful insights for long-term investment decision-making in the nickel mining sector. Keywords:  Bidirectional Long Short Term Memory, Forecasting, Genetic Algorithm, Macroeconomics, Stocks
Co-Authors Abghaza Bayu Kusuma Wardhana Brodjol Sutijo Suprih Ulama Moch Abdillah Nafis Sri Pingit Wulandari