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Hybrid filtering methods for feature selection in high-dimensional cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; Mansor, Mahayaudin M.; Yusoff, Marina
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6862-6871

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain.

Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; M. Mansor, Mahayaudin; Md Ghani, Nor Azura; Yusoff, Marina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3101-3110

The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.

IDENTIFIKASI TANAMAN PAKAN DAN PERILAKU LEBAH (Trigona sp.) DI DAERAH PENYANGGA KAWASAN TAMAN NASIONAL BOGANI NANI WARTABONE Ibrahim, Nurain; Febriyanti; Ahmad, Jusna; Pratama Solihin , Angry; Tri Nugroho, Bagus; Zakaria, Zuliyanto
Jurnal Biogenerasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1, Agustus 2024 - Februari 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/biogenerasi.v10i1.4590

The purpose of this study is to identify the kind of feed plant and Trigona sp. behavior in the buffer area of Bogani Nani Wartabone National Park. It is a quantitative descriptive method. Bee trips to feed trees were directly observed in order to identify the feed plants, and the results were confirmed through examination of the morphology of the pollen. Acetolysis is a technology used in the pollen extraction process. The study's findings identified six different plant species that provide food for Trigona sp. bees: Zinnia peruviana, Mangifera indica, Arachis hypogaea, Turnera subulata, and Antingonon leptopus. According to the results of the pollen identification process, eight plant species Antingonon leptopus, Cocos nucifera, Turnera ulmifolia, Turnera subulata, Chamaecrista calycioides, Arachis hypogaea, and Sida rhombifolia contributed as a source of feed. A. leptopus and C. nucifera are the two plants that receive the most visits, and 08.00–12.00 is the busiest hour.

ANALISIS PENDAPATAN DAN NILAI TAMBAH USAHA PRODUK OLAHAN NIRA AREN (GULA AREN) DI DESA MONGIILO KECAMATAN BOULANGO ULU KABUPATEN BONE BOLANGO Ibrahim, Nurain; Indriani, Ria; Sirajuddin, Zulham
Viabel : Jurnal Ilmiah Ilmu-Ilmu Pertanian Vol 18 No 1 (2024): Mei 2024
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/viabel.v18i1.3442

The objectives of this study are twofold: firstly, to assess the viability of processing palm sugar products, and secondly, to determine the additional value that palm sugar brings to Mongiilo Village in the Bone Bolango Regency. This investigation spanned from September to December 2022, involving 67 farmers as respondents. The research utilized both primary and secondary data. Employing a saturated sampling method (census), all respondents were interviewed, ensuring a comprehensive representation of the population. The analytical approach adopted for assessing added value was the hayami method, while the Return Cost Ratio (R/C Ratio) was employed for overall analysis. The findings revealed an R/C ratio of 3.72, surpassing the threshold of 1. This implies that cultivating palm sugar in Mongiilo Village, Bulango Ulu District, Bone Bolango Regency, is economically justified. The study determined an added value of Rp. 761.11/kg, with palm sugar craftsmen in Mongiilo Village generating a profit of Rp. 587.83, equivalent to 19.59% of the total production.

Visualization Tools for Backward Elimination Technique in Multiple Regression Time Series Modelling of CO2 Emissions in Malaysia Mansor, Mahayaudin M.; Ibrahim, Nurain; Zakaria, Roslinazairimah; Suhaila, Jamaludin; Miswan, Nor Hamizah; Shaadan, Norshahida
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3012

Understanding multiple regression time series modelling is crucial because the procedures involve intricate statistical methods. This study incorporates a flowchart that clearly illustrates the steps for modelling a response variable affected by several explanatory variables via the backward elimination technique. The first objective of this study is to utilise ten graphical tools, comprising charts and tables, for visual assessment to support formal evaluations in model diagnostics using R programming. The aim is to provide comprehensive insights and improve the overall understanding of the modelling procedures. The visualisation tools include criteria for multicollinearity, goodness-of-fit, and underlying assumptions of normality, homoscedasticity, zero serial correlation, and volatility in the residuals. The second objective involves implementing modelling procedures to obtain a well-specified model in a real-world context, demonstrating its practical value and implications. In this instance, the selected response variable is carbon dioxide (CO2) emissions, significantly contributing to global warming. In Malaysia, CO2 emissions increased continuously from 1990 to 2022, with an alarming average annual growth rate of 4.9%. The visual diagnostics have helped guide the elimination of some explanatory variables in the initial model and refined the models, resulting in a well-specified final model that is parsimonious and explains 98.6% of the variability in CO2 emissions. The final model suggests that high fossil fuel use and GDP per capita are contributing factors to increased CO2 emissions in Malaysia. The study recommends government action and investment in renewable energy to reduce CO2 emissions by 45% by 2030 and achieve net-zero emissions by 2050.

IDENTIFIKASI TANAMAN PAKAN DAN PERILAKU LEBAH (Trigona sp.) DI DAERAH PENYANGGA KAWASAN TAMAN NASIONAL BOGANI NANI WARTABONE Ibrahim, Nurain; Febriyanti; Ahmad, Jusna; Pratama Solihin , Angry; Tri Nugroho, Bagus; Zakaria, Zuliyanto
Jurnal Biogenerasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1, Agustus 2024 - Februari 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/biogenerasi.v10i1.4590

The purpose of this study is to identify the kind of feed plant and Trigona sp. behavior in the buffer area of Bogani Nani Wartabone National Park. It is a quantitative descriptive method. Bee trips to feed trees were directly observed in order to identify the feed plants, and the results were confirmed through examination of the morphology of the pollen. Acetolysis is a technology used in the pollen extraction process. The study's findings identified six different plant species that provide food for Trigona sp. bees: Zinnia peruviana, Mangifera indica, Arachis hypogaea, Turnera subulata, and Antingonon leptopus. According to the results of the pollen identification process, eight plant species Antingonon leptopus, Cocos nucifera, Turnera ulmifolia, Turnera subulata, Chamaecrista calycioides, Arachis hypogaea, and Sida rhombifolia contributed as a source of feed. A. leptopus and C. nucifera are the two plants that receive the most visits, and 08.00–12.00 is the busiest hour.

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