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Lalu Moh. Arsal Fadila
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Perbandingan Kinerja Metode Hybrid KNNI-GA dan MissForest Dalam Menangani Missing Values Lalu Moh. Arsal Fadila; Siti Muchlisoh
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.681 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1315

Abstract

A good and correct survey business process is to select a representative sample in order to obtain quality data. However, one of the relevant problems in data quality is the presence of missing values. Missing value is found in almost all large-scale data collections. Missing values ​​can cause all sorts of problems. Therefore, it must be addressed. One way to overcome missing values ​​is the imputation method. Hybrid KNNI-GA and missForest are imputation methods that can be used to handle missing values. Hybrid KNNI-GA uses a genetic algorithm to select the optimum k value and requires predictor variables to perform imputation. Meanwhile, missForest forms a model to carry out the imputation process. This study compares the hybrid KNNI-GA and missForest in dealing with missing values ​​in terms of estimator accuracy and computational performance. The simulation results obtained, the KNNI-GA hybrid is better than missForest in terms of estimator accuracy. Meanwhile, missForest's computational performance is more stable than the KNNI-GA hybrid.
Analisis Perkembangan Ketahanan Pangan di Indonesia : Pendekatan Menggunakan Big Data dan Data Mining Lalu Moh. Arsal Fadila; Nadia Arsyta Putri
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1890

Abstract

Food security is a crucial topic for Indonesia as it is intricately linked to social, economic, and political aspects. The majority of Indonesia's population relies on the agricultural sector to meet their daily needs, both in terms of economic livelihood and food nutrition. Therefore, the Indonesian government must ensure that the food needs of its population are sustainably met by emphasizing the development of food security. The process of food security development requires accurate and up-to-date data. Utilizing Big Data has become an alternative to fulfill the need for large, accurate, and efficiently manageable data. One component of Big Data is Google Trends. In this research, Google Trends was analyzed using LSTM and K-Means Clustering methods. The results showed that the LSTM model was able to predict the trends related to food security in Indonesia quite effectively. Furthermore, the topics related to food security from Google Trends could be clustered using K-Means Clustering, resulting in the formation of three clusters. Of particular concern is Cluster 1, as it indicated relatively low information regarding the topic of food security in Indonesia.
Co-Authors Nadia Arsyta Putri Siti Muchlisoh