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All Journal Sistemasi: Jurnal Sistem Informasi Prosiding Seminar Nasional Official Statistics
Japany, Adham Malay
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Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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Penerapan Model First-Order Autoregressive Error dalam Analisis Daya Saing Ekspor Crude Palm Oil Indonesia Tahun 1995-2021 Japany, Adham Malay; Dino Pardede, Marsandhi Evan; Humaira, Rania; Yuliana, Rita
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.v2023i1.1864

Abstract

Indonesia's Crude Palm Oil (CPO) commodity has advantages and has the potential to increase the country's economic growth. Indonesia is the largest CPO exporter in the world. However, in recent years CPO exports have continued to fluctuate. The potential and capability must be followed by quality improvement, so that later it can have an active role in international trade. Therefore, this study aims to identify an overview and analyze the factors that affect the competitiveness of Indonesia's CPO commodity exports in the world market. This study uses time series data from 1995 to 2021. The competitiveness analysis approached by Revealed Comparative Advantage (RCA) applies the first-order autoregressive error model method. The results show that during the 1995-2021 period, the competitiveness of CPO exports and CPO production tends to experience a positive trend, FDI tends to experience a negative trend, and real exchange rates tend to fluctuate. Furthermore, real exchange rates and CPO production affect the level of competitiveness of Indonesia's CPO exports.
Implementasi Small Area Estimation Hierarchical Bayes - Beta Difference Benchmark dalam Estimasi NEET Lulusan Perguruan Tinggi Salis, Dian Rahmawati; Japany, Adham Malay; Rodliyah, Ratih; Ibad, Syaikhul; Pulungan, Ridson Al farizal; Ramadhan, Yogi
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2285

Abstract

The survey data generated by BPS serves as the primary data source for calculating various SDGs indicators. However, not all indicators can be reliably estimated, particularly at detailed disaggregation levels. Some indicators face issues due to sample inadequacy, resulting in high Relative Standard Errors (RSEs) if estimated directly. One such indicator is the percentage of young college graduates who are neither in education, employment, nor training (NEET). This indicator is only available at the provincial level, with disaggregation based on other characteristics only available at national level. Therefore, this study aims to estimate NEET among college graduates at the regency/city level in Sumatra Island for the year 2023 using the SAE HB Beta model. To maintain consistency with direct estimates at the provincial level, which have shown sufficiently low RSEs, a benchmarking process will be conducted using the difference benchmark method. Based on the findings, the difference benchmark method enhances the validity of the estimation results using the SAE HB Beta model.
Handling of Data Imbalance in Classification of Regencies/Municipalities in Eastern Indonesia Japany, Adham Malay; Wijaya, Yuliagnis Transver
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.2862

Abstract

Imbalance of data between classes can result in incorrect predictions in classification, which can cause problems in decision making. Eastern Indonesia (KTI) is one of the regions that has a Human Development Index (HDI) below the national HDI, so increasing human potential in the production process in KTI must be focused on. In the categorization of regencies/municipalities in KTI there is imbalanced data. This shows that human development between regions in KTI is still uneven. For this reason, a classification of regencies/municipalities based on HDI into certain categories is carried out accurately and quickly. The classification results are expected to help the government in determining future strategic steps to improve the quality of human resources in KTI. One method that can handle data imbalance is Synthetic Minority Over-sampling Technique (SMOTE), using three classification algorithms, namely Support Vector Machine (SVM), K-Nearest neighbors (KNN), and Random Forest (RF). It was found that with the handling of data imbalance and the application of the k-fold cross validation method, the three algorithms showed a significant increase in accuracy. Therefore, handling data imbalance is proven to be able to improve the performance of the applied classification algorithms.
Co-Authors Dino Pardede, Marsandhi Evan Humaira, Rania Ibad, Syaikhul Pulungan, Ridson Al farizal Rita Yuliana Rodliyah, Ratih Salis, Dian Rahmawati Wijaya, Yuliagnis Transver Yogi Ramadhan