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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Applied Food Technology JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) The Indonesian Journal of Computer Science
Alifviansyah, Kevin
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Other

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A Analisis Perbandingan Kinerja Metode Ensemble Bagging dan Boosting pada Klasifikasi Bantuan Subsidi Listrik di Kabupaten/Kota Bogor Cintari, Nanda Putri; Alifviansyah, Kevin; Tsabitah, Dhiya Ulayya; Sartono, Bagus; Firdawanti, Aulia Rizki
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4537

Abstract

The classification of electricity subsidy recipients is an crucial step to ensure that the government's social assistance program is distributed in a targeted manner, so an appropriate analysis method is needed. This research compares the Bagging and Boosting ensemble methods for the classification of households receiving electricity subsidies in Bogor Regency and City using Susenas 2023 data totaling 2002 households. The bagging method uses Random Forest and Extra Trees, while boosting includes CatBoost and LightGBM. The results showed that the Extra Trees method of bagging provided the best performance with 91% accuracy, 95% F1score, and 97% sensitivity. Factors such as ownership of electronic goods and modern facilities, such as ownership of air conditioners, laptops, and televisions are the most significant variables in influencing the classification of electricity subsidy recipients. With high accuracy and minimal bias, this model effectively supports data-driven policies for electricity subsidy distribution. This research is expected to be a strategic recommendation for the government to improve the effectiveness of the electricity subsidy program to be more efficient, well-targeted, and support the improvement of people's welfare.
Identification of Latent Dimensions of Digital Readiness and Typology of Districts/Cities in Indonesia Using PCA and K-Means Clustering Sari, Jefita Resti; Fahira, Fani; Zahra, Latifah; Fitrianto, Anwar; Alifviansyah, Kevin
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11487

Abstract

Digital transformation is a key agenda in Indonesia’s national development that requires balanced readiness across regions. However, the level of digital readiness among districts and cities still varies widely, highlighting the need for a typology that can comprehensively describe existing disparities. This study aims to identify the latent dimensions of digital readiness and to develop a regional typology of Indonesian districts/cities using Principal Component Analysis (PCA) and K-Means clustering. The data were obtained from the 2024 Indonesian Digital Society Index (IMDI), which consists of four pillars—Infrastructure and Ecosystem, Digital Skills, Empowerment, and Employment—with ten sub-pillars. PCA reduced these correlated indicators into two main latent components, namely Digital Capacity and Participation and Digital Infrastructure Foundation, which together explain 70.4% of the total variance. Cluster validation using the Silhouette Score and Davies–Bouldin Index (DBI) showed that K = 2 yielded the best internal validity (Silhouette = 0.402; DBI = 0.906), but a three-cluster configuration (K = 3) was adopted to obtain a more interpretable typology of high-, medium-, and low-readiness regions (Silhouette = 0.346; DBI = 1.007). Spatial mapping reveals that high-readiness districts are concentrated in Java, Bali, and parts of Sumatra, whereas low-readiness areas dominate eastern Indonesia. These findings confirm persistent digital inequality across regions and provide a quantitative basis for targeted policy interventions, including infrastructure development, digital literacy programs, and innovation ecosystem strengthening, to support an inclusive digital transformation in Indonesia.
Multivariate Exploration of Food Security in the Sulampua Region Identification of Clusters and Dominant Dimensions of Food Security Saputra, Wawan; Alfiryal, Naufalia; Prasetya, I Putu Gde Inov Bagus; Fitrianto, Anwar; Alifviansyah, Kevin
Journal of Applied Food Technology Vol 12, No 2 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17728/jaft.29754

Abstract

Food security is a strategic issue closely related to economic development, community welfare, and the achievement of sustainable development goals. The Food Security Index (FSI) is an important instrument for measuring food security conditions at the provincial and district/city levels. However, FSI performance in Indonesia still shows regional disparities, particularly in Sulawesi, Maluku, and Papua (Sulampua), which tend to have low scores. This study aims to explore patterns of food security and vulnerability in Sulampua through multivariate analysis and regional clustering using K-Means and K-Medoids (PAM) methods. The analysis begins with Principal Component Analysis (PCA) to reduce the dimensionality of FSI indicators and identify dominant factors contributing to data variation. The PCA results show that the first three components explain more than 77% of the variance, with dominant factors including poverty, food expenditure, basic infrastructure access, as well as health and nutrition indicators. The clustering analysis produces two main groups: cluster 1, which includes the majority of districts/cities in Sulawesi and Maluku with relatively better food security, and cluster 2, consisting of 16 districts/cities in Papua with significant food insecurity. Cluster validity evaluation indicates that the K-Medoids method performs better than K-Means, being more robust to outliers and producing more consistent cluster separation. This study contributes to the literature by providing multivariate visual exploration and regional classification based on FSI indicators, which can serve as a basis for formulating more targeted food security policies in the Sulampua region.
KAJIAN EKSPLORASI TENTANG POLA KESEJAHTERAAN MULTIDIMENSI DI JAWA BARAT MENGGUNAKAN ANALISIS GEROMBOL Az-Zahra, Putri Nisrina; Tangdilomban, Claudian Tikulimbong; Mutmainah, Zamrah; Fitrianto, Anwar; Alifviansyah, Kevin; Erfiani, Erfiani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9307

Abstract

Kesejahteraan multidimensi mencerminkan kualitas hidup yang melampaui indikator tunggal seperti IPM. Penelitian ini berfokus pada eksplorasi dan visualisasi pola kesejahteraan multidimensi di Jawa Barat menggunakan algoritma K-Means dan HDBSCAN. Data Susenas Maret 2024 mencakup 12 variabel dalam empat dimensi: pendidikan, kesehatan, ekonomi, dan fasilitas rumah tangga. Reduksi dimensi dilakukan dengan PCA sebelum clustering. Hasil menunjukkan HDBSCAN lebih optimal dibandingkan K-Means, dengan Silhouette Score 0,558, Calinski-Harabasz Index 41,584, dan Davies-Bouldin Index 0,603. Visualisasi cluster mengungkap ketimpangan antarwilayah, di mana daerah perkotaan cenderung lebih sejahtera, sedangkan pedesaan dan pinggiran menunjukkan variasi yang lebih beragam.
Co-Authors Alfiryal, Naufalia Aulia Rizki Firdawanti Az-Zahra, Putri Nisrina Bagus Sartono Cintari, Nanda Putri Erfiani Erfiani Fahira, Fani Fitrianto, Anwar Mutmainah, Zamrah Prasetya, I Putu Gde Inov Bagus Sari, Jefita Resti Tangdilomban, Claudian Tikulimbong Tsabitah, Dhiya Ulayya Wawan Saputra Zahra, Latifah