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Muhammad Arif Kurniawan
Universitas Insan Pembangunan Indonesia
Computer Science & IT

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OPTIMALISASI PENGELOLAAN DATA TANAH MENGGUNAKAN ARCGIS DI BADAN BANK TANAH Andi Rukmana; Angger Styo Yuniarti; Samsul Makin; Muhammad Arif Kurniawan; Ferdi Kuswandi
IPSIKOM Vol. 13 No. 1 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i1.419

Abstract

Badan Bank Tanah, which is primarily engaged in the land sector, requires technology that can manage maps centrally and can be used collaboratively with both internal and external providers through service connections to support analysis and monitoring of HPL assets by related stakeholders based on the level of access provided. Currently, Badan Bank Tanah does not have a centralized GIS system, the process of creating shape files and map polygons is still carried out by each staff. This results in the information, database types, and data structures created being non-standard, and results in the shape file and map polygon databases being stored on each staff's work tools. The risk that can occur from this is the loss or misinformation needed, so that if needed, it takes time to prepare the data. The solution to the application being sought is software that has the capability to store data in a standardized manner, can be connected to external GIS applications through service methods, and is centralized so that the data can be stored in a secure environment
ANALISIS SENTIMEN MASYARAKAT TWITTER TERHADAP KEBIJAKAN EFISIENSI ANGGARAN KEMENTERIAN MENGGUNAKAN SVM Muhammad Arif Kurniawan; Samsul Makin; Angger Styo Yuniarti; Andi Rukmana
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.452

Abstract

Sentiment analysis of the ministry's budget efficiency policy is crucial to understanding public responses to government policies. This study employs the support vector machine (SVM) method to classify positive and negative sentiments from 1,418 tweets collected through crawling using Twitter API v2 between February 10 and 22. The text processing steps include case folding, cleaning, tokenizing, stopword removal, stemming, and weighting using the term frequency-inverse document frequency (TF-IDF) method. The analysis results indicate that negative sentiment dominates over positive sentiment, reflecting public criticism and dissatisfaction with the policy. The SVM model was evaluated using k-fold cross-validation with k values ranging from 2 to 10, achieving the best accuracy of 94.76% with 10-fold validation. Evaluation using the confusion matrix showed a precision of 92.85%, a recall of 91.32%, and an AUC of 0.972, indicating excellent model performance in sentiment classification. These findings suggest that the SVM model is effective in analyzing public sentiment toward government policies and can be further developed by enriching features and comparing it with other algorithms to enhance prediction accuracy.
Co-Authors Andi Rukmana Andi Rukmana Angger Styo Yuniarti Ferdi Kuswandi Samsul Makin