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All Journal JAIA - Journal of Artificial Intelligence and Applications The Indonesian Journal of Computer Science
Andi Kurnianto
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Data Mapping System Of Riau Province Fire Potential Using K-Means Clustering Method Rahmaddeni Deni; Andi Kurnianto
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 1 (2020): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.105 KB) | DOI: 10.33372/jaia.v1i1.640

Abstract

According to a report from the Riau Province BLHK states that hotspots in Riau Province are always present every year despite the number of hotspots that have been suppressed (http://dislhk.riau.go.id/). One of the causes is the frequent land clearing occurred as a trigger from a hotspot in Riau Province. There is a need for countermeasures as soon as possible to overcome the problem of hotspots that will cause forest fires. These problems need to be watched out quickly, one of which is to know in advance the hotspots that are likely to emerge based on existing data. Data mining processing is very suitable to be applied in order to produce relevant data to find out the possibility of hotspots. In this study the data grouping was done in the form of a visualization of hotspot mapping using the K-means Clustering method. The parameters used include 3 number of clusters (critical, alert, vigilant), 12 regencies / cities in Riau Province and 3 attributes (hotspots, number of fires, number of events). With the results of the visualization of the mapping using the K-means Clustering method, it is expected to be able to help the relevant parties, namely the Riau Provincial Forest Service in handling early the hotspots that are likely to emerge.
Tingkat Kematangan Tata Kelola Data Lembaga Pemerintah Yang Bertugas Di Bidang Pangan Abhimata Ar Rasyiid; Betty Purwandari; Andi Kurnianto
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4191

Abstract

In order to support the national food security program, Sistem Informasi Pangan dan Gizi (SIPG) was built. This system was built by one of the government institutions tasked with food. In supporting its function, this system integrates data from internal and external institutions. Currently, not all data can be integrated into the system. The absence of policies related to reference codes and mechanisms for how data is obtained, processed, and displayed is one of the causes. To overcome this, the implementation of data governance is needed. Before doing this, it is necessary to assess the current level of data governance maturity. The assessment will provide an overview of whether the current data governance has been carried out correctly. The assessment was carried out using the Stanford Data Governance Maturity Model. Assessment results show that the current level of maturity is at level 2. While the expectation to be achieved is at level 4. To achieve the expected level of maturity, 48 recommendations are given.
Co-Authors Abhimata Ar Rasyiid Purwandari, Betty Rahmaddeni Rahmaddeni