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All Journal CogITo Smart Journal
Isnen Hadi Al Ghozali
Universitas Budi Luhur
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Teknik Optimasi Database dengan Logic Execution Optimization pada Microservices Architecture Isnen Hadi Al Ghozali; Mohammad Shiddiq Antarressa; Samidi Samidi
CogITo Smart Journal Vol. 9 No. 1 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i1.444.60-72

Abstract

Microservices architecture, a distributed framework architecture that allows changes to one module without interfering with other modules. The implementation of this architecture has its own challenges. The get-list-attachment API running on this architecture takes an average of 12.5 seconds to serve data. This needs to be considered because business processes require shorter access times to support decision making. The research objective is to obtain query response time efficiency for accounting applications. To achieve this, the research uses database optimization techniques with logic execution optimization microservices architecture. This study obtained the source of information from the Accounting Harmony Accounting Module, which has an API (get-list-attachment) with data sourced from Service Accounting (581253 records) and Service Users (2182 records). Based on a series of tests carried out, several services need to be added with APIs to improve the microservices architecture to accept bulk parameters that generate a list of objects so that data presentation is more optimal. After doing a series of engineering on microservices architecture and indexing application, query response time performance increased by 49.22% for Service Accounting module.
Eksplorasi Kerangka Manajemen Risiko Proyek untuk Perusahaan Teknologi Informasi Isnen Hadi Al Ghozali; Samidi Samidi; Andy Rio Handoko
CogITo Smart Journal Vol. 9 No. 2 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i2.517.266-279

Abstract

 Based on CHAOS 2020: Beyond Infinity Overview, reported by the Standish Group, only 31% of  IT projects were successfully implemented, while 50% of projects were challenged and 19% of projects failed. Many project managers less awareness about SRM and have a partial understanding of risk. The purpose of this study is to develop a project risk management framework for listing companies in the information technology sector. The sample for this study is 35 annual reports of technology companies listed on IDX. This study identified 122 types of project risks from 33 companies' annual reports. This study uses an exploratory study approach. The proposed framework includes three stages, namely the root cause, risk assessment, and performance stages. At the root cause stage, the identification of risks from elements of the business environment becomes the basis for measuring risk treatment. In the next stage, the identified risk treatment is measured through identify, analysis, and verification activities with the support of communication, documentation, and evaluation. The measurement results are classified into three major dimensions, namely cost, time, and quality. The final stage of the framework is in the form of residual performance risk and a risk mitigation action plan.
Analisis Komparatif Algoritma Clustering Data Kinerja Anggaran Pemerintah Isnen Hadi Al Ghozali; Ibnu Afan; Triardani Lestari
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.611.578-591

Abstract

The government's budget performance is a benchmark for the government's success in optimizing people's money to achieve national goals. Even though performance measurement has reached the Work Unit level, the data formed still do not have a specific grouping, in the sense of unstructured data. The purpose of this research is to find the best clustering algorithm for classifying budget performance data. The data used is budget performance data for 19,460 Indonesian Government Work Units. The data is sourced from the SMART application and the OM SPAN application. This research uses a comparative study approach for the K-Means algorithm, DBSCAN, and agglomerative hierarchical clustering (AHC). Evaluation of the clustering results formed using the Davies-Bouldin Index (DBI) method. The AHC algorithm with k = 6 achieved the lowest DBI value of 0.3583472. The DBI value for the DBSCAN algorithm with MinPts = 10 is 0.5398259. However, the AHC algorithm is not good in terms of ease of implementation. Therefore, the K-means algorithm with parameters k = 10 is the best alternative. The K-Means algorithm gets a DBI value of 1.052678. The K-Means algorithm produces 10 clusters. Based on knowledge extraction, it is determined that cluster 2 and cluster 5 are ideal clusters in terms of budget performance. While the clusters that require attention are cluster 1, cluster 3, cluster 4, and cluster 8.
Co-Authors Andy Rio Handoko Ibnu Afan Mohammad Shiddiq Antarressa Samidi Samidi Samidi Samidi Triardani Lestari