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All Journal Rekayasa Teknologi Journal Sensi: Strategic of Education in Information System
Martono, Aris
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Systems Analisys Of National Employment From The Technological Aspect And Working Mechanisms Martono, Aris
Rekayasa Teknologi Vol 2 No 2 (2011): Rekayasa Teknologi
Publisher : Rekayasa Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.588 KB)

Abstract

National employment systems currently lack the optimal data fromthe input data nakertrans service districts / cities have been cut off due tostructural relations official with the center after implementation of regionalautonomy laws since 1999. Besides the force personnel in each unit district/ city level of skill and expertise less understood areas of employment forpersonnel and units of work is a fusion of the department of labor office, theoffice of the department of resettlement,social services and education services. The stakeholders involved on the mechanisms of national employmentsystems also vary depending upon the region. Region I, job seekers andusers of workforce capable of using the internet as a means to earn ayellow card for the job seekers and Obligation Report sent to the officesnakertrans Jobs district / city for the users of labor. Region II, as theinternet inaccessible areas but the population is able to use scanners. Jobdata sent by fax to the offices nakertrans district / city. Job seekers canregister directly to the tribal district offices to get a yellow card. Region IIIexplained that this region does not reach the Internet and its inhabitantsare not able to use the scanner then the mechanism of the Employmentsystem as region II. Building a new organizational structure in the nationalemployment system so that new bodies are performing theirduties and functions it could achieve the expected goals. Build a network of internet and intranet technology to record the new jobseekers and sends vacancies available quickly and easily.
Model Deteksi Penyimpangan Keuangan Medis Menggunakan Gradient Boosted Tree (GBT ) Pada Rumah Sakit ABC Martono, Aris; Padeli, Padeli
Journal Sensi: Strategic of Education in Information System Vol 10 No 1 (2024): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v10i1.3115

Abstract

Tujuan penelitian ini yaitu untuk mengetahui penyimpangan keuangan yang terjadi di lingkungan Rumah Sakit. Penyimpangan transaksi keuangan ini melibatkan aktivitas dokter, pembuatan resep dan apotik atau farmasi serta bagian keuangan Rumah Sakit. Setiap dokter yang mengeluarkan resep untuk pengobatan pasien, diharapkan pasien membeli obat di apotik Rumah Sakit itu sendiri sehingga transaksi keuangannya menjadi pemasukan bagi Rumah Sakit. Namun sebaliknya, hal ini bisa mempersulit mengetahui pemasukan kas yang diperoleh dari setiap dokter terkait resep yang dikeluarkan. Oleh karenanya penelitian ini dilakukan dengan membuat model untuk mengetahui penyimpangannya. Untuk mendapatkan model yang terbaik dilakukan evaluasi model terhadap algoritma Gradient Boosted Tree(GBT) dan Random Forest(RF). Hasilnya adalah AUC (Area Under the Curve) model GBT = 0.976 dan AUC model RF = 0.964 yang menunjukkan bahwa algoritma GBT pilihan terbaik untuk pemrosesan penyimpangan transaksi keuangan dataset medis di Rumah Sakit ABC.
Credit Risk Prediction Model Using Support Vector Machine with Parameter Optimization in Banks Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip; Santoso, Sugeng; Sunandar, Endang
Journal Sensi: Strategic of Education in Information System Vol 10 No 2 (2024): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v10i2.3463

Abstract

Abstract This research aims to determine the Support Vector Machine (SVM) model with Parameter Optimization in predicting loan worthiness to avoid the risk of bad credit at the Bank. Every bank tries to market financial loan products with very strict requirements. One of the requirements is that the company's financial reports must be healthy if it borrows money from a bank to develop the company's business. In the credit analysis process, there are 19 financial factors that must be measured from dozens or even hundreds of companies proposing financial loans, making it difficult for credit analysts to make decisions about whether these companies are worthy of borrowing or not. Therefore, this research was carried out by comparing the two models, namely SVM with parameter optimization and SVM with parameter optimization and Particle Swarm Optimization (PSO) to select the best model. The research results show that the Area Under Curve (AUC) criteria with validation number of folds (nof) = 10 and nof = 5 are 98.80% and 98.80%, meaning good and stable in the SVM model with parameter optimization. Meanwhile, the SVM model with parameter optimization and PSO has better AUC for validation nof=5 (99%) but for AUC with validation nof=10 (98.30%) it is less good.
Co-Authors Apriyani, Dwika Kuspriambodo, Aji Padeli Padeli Sugeng Santoso Suhaepi, Muhamad Iip Sunandar, Endang Wahyu Hidayat Yulianjani, Arsi