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All Journal Rekayasa Teknologi Journal Sensi: Strategic of Education in Information System MAVIB Journal : Jurnal Multimedia Audio Visual and Broadcasting
Martono, Aris
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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Journal : Journal Sensi: Strategic of Education in Information System

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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.
Employee Attendance Optimization Using QR Code Model with Reed Solomon Error Correction for Data Security and Accuracy Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3762

Abstract

This research aims to determine the process of creating a quick response code (QR code) model with Reed Solomon error correction for employee attendance at the Company. Fingerprint attendance systems, even though they are more sophisticated, still have disadvantages, such as difficulties in use in unhygienic environments, as well as high costs for installing the device. Apart from that, traditional attendance is also less flexible in managing employees who work in the field or employees who do not work in the main office. Companies that have many branches or employees who work outside the main office often have difficulty monitoring absenteeism effectively and accurately. The mechanism of this QR code model is carried out through several steps, namely: coding QR codes based on employee ID numbers, grouping encoder data every 8 bits, converting encoder data to binary format, error correction using the Reed Solomon algorithm, creating error correction codes (EC). ) in polynomial form, calculating error correction data based on the correspondence and index of integer numbers in the Galois Field (GF), calculating the function f′(x) through an iterative division process until completion, determining the remainder of the division in the form of R(x), as well as merging encoder data with error correction code as result end. With this mechanism, the QR code-based attendance system is able to maintain data security and accuracy while minimizing the occurrence of anomalies during the work attendance process.
Performance Evaluation of ARIMA and LSTM Models with Product Inventory Demand in Production Companies Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip; Tia Wulandari, Anur Rahmah
Journal Sensi: Strategic of Education in Information System Vol 11 No 2 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i2.4068

Abstract

This study aims to evaluate and compare the performance of two time series forecasting approaches: the classical statistical ARIMA model and the deep learning-based LSTM model, in the context of forecasting product inventory demand in a production company. The data used consists of historical daily demand records, totaling 100 and 200 records, which were analyzed to identify linear and non linear patterns. The ARIMA model was selected for its reliability in modeling stationary and seasonal data, while the LSTM model was utilized to capture complex temporal patterns through its layered neural network architecture. The test results using the MSE and RMSE metrics show that in both datasets, the ARIMA model has better prediction performance (100 records, RMSE=45.61% and 200 records, RMSE=44.72%) compared to LSTM, namely 100 records, RMSE=45.93% and 200 records, RMSE=49.54%. Although LSTM excels in handling non-linear dynamics, ARIMA outperformed it on data with linear. This study highlights the importance of selecting forecasting models based on data characteristics and suggests opportunities for future exploration of hybrid models. The theoretical and empirical foundations of this research are supported by the works of Hyndman & Athanasopoulos (2018), Hochreiter & Schmidhuber (1997), and Makridakis et al. (2018), which provide critical insight into predictive modeling for time series analysis.
Co-Authors Apriyani, Dwika Aulia, Frisca Jihan Desrianti, Dewi Immaniar Kuspriambodo, Aji Padeli Padeli Rahma, Alfaini Lailatur Sugeng Santoso Suhaepi, Muhamad Iip Sunandar, Endang Tia Wulandari, Anur Rahmah Wahyu Hidayat Yulianjani, Arsi