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All Journal International Journal of Electrical and Computer Engineering Jurnal Studi Manajemen dan Bisnis
Refianto Damai Darmawan
IPB University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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TINGKAT PENGEMBALIAN INVESTASI PENDIDIKAN PADA LULUSAN FAKULTAS EKONOMI DAN MANAJEMEN (Studi Kasus Lulusan yang Bekerja pada Sektor Formal) Hilma Gusparima; Refianto Damai Darmawan; Lindawati Kartika
Jurnal Studi Manajemen dan Bisnis Vol 9, No 2 (2022): Desember
Publisher : Trunojoyo University of Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jsmb.v9i2.15878

Abstract

Pengembalian investasi pendidikan di Indonesia menunjukkan angka yang konsisten dan semakin meningkat untuk jenjang pendidikan yang semakin tinggi. Namun jika melihat data, Angka Partisipasi Kasar (APK) di Indonesia mengalami penurunan pada jenjang pendidikan. Sehingga tujuan penelitian ini adalah untuk mengetahui hasil Return on Higher Education Investment (ROHEI) dan kelayakannya pada lulusan Fakultas Ekonomi dan Manajemen IPB sebagai perguruan tinggi negeri di wilayah Jabodetabek.  Jenis data yang digunakan dalam penelitian adalah data kuantitatif dan kualitatif melalui data primer dan sekunder. Metode penentuan dan penarikan sampel yang digunakan adalah nonprobability sampling dan purposive sampling. Jumlah sampel dalam penelitian ini sebanyak 150 orang. Metode analisis yang digunakan adalah analisis deskriptif dan pengembalian investasi. Analisis deskriptif digunakan untuk mengolah data pada karakteristik responden. Adapun analisis pengembalian investasi berupa benefit cost ratio (BCR), rate of return (ROR), cost benefit analysis (CBA), Payback Period (PP), net present value (NPV), dan internal rate of return (IRR) serta analisis loyalitas lulusan menggunakan net promoter score (NPS). Hasil penelitian ini menunjukkan, bahwa kelayakan investasi pendidikan dari gaji responden dinyatakan layak namun hasil penghitungan terkait nilai manfaat berdasarkan tabungan dinyatakan tidak layak. Loyalitas responden terhadap Fakultas Ekonomi dan Manajemen mayoritas berada dalam kategori passive. Kata Kunci: Investasi, Lulusan, Pendapatan Pendidikan, ROHEI, Tabungan
Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit Refianto Damai Darmawan; Wisnu Ananta Kusuma; Hendra Rahmawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3111-3123

Abstract

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.
Co-Authors Hendra Rahmawan Hendra Rahmawan Hilma Gusparima Lindawati Kartika Wisnu Ananta Kusuma