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All Journal BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Jurnal Sains dan Teknologi Journal of Informatics and Advanced Computing
Rizki Hesananda
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Profiling Calon Mahasiswa Program Studi Informatika Menggunakan Decision Tree Rizki Hesananda; Ninuk Wiliani; Latifah
BRITech, Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Vol 2 No 1 (2020): Periode Juli
Publisher : Institute Teknologi dan Bisnis Bank Rakyat Indonesia

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Abstract

Prospective student data can be used as important information for academic community, therefore proper data management needed to process it. This research uses the prospective student throught the 2020 APERTI scholarship path as the basis for the classification of prospective students which wasa previously done manually using Microsoft Excel so that the classification process is not optimal. The process of identifying profiles uses data mining to determine marketing plans and pattern of prospective students with a profile classification process as well as offering recommendations for them. This research used decision tree (C4.5). The attributes used for the classification process are father’s job, mother’s job, gender, school type, major and the choice of the chosen study program. The result of this research can be used to help sort out prospective students according to the informatics study program.
Rancang Bangun Aplikasi Pengajuan dan Perhitungan Lembur Pekerja untuk Meningkatkan Efisiensi SDM di BRI Cabang Veteran Rizki Hesananda; Nurrahman Putra Surya Kencana
Journal of Informatics and Advanced Computing (JIAC) Vol 5 No 2 (2024): Journal of Informatics and Advanced Computing
Publisher : Teknik Informatika Universitas Pancasila

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Abstract

Pengelolaan perhitungan lembur secara manual pada bagian Penunjang Operasional Layanan SDM PT. Bank Rakyat Indonesia (Persero), Tbk Kantor Cabang Jakarta Veteran sering kali memakan waktu dan tidak efisien. Proses ini melibatkan pengumpulan dokumen lembur fisik, yang menyebabkan keterlambatan dan potensi kesalahan dalam penginputan data. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi perhitungan lembur berbasis web menggunakan PHP dan MySQL guna meningkatkan efektivitas proses pengajuan lembur. Metode pengembangan yang digunakan adalah Waterfall Model, dimulai dari analisis kebutuhan melalui observasi lapangan, perancangan sistem dengan UML dan High Fidelity Mockup, implementasi menggunakan PHP dan HTML, serta pengujian dengan metode Black Box Testing. Hasil evaluasi menunjukkan bahwa rata-rata waktu pengerjaan lembur oleh petugas SDM tanpa sistem adalah 186 menit, sedangkan dengan sistem informasi perhitungan lembur menjadi 33 menit. Dengan demikian, sistem yang dikembangkan mampu menghemat waktu pengerjaan hingga 153 menit atau sekitar 82,3%. Kesimpulannya, sistem informasi yang diusulkan dapat meningkatkan efektivitas dan efisiensi dalam proses pengajuan lembur serta meminimalkan kesalahan penginputan data. Sistem ini diharapkan dapat diintegrasikan dengan aplikasi web BRI Human Capital (BRIHC) untuk mendukung pengolahan data yang lebih lanjut.
Prediksi Penjualan Aerosol Menggunakan Algoritma ARIMA, LSTM Dan GRU Nendi Sunendar; Harjono P. Putro; Rizki Hesananda
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 1 (2025): Februari 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i1.4868

Abstract

The advancement of information technology has significantly enhanced operational efficiency by enabling companies to process data more effectively and make better decisions. In a highly competitive global market, distributors face major challenges, including shorter product life cycles and fluctuating customer demand. These factors impact stock and production management, necessitating more accurate predictive solutions to optimize production planning. This study aims to compare the performance of ARIMA, LSTM, and GRU models in sales forecasting using time series forecasting methods. ARIMA represents a traditional statistical approach, while LSTM and GRU, based on deep learning, are capable of capturing complex data patterns. The models are evaluated using MSE, RMSE, MAE, and MAPE metrics. The results indicate that LSTM outperforms other models with a MAPE of 10.76%, followed by ARIMA (11.23%) and GRU (11.47%). LSTM excels in identifying long-term trends and seasonal patterns, while GRU achieves nearly comparable accuracy with a shorter training time. ARIMA, despite its simplicity, struggles to handle non-linear patterns. These findings provide valuable insights for companies in selecting the most suitable predictive model to optimize supply chain management, enhance operational efficiency, and support more informed decision-making.
Co-Authors Harjono P. Putro Latifah Nendi Sunendar Ninuk Wiliani Nurrahman Putra Surya Kencana