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All Journal Jurnal Informasi dan Teknologi Jurnal Bumigora Information Technology (BITe)
Puspa Dewi, Nindian
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis dan Implementasi VPN pada VPS untuk Peningkatan Aksesibilitas Jaringan di Lingkungan Perguruan Tinggi Ubaidi; Puspa Dewi, Nindian
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i3.409

Abstract

Perguruan Tinggi merupakan institusi pendidikan dengan lingkungan jaringan yang kompleks dan luas. Mahasiswa, dosen, dan tenaga kependidikan memerlukan akses yang aman dan terpercaya ke berbagai sumber daya dan layanan di jaringan kampus, termasuk akses ke data mahasiswa, penelitian, perpustakaan digital, dan sistem administratif. Karena itulah diperlukan infrastruktur yang aman dengan biaya yang efisien. Implementasi VPN (Virtual Private Network) merupakan salah satu teknologi yang dapat memberikan akses jaringan yang aman, mudah dan memiliki kecepatan transfer yang baik bagi para pengguna. VPN memungkinkan pengguna untuk mengenkripsi lalu lintas data mereka saat terhubung ke jaringan kampus, sehingga menjaga keamanan dan kerahasiaan informasi. Selain itu untuk efisiensi infrastruktur dapat dilakukan dengan implementasi VPS yang memungkinkan perguruan tinggi untuk menyediakan lingkungan server terisolasi yang dapat digunakan untuk berbagai tujuan dengan tetap menghemat biaya dan sumber daya, serta meningkatkan skalabilitas infrastruktur mereka.
Application of Profile Matching in Determining Employee Annual Bonuses Puspa Dewi, Nindian; Ramadhani, Nilam; Darmawan, Irwan; Ubaidi, Ubaidi; Syahroni, Abd Wahab
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i2.542

Abstract

In the competitive business world, companies are required to have an effective and objective performance appraisal system. One crucial aspect of employee performance appraisal is the annual bonus distribution. One effort to improve employee performance is by providing annual bonuses. For this reason, currently, many companies are implementing this bonus so that their employees are motivated to improve and increase the quality of their work. One of them is a CV. Laras Alam Pamekasan. CV. Laras Alam Pamekasan is a company operating in the tobacco sector specifically supplying PT. Djarum Tobacco. Every year, CV. Laras Alam provides annual bonuses according to employee performance. However, bonuses are still based on rough calculations. That's why research was conducted to produce a decision support system in determining employee annual bonuses based on performance. This research uses the Profile Matching method. Profile Matching is one method in performance appraisal systems that uses a comparative approach between employee profiles and predefined ideal profiles. The aspects used in determining employee annual bonuses are the Work Attitude Aspect including criteria, behavior, responsibility, and cooperation. Performance Aspects include criteria, discipline, absenteeism, acceptance of additional assignments, and loyalty. From this research, a trial was carried out on 22 employees and then calculations and rankings were carried out.
Optimalisasi Model Bahasa dan Sistem Ekonomi Berbasis Teks dengan Proximal Policy Optimization: Studi Kasus dalam NLP Modern Darmawan, Irwan; Ramadhani, Nilam; Nazir Arifin, Mohammad; -, Ubaidi; Puspa Dewi, Nindian; Innuddin, Muhammad
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i1.5222

Abstract

Background: This study investigates the use of the Proximal Policy Optimization (PPO) algorithm in two text-based case studies: alignment of large language models (LLMs) with human preferences and dynamic pricing based on customer reviews. In the LLM case, PPO combined with preference-based learning significantly improves alignment, BLEU, and human-likeness scores.Objective: This research aims to evaluate PPO’s effectiveness in text-based decision-making through these two cases.Methods: The method employed is reinforcement learning experimentation using the PPO approach. For the LLM case, PPO is integrated with preference learning to enhance alignment, BLEU, and human-like output. Meanwhile, in the economic scenario, PPO produces adaptive pricing strategies with high accuracy or low Mean Absolute Error (MAE) and the best cumulative rewards, outperforming the A3C and DDPG algorithms. Cross-validation and ablation studies assessed PPO’s generalization capability and the contribution of reward components, clipping, and exploration strategies.Result: The findings demonstrate that PPO excels across distinct domains and offers a stable and efficient solution for text-based tasks.Conclusion: The findings confirm its flexibility for various NLP applications and intelligent decision-making systems 
Co-Authors -, Ubaidi Abd. Wahab Syahroni Irwan Darmawan Muhammad Innuddin Nazir Arifin, Mohammad Ramadhani, Nilam Ubaidi Ubaidi, Ubaidi