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All Journal TIERS Information Technology Journal Klabat Journal of Management
Swilugar, Ayu
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Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Feasibility Analysis of Industrial Waste Reduction Investment: Potential of Fruit Basket and Mat Products: Analisis Kelayakan Investasi Pengurangan Limbah Industri: Potensi Produk Keranjang Buah dan Keset Anasrul, Rahmad Fajri; Haryo, Afrido; Tristiadi, Aloysius Kalis; Swilugar, Ayu; Bagaskara, Caesarius Haryo; Vyanti, Jessica
Klabat Journal of Management Vol. 7 No. 1 (2026): Klabat Journal of Management (in progress)
Publisher : Faculty of Economics and Business, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60090/kjm.v7i1.1343.115-126

Abstract

Unprocessed waste has become a major issue in the world today. In 2021, Indonesia's environmental statistics recorded that approximately 24.5% of the total waste generated was not properly managed. One of the subjects of this issue is palm oil and textiles. Palm oil is a commodity that has experienced rapid growth, with a growth rate of 22.72% from 2017 to 2018. Meanwhile, textile waste is commonly found, especially in garment companies. This research aims to conduct an investment feasibility analysis from a financial perspective for industrial waste reduction, focusing on the business potential of fruit baskets made from palm oil waste and mats made from fabric scraps. The products generated from this waste can provide a sustainable solution while also generating financial profit. Therefore, a feasibility analysis was carried out for these two products. The investment feasibility analysis method is used to evaluate the potential success of these two waste-reducing products. The feasibility analysis involves calculating initial costs, revenue, operational costs, and cash flow projections over a certain period. Additionally, various risk factors and relevant assumptions are also considered in this analysis. The results of the feasibility analysis conclude that the fruit basket made from palm oil waste is more feasible than the mat made from fabric scraps.
Operating Room Scheduling Optimization Under Surgeon and Nurse Constraints Using Genetic Algorithm Swilugar, Ayu; Herliansyah, Muhammad Kusumawan
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7164

Abstract

Operating room scheduling is a complex problem due to the limited availability of surgeons, nurses, and operating rooms, as well as the variability in surgery durations. Inaccurate predictions or scheduling may cause conflicts such as overlapping surgeon schedules, violations of contamination level restrictions, and unavailability of nurses or rooms, ultimately reducing the quality of hospital services. This study integrates multiprocedure surgery duration prediction using machine learning with scheduling optimization based on genetic algorithms. The prediction model considers the American Society of Anesthesiologists (ASA) physical status classification, patient profiles, and sets of surgical procedures variables. Scheduling optimization employs a lexicographic approach with three main objectives: minimizing patient waiting time, nurse overtime, and operating room idle time, while ensuring surgeon presence during critical phases and nurse availability according to shifts. The results show that the Catboost algorithm achieves the best prediction performance. Incorporating the ASA variable reduces prediction errors by 33.880 minutes in MAE and 55.575 minutes in RMSE compared to model without the ASA feature. The optimization model successfully eliminates all scheduling conflicts, ensuring full compliance with medical procedure constraints. Recovery bed utilization remains efficient, with a maximum of five units used, representing less than 50% of the total capacity.
Co-Authors Anasrul, Rahmad Fajri Bagaskara, Caesarius Haryo Haryo, Afrido Muhammad Kusumawan Herliansyah Tristiadi, Aloysius Kalis Vyanti, Jessica