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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM
Elvis Sastra Ompusunggu
Universitas Prima Indonesia
Computer Science & IT

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Triple Exponential Smoothing Analysis in Predicting Numbers Request for Delivery of Logistics CV. Lotus Mas Express Elvis Sastra Ompusunggu; Andrean Wirjana; Silemberesen Silemberesen; Dea Junia Dea Junia
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.204 KB)

Abstract

CV. Teratai Mas Express is a company engaged in services, namely transportation services or commonly called expeditions. But this company specializes in transportation logistics. The problem that is often faced by this company is that it often suffers losses due to being unstructured in terms of stock transportation inventory to the number of delivery requests. Especially in certain seasons and usually the corn harvest season. Demand rose, but companies often made mistakes in providing their freight. Sometimes advantages and sometimes disadvantages. Excess or shortage of these supplies in a high level. Suppose the company provides 14 transportations but only 8 is used or vice versa, resulting in a large loss. For this reason, a precise prediction calculation is needed so that the number of logistics delivery requests can be predicted efficiently in order to reduce large losses. The prediction method that I use is Triple Exponential Smoothing. This method is suitable for use in this case because the number of logistics delivery requests increases in certain seasons and this method can analyze it.
PENENTUAN KELAYAKAN PROMOSI PEGAWAI MENGGUNAKAN ALGORITMA RANDOM FOREST CLASSIFIER DAN XGBOOST CLASSIFIER Elvis Sastra Ompusunggu; Aleksander Nainggolan; Meri Kristina Sihombing
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.949

Abstract

Perkasa Internusa Mandiri is a company engaged in the field of Property Management. One of the problems employees to become permanent employees is that it still needs to be more effectives; this raises doubts in decision-making, allowing mistakes to occur. In this context, to solve problems at PT. Perkasa Intenusa Mandiri uses a classification algorithm to provide a more objective and transparent solution. This study aims to apply a data-based approach using the Random Forest Classifier and XGBoost Classifier algorithms to assess the feasibility of promoting contract employees to permanent employees. In this study, the Random Forest classification algorithm and the XGBoost Classifier have also produced good accuracy values, whereas the Random Forest has an accuracy of 86.8%. In comparison, the XGBoost Classifier has an accuracy of 83.5%. Both models have good performance (because the accuracy is above 80%) and can be implemented in real-world cases in the future.
Co-Authors Aleksander Nainggolan Andrean Wirjana Dea Junia Dea Junia Meri Kristina Sihombing Silemberesen Silemberesen