Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Sisfo: Jurnal Ilmiah Sistem Informasi Formosa Journal of Computer and Information Science
Elvis Sastra Ompusunggu
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Analysis of Smartphone Addiction of Immanuel Medan Students Using Data Mining Classification Method (Naive Bayes and C4.5) Berna Susanti Br Tarigan; Resa Jesiulina H; Elvis Sastra Ompusunggu
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27184

Abstract

Smartphone addiction among students is a problem that interferes with their concentration while studying, social interactions, and their academic motivation. This study analyzed the level of smartphone addiction among students of SMKS Immanuel Medan using the Naive Bayes classification algorithm and the C4.5 decision tree. This study adopts a comparative quantitative approach using the phases of Knowledge Discovery in Databases (KDD), including data collection, data cleaning, data selection, data transformation, data mining, and evaluation. The research data was collected by distributing questionnaires to 100 students at SMKS Immanuel Medan. The study variables included age, gender, duration of smartphone use, purpose of smartphone use, dominant type of social media, and the level of smartphone addiction as target variables. The classification was carried out using RapidMiner software with a 70:30 training and testing data split. Model evaluation was carried out using a confusion matrix with the parameters of accuracy, precision, recall, and F1 score. The results show that the C4.5 decision tree algorithm gives better results than the Naive Bayes algorithm. The C4.5 algorithm achieved 90% accuracy, 88.9% precision, 80% recall, and 84% F1 score, while the Naive Bayes algorithm achieved 80% accuracy, 80% precision, 66.7% recall, and 73% F1 score. This research contributed to the development of a simple web-based expert system that helps schools assess the level of smartphone addiction among students quickly, objectively, and systematically, so that it can be used as a decision-making tool to monitor smartphone use in the education sector.
Comparasion of Support Vector Machine and Decision Tree Methods in Sentiment Analysis of Social Media X User Toward the Free Nutritious Meal Program Olinda Nathaniel Mendrofa; Deva Angriani; Elvis Sastra Ompusunggu
Formosa Journal of Computer and Information Science Vol. 5 No. 2 (2026): August 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i2.16668

Abstract

The research stages include a preprocessing process consisting of cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. Furthermore, the TF-IDF method is used to extract text features so that the data can be represented in numerical form. The dataset was then divided into training data and test data with an 80:20 ratio. The model evaluation was carried out using a confusion matrix by paying attention to the values of accuracy, precision, recall, and F1-Score. The results showed that the Support Vector Machine algorithm had better performance than Decision Tree in classifying the sentiment of social media users X towards the Free Nutritious Meal Program. The SVM algorithm obtained an accuracy score of 75.42%, precision of 74.65%, recall of 75.42%, and F1-Score of 74.96%. Meanwhile, the Decision Tree algorithm produced an accuracy value of 72.08%, precision of 73.33%, recall of 72.08%, and F1-Score of 72.59%.
Co-Authors Aleksander Nainggolan Andrean Wirjana Berna Susanti Br Tarigan Dea Junia Dea Junia Deva Angriani Meri Kristina Sihombing Olinda Nathaniel Mendrofa Resa Jesiulina H Silemberesen Silemberesen