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Integra: Journal of Integrated Mathematics and Computer Science
Published by Universitas Lampung
ISSN : -     EISSN : 31091792     DOI : https://doi.org/10.26554/integrajimcs
Core Subject : Science, Education,
Computer Science & IT Mathematics
Integra : Journal of Integrated Mathematics and Computer Science is the international journal in the field of Mathematics and Computer Science. Integra : Journal of Integrated Mathematics and Computer Science publish original research work both in a full article or in a short communication form, review article, and technical article in the field of Mathematics and Computer Science. Scope of this journal is : Mathematics Applied Mathematics Statistics Applied Statistics Data Science Computer Science
Arjuna Subject : Matematika - Matematika Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2024): November" : 5 Documents clear
Comparison of Naïve Bayes and Random Forest Models in Predicting Undergraduate Study Duration Classification at the University of Lampung Hestina P., Shelvira; Widiarti; Nuryaman, Aang; Usman, Mustofa
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241317

Abstract

This study aims to compare the performance of the Naïve Bayes and Random Forest classification algorithms in predicting the study duration of undergraduate students in the Mathematics Study Program at the University of Lampung. The dataset consists of 537 graduation records from 2020–2024. The research steps include data preprocessing, data partitioning (train-test split and k-fold cross validation), model building, and evaluation using a confusion matrix. The results show that the Random Forest algorithm achieved the highest accuracy of 94.44%, outperforming Naïve Bayes which reached a maximum accuracy of 92.59%. These findings suggest that Random Forest is more effective for classifying student study durations. These findings suggest that Random Forest is more effective for classifying student study durations.
Georaphically Weighted Ridge Regression Modelling on 2023 Poverty Indicators Data in the Provinces of West Kalimantan and Central Kalimantan Anjani, Syarli Dita; Widiarti; Utami, Bernadhita Herindri Samodera; Usman, Mustofa; Handayani, Vitri Aprilla
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241320

Abstract

Regression analysis is a method to explain the relations between independent variables and a dependent variable. Linear regression analysis relies on certain assumptions, one of the assumption is homogeneity. However, there is a situation when the variance at each observation differs or called spatial heterogeneity.This issue can be solved using Geographically Weighted Regression (GWR), a statistical method that can be fixed spatial heterogeneity by adding a local weighted matrix, the result in GWR model is a local model for each observation point. However, GWR has a limitation, it cannot handle multicollinearity. Ridge regression is a method used to solved multicollinearity by adding a bias constant (λ). A GWR model that contains multicollinearity and fixed using ridge regression is known as Geographically Weighted Ridge Regression (GWRR).
Application of GSTARMA Spatial-Temporal Model for Inflation Analysis in South Sulawesi Province Sari, Dede Ratna; Widiarti; Nurvazly, Dina Eka; Usman, Mustofa; Loves, Luvita
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241322

Abstract

The Generalized Space-Time Autoregressive Moving Average (GSTARMA) model is a development of the time series model that can capture both spatial and temporal dynamics simultaneously. This study uses the GSTARMA model to analyze inflation data in five cities in South Sulawesi Province from January 2017 to October 2024. The GSTARMA model obtained is GSTARMA (1,0,1) with a cross-correlation normalization spatial weight matrix. The results of the analysis indicate a spatial influence between locations and temporal relationships in the inflation data.
The Use of Dijkstra's Algorithm in Determining the Shortest Path of Expedition in Bandarlampung Assiva, Adelia; Puspita, Resta Meyliana; Sihombing, Riska Romauli; Chasanah, Siti Laelatul; Mustika, Mira
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241323

Abstract

Delivery of goods is a problem faced by freight forwarders/expedition companies. Determining an efficient route will determine the speed and cost of delivery. This is faced by most expedition companies, including one of the expedition companies in the city of Bandarlampung, namely the J&T Express expedition. There are 20 J&T Express branches in Bandarlampung city. If someone wants to send an item but one of the branches is closed or not available then he will try to determine the next closest branch. In this study, the shortest path from J&T on Pagar Alam to 19 other branches in Bandarlampung will be determined using Dijkstra’s Algorithm.
Traffic Violation Modeling Using K-Means Clustering Method: A Case Study in Bandung, Indonesia Junaidi, Akmal; Manurung, Yunita Rosalina; Shofiana, Dewi Asiah; Lumbanraja, Favorisen Rosyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241326

Abstract

Violations of traffic regulations are both an issue and a problem that persists as a feature of life, especially in metropolitan regions such as Bandung. Traffic violation has both behavioral and environmental patterns, with different types of violations occurring at different times during the day. This negligence stems largely from not properly equipping the vehicle with the necessary documents, especially for drivers who do not pay attention to proper document preparation. With the goal of increasing road safety, law enforcement bodies face the ongoing challenge of managing rising traffic violation rates which results in a growing backlog of violation cases and a corresponding backlog workload for police departments. Comprehensive preventive strategies for the problem are extremely difficult to implement in the absence of streamlined mechanisms for the efficient allocation of limited police resources. Currently, agencies responsible for managing violation records are still using a manual desktop system based on Microsoft Excel spreadsheets. This method impedes the analysis of large datasets to derive actionable insights that could inform targeted, data-driven strategies needed to guide proactive measures. In this regard, this study attempts to implement the K-Means clustering technique in order to identify and classify high-incidence traffic violation areas in Bandung. Using this technique, the research classifies the city into three violation risk clusters: very prone, prone, and moderately prone areas. The map of the classes demonstrates the distribution of these clusters spatially, illustrating clearly and vividly how stakeholders can visualise the pattern of traffic violations. This method improves the understanding of data and at the same time boosts purposeful planning for the safety and public traffic order anticipations.

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