Computing and Information Processing Letters
Journal title Computing and Information Processing Letters Initials CIP Abbreviation Comput. Inf. Process. Lett. Frequency 2 issues per year DOI Prefix 10.31315 Online ISSN 2809-1256 Editor-in-chief Dr. Awang Hendrianto Pratomo, S.T., M.T. Managing Editor Shoffan Saifullah Publisher Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta Citation Analysis Google Scholar Computing and Information Processing Letters aims to keep abreast of the current development and innovation in the area of Computer Science, Information System and Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related disciplines, including theories, methods, tools, technologies, applications, and so on. High-quality experimental papers that address topics of sufficiently broad interest may also be considered. More detailed information about the topics of interest and submission format can be found in the Guide for Authors.
Universitas Pembangunan Nasional Veteran Yogyakarta
License and Copyright Agreement In submitting the manuscript to the journal, the authors certify that: They are authorized by their co-authors to enter into these arrangements. The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read SITech's Posting Your Article Policy. That it is not under consideration for publication elsewhere, That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out. They secure the right to reproduce any material that has already been published or copyrighted elsewhere. They agree to the following license and copyright agreement. Copyright Authors who publish with Computing and Information Processing Letters agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. Licensing for Data Publication Science in Information Technology Letters use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data: Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default) Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/ Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/ Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article. Open Data and Software Publishing and Sharing The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code, or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article. Datasets and software should be deposited and permanently archived in appropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g., DOI or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the article's reference list with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others. Small data may also be published as data files or packages supplementary to a research article; however, the authors should prefer a deposition in data repositories in all cases.
Image Captioning With Attention Mechanism Using Convolutional Neural Network and Gated Recurrent Unit on the Picture of Tourism in the Special Region of Yogyakarta
This study’s purpose is to apply Convolutional Neural Network (CNN) using ResNet-50 feature extraction and GRU also using Attention Mechanism to generate image captions automatically with dataset of tourism pictures in Special Region of Yogyakarta using Indonesian language and to know the accuracy. Design/methodology/approach: Using CNN method with ResNet-50 and GRU and attention mechanism. Findings/result: The results of the test carried out using the BLEU score got the BLEU-1,2,3,4 score 42.64, 32.56, 18.55, 16.27. Originality/value/state of the art: This study uses datasets taken from 10 tourist attractions in Yogyakarta, and also in this study using the CNN method with feature extraction ResNet-50, GRU and using the Attention Mechanism.
Copyright (c) 2025 Computing and Information Processing Letters
Optimization of Triple Exponential Smoothing Parameters Using GSSMV Algorithm and Genetic Algorithm for Determining the Best Forecasting Optimization Model and Predicting the Number of Train Passengers
This study compares two optimized Triple Exponential Smoothing (TES) forecasting models for predicting the number of Indonesian train passengers on Java: TES optimized with the GSSMV algorithm (TES-GSSMV) and TES optimized with a genetic algorithm (TES-Genetics). Using 124 time-series data points, both models estimate TES parameters (alpha, beta, gamma) and are evaluated using MAPE. Results show TES-Genetics achieves better prediction accuracy than TES-GSSMV, with a MAPE improvement of 1.393%, so TES-Genetics is selected as the best forecasting optimization model and its alpha, beta, gamma parameters are used for final passenger predictions.
Copyright (c) 2026 Computing and Information Processing Letters
Optimization of Foodstuffs for Patients with Hypertension Using the Improved Particle Swarm Optimization Method
Purpose: to find out the optimal food composition for people with hypertension by considering nutrition and the price of the food ingredients used using the Improved Particle Swarm Optimization method, so that it can help people with hypertension in regulating the composition of the food that must be consumed. Findings/result: From this research, recommendations for food composition are produced according to the conditions or circumstances of the user. With recommendations, the food is divided into 3 portions, namely breakfast, lunch and dinner. Breakfast and lunch consist of staple foods, vegetable sources, animal sources, vegetables and fruits. While dinner consists of staple food, vegetable sources, animal sources, vegetables and fruits without using fruit foods, Originality/value/state of the art: This study considers the price and nutrition of the food ingredients used to produce an optimal food composition for people with hypertension. As well as in research using the Improved Particle Swarm Optimization method.