Computing and Information Processing Letters
type: channel
description: 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.
publisher: Universitas Pembangunan Nasional Veteran Yogyakarta
language: en-US
publication name: Computing and Information Processing Letters
issn: 2809-1256
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items: Image Captioning With Attention Mechanism Using Convolutional Neural Network and Gated Recurrent Unit on the Picture of Tourism in the Special Region of Yogyakarta
items: https://jurnal.upnyk.ac.id/index.php/cip/article/view/16414
items: Optimization of Foodstuffs for Patients with Hypertension Using the Improved Particle Swarm Optimization Method

Image Captioning With Attention Mechanism Using Convolutional Neural Network and Gated Recurrent Unit on the Picture of Tourism in the Special Region of Yogyakarta
type: item
description: 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.
creator: Basrizal Reza Astana
creator: Dessyanto Boedi Prasetyo
creator: Wilis Kaswidjanti
rights: Copyright (c) 2025 Computing and Information Processing Letters
date: 2025-12-24
publication date: 2025-12-24
volume: 4
number: 1

https://jurnal.upnyk.ac.id/index.php/cip/article/view/16414
type: item
title: 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
description: 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.
creator: Adrian Fathur Setyawan
creator: Rifki Indra Perwira
creator: Heriyanto
rights: Copyright (c) 2026 Computing and Information Processing Letters
date: 2026-01-22
publication date: 2026-01-22
volume: 4
number: 1

Optimization of Foodstuffs for Patients with Hypertension Using the Improved Particle Swarm Optimization Method
type: item
description: 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.
creator: Muhamad Setiawan Wicaksono
creator: Novrido Charibaldi
creator: Nur Heri Cahyana
rights: Copyright (c) 2026 Computing and Information Processing Letters
date: 2026-01-22
publication date: 2026-01-22
volume: 4
number: 1