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INDONESIA
Journal of Computer Science and Engineering (JCSE)
Published by Institute of Computer Science and Engineering
ISSN : -     EISSN : 27210251     DOI : https://doi.org/10.36596/jcse
Core Subject : Science,
Computer Science & IT
Computer Architecture, Processor design, operating systems, high-performance computing, parallel processing, computer networks, embedded systems, theory of computation, design and analysis of algorithms, data structures and database systems, theory of computation, design and analysis of algorithms, data structures and database systems, artificial intelligence, machine learning, data science, Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 6, No 1: February (2025)" : 5 Documents clear
Expert System for Early Detection of High-Risk Pregnancy Conditions Using Certainty Factor and Forward Chaining Methods Agus, Fahrul; Vadlisky, Febria Dwi; Hamdani, Hamdani
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

The maternal mortality rate is the proportion of deaths that occur during pregnancy due to disorders that specifically impact the uterus. Experts attribute the high number to a lack of knowledge and delays in its management. Samarinda, located in East Kalimantan, has the second highest mortality rate, following Kutai Kartanegara. Hence, the implementation of an early detection system is important to effectively address this issue. The objective of this study is to develop an expert system that utilizes the certainty factor technique to identify high-risk factors in pregnant women before delivery. This study identified three high-risk conditions in pregnant women: preeclampsia, gestational diabetes mellitus (GDM), and constipation. There are a total of 22 symptoms associated with each condition, and for each disease, there are three distinct treatment options available. An expert in the field of obstetrics and gynecology provided the research data. The research yields an expert system that demonstrates accuracy by comparing 10 test data sets from both human experts and computing systems. The system achieved a 90% accuracy rate. Through the use of an expert system methodology, we expect this system to be a valuable resource for pregnant women and healthcare professionals seeking early detection of high-risk diseases in pregnant women.
Optimizing Transportation Routes and Costs in Crude Palm Oil Supply Chains using Linear Programming: A Case Study of PT.X, OKU Regency, South Sumatera Pramestari, Diah; Djatna, Taufik
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Transportation is a crucial factor that significantly impacts the supply chain. Because there aren't enough roads and other ways to get around, it's hard for one of the companies in OKU Regency that makes crude palm oil to get its products to customers, especially when it rains. The company that produces cooking oil received a delivery of crude palm oil, accounting for 90% of total production, from PT.X. The delivery of crude palm oil to PT.Y was received later than the original schedule. Number formulas that are meant to solve PT.X transportation problems can be written in terms of linear programming methods. From the stages of collecting, processing data and testing formulations using the LINDO application, it was discovered that under ideal conditions there are 9 alternative transportation routes from origin city B to destination city TJ with the shortest time of 9 hours. The delivery of transportation mode uses 12 tanks with 9.5 tons capacity and 17 tanks with 8 tons capacity and the lowest transportation expenses are Rp. 487.580. The results of the general mathematical formulation can then be used to solve transportation problems for other cases with similar conditions to PT.X. This mathematical formulation can be used for making decisions in selecting transportation routes, selecting modes and transportation costs. Changes in production factors such as capacity, number of orders, travel time, delivery distance, fuel prices, and toll rates can change the decisions made by entering their actual values in the general mathematical formula that has been produced.
Employment of Convolutional Neural Networks in an Eye Disease Detection Application Leveraging Tensorflow.js Arifin, Zainal; Santoso, Firman; Susanto, Adi
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Cataract and glaucoma are the leading causes of vision impairment worldwide,according to data from the World Health Organization. In Indonesia, theseconditions rank first in Southeast Asia and second globally, as evidenced bydata from the Ministry of Health's Roadmap of Visual Impairment ControlProgram in Indonesia 2017-2030. Early detection of these diseases is crucialfor preventing blindness. This study aims to classify eye diseases using a native-architecture Convolutional Neural Network (CNN) classification method withthe novel inclusion of three non-fundus or real-eye image subsets. The CNNimplementation in this study employs 100 epochs and achieves an accuracy of98.67%. The saved model from this research will be deployed usingTensorFlow.js, a framework or library derived from TensorFlow.
Algorithms for Question Answering to Factoid Question Fadhila, Raihan Pambagyo; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

The development of transformer-based natural language processing (NLP) has brought significant progress in question answering (QA) systems. This study compares three main models, namely BERT, Sequence-to-Sequence (S2S), and Generative Pretrained Transformer (GPT), in understanding and answering context-based questions using the SQuAD 2.0 dataset that has been translated into Indonesian. This research uses the SEMMA (Sample, Explore, Modify, Model, Assess) method to ensure the analysis process runs systematically and efficiently. The model was tested with exact match (EM), F1-score, and ROUGE evaluation metrics. Results show that BERT excels with an Exact Match score of 99.57%, an F1-score of 99.57%, ROUGE-1 of 97%, ROUGE-2 of 30%, and ROUGE-L of 97%, outperforming S2S and GPT models. This study proves that BERT is more effective in understanding and capturing Indonesian context in QA tasks. This research offers explanations for the implementation of Indonesian-based QA and can be a reference in the development of more accurate and efficient NLP systems.
Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization Oktiana, Milda Safrila; Sulistyo, Satria Harya; Zahwa, Refina Nur; Chair, Luthfi Muhammad; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

The Plasmodium parasite, which spreads through the bite of the Anopheles mosquito, causes malaria, a significant global health concern. Notwithstanding attempts to curtail its proliferation, malaria continues to be a predominant cause of mortality in tropical nations, especially in Sub-Saharan Africa and certain regions of Southeast Asia. Timely identification and precise diagnosis are essential for effective treatment. This research seeks to create a malaria classification model using deep learning based on the EfficientNetV2B0 architecture. The model is engineered to identify malaria parasite infections in microscopic images of erythrocytes. The dataset used is an open-source collection of photographs depicting red blood cells categorised as either infected or uninfected with malaria. The development method encompasses multiple critical stages, beginning with data collection, followed by preprocessing, data augmentation, and modelling using transfer learning with the EfficientNetV2B0 model. Bayesian optimisation is used to improve the model's accuracy by adjusting its hyperparameters. Assessment metrics, including accuracy, precision, recall, and F1-score, are used to evaluate the trained model's performance. The results show that the model has an accuracy of 96%, with equivalent precision, recall, and F1-scores for both the infected (under the heading "Parasitised") and uninfected (under the heading "Uninfected") groups. The model is extremely effective in diagnosing malaria, making it a valuable diagnostic tool for malaria control and prevention, especially in resource-constrained locations.Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization

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