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Rizki Wahyudi
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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 61 Documents
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Comparison of Moora and Waspas Methods for Recommendations of Cayenne Pepper Seeds Wardani, Agus Tri; Hamdani, Hamdani; Agus, Fahrul
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Multiple Criteria Decision Making (MCDM) encompasses several methodologies, including MOORA and WASPAS. These strategies demonstrate unique approaches and produce varying results. The main aim of this work is to provide a comparative analysis of the MOORA and WASPAS procedures. To achieve this objective, we conduct a detailed analysis that specifically examines five parameters related to cayenne pepper seeds: prospective crop yields, optimal harvesting time, recommended conditions for highland cultivation, weight of 1000 seeds, and plant height. The study utilizes the sensitivity test approach in a comparative analysis framework to ascertain the superior method. The computations using both the MOORA and WASPAS methods determine that the Bisi Hp 35 (A3) alternative is the best choice. This alternative has a MOORA preference value of 0.1463, while the WASPAS approach gives it a preference value of 0.8374. Next, we perform a sensitivity test by increasing the weight criteria for each criterion by 0.5 and 1. The sensitivity analysis indicates that the MOORA approach has a level of 380, whereas the WASPAS method has a level of 376. The data suggest that the MOORA method is more effective than the WASPAS method when it comes to making recommendations for cayenne pepper seeds.
Managing Student Mobility in Cameroon’s University Ecosystem: A FORM/BCS Approach Ngoumou, Amougou; Roger, Atsa Etoundi; Ndjodo, Marcel Fouda
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Nowadays, student mobility cannot be avoided in Cameroon university ecosystem. This phenomenon has many causes. Certain students live with their parents who are civil servants and they have to move with them when they are sending to a different region; another situation is link to universities newly created in cities where the cost of the live is better than in the home university city and students prefer move to these new universities. Since student circuit is not the same in each university, it is difficult to find his level in the new university and which courses he has to follow in order to complete his training. This problem is crucial in Cameroon university ecosystem and we tackle it in this paper. The Feature Oriented Reuse Method with Business Component Semantics (FORM/BCS) is a software domain engineering method that has been proposed to design an adaptable architecture for systems belonging in a same business domain. In this work, we apply the FORM/BCS method to manage student mobility in Cameroon university ecosystem. The result of this work is a management model that allows once a student gets an enrolment in a new university, to transfer credit from the student home university to the host one.
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
Comparative Analysis of Parameter-Efficient-Fine-Tuning and Full Fine-Tuning Approaches for Indonesian Dialogue Summarization using mBART Aji, Ananda Bayu; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

This study addresses the urgent need for efficient Indonesian dialogue summarization systems in remote working contexts by adapting the multilingual mBART-large-50 model. The DialogSum dataset was translated into Indonesian using Opus-MT, and two fine-tuning approaches—full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) with LoRA—were evaluated. Experiments on 1,500 test samples revealed that full fine-tuning achieved superior performance (ROUGE-1: 0.3726), while PEFT reduced energy consumption by 68.7% with a moderate accuracy trade-off (ROUGE-1: 0.2899). A Gradio-based interface demonstrated practical utility, enabling direct comparison of baseline, fine-tuned, and PEFT models. Critical findings include translation-induced terminology inconsistencies (e.g., "Hebes" vs. "Hebei") and context retention challenges in long dialogues. This work contributes a scalable framework for low-resource language NLP and provides actionable insights for optimizing computational efficiency in real-world applications.This study addresses the urgent need for efficient Indonesian dialogue summarization systems in remote working contexts by adapting the multilingual mBART-large-50 model. The DialogSum dataset was translated into Indonesian using Opus-MT, and two fine-tuning approaches, full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) with LoRA, were evaluated. Experiments on 1,500 test samples revealed that full fine-tuning achieved superior performance (ROUGE-1: 0.3726), while PEFT reduced energy consumption by 68.7% with a moderate accuracy trade-off (ROUGE-1: 0.2899). A Gradio-based interface demonstrated practical utility, enabling direct comparison of baseline, fine-tuned, and PEFT models. Critical findings include translation-induced terminology inconsistencies (e.g., "Hebes" vs. "Hebei") and context retention challenges in long dialogues. This work contributes a scalable framework for low-resource language NLP and provides actionable insights for optimizing computational efficiency in real-world applications.
Hyperband‑Optimized LightGBM and Ensemble Learning for Web Phishing Detection with SHAP‑Based Interpretability Wahyudi, Rizki
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

This study evaluates the performance of three tree boosting algorithms, Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM), in detecting phishing websites using a phishing dataset based on HTML, URLs, and network features. Two hyperparameter optimization strategies were tested: Hyperband search (HalvingRandomSearchCV) and stacking ensemble combining all three models. The evaluation was conducted based on five main metrics: accuracy, precision, recall, F1-score, and AUC‑ROC. The results indicate that LightGBM tuned via Hyperband achieved the highest performance (accuracy 0.9724; AUC‑ROC 0.9702), followed by ensemble tuned (accuracy 0.9697; AUC‑ROC 0.9684). SHAP analysis was used to interpret the contribution of key features in predicting phishing websites. The AUC‑ROC difference of 0.0034 points from the XGBoost baseline (0.9668) confirms the effectiveness of Hyperband tuning and stacking ensembles for phishing detection
Statistical Analysis of Adaptive Thresholding Algorithms for Denoising Signature Images Choudhury, Ruhiteswar; Deb Roy, Tanusree
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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This study explores the efficacy of adaptive thresholding techniques in denoising signature images captured under varying lighting conditions. Signature images from multiple individuals were obtained in different illumination scenarios, and three prominent adaptive thresholding algorithms, namely histogram thresholding, Otsu’s method, and the Gaussian Mixture Model (GMM), were applied to the noisy images. The performance of each technique was rigorously evaluated using root mean square error (RMSE) and correlation coefficient metrics. The findings reveal that the Gaussian Mixture Model significantly outperformed both histogram thresholding and Otsu’s method, achieving superior noise reduction and better preservation of essential information. This was evidenced by lower RMSE values and higher correlation coefficients. These results suggest that the Gaussian Mixture Model is a highly effective technique for denoising signature images, particularly under varying lighting conditions. Its superior performance underscores its potential as a robust tool for enhancing the clarity and accuracy of signature verification systems. This study provides valuable insights into the application of adaptive thresholding techniques in image processing, highlighting the advantages of the Gaussian Mixture Model over traditional methods. The implications of this research are substantial for fields that rely on precise signature recognition and verification, such as banking, legal documentation, and security systems. This study specifically focuses on signature segmentation as a preprocessing step for signature verification systems. It does not directly address full document verification but aims to improve segmentation accuracy under varying lighting conditions, which is a foundational component in document authentication pipelines.    

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