cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Purwono
Contact Email
purwono@uhb.ac.id
Phone
+62281-6843493
Journal Mail Official
ikomti@uhb.ac.id
Editorial Address
Jl. Raden Patah No. 100, Ledug, Kecamatan Kembaran Kabupaten Banyumas - Jawa Tengah
Location
Kab. banyumas,
Jawa tengah
INDONESIA
Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Published by Universitas Harapan Bangsa Purwokerto
ISSN : 27464237     EISSN : 27464237     DOI : https://doi.org/10.35960/ikomti.v2i2
Core Subject : Science,
Computer Science & IT
Jurnal Ilmu Komputer dan Teknologi (IKOMTI) focuses on Computer Science, Information Systems, Information Technology and its implementation. IKOMTI is peer review, electronic, and open access journal. IKOMTI is seeking an original and high-quality manuscript. Areas of interest in Computer Science, Information Systems, and Information Technology include but are not limited to the following topics: 1. Computer Science - Application Technologies - Application Development - Artificial Intelligence - Cloud Computing - Computational Theory and Mathematics - Computer Hardware and Architecture - Computer Optimization - Digital Image Processing - Internet of Things - Machine Learning - Soft Computing - Software Engineering 2. Information Technology - Enterprise Architecture - Human Computer Interaction - Industrial Organization - Information/Data Security - IT Governance - IT Infrastructure /Operations - IT Operation Management - IT Organizations and Risk Management - IT Procurement - IT Strategic Planning - Networks and/or Telecommunications - System Integration - etc. 3. Information Systems - Big Data - Business Intelligence - Data and Knowledge-Based System Architectures - Data mining - Decision Support Systems - E-Business - E-Government - Health Information Systems - Information Management - IS/IT Project Management - New Technology Acceptance - Supply Chain Information Systems - System Analysis and Design - User Experience and Design - etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 98 Documents
 6   7   8   9   10 
Prediksi Tingkat Obesitas Menggunakan Logistic Regression Dan Xgboost Berbasis Faktor Gaya Hidup Dan Antropometri Indah Theresia Sibuea; Delima Sitanggang; Jamaluddin Hutajulu
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2437

Abstract

Obesity is a growing health problem that can be influenced by various factors, such as diet, physical activity, and anthropometric conditions. This study aimed to predict obesity levels based on lifestyle and anthropometric factors using machine learning methods, namely Logistic Regression and XGBoost. The dataset used was obtained from the Kaggle platform, with a total of 2,111 data points and 17 attributes. The research process began with data preprocessing (label encoding and standard scaler), train-test data split (80:20), and validation using 5-Fold Stratified Cross-Validation. The results showed that the Logistic Regression model achieved 87.47% accuracy, while the XGBoost model achieved 95.98% accuracy in the train-test split and 94.35% ± 1.67% in the 5-Fold Cross-Validation. Feature importance analysis showed that Weight, Height, family_history_with_overweight, FAVC, and FAF were the most dominant predictors. Based on this comparison, the XGBoost model showed better performance and is recommended for predicting obesity levels.
Perbandingan Decision Tree dan Neural Network dalam Prediksi LVEF pada Pasien Gagal Jantung Eva Rahmawati; Mieke Nurmalasari; Hosizah Markam; Dhiar Niken Larasati
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2438

Abstract

Heart failure is a cardiovascular disease with a high mortality rate, affecting more than 64 million people worldwide, with a one-year fatality rate of 33%. To assess cardiac performance, the Left Ventricular Ejection Fraction (LVEF) indicator was used, which reflects the ability of the left ventricle to pump blood. Therefore, an analytical approach is needed to predict LVEF values more accurately based on patient characteristics. This study aimed to compare the performance of Decision Tree and Neural Network algorithms in predicting LVEF values in patients with heart failure. Data processing was conducted using Orange Data Mining, utilizing risk factor variables as the basis for classification. The approach used was supervised learning, involving 381 heart failure patient records that were split using an 80:20 train-test split technique, resulting in 305 training data and 76 testing data. Class imbalance in the training data was handled using the SMOTE method before the modeling process. The experimental results show that the model built using the Neural Network algorithm provides better performance than the Decision Tree. This was indicated by an AUC value of 64.8%, classification accuracy of 57.9%, F1-score of 55.8%, precision of 57.6%, and recall of 57.9%. In addition, based on the confusion matrix evaluation, the Neural Network algorithm was able to achieve higher accuracy and recall levels in most LVEF categories. Based on these results, it can be concluded that the Neural Network is a more effective method for predicting LVEF values in patients with heart failure. This model is considered more capable of recognizing complex data patterns, thereby producing better predictive accuracy compared with the Decision Tree algorithm.
Penerapan Logistic Regression pada Data Tidak Seimbang untuk Sistem Pendukung Keputusan Deteksi Dini Dropout Mahasiswa ismail setiawan; Apolinario dos Santos; Aisyah Mutia Dawis
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2458

Abstract

Student dropout negatively impacts both university performance and the futures of individual students. Identifying at-risk students during the early stages of their studies is crucial for effective academic intervention. This study aims to develop an Early Warning System (EWS) using machine learning to predict dropout potential based on academic and demographic data from the first three semesters. To address the extreme class imbalance in the dataset, a cost-sensitive learning approach using class weighting was applied. Three classification algorithms were evaluated: Random Forest, XGBoost, and Logistic Regression. Performance evaluation using the Area Under the Precision-Recall Curve (AUC-PR) metric demonstrated that Logistic Regression outperformed the tree-based ensemble models, achieving the highest score of 0.6454. Feature interpretation revealed that low parental income, poor minimum attendance, and early course failure were the primary risk factors for dropping out. Furthermore, the best model was successfully implemented into a web-based Decision Support System (DSS) prototype dashboard. This system provides university management and academic advisors with an interactive tool for conducting proactive, data-driven early interventions.
Optimasi Support Vector Machine Menggunakan Grid Search Untuk Prediksi Jenis Penyakit Stroke I Gusti Agung Harkit Brahmantya; I Nyoman Sukajaya; Raphita Yanisari Silalahi
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2493

Abstract

This study aims to improve the classification performance of stroke types by optimizing the Support Vector Machine (SVM) algorithm using the Grid Search method. The dataset used consists of medical records of stroke patients in 2024 from RSUD Buleleng, comprising 610 patient records with 13 clinical attributes as input variables and stroke type as the target variable. The research stages include data preprocessing (data cleaning and label encoding), data splitting with an 80:20 ratio, building an SVM model without optimization, and parameter optimization using Grid Search. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model without optimization achieved an accuracy of 0.92, precision of 0.92, recall of 0.93, and F1-score of 0.92. After applying Grid Search optimization, the best parameters obtained were a polynomial kernel with , , , and , which improved the model performance to an accuracy of 0.95, precision of 0.94, recall of 0.95, and F1-score of 0.94. These results indicate that parameter optimization using Grid Search can effectively enhance the performance of the SVM model in classifying stroke types more accurately.
Artificial Intelligence for a Smarter Future: Trends, Applications, and Research Directions Zohaib Hassan Sain; Tri Pujiani; Ida Dian Sukmawati; Jaime Da Costa Lobo Soares
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2498

Abstract

Artificial Intelligence (AI) has emerged as the defining technological force of the twenty-first century, fundamentally transforming industries, reshaping scientific inquiry, and reconfiguring the boundaries of what machines can accomplish. This paper presents a comprehensive quantitative and qualitative review of AI's most recent advances, covering key paradigms including Natural Language Processing (NLP), deep learning, computer vision, reinforcement learning, generative AI, and federated learning. Methodologically, this study adopts a structured narrative review: 48 peer-reviewed articles and authoritative technical reports published between 2019 and 2024 were retrieved from Scopus, IEEE Xplore, Web of Science, the ACL Anthology, and arXiv, and then screened and synthesized thematically across six AI paradigms. A systematic analysis of benchmark performance data across leading AI models, including GPT-4, Gemini Ultra, and AlphaFold 2, demonstrates measurable progress in accuracy, efficiency, and versatility. This study further maps the critical challenges impeding AI's responsible deployment of AI: data bias, computational cost, lack of explainability, adversarial vulnerabilities, and regulatory fragmentation. Drawing on evidence from recent peer-reviewed literature and industry reports, we propose a structured roadmap for future research directions, including Artificial General Intelligence (AGI), Explainable AI (XAI), Green AI, quantum machine learning, and human-AI collaboration frameworks. Our analysis underscores the urgent need for interdisciplinary research, ethical governance, and sustainable AI design principles to ensure that AI development aligns with the long-term values and societal goals of humanity.
Evaluasi Aksesibilitas Learning Management System (LMS) Perguruan Tinggi Berdasarkan Standar WCAG 2.1 bagi Mahasiswa Disabilitas Lukman Santoso; Agus Waryanto; Neilin Nikhlis; Dani Sasmoko
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2509

Abstract

This study evaluates the accessibility of 14 higher education Learning Management Systems (LMS) in Semarang City based on WCAG 2.1 standards using the WAVE tool to support SDG 4 (Quality Education). Employing a descriptive quantitative-evaluative approach on 28 public pages (homepages and login pages), the results revealed suboptimal and varying compliance. The platforms were categorized into three issue clusters: low (3 LMS), medium (7 LMS), and high (4 LMS). The most dominant violations occurred in the Perceivable principle (57.1%) due to contrast errors and the Understandable principle (35.7%) due to structural inconsistencies, with homepages generally exhibiting higher error rates than login pages. These findings confirm a functional digital divide for students with disabilities, necessitating a shift in campus IT governance toward a user-centered inclusive design.
Evaluasi Perbandingan Model Machine Translation untuk Penerjemahan Dataset Etika Penggunaan AI Caroline Angelia Setiawan; Aris Tjahyanto
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2516

Abstract

The development of Large Language Models (LLMs) and Artificial Intelligence (AI)-based technologies has increased the demand for multilingual chatbots for AI ethics education. However, language differences between chatbot training data and user language remain a challenge that can affect the interaction quality.Although machine translation has been widely used to support multilingual chatbots, studies comparing the impact of different translation models on translation quality, particularly in the domain of AI ethics, remain scarce. This study aims to compare and select the best machine translation model in the field of artificial intelligence ethics. The dataset was obtained from UNESCO’s Recommendation on the Ethics of Artificial Intelligence document and generated using a Retrieval-Augmented Generation (RAG) approach based on LLMs. The dataset consisted of 1,000 English-language questions that were later translated into Indonesian using an LLM and manually validated. The Indonesian-language dataset was used as input for back-translation into English using several machine translation methods, namely Google Translate, MarianMT, and M2M-100. The evaluation was conducted using BLEU and METEOR metrics. The results indicate that Google Translate achieved the highest performance, with a BLEU score of 52.2% and a METEOR score of 81.7%, whereas the lowest performance was observed in MarianMT Multi-EN, with a BLEU score of 18.95% and a METEOR score of 56.19%. The findings also indicate that increasing the number of parameters in the M2M-100 model improved the translation quality. This study demonstrates that machine translation has significant potential for supporting multilingual chatbots, particularly in the field of AI ethics.
Analisis Sentimen Keluhan Layanan Cash on Delivery (COD) pada Platform Shopee Menggunakan IndoBERT Musa Rahanra; Rooy marthen Thaniket; Nicodemus Rahanra; Hermanus J Suripatty
Jurnal IT UHB Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v7i2.2545

Abstract

This study aims to analyze user review sentiment regarding the Cash on Delivery (COD) service on the Shopee e-commerce platform using the IndoBERT model. The dataset consisted of 1,840 unique Google Play Store reviews related to COD collected from September 25, 2018, to December 29, 2023. The stages of the research included data collection, text preprocessing, sentiment labeling, and modeling using IndoBERT. The results show that 48.4% of the reviews were negative, 41.1% positive, and 10.5% neutral. The IndoBERT model achieved an accuracy of 92.1%, with a weighted precision of 0.923, weighted recall of 0.921, and weighted F1-score of 0.922. The results indicate that the IndoBERT method is capable of classifying sentiments with good performance. The findings provide an overview of users’ perceptions of the COD service on Shopee. However, this study is limited to the use of a dataset from a single platform and cannot be generalized to all e-commerce platforms in Indonesia. Future research should use cross-platform datasets to obtain more comprehensive results.

Page 10 of 10 | Total Record : 98

 6   7   8   9   10 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol 7 No 2 (2026): Jurnal Ilmu Komputer dan Teknologi Vol 7 No 1 (2026): Jurnal Ilmu Komputer dan Teknologi Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi Vol 6 No 2 (2025): Jurnal Ilmu Komputer dan Teknologi Vol 6 No 1 (2025): Jurnal Ilmu Komputer dan Teknologi Vol 5 No 3 (2024): Jurnal Ilmu Komputer dan Teknologi Vol 5 No 2 (2024): Jurnal Ilmu Komputer dan Teknologi Vol 5 No 1 (2024): Jurnal Ilmu Komputer dan Teknologi Vol 4 No 2 (2023): Jurnal Ilmu Komputer dan Teknologi Vol 4 No 1 (2023): Jurnal Ilmu Komputer dan Teknologi Vol 3 No 1 (2022): Jurnal Ilmu Komputer dan Teknologi Vol 2 No 2 (2021): Jurnal Ilmu Komputer dan Teknologi Vol 2 No 1 (2021): Jurnal Ilmu Komputer dan Teknologi Vol 1 No 2 (2020): Jurnal Ilmu Komputer dan Teknologi Vol 1 No 1 (2020): Jurnal Ilmu Komputer dan Teknologi More Issue