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 85 Documents
 5   6   7   8   9 
Deteksi Lesi Cacar Monyet pada Citra Dermatologi Menggunakan Metode YOLOv7 Ali Sya'bana Syukurillah; Anggit Wirasto; Retno Agus Setiawan
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2015

Abstract

Monkeypox is an infectious disease characterized by skin lesions that are often difficult to distinguish from other pox-related conditions, which complicates diagnosis in resource-limited settings. This study aims to implement YOLOv7 for detecting monkeypox lesions in dermatological images and to evaluate its accuracy. The dataset consisted of 1,500 annotated images resized to 512×512 pixels, monkeypox was used as the target class, while chickenpox and cowpox were included as comparison/non-target classes to support the differentiation of lesions during model training and evaluation. The YOLOv7 model was trained for 50 epochs using default configurations and a transfer learning approach, with a data split of 70% for training, 20% for validation, and 10% for testing. Training results showed an mAP@0.5 of 89.1% and an mAP@0.5:0.95 of 59.2%. Meanwhile, on the testing stage using original (non-augmented) data, the model performance decreased, achieving an mAP@0.5 of 75.3% and an mAP@0.5:0.95 of 44.9%.
Perancangan Sistem Filling Rekam Medis di Rumah Sakit Umum Tabanan Aditya, Made Wahyu Aditya; Ika Setya Purwanti
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2023

Abstract

The efficiency of medical record management plays an essential role in improving the quality and accuracy of hospital services. At Tabanan General Hospital, several issues were identified in the manual medical record filling process, including incomplete borrowing data and document return delays exceeding 24 hours. These problems were mainly caused by the use of manual expedition books, which are less effective and efficient. This study aims to design an electronic medical record filling system to support accurate and timely document management. The research employed a descriptive qualitative method combined with a waterfall design approach. The system design included the development of Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), and user interface prototypes. The resulting system design is considered acceptable based on expert validation and is recommended for implementation at Tabanan General Hospital to improve efficiency, data completeness, and tracking accuracy in the medical record management process.
Analisis Komparatif Algoritma Machine Learning dengan Metrik Akurasi, Presisi, Recall, dan F1-Score pada Dataset Kacang Kering Helmiyah, Siti; Pramestiawan, Rico
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2031

Abstract

This study aims to compare the performance of five machine learning algorithms in classifying dry bean varieties as an effort to support quality detection systems for agricultural products. Issues related to authenticity and food safety that frequently occur, such as rice adulteration, highlight the importance of fast and accurate methods for variety identification. The study utilizes the Dry Bean Dataset from the UCI Machine Learning Repository, which consists of 13,611 samples with 16 numerical features and 7 classes of bean varieties. Five algorithms were tested, including K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). The data were divided into 80% for training and 20% for testing, and evaluated using accuracy, precision, recall, and F1-Score metrics. The results show that the SVM algorithm achieved the best performance with an accuracy of 92.43% and an F1-Score of 93.61%, followed by Logistic Regression and Random Forest. The confusion matrix analysis indicates that most varieties were correctly classified, although some misclassifications occurred among classes with similar morphological characteristics such as Dermason, Seker, and Sira. Based on these findings, it can be concluded that selecting the appropriate algorithm is crucial in applying machine learning for agricultural product classification. Evaluation using multiple metrics provides a more comprehensive performance overview compared to relying solely on accuracy. This approach has the potential to support more efficient automation in the identification of agricultural product varieties.
Implementasi Platform Mobile dan Cloud untuk Otomatisasi Layanan Pelanggan pada Midnet Home Networking Charderra Eka Bagas Sanjaya; Ryan Putra Laksana
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2033

Abstract

This research focuses on developing an Android application for internet payment and installation, which utilizes Extreme Programming (XP) and Cloud Computing. The main objective is to improve the efficiency of development and quality of the application, as well as making it easier for users to access information, apply for installation, and make payments for Midnet Home Networking internet services. The research method includes feature planning through interviews, ERD and cloud architecture design, and implementation using Laravel and Flutter frameworks, supported by Midtrans Payment Gateway. The results showed that the application of XP successfully improved development efficiency and adaptability to changing user needs. The practical contribution of this research is the creation of an adaptive and effective internet payment and installation application, which also facilitates the management of customer data, package data, and transactions by the admin. Black Box testing shows the application's functionality runs as expected, while a System Usability Scale (SUS) evaluation yields an average score of 80.1, indicating a good level of usability and user acceptability.
Perbandingan Algoritma Klasifikasi Sentimen pada Ulasan Aplikasi Mobile JKN Maulana, Ramadhoni Gibran; Budiman, Muhammad Shidiq; Prasetyo, Tegar Hardiansyah; Karimuddin, Muhammad Fadhlan; Adriansyah, Rizqy; Hendrian, Yayan; Kinanti, Shynde Limar
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.1891

Abstract

Sentiment classification plays an important role in evaluating public response to digital services such as BPJS Kesehatan's Mobile JKN application. This study aims to compare the performance of three machine learning algorithms-Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) for classifying user sentiment based on reviews in the Google Play Store. A total of 10,000 user reviews were collected using Python and processed on Google Colab. The research process includes text pre-processing, sentiment labeling based on ratings, data splitting, and model training. Evaluation was conducted using accuracy, precision, recall, F1 score, and confusion matrix metrics. The results show that the SVM algorithm provides the best accuracy of 90.9%, followed by Naive Bayes (90.3%) and KNN (86%). These findings prove that SVM is the most effective model for sentiment classification in the context of public services and provide important insights for government policy evaluation and digital service improvement.

Page 9 of 9 | Total Record : 85

 5   6   7   8   9 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue 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