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Ipung Dwiansyah
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JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Published by Universitas Muhammadiyah Jember
ISSN : 25025724     EISSN : 25415735     DOI : https://doi.org/10.32528/justindo
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
JUSTINDO is a scientific journal managed by the informatics engineering study program at the University of Muhammadiyah Jember as a publication media for research articles in the field of systems and information technology which covers the following topics: Software engineering, Games, Information Retrieval, Computer networks, Telecommunication, Internet, Internet of Things, Cloud Computing, Wireless technology, Network security, Multimedia technology, Mobile Computing, Parallel / Distributed Computing, Development, management and utilization of Information Systems, Organizational Governance, Enterprise Resource Planning, Enterprise Architecture Planning, e-Businness, e-Commerce, e-Learning, Data mining, Text mining, Machine Learning, Data warehouse, Online Analytical Processing, Artificial Intelligence, Decision Support System, and Mathematics. JUSTINDO is issued twice a year in February and August. The editor invites research lecturers, reviewers, practitioners, industry, and observers to contribute to this journal. JUSTINDO provides a platform for scientists and academics throughout Indonesia to promote, share and discuss new issues and the development of information systems and information technology. JUSTINDO aims to achieve the theory and application of this sophisticated field. In 2017, JUSTINDO already has an ISSN both printed and online, for ISSN (Print) is 2502 - 5724 and for ISSN (Online) is 2541 - 5735
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 7 Documents
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Search results for , issue "Vol. 11 No. 1 (2026): JUSTINDO" : 7 Documents clear
Topic Analysis in Political Speech Video Transcripts Using the Latent Dirichlet Allocation (LDA) Method Septiara, Dhea Intan; Deni Arifianto; Wiwik Suharso
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.4044

Abstract

Political speeches are an important medium for conveying a country’s leader’s vision, mission, and policy directions to the public. This study aims to identify and analyze the main topics in the video transcripts of President Joko Widodo’s political speeches during the 2014–2024 period using the Latent Dirichlet Allocation (LDA) method. The data consist of 185 press conference speech videos obtained from the Indonesian Cabinet Secretariat’s YouTube channel and converted into text using speech-to-text technology. The dataset is divided into 81 videos from the 2014–2023 period as training data and 104 videos from 2024 as testing data. The analysis process includes text preprocessing, rule-based automatic labeling, LDA model training, and evaluation using coherence score and perplexity. The results show that in the training data, the topics of Infrastructure and Economy are the dominant topics, reflecting the government’s focus on physical development and economic growth. In contrast, in the 2024 testing data, Healthcare emerges as the most dominant topic, followed by the topics of Infrastructure, Economy, Education, and Technology. The Infrastructure topic consistently achieves the highest coherence score of 0.85, indicating strong semantic consistency among its constituent terms. This study contributes to understanding the temporal dynamics of political communication and demonstrates the effectiveness of LDA in analyzing political speech data derived from video transcripts.
Clinical Image Classification of Cattle Foot and Mouth Disease Based on Convolutional Neural Network Reza, Muammar Reza Pahlawan; Sahriani; Shabarul Mukjizat
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.4737

Abstract

Foot-and-Mouth Disease (FMD) is a highly contagious viral outbreak affecting cattle and causes significant economic losses to the national livestock industry. The limited availability of veterinary experts in the field often leads to delayed diagnosis, which contributes to the rapid spread of the virus. This study aims to develop an intelligent computational model capable of automatically diagnosing clinical symptoms of FMD from digital images using a deep learning approach based on Convolutional Neural Networks (CNNs). The research methodology begins with the collection of a dataset consisting of images of cattle mouths and hooves, categorized into two classes: FMD-infected and healthy. The preprocessing stage involves image resizing and pixel normalization, followed by data augmentation techniques such as rotation and flipping to reduce overfitting. The model architecture is designed using a sequence of convolutional layers, pooling layers, and fully connected layers to automatically extract visual features related to lesion characteristics. Based on the experimental results, the proposed model achieves high classification performance, with a validation accuracy of 95%. The dataset used in this study consists of 1,000 image samples, with a data split ratio of 70% for training, 15% for validation, and 15% for testing. In addition to accuracy, the classification performance demonstrates a recall of 96%, F1-score of 94%, and precision of 91%.The findings of this study confirm that a computer vision–based approach can serve as a reliable tool for early diagnostic assistance, offering fast and accurate detection to support better decision-making in livestock health management.
Ultrasound Image Classification of Breast Cancer Using MobileNet Arwoko, Heru; Sofia Ariyani
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.4860

Abstract

Breast cancer is one of the most prevalent diseases affecting women and has a high mortality rate if not detected at an early stage. Therefore, the development of an automated and accurate system for breast cancer diagnosis is of critical importance. One of the most commonly used methods for early breast cancer detection is medical ultrasonography (US) imaging, as it is safe and easily accessible. However, ultrasound images suffer from several limitations, including low image quality, high noise levels, and heterogeneous characteristics, which make the classification of cancer types challenging. In this study, a transfer learning approach is employed for breast ultrasound image classification by utilizing the MobileNet architecture, which is lightweight and computationally efficient, to enhance model performance. The classification task is performed on three classes: benign tumors, malignant tumors, and normal tissue. The dataset used is the BUSI (Breast Ultrasound Images) dataset obtained from Baheya Hospital, Cairo, Egypt, consisting of 780 breast ultrasound images. Experiments are conducted using several pre-trained architectures, including MobileNet, MobileNetV2, Xception, and InceptionV3. The evaluation results demonstrate that the MobileNet architecture achieves the best performance with an F1-score of 89%. These results indicate that the proposed approach is effective for classifying ultrasound images, as features are automatically and globally learned by the neural network without requiring manual geometric feature analysis.
Certificate Printing System Integrated with RESTful API JSON on Quistiq Application Dorthea Elvita Harefa; Fikrah Kristo Fotriman Waruwu; Dian Maharani Buulolo; Christine Jenny Puspita Zega; Ester Ratna Cahyani Zega; Devi Chrisman Lase
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.4991

Abstract

Quistiq is a web-based online quiz application using native PHP. The certificate creation process for passing participants is still manual, requiring 5-7 minutes per certificate with a 12% error rate. This research develops a certificate printing system integrated with Quistiq through RESTful API JSON to automate graduation validation and certificate printing. The research method uses Software Development Life Cycle (SDLC) Waterfall model including requirements analysis, design, client-server architecture design, implementation using native PHP, MySQLi, Bootstrap, CSS, and JavaScript with JSON format data exchange, as well as testing and maintenance phases. Functionality testing uses black-box testing method on 6 test cases and API performance testing uses Postman with 100 requests. The test results show that the system has a functionality success rate of 99.2% with all features running according to specifications. API performance shows an average response time of 1.9 seconds, PDF certificate generation time of less than 2 seconds, and request success rate of 99.2%. The system successfully increases the efficiency of the certificate creation process by 85% compared to the manual process, reduces input data errors from 12% to 0.8%, and ensures certificate format consistency. The implementation of RESTful API JSON proves effective in automating the certificate printing process and significantly improving system accuracy and efficiency.
Sentiment Classification of Aci Application Reviews Using N-Gram Features And Support Vector Machine (SVM) Algorithm Wijaya Kusuma, Ageng; Moh. Dasuki; Wiwik Suharso
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.5020

Abstract

The transformation of information technology has created significant opportunities for the application of Natural Language Processing (NLP) in text-based sentiment analysis, particularly in exploring user opinions toward application-based services. This study aims to analyze the sentiment of user reviews of the ACI (Aku Cinta Indonesia) online motorcycle taxi application available on the Google Play Store by applying the N-gram method and the Support Vector Machine (SVM) algorithm. A total of 1,419 reviews were collected, and after data preprocessing and lexicon-based sentiment labeling, 239 final samples were obtained and categorized into positive and negative sentiments. Feature extraction was performed using combinations of unigram, unigram + bigram, and unigram + trigram, with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. Furthermore, the classification process was carried out using a linear kernel Support Vector Machine with an 80:20 split between training and testing data. The experimental results show that the unigram+ bigram model achieved the highest accuracy of 96%, followed by unigram + trigram at 94% and unigram at 90%, with all precision, recall, and F1-score values across the three models exceeding 88%. These findings indicate that the unigram + bigram combination represents word context more effectively than unigram while remaining more efficient than unigram + trigram, thereby improving the sentiment classification accuracy of the SVM model without significantly increasing computational complexity.
Electric Vehicle Sentiment Analysis Using a Comparison of Naïve Bayes and Support Vector Machine Faizah Dian Herawati; Frederik Samuel Papilaya
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.5026

Abstract

The development of electric vehicles in Indonesia has sparked various opinions from the public, which are often shared on social media, especially X. These opinions need to be analyzed to understand how the public views the policies and implementation of environmentally friendly vehicles. This study aims to examine public sentiment toward electric vehicles by comparing two types of text classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM), using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data used is Indonesian-language tweets collected through a crawling process, which then undergoes several pre-processing stages such as cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. After that, the data was labeled for sentiment into three categories: positive, negative, and neutral, before being processed using a classification algorithm. To evaluate the model's performance, a confusion matrix was used, which shows the algorithm's performance based on accuracy, precision, recall, and F1-score values. The research results show that the Naïve Bayes algorithm has better results with an accuracy of 92%, while SVM achieves an accuracy of 76%. Therefore, the Naïve Bayes algorithm is considered more suitable for analyzing the sentiment of tweets related to electric vehicles in Indonesia.
Website-Based STEM Learning Using Encyclopedias for Students with Special Needs Saifudin, Ilham; Suharso, Wiwik; Mubaroq, Syahrul
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.5029

Abstract

In the world of education, we continue to create and innovate to improve the learning process and achieve desired learning outcomes. One of them is using digital-based learning (Digital Learning) which can be accessed with information technology. This research aims to determine the response to implementing a video blog-based learning system with a STEM (Science, Technology, Engineering, and Mathematics) approach. Apart from that, students will be given assignments using the Encyclopedia Website tool which contains formulas and logical flows related to Algorithms and Complexity courses. This will produce meaningful, quality learning that can be used by the wider community, especially to make it easier for students with special needs to learn to compile a simple algorithm. The method applied in implementing a website for students with special needs is a usability test with Nielsen' Attributes of Usability (NAU). Researchers carry out website evaluations with the aim of measuring and knowing the level of success of the website, how easy it is for users to understand. The research results showed that (1) the usability test with Nielsen' Attributes of Usability (NAU) can be used to measure the quality of website usability; (2) testing success rate of 100% because there were no failures in testing the search feature. Testing found a test pattern, namely that the more search keywords there are, the greater the number of document links produced, and the time required, however, it can significantly increase the highest similarity value of 0.21; (3) the respondents' conclusions show that the learnability, memorability, efficiency, errors attributes have an average helpful conclusion of 11, while the Satisfaction attribute has a very helpful average conclusion of 7.

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