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All Journal Teknika Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Building of Informatics, Technology and Science Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Ilmu Komputer dan Informatika Brilliance: Research of Artificial Intelligence TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 Electronic Journal of Education, Social Economics and Technology MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Pengabdian Masyarakat Ilmu Komputer Journal Serambi Engineering (JSE) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Lidya Rosnita
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Journal : International Journal of Engineering, Science and Information Technology

 1 
Performance Analysis Algorithm Classification and Regression Trees and Naive Bayes Based Particle Swarm Optimization for Credit Card Transaction Fraud Detection Afridah, Rita; Ula, Munirul; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.523

Abstract

With the advancement of technology, credit cards have become a popular tool for transactions, both physically and online, due to their ease of use and seamless integration with banking systems. However, with the increasing use of credit cards, the cases of fraud have also risen, resulting in financial losses for both cardholders and banks. To address this issue, effective and efficient credit card transaction fraud detection has become a top priority. Using machine learning algorithms is one of the techniques that can be employed to detect fraud in credit card transactions. The purpose of this research is to determine the performance and find the best method of the CART algorithm, Naive Bayes, and their combination with Particle Swarm Optimization (PSO) in detecting fraud in credit card transaction histories. The data used consists of 568,630 big data entries with parameters including id, V1-V28, amount, and class. The research results obtained are as follows: the accuracy of the Naive Bayes algorithm is 93.15%, precision is 94%, recall is 93%, and AUC is 0.99. For the CART algorithm, the accuracy is 99.96%, with precision and recall at 100%, and AUC at 1.00. Additionally, the Naive Bayes algorithm combined with PSO achieved an accuracy of 98.50%, precision and recall of 98%, and AUC of 1.00. Lastly, the CART algorithm combined with PSO reached an accuracy of 99.97%, with precision and recall at 100%, and AUC at 1.00. It can be concluded that the best method resulting from the tests conducted is the Classification and Regression Trees method combined with Particle Swarm Optimization.
Mobile Learning Application Tahsin Al-Quran Using Dynamic Time Warping Method Based on Adroid Nasution, Wahidatunnisa; Ula, Munirul; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.512

Abstract

This research aims to design and build an Android-based Quran tahsin learning mobile application using the Dynamic Time Warping (DTW) method. This application offers tajweed learning features and voice exercises to find out the readings of Al-Quran readers. The DTW method is used to analyze the similarity between the user's voice pattern and the reference voice pattern in the application. The research methods used include reference collection, direct observation, and literature study. The application is designed with a user-friendly interface and equipped with an accurate ability evaluation feature, so that users can find out their weaknesses and strengths in learning Qur'an tahsin. Based on the test results, out of 42 voice data tested, 38 data were successfully recognized correctly and 4 data had errors. The average accuracy rate of this application reached 90.47%. This application is designed to overcome some of the main problems in learning Quran tahsin: lack of understanding of basic tahsin techniques, lack of appropriate learning tools, difficulty in evaluating skills, and lack of motivation to learn. With this application, users can learn Quran tahsin more easily and effectively through interactive and varied methods. Evaluation of users' ability to recite Quranic verses can also be done accurately, so that users can know their strengths and weaknesses in tahsin learning. The implementation of this application is expected to make a significant contribution in improving the quality of Quran tahsin learning among the wider community.
Analysis of Public Sentiment Towards Celebrity Endorsment On Social Media Using Support Vector Machine Syahputra, M Oriza; Bustami, Bustami; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.543

Abstract

Analysis of public sentiment towards celebrity endorsements on social media is very important to understand the public's response to promotional campaigns involving celebrities. In this study, we combine the VADER labeling method with the Support Vector Machine (SVM) method to analyze public sentiment toward celebrity endorsements on social media. Data is taken from various social media sources such as Twitter, Instagram, and Facebook. The data is pre-processed to ensure data accuracy and relevance and then labeled with the VADER method to determine the positive, negative, or neutral sentiment of the text. The labeled data is then extracted for features and used to train the SVM model. The trained SVM model is then validated using test data to measure its accuracy and performance. The results of the analysis provide useful insight into public sentiment towards celebrity endorsements on social media and can provide recommendations for stakeholders regarding this matter. Overall, combining the VADER labeling method with SVM in analyzing public sentiment towards celebrity endorsements on social media shows more accurate results and can provide practical benefits in marketing and promotional strategies. The results shown using the Support Vector Machine method with a ratio of 80:20 can provide average precision results of 77%, recall of 100%, f1-score of 87%, and accuracy of 76.92%. Twitter application user sentiment shows that 77% (338 data) of Twitter user reviews provide positive sentiment and 23% (119 data) provide negative sentiment reviews from a total of 517 data. Suggestions from researchers are that in future research they can add more data to make modeling easier to provide higher accuracy values. Using other classification and performance evaluation methods, such as Naive Bayes, Decision Tree, Fuzzy, or Deep Learning. Use other data processing tools, such as RapidMiner, Jupyter Notebook, RStudio, or others.
Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things Rosnita, Lidya; Ikhwani, Muhammad; Aidilof, Hafizh Al Kautsar; Salamah, Salamah; Hamsi, Widia; Rangkuti, Haris Yunanda
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.566

Abstract

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.
Grouping Sales Levels Smartphone Of Offline Store Using BIRCH Clustering Algorithm Rahmadani Sari, Putri Dwi; Qamal, Mukti; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.558

Abstract

From 2020 to 2024, TM_Store and Jaya Com exhibited different sales patterns based on cluster analysis using the BIRCH algorithm. The background of this research is to provide strategic insights to both stores for improving their sales performance through data analysis. The sales data used includes brand, type, month, year, stock quantity, quantity sold, unit price, and total sales. The BIRCH method was chosen for its effectiveness in handling large datasets and providing accurate clustering results. The clustering results indicate a significant increase in the "Moderate" category, from 12 sales in 2020 to 354 sales in 2023. Meanwhile, the "Very High" category also saw an increase from 5 sales in 2020 to 97 sales in 2023, with sales in the "Very Low" category remaining high at 70 sales in 2023. On the other hand, Jaya Com was dominated by the "Very High" category, with a sharp increase from 25 sales in 2020 to 597 sales in 2023. The "High" category also showed significant growth, from 6 sales in 2020 to 98 sales in 2023. This data indicates that Jaya Com focuses on high-performance products, while TM_Store shows a more balanced distribution across various sales categories. Based on the analysis, Jaya Com had 1988 data points with 1984 cluster points, whereas TM_Store had 2012 data points with 1811 cluster points. Overall, the study concludes that the BIRCH algorithm can identify significant sales patterns in both stores, aiding in the development of more effective and efficient promotional strategies tailored to each sales category's performance.
Plagiarism Detection Application for Computer Science Student Theses Using Cosine Similarity and Rabin-Karp Ansyari, Taufik Habib; Abdullah, Dahlan; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.686

Abstract

Plagiarism detection is critical in maintaining academic integrity, particularly in higher education. This study focuses on developing a plagiarism detection application for Computer Science student theses. The application leverages the Cosine Similarity and Rabin-Karp algorithms to accurately and efficiently detect textual similarities. Developed using JavaScript, the application provides an intuitive interface and reliable performance, making it a practical tool for educational institutions. The application includes features allowing users to upload thesis documents, analyze textual content, and measure plagiarism levels by comparing them to an existing dataset. The Cosine Similarity algorithm measures the overall similarity between documents, while the Rabin-Karp algorithm focuses on identifying exact matches in phrases and sentences. The results demonstrate the efficacy of both algorithms. For titles, the Cosine Similarity algorithm achieved a 100% similarity rate for identical documents while detecting minor plagiarism with a similarity level of 5.86% for other documents. For abstracts, it achieved 100% similarity for the first document, 2.78% for the second document, and 8.37% for the third document. These findings highlight the algorithm's ability to detect exact matches and partial overlaps in textual content. The Rabin-Karp algorithm showed comparable performance, particularly in detecting phrase-level similarities. For titles, it recorded 100% similarity for identical documents, 11.42% for the second document, and 16.92% for the third document. For abstracts, the algorithm also achieved 100% similarity for the first document, 11.42% for the second document, and 16.81% for the third document. The study confirms that both algorithms complement each other in detecting different forms of plagiarism. The Cosine Similarity algorithm excels in identifying global patterns of similarity, while the Rabin-Karp algorithm is more suited for finding exact matches in specific phrases or sentences. This dual approach provides a comprehensive solution for detecting plagiarism in academic theses. The findings from this research are promising and highlight the potential of the application as a reliable tool for ensuring academic integrity. Future improvements could include expanding the dataset, enhancing the user interface, and integrating additional algorithms for cross-language plagiarism detection. This application contributes to academic honesty and is a valuable resource for educators, researchers, and students in combating plagiarism effectively. 
Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach Bancin, Udurta; Bustami, Bustami; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.592

Abstract

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.
Financial Management System for Final-Year University Students Implementing a Rule-Based Method on the Android Platfor Azzahra Iskandar, Farah; Fikry, Muhammad; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.703

Abstract

Final-year students face significant financial challenges due to limited income and low financial literacy. This often leads to uncontrolled spending, lack of awareness about financial habits, and imbalanced income and expenses. This study proposes a rule-based financial management system to help students organize, monitor, and analyze their finances. The analysis shows that students' main expenses are food, transportation, and housing, with financial risks arising from irregular financial recording and overspending. The proposed system includes features like expense categorization, budget limits, monthly reports, notifications for unusual spending, and reminders to record finances. Applying a rule-based approach, the system offers personalized recommendations, such as budget control for specific categories and early warnings for disproportionate expenses. This application aims to improve financial awareness, help students prioritize spending, and reduce financial risks. It also serves as a financial education tool to support students in achieving financial stability as they transition into professional life.
Co-Authors Afif, Muhammad Athallah Afridah, Rita Aidilof, Hafizh Al Kausar Aidilof, Hafizh Al Kautsar Al Kautsar Aidilof, Hafizh Amelia, Ulva Amir Fauzi Ansyari, Taufik Habib Armaya, Devira Yuda Asrianda Asrianda Azzahra Iskandar, Farah Bancin, Udurta Bustami Bustami Dahlan Abdullah Deassy Siska Dela, Monisa Dian Putri, Yohana Diana, Mhd. Arief Efendi, Syahril Efendi, Syahril Elma Fitria Ananda Eva Darnila Eva Darnila Fachry Abda El Rahman Fadlisyah Fadlisyah Fasdarsyah Fasdarsyah Fidyatun Nisa Fuadi, Wahyu Furqan, Hafizul Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Ikramina ikramina ikramina, Ikramina Jange, Beno Khairul Amna, Khairul Kurniawati Kurniawati Lina Mardiana Harahap Mara Wahyu Alamsyah Pane Micola Azwir, Andrea Muhammad Azhari Muhammad Fajri Muhammad Fikry Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muhammad Muhammad Zarlis Muhammad Zarlis, Muhammad Muharry Yusdartono, Habib Mukti Qamal Mulyadi, Rizki Munirul Ula Muzaffar Rigayatsyah Nanda Sitti Nurfebruary Nasution, Wahidatunnisa Naturizal, Rayhan Naza Amarianda Nur Ismiza Nurdin Nurfebruary, Nanda Sitti Nurhaliza Bin Aras Nurqamarina Nurul Aula Nurwijayanti Pasaribu, Hafni Maya Sari Pratiwi, Dinda Pulungan, Fauzi Irham Putri, Sri Raihan Rachman, Aulia Rachmat Triandi Tjahjanto Rahmadani Sari, Putri Dwi Rahmat Triandi Rangkuti, Haris Yunanda Rian Kelana Putra Rini Meiyanti Risawandi, Risawandi Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Safriana Safriana Safwandi Safwandi, Safwandi Said Fadlan Anshari salamah salamah Samosir, Dini Kairiyah Saputri, Rifa Andriani Siti Maimunah Sujacka Retno Syahputra, M Oriza Ulva Ilyatin Wahyu Fuadi Yesy Afrillia Yunanda Rangkuti, Haris Zalfie Ardian Zara Yunizar Zulfadli Zulfadli