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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Informatika Kaputama (JIK) Jurnal Informasi dan Teknologi Vocatech : Vocational Education and Technology Journal JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Pengabdian kepada Masyarakat Nusantara JINAV: Journal of Information and Visualization International Journal of Engineering, Science and Information Technology MALLOMO: Journal of Community Service Journal of Renewable Energy, Electrical, and Computer Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Pengabdian Masyarakat Bangsa MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Informatika: Jurnal Pengembangan IT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Yesy Afrillia
Universitas Malikussaleh
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Journal : international journal of engineering science and information technology

 1 
Cosmetic Shop Sentiment Analysis on TikTok Shop Using the Support Vector Machine Method Rahmawati, Rahmawati; Fuadi, Wahyu; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 4, No 2 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i2.498

Abstract

User reviews are crucial in today's digital world for determining a product's quality. Nevertheless, these remarks are frequently disorganized and erratic, which confuses people and makes it challenging for them to make wise purchases. The erratic character of these reviews breeds uncertainty and makes determining a product's actual value more difficult. To help consumers more effectively evaluate and select products on platforms such as TikTok Shop, this study uses sentiment analysis tools. It hopes to accomplish this by improving the overall shopping experience and empowering customers to make more confident and informed selections. This research aims to assist consumers in evaluating and selecting products on TikTok Shop, an online shopping platform, by employing sentiment analysis techniques that help consumers make more informed decisions. In this study, a total of 500 comments from TikTok Shop users were collected as data. 350 comments have been set aside for training and 150 comments were set aside for testing. Data was gathered employing scraping, an automated process that makes use of the Python library's Selenium module to retrieve data from the internet. We employed the Support Vector Machine approach, an efficient machine learning tool for text classification, to assess the comments. 121 comments were categorized as having positive sentiment and 29 as having negative sentiment based on the test results. The system successfully recommended the "Ourluxbeauty" cosmetics store as a shop with many positive sentiments, indicating a recommendation level of 0.7 on the positive sentiment scale. The system's accuracy was measured using a Confusion Matrix, resulting in an accuracy rate of 78% and an inaccuracy rate of 22%. This demonstrates that the system can accurately classify comment sentiments and has significant potential for application in e-commerce practices to enhance the online shopping experience.
Expert System for Diagnosing Dengue Fever with Comparison of Naïve Bayes and Dempster Shafer Methods Susanti, Neli; Nurdin, Nurdin; Afrillia, Yesy
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.v5i1.691

Abstract

An expert system for diagnosing dengue fever (DF) using a comparison of the Naive Bayes and Dempster Shafer methods aims to provide a solution to assist medical personnel in diagnosing this disease. Dengue fever is a disease caused by the dengue virus infection through the bite of Aedes mosquitoes. It has symptoms similar to other diseases and requires rapid and accurate diagnosis. The Naive Bayes and Dempster Shafer methods were chosen because both have different approaches to handling uncertainty and imprecise information. The Naive Bayes method is a probability-based classification that assumes independence between features. Meanwhile, Dempster Shafer is an approach to handling uncertainty. Therefore, comparing Naive Bayes and Dempster Shafer allows for classification with structured and fairly straightforward data, offering accuracy and flexibility in dealing with uncertainty. Applying this expert system with these methods can help in the faster and more accurate diagnosis of DF and provide better recommendations in situations where the available data is incomplete or ambiguous. From the test data calculations, the two methods show that the Naive Bayes method has a higher percentage value of 93%, while Dempster Shafer has 86%.
Sentiment Analysis of Customer Satisfaction Towards Shopee and Lazada E-commerce Platform Using the Random Forest Algorithm Classifier Dewi, Tursina; Asrianda, Asrianda; Afrillia, Yesy
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.v5i1.692

Abstract

In the digital era, e-commerce platforms like Shopee and Lazada have become the primary channels for online transactions in Indonesia, significantly shaping consumer behaviour and business strategies. This study analyses and compares consumer sentiment towards product reviews on these platforms, focusing on three prominent stores: Skintific, Originote, and Azarine. The research utilized a dataset of 4,500 comments collected from both platforms, with 3,600 comments allocated for training and 900 comments for testing. The sentiment analysis used a lexicon-based approach and machine learning techniques to ensure accuracy and reliability. The results reveal that the Skintific store achieved 88% positive sentiment on Shopee and 84.1% on Lazada. The Originote store recorded 81.4% positive sentiment on Shopee and 91.5% on Lazada, while the Azarine store achieved 87.8% on Shopee and 77.9% on Lazada. These findings highlight variations in consumer sentiment between platforms, which platform-specific features and user demographics may influence. This study provides valuable insights for businesses to tailor their marketing strategies and improve customer engagement on different e-commerce platforms.
Application of K-Medoids Clustering Method on Disease Clustering Based on Patient Medical Records Fatika, Dian; Bustami, Bustami; Afrillia, Yesy
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.v5i1.679

Abstract

Dr. Fauziah Bireuen Regional General Hospital (RSUD) faces daily challenges in managing the ever-increasing medical record data. Currently, the medical record data only consists of reports containing information on the number of patients and their diseases, which are then archived without further processing to generate valuable information. This research aims to cluster diseases based on patient medical records using the K-Medoids Clustering method, thereby providing information on the patterns of disease spread in various regions of the Bireuen Regency. The data used are patient medical records from RSUD Dr. Fauziah Bireuen from 2021–2023, focusing on five common diseases: stroke, hypertension, schizophrenia, dyspepsia, and pneumonia. We conducted Clustering in 17 sub-districts in Bireuen Regency using the K-Medoids method and determined the optimal number of clusters using the Elbow method. The research results show that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The results showed that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The cluster distribution for stroke disease consists of 7 sub-districts in the high cluster, 7 in the medium, and 3 in the low. Hypertension disease consists of 6 sub-districts in the high cluster, 3 in the medium, and 8 in the low. Schizophrenia disease comprises seven sub-districts in the high cluster, 8 in the medium, and 2 in the low. Dyspepsia disease includes six sub-districts in the high cluster, 2 in the medium, and 9 in the low. Meanwhile, pneumonia disease consists of 8 sub-districts in the high cluster, 5 in the medium, and 4 in the low.
Public Facility Recommendation System in Subulussalam City Using Fuzzy C-Means Algorithm Berutu, Indah Fachlira; Dinata, Rozzi Kesuma; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.873

Abstract

Subulussalam City, as one of the autonomous regions in Aceh Province, Indonesia, has excellent potential to develop public facilities to improve the quality of life for its residents. Recommendation systems have become an effective solution in helping users find relevant information based on the preferences and needs of the community. This research focuses on developing a recommendation system using the Fuzzy C-Means algorithm. This algorithm is one of the clustering methods capable of handling uncertainty and ambiguity in data. This study aims to develop and analyze a public facility recommendation system in Subulussalam City using the Fuzzy C-Means algorithm. The dataset in this study was obtained from the Youth, Sports, and Tourism Office of Subulussalam City and the results of a research questionnaire. Regarding the names of each public facility, it provides information about the location and various forms of visitor assessments, including evaluations related to accessibility, facilities, costs, environment, and visitor experiences, using a rating scale of 1-5. Based on the testing results, the Fuzzy C-Means clustering algorithm can group facilities based on characteristics and user preferences, resulting in more personalized and relevant recommendations. The data to be clustered is divided into two categories: recommended and not recommended. The study's results using the Fuzzy C-Means algorithm show the final grouping based on the degree of membership from the last iteration of each public facility, with cluster 1 containing 31 locations and cluster 2 containing 31 locations.
Classification Of Outpatient Visit Status Walking at Dr. Zubir Mahmud Hospital Using Algoritma C4.5 Fikria, Putri; Dinata, Rozzi Kesuma; afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.865

Abstract

This study aims to classify the status of outpatient visits at RSUD Dr. Zubir Mahmud into three main categories, namely "Very Urgent", "Urgent", and "Not Urgent”, using the C4.5 algorithm. The web-based system uses the PHP programming language and MySQL database to ensure ease of implementation and efficient data management. The classification process is done by setting threshold parameters, calculating entropy, and the gain ratio to form an accurate and reliable decision tree. The results show that the C4.5 algorithm can classify patient visit data with a reasonably high accuracy rate, which is 93.75% for 2022 data and reaches 100% for 2023 data. In 2022 the “Very Urgent" category had 9 True Positives (TP); in 2023, the number remained consistent. However, in both years, there were also False Negatives in the same category, with 4 cases in 2022 and 5 cases in 2023. The "Urgent" and "Not Urgent" categories show suboptimal classification performance due to uneven data distribution, which causes the precision and recall values in these categories low. Model evaluation was conducted using evaluation metrics such as precision, recall, and F1 score. The evaluation results show that the model works very well in identifying high-priority categories, but further development is needed to improve classification in other categories. This system is expected to be a reliable tool in decision-making in health services, especially in determining the priority of patient services appropriately and efficiently. With further development, this system has the potential to be widely applied in various other hospitals.
Expert System For Detecting Soil Fertility Levels for Oil Palm Cultivation Using the Fuzzy Tsukamoto Yanti, Winda; Yunizar, Zara; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.884

Abstract

Soil fertility is one of the critical factors that affect the productivity of oil palm plants. Inappropriate soil fertility levels can cause suboptimal plant growth and even crop failure. Low public knowledge about soil fertility is also a significant factor. This research aims to build an expert system that can detect the soil fertility level for oil palm plants using the fuzzy Tsukamoto method. This system uses three main parameters as a reference: soil acidity (pH), soil moisture, and soil texture. The fuzzy Tsukamoto method was chosen because it can handle uncertain data and provide more flexible results. The system was developed web-based using the PHP programming language and MySQL database, and tested on 49 soil data points from the Agricultural Extension Center of Matangkuli District, North Aceh Regency. The system successfully detected soil fertility levels accurately and consistently. Tests were conducted on 49 soil sample data from various villages in Matangkuli District, North Aceh Regency, where soil fertility in the Low category was found in 43 villages with a percentage of 84%, soil fertility in the Medium category was found in 6 villages with a rate of 16% and soil fertility in the High category was not found in any town of Matangkuli District with a percentage of 0% with valid fertility classification results and by expert judgment. With this system, farmers and agricultural extension workers can be helped to make the right decisions regarding the feasibility of land for planting oil palm plants.
Measuring the Level of Security Awareness of Smartphone Users Among Universitas Malikussaleh Students Using the Fuzzy Analytical Hierarchy Process Method Andreansyah, Sabda; Ula, Munirul; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.861

Abstract

Technology is developing rapidly; its benefits are manifold. The development of technology, especially smartphones, Has become a part of everyday life that cannot be distinguished anymore. The increasing number of smartphone users has also impacted the rising information security and privacy cases caused by a lack of awareness of spam, malware, and phishing. Many users upload personal information such as photos, phone numbers, and addresses without antivirus protection. This study aims to identify security and privacy challenges in smartphone use by measuring problems in the dimensions of attitude (knowledge), knowledge (attitude), and behavior (behavior). There are five focus areas: Backdoor, hardware, and AndroidOS, which is still low compared to applications and permissions. The method used the Analytical Hierarchy Process (AHP) with the Fuzzy concept to measure the level of information security awareness of Malikussaleh University students who use Android phones. The results showed that the overall level of understanding was good (80%). Although the attitude and behavior dimensions showed good awareness, the knowledge dimension was moderate. This may be why information security breaches still often occur among Android phone users. Faculty of Economics, Less Aware: 23 people Unaware: 1 person. Faculty of Social and Political Sciences, Less Aware: 24 people. Faculty of Teacher Training and Education, Less Aware: 21 people. Faculty of Law, Less Aware: 24 people. Faculty of Medicine, Less Aware: 27 people and Aware: 3 people. Faculty of Agriculture, Less Aware: 30 people. Faculty of Informatics Engineering, Less Aware: 70 people and Aware: 5 people. Total Awareness, Less Aware: 199 people, nine people, and Unaware: 1 person.
Co-Authors Abadi, Sabani Abdul Hadi Abil Khairi Adek, Rizal Tjut Aldo januansyah. H Ananda Faridhatul Ulva Andreansyah, Sabda Annas, Muhammad Aqmal, Jamalul Arif, Abdul Halim Arif, M. Arif Saputra Arifa, Cut Hilma Asmi, Nurul Annisa Asrianda Asrianda Asrifan, Andi Aswandi, Sakti Ayu Indah Lestari Berutu, Indah Fachlira Bustami Bustami Dahlan Abdullah Dasril Dasril David Sarana Deassy Siska Dewi, Tursina Dika, Farhan Dinanti, Intan Putri EDI YUSUF, EDI eva darnila, eva darnila Fadlisyah Fadlisyah Fadlisyah Fajar, Kasihan Muhammad Fakhruddin Ahmad Nasution Fatika, Dian Fidyati, Fidyati Fikria, Putri Fuadi, Wahyu Hafidh Rafif, Teuku Muhammad Harahap, Ilham Taruna Herman Fithra Hidayat, Amam Taufiq Ilsa Hidayat Jamalul Aqmal Julianansa, Ririn Kautsar, Al Khairuni Khairuni Lidya Rosnita Mahesa Reglisalo Maida, Eka Muhammad Fikry Muhammad Muhammad Muhammad Yusuf Mukhlis Mukhlis Mukti Qamal Munirul Ula Muzaffar Rigayatsyah Muzaffar Rigayatsyah NELI SUSANTI, NELI Nurdin Nurdin Nurqamarina Rahma, Mutiara Rahmawati, Rahmawati Rini Meiyanti Risawandi, Risawandi Riza, Saiful Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Rozzi Kesuma Dinata Safriand, Safriand Sayed Fachrurrazi Selian, Riko Ardiansyah Siregar, Winda Ramadhani Sofyan Sofyan Suci Ramadani Sujacka Retno Teuku M. Arief Afwan Uliana, Lisa Ulva Ilyatin Veri Ilhadi Wahyu Fuadi Wahyu Fuadi Wardana, Ade Bagus Widari, Liz Ayu Yanti, Winda Zara Yunizar Zuhra, Elviza Zulfan