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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)
Yesy Afrillia
Universitas Malikussaleh
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Web Application Firewall (WAF) Design to Detect and Anticipate Hacking in Web-Based Applications Annas, Muhammad; Adek, Rizal Tjut; Afrillia, Yesy
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 3 (2024): Journal of Advanced Computer Knowledge and Algorithms - July 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i3.16315

Abstract

Data leakage cases have recently been rampant in Indonesia. One of the biggest is the leak of user data from BPJS Health in 2021, this data leak is certainly very detrimental to users. This research develops a Web Application Firewall (WAF) using ModSecurity and OWASP Core Rule Set to protect web applications from SQL Injection and XSS attacks. The methodology involves analyzing the functionality of the existing system using UML, with DVWA and WordPress as test objects. Results showed 100% SQL Injection and 99.8% XSS attack detection, with logs recording attacks in real-time. The findings emphasize the importance of WAF integration with web application built-in security, making significant contributions in the design and implementation of resilient WAFs, as well as improving resilience against evolving cyber threats.
Pemanfaatan Augmented Reality (AR) untuk Pembelajaran Geometri Berbasis Android di MTsN 1 Kota Lhokseumawe Afrillia, Yesy; Hidayat, Amam Taufiq; Ilhadi, Veri; Nasution, Fakhruddin Ahmad
Jurnal Malikussaleh Mengabdi Vol. 3 No. 2 (2024): Jurnal Malikussaleh Mengabdi, Oktober 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i2.18769

Abstract

MTsN 1 Kota Lhokseumawe merupakan sekolah di bawah Kementerian Agama yang cenderung tertinggal dalam hal perkembangan teknologi dibandingkan sekolah-sekolah umum di bawah Kemendikbud. Program Pengabdian kepada Masyarakat ini bertujuan untuk memperkenalkan penggunaan teknologi Augmented Reality (AR) sebagai media pembelajaran pengenalan objek geometri berbasis Android. Tujuan kegiatan ini adalah untuk menciptakan lingkungan pembelajaran Smart Education, dengan memanfaatkan teknologi AR untuk meningkatkan pemahaman siswa terhadap materi geometri dalam mata pelajaran Matematika. Metode yang digunakan mencakup observasi, koordinasi, dan pengumpulan data melalui teknik analisis data interaktif. Hasil kegiatan ini meliputi publikasi di media massa, submission ke jurnal Sinta 5, serta dokumen kerjasama dan penerbitan HKI. Inovasi utama yang dihasilkan adalah aplikasi AR berbasis Android, yang memberikan visualisasi objek geometri secara real-time kepada siswa, sehingga dapat meningkatkan mutu pembelajaran baik bagi siswa maupun guru di madrasah. Hasil ini diharapkan dapat menyetarakan mutu pengajaran di madrasah dengan sekolah-sekolah umum.
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.
Pelatihan Pembuatan Media Pembelajaran Online dan Perakitan Komputer Pada Sekolah di Desa Paloh Lada Kecamatan Dewantara Bustami, Bustami; Muhammad, Muhammad; Yunizar, Zara; Rosnita, Lidya; Meiyanti, Rini; Afrillia, Yesy; Hafidh Rafif, Teuku Muhammad; Harahap, Ilham Taruna
MEUSEURAYA - Jurnal Pengabdian Masyarakat Vol.1 No.2 (Desember 2022)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat STAIN Teungku Dirundeng Meulaboh

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.218 KB) | DOI: 10.47498/meuseuraya.v1i2.1436

Abstract

Pesatnya perkembangan teknologi informasi saat ini secara tidak langsung juga “memaksa” kita untuk dapat mengikuti perkembangannya, bukan hanya bagi kita yang memang bergerak di bidang IT, namun juga bagi kita yang bergerak disemua bidang, salah satunya di bidang Pendidikan. Teknologi informasi menjadi kebutuhan primer bagi kita yang membutuhkan efisiensi dalam berkegiatan. Guru dan siswa juga merasakan langsung bagaimana teknologi berperan dalam kegiatan Pendidikan, pembelajaran secara daring di masa covid menjadi puncak dari pemanfaatan teknologi didunia Pendidikan. Salah satu point penting dari kegiatan pembelajaran daring adalah pemanfaatan media pembelajaran daring, misalnya google classroom. Kegiatan pengabdian ini bertujuan untuk membantu Guru dan juga siswa/I memanfaatkan teknologi dalam kegiatan pembelajaran. Kegiatan pengabdian ini terdiri dari dua kegiatan besar, yaitu Pelatihan pembuatan media pembelajaran online yang diberikan kepada para guru dan kegiatan pelatihan perakitan komputer dan instalasi komputer kepada para murid. Kegiatan ini dilakukan pada MTsS Jabal Nur dan MTsN 2 Aceh Utara, Kecamatan Dewantara, Kab. Aceh Utara. Output dari kegiatan ini adalah Jurnal yang di submit pada Jurnal Rambieden dan Publikasi media massa. Selain itu, kegiatan ini juga memberikan pemahaman pada para guru dalam pemanfaatan media pembelajaran online dan dapat diterapkan dalam kegiatan pembelajran, sedangkan bagi siswa, kegiatan pelatihan ini memberikan pengetahuan pada mereka tentang perkembangan teknologi informasi.
Comparison of Random Forest Algorithm Classifier and Naïve Bayes Algorithm in Whatsapp Message Type Classification Hadi, Abdul; Qamal, Mukti; Afrillia, Yesy
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.21227

Abstract

This study compares the effectiveness of Random Forest and Naïve Bayes algorithms in classifying WhatsApp messages into three categories: normal, promotional, and fraudulent messages. With over 2.78 billion active users worldwide and 90% of Indonesian internet users utilizing WhatsApp, the platform's end-to-end encryption creates challenges for automatic spam detection, necessitating machine learning approaches. A dataset of 300 messages, equally distributed across the three categories, underwent preprocessing including cleansing, case folding, stopword removal, normalization, and stemming before being converted to numerical form using TF-IDF vectorization. Experimental results demonstrated that Naïve Bayes outperformed Random Forest with higher accuracy (88.67% vs. 86.00%), precision (89.64% vs. 88.95%), recall (88.67% vs. 86.00%), and F1-score (88.61% vs. 85.99%). Cross-validation analysis with 10-fold validation further confirmed Naïve Bayes' superior consistency and stability across all evaluation metrics. Additionally, Naïve Bayes exhibited remarkable computational efficiency, requiring only 0.13 seconds for training compared to Random Forest's 3.65 seconds. Confusion matrix analysis revealed Naïve Bayes' particular effectiveness in distinguishing between normal and fraudulent messages, crucial for preventing users from falling victim to scams. The model successfully identified key fraud indicators such as "claim," "account," and "verification" while demonstrating precision in ambiguous cases. These findings contribute significantly to developing more effective spam detection systems for encrypted messaging platforms where traditional filtering mechanisms cannot be applied, ultimately enhancing user safety and experience through automated identification of potentially harmful content.
Diet Recommendation Application for Diabetes Patients Using the Preference Selection Index Method Siregar, Winda Ramadhani; Yunizar, Zara; Afrillia, Yesy
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.17810

Abstract

Diabetes mellitus is a chronic condition characterized by elevated blood glucose levels. Effective diet management is crucial for controlling this condition and preventing serious complications. This study aims to develop a meal recommendation application for diabetes patients using the Preference Selection Index (PSI) method. The data used include user identity, health conditions, food preferences, and the nutritional content of meal menus. The PSI implementation process involves several key steps: collecting user data, normalizing nutritional values based on the minimum and maximum values in the database, adjusting the criterion weights according to the user's health conditions and food preferences, and calculating the PSI for each meal menu. The study results show that this application can provide meal recommendations that match the nutritional needs and health conditions of users. From a total of 10 user data analyzed, 50% received "Red Bean Soup with Vegetables" as the best menu, 30% received "Grilled Chicken Breast with Vegetables," and 10% each received "Grilled Chicken with Green Beans" and "Quinoa Salad with Avocado." The conclusion of this study is that the PSI method is effective in helping diabetes patients select an optimal diet, which can assist in better managing their condition and improving their quality of life. Suggestions for future research include increasing the variability of nutritional data, integrating with wearable technology, and developing reminder and education features.
Classification of Nutritional Status of Pregnant Women at Risk of Stunting in Prospective Babies Using the Support Vector Machine (SVM) Algorithm Afrillia, Yesy; Fadlisyah, Fadlisyah; Asmi, Nurul Annisa
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22393

Abstract

Stunting describes the existence of chronic nutritional problems, influenced by the condition of mothers/mothers-to-be, fetal period, and infants/toddlers, including diseases suffered during toddlerhood. According to a WHO report quoted from Riskesdas, in 2018 the stunting target in Indonesia was 20%, but in 2013 the stunting rate was 37.2%, but in 2018 there was a decrease to 30.8%. However, the stunting rate in Indonesia is still very high and far from what is targeted by WHO. The method with the best level of accuracy for classification in this study is SVM. This study uses the Support Vector Machine (SVM) method as criteria and attributes which take benchmarks in pregnant women with attributes as a reference including gestational age, maternal weight, blood pressure, and pregnancy problems. The reason for taking benchmarks in pregnant women is because in the first 1000 days of a baby's life determines the baby's nutrition. The first 1000 days of life or 1000 HPK is a critical period in the growth and development of children starting from the beginning of pregnancy (270 days) to 2 years old (730 days). Data was obtained from the Tanah Luas Health Center totaling 684 data on pregnant women. The process of manual calculation is data normalization, kernelization, calculating the alpha and alpha delta Ei values, calculating weights, calculating bias values, and calculating f(x) values. In this study, the dataset totaled 680 data with 544 training data and 136 test data with the criteria of gestational age, pregnant woman's weight, blood pressure, and pregnancy problems. The accuracy obtained was 38.90 %. The variables that have the most influence on this classification are 3, namely the weight of pregnant women, blood pressure, and complaints experienced in pregnant women.
Pemilihan Tempat Kost Menggunakan Metode Multi Attribute Utility Theory Dan Algoritma A* Yesy Afrillia; Wahyu Fuadi; Ayu Indah Lestari
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 2 (2023): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i2.6279

Abstract

Overseas students usually have difficulty finding boarding houses around Malikussaleh University. To get information on boarding houses, they have to search manually, so the time needed to find a boarding house can be very long. The purpose of this research is to produce a Geographic Information System that can make it easier for students to find information about the location of boarding houses, provide recommendations in selecting boarding houses, and find the shortest distance for each boarding house to campus. By using the MAUT method combined with an algorithm A* can provide recommendations in selecting boarding houses and provide the shortest distance from the location of each boarding house to the Campus. The results of this study resulted in recommendations for boarding houses based on the results of ranking using the MAUT method. The boarding houses with the top three rankings are 4G Boarding House, Ceiza Boarding House, and Hj Boarding House. Madriah. With distance of 0.75 km, 2.01 km and 1.38 km. With an algorithmA* to find the closest route it can be concluded that the MAUT method and algorithm A* is a combination that can be used in evaluating boarding houses and solutions in searching for the shortest distance.
Co-Authors Abadi, Sabani Abdul Hadi Adek, Rizal Tjut 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 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 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 Wardana, Ade Bagus Widari, Liz Ayu Yanti, Winda Zara Yunizar Zuhra, Elviza Zulfan