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Applied Information Technology and Computer Science (AICOMS)
Published by Politeknik Negeri Ketapang
ISSN : -     EISSN : 29647703     DOI : https://doi.org/10.58466/aicoms
Core Subject : Science,
Computer Science & IT
Applied Information Technology and Computer Science (AICOMS) is an online version of national journal in Bahasa Indonesia and English, published by Department of Informatics Engineering, Politeknik Negeri Ketapang. AICOMS also has a print version. AICOMS also invites academics and researchers in the field of information technology, particularly from informatics engineering and information systems research to submit their articles. The articles to be published is an original work and has never been published. Incoming articles will be reviewed by a team of reviewers from internal and external sources.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol 4 No 2 (2025)" : 8 Documents clear
Analisis Komparatif Support Vector Machine dan Random Forest untuk Deteksi Email Phishing Purnama Sari, Indah; Krianto Sulaiman , Oris; Apdilah , Dicky; Simanjuntak , Pastima
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1806

Abstract

Information and communication technology has rapidly advanced, bringing significant changes to daily life. With these advancements, access to information has become faster and easier; however, this convenience also introduces challenges, particularly concerning personal data security. One common cybercrime is email phishing, where attackers use malicious links to encrypt user data or devices and demand a ransom to restore access. Phishing emails often resemble official messages from trusted sources, making recipients unaware of the potential threat. To minimize such risks, technology can be utilized to automatically classify phishing emails. This study focuses on developing a machine learning model for automatic phishing email classification. The dataset used consists of 18,650 emails, including 11,322 non-phishing and 7,328 phishing emails. The proposed models employ two algorithms: Support Vector Machine (SVM) and Random Forest. To optimize performance, hyperparameter tuning was conducted using GridSearchCV. The experimental results demonstrate that the SVM algorithm achieved an accuracy of 97.27%, while the Random Forest algorithm achieved 96.51%. These findings indicate that the developed models can effectively support efforts to anticipate and mitigate phishing email threats..
Opini Public Terhadap Liga Korupsi Indonesia Pada Platform Youtube menggunakan Naive Bayes dan SMOTE Ardiansyah, Aldi; Bella Permata Sihombing Permata , Mecha; Rachmat, Nur
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1810

Abstract

This study analyzes public opinion regarding the Corruption League in Indonesia by utilizing the Naïve Bayes method combined with the Synthetic Minority Oversampling Technique (SMOTE). The Corruption League is a compilation of corruption cases involving public officials, politicians, and other parties in Indonesia. In this research, Naïve Bayes is employed for sentiment classification, while SMOTE is used to address class imbalance within the dataset, which was collected from YouTube comments. The methodology consists of several stages, including data collection, labeling, preprocessing, classification, and model evaluation. The results reveal that Naïve Bayes without SMOTE achieves high performance in identifying the negative class but struggles significantly in recognizing the positive class, leading to an imbalanced classification outcome. Conversely, when Naïve Bayes is combined with SMOTE, the model’s performance becomes more balanced, showing a notable improvement in detecting the positive class. Additionally, accuracy increases from 79.7% to 84.3%. This study provides valuable insights into public perceptions and demonstrates the effectiveness of classification methods in the context of corruption issues in Indonesia.
Keyword Extraction Abstrak Jurnal Ilmiah Menggunakan Metode TF-IDF dan KeyBERT Suhartoyo, Rayvin; Julyo Armando Davincy Lin, Valen; Irsyad, Hafiz; Rahman, Abdul
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1814

Abstract

Keyword extraction is a significant technique in natural language processing (NLP) that serves to summarize the essence of a document, such as a scientific journal summary. This study aims to analyze the effectiveness of two keyword extraction methods, namely Term Frequency-Inverse Document Frequency (TF-IDF) and KeyBERT, in finding significant keywords from a collection of scientific journal abstracts. The dataset used consists of several scientific journal abstracts accompanied by manual keywords as a basis for assessment. The TF-IDF method relies on the frequency of words in the document, while KeyBERT utilizes a cosine similarity approach based on the BERT transformer model to determine the most meaningful keywords. The research findings show that the KeyBERT method and the TF-IDF method have a moderate level of similarity with semantic similarity values ​​of 0.578 for the KeyBERT method and 0.469 for the TF-IDF method, respectively. These results show significant potential for the use of machine learning and deep learning-based models with both methods for topic classification systems, especially in the fields of information retrieval and text mining.
Rancang Bangun Game Edukasi Interaktif sebagai Media Pembelajaran Mufrodat Bahasa Arab Sekolah Dasar (Studi Kasus: SD Al-Ihsan Islamic School) Assidiq, Ilham; Hidayat, Hidayat
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1815

Abstract

Effective learning requires innovation to enhance student engagement and understanding of the material. This study aims to develop an educational game for learning mufrodat (Arabic vocabulary) for fifth-grade elementary school students using Unity 3D and the Game Development Life Cycle (GDLC) approach. The GDLC method consists of five stages: concept, pre-production, production, testing, and release, ensuring a systematic game development process. The game is designed to provide an interactive and enjoyable learning experience, enabling students to acquire Arabic vocabulary through gameplay activities. Unity 3D was chosen for its ability to create dynamic and interactive game environments. The testing results indicate that the developed educational game is effective in enhancing students’ understanding and retention of Arabic vocabulary, with positive feedback received from both students and teachers. Future development may include the addition of more comprehensive learning features and adaptations for various levels of difficulty.
Perancangan UI/UX Pada Aplikasi Elaruna Dengan Metode Design Thinking Caroline, Fellycia; Angelica, Steffanie; Fajar Ariansyah, Muhammad; Devina Suryanto, Serenity; Rizky Pribadi, Muhammad
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1825

Abstract

Indonesia has great potential in the tourism sector due to its rich culture and natural beauty, but it still faces challenges in the form of limited access to integrated and reliable information for tourists. This study aims to design a mobile-based tourism information application user interface (UI/UX) that provides quick, accurate, and user-friendly access to information. The method used is Design Thinking, which consists of five stages: Empathize, Define, Ideate, Prototype, and Test. The design process was carried out using a user-centered approach to ensure the design meets the needs and preferences of tourists. Test results show that most users find the application interface easy to use, visually appealing, and clearly navigable, with 91.3% of respondents stating that the navigation is easy and 73.9% feeling that the application runs smoothly. This demonstrates that the Design Thinking approach is effective in producing design solutions that are responsive to user needs. This study is expected to contribute to the development of digital tourism applications in Indonesia and serve as a foundation for further research in the development of features and broader integration of information technology.
Penerapan Penetration Testing pada Sistem EasyCart dalam Menghadapi Ancaman Keamanan Siber Fahrul Reza, Mochamad; Sutanto, Imam
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1937

Abstract

Information security in e-commerce applications is a crucial aspect in maintaining the integrity, confidentiality, and availability of user data. The method used is penetration testing with a black-box and grey-box approach, referring to the Penetration Testing Execution Standard (PTES) and the OWASP Top 10 framework for 2021. The testing was conducted through the seven PTES phases: Pre-engagement Interactions, Intelligence Gathering, Threat Modeling, Vulnerability Analysis, Exploitation, Post-Exploitation, and Reporting. The testing environment was run locally using tools such as Burp Suite, OWASP ZAP, Nikto, SQLMap, and Nmap. The testing results identified 20 vulnerabilities with high, medium, and low risk levels, including Cross-Site Scripting (XSS), SQL Injection, Broken Access Control, and Security Misconfiguration. Mitigation recommendations are based on ISO/IEC 27001:2022 controls, specifically Annex A.5 (information security policy), A.8 (asset management), and A.12 (operational security). This research contributes to the understanding and application of standards-based security testing in simulation applications, while emphasizing the importance of input validation, secure system configuration, and regular updates as mitigation measures against cyber threats.
Sistem Pemantauan Detak Jantung Berbasis ESP32 Menggunakan Sensor AK90 Dengan Antarmuka Web Chalik, Aqsa; Rohman, Hidayatul; Rosdiansyah, Luthfia; Fazrian, Alief; Hidayat, Hidayat
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1958

Abstract

Heart rate monitoring plays a crucial role in maintaining cardiovascular health, enabling early detection of disorders such as arrhythmia. This study aims to develop an Internet of Things (IoT)-based heart rate monitoring system using the AK90 sensor and ESP32 microcontroller integrated with a web interface to support real-time monitoring and digital data storage. The system was developed using the Waterfall approach, consisting of requirement analysis, system design, implementation, testing, and deployment in a Posyandu environment. The AK90 sensor utilizes the principle of photoplethysmography to detect heart rate, with data processed by the ESP32, displayed on an OLED screen, and stored via a local web server. Testing on four subjects showed that the system was able to record heart rates within the normal range (60–100 BPM) after a 10-second sensor stabilization period, although initial readings often showed a value of 0 due to the initialization process. This system offers a practical, affordable, and standalone solution for heart health monitoring, with potential for future enhancements such as notifications and historical data analysis to support medical diagnosis.
Opini Publik terhadap Isu Pengoplosan Pertamax di Youtube Menggunakan Metode Naive Bayes Adikara Alif Nurrahman; Moza , Earlando; Md, Ramanda; Rizvi Roshan , Muhamad; Rizky , Ahmad; Irsyad , Hafiz
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1990

Abstract

This study aims to explore public perceptions regarding the issue of Pertamax fuel adulteration, a topic that has sparked widespread discussion on YouTube, by employing sentiment analysis techniques based on the Naive Bayes algorithm. This issue has attracted significant public attention and become a trending topic on social media, particularly on the YouTube platform. The data analyzed in this research consist of user comments responding to the issue. The Naive Bayes algorithm is used to classify sentiments in the comments into three categories: positive, negative, and neutral. To address the imbalanced distribution of data, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The results show that before applying SMOTE, the model achieved an accuracy of only 48%, with a precision of 0.48, recall of 0.36, and an F1-score of 0.41 for the negative category, as well as a precision of 0.48, recall of 0.56, and an F1-score of 0.52 for the positive category. After implementing SMOTE, the model's accuracy increased significantly to 88%, with a precision of 0.91, recall of 0.93, and an F1-score of 0.92 for the negative category. For the positive category, precision improved to 0.80, although recall decreased to 0.75, yielding an F1-score of 0.77. The average precision, recall, and F1-score (macro average) after applying SMOTE reached 0.85, 0.84, and 0.85, respectively, representing a substantial improvement compared to the results before SMOTE. This study highlights the importance of using SMOTE to enhance sentiment analysis accuracy, particularly in addressing class imbalance issues within the dataset.

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