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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 431 Documents
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Analysis of Information Security Management System Implementation at BSN Arianty, Kiki Puspo
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8211

Abstract

SNI ISO/IEC 27001:2013, adopted by the National Standardization Agency of Indonesia (BSN), is a national standard derived from the international ISO/IEC 27001 published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). This study evaluates the effectiveness of BSN's Information Security Management System (ISMS) implementation, focusing on compliance with international standards, risk management strategies, and organizational commitment to safeguarding information. Employing qualitative descriptive methods, data were collected through interviews, document analysis, and observations. The findings highlight the critical roles of leadership commitment, comprehensive risk assessments, and regular system evaluations in achieving ISMS objectives. Despite significant achievements, including obtaining Integrated Management System certification in 2023, challenges persist in optimizing resources and adapting to emerging security threats. Recommendations include enhancing staff capabilities, investing in advanced technologies, and transitioning to the updated SNI ISO/IEC 27001:2022 standard. This study reinforces the importance of ISMS in protecting sensitive information, fostering trust, and aligning with global best practices.
Optimasi Bobot Kelas LSTM untuk Deteksi URL Phishing pada Dataset Tidak Berimbang Handoyo, Tri Ferli; Putra, Muhammad Pajar Kharisma
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8128

Abstract

Phishing URL detection is one of the main challenges in cybersecurity, considering the ever-increasing threats affecting internet users globally. This research aims to develop a Long Short-Term Memory (LSTM) based deep learning model to detect phishing URLs with high accuracy. The dataset used consists of 651,191 URLs, which are divided into four categories: benign, defacement, phishing, and malware. The dataset is processed through preprocessing stages, including URL cleaning and feature extraction. The LSTM model is applied with optimized hyperparameter configurations to learn patterns from the dataset. The results showed that the model was able to achieve significant accuracy during the training and validation process. Evaluation on external datasets shows that the model performs well in the benign and defacement categories, with relatively high precision and recall. However, challenges were identified in the malware and phishing categories, where recall was low due to dataset imbalance and lack of feature representation. Further analysis showed a model bias towards the majority class, as well as difficulty in detecting URLs in the minority class. This research shows the potential of using LSTM-based deep learning in phishing URL detection, but also emphasizes the importance of further optimization, such as adjusting class weights, oversampling, or using additional features. It is hoped that the resulting model can be an initial solution in improving cyber security, especially in detecting phishing threats in real-time.
Performance and Security Analysis of Lightweight Hash Functions in IoT Mufidah, Nada Fajri; Nuha, Hilal Hudan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.7633

Abstract

The rapid proliferation of Internet of Things (IoT) devices across various sectors, including healthcare, automotive, smart homes, and agriculture, has created a need for robust security measures that do not compromise the limited resources of these devices. This study analyses the performance and security of several lightweight cryptographic hash functions, specifically SHAKE128, BLAKE2s, SHA-256, SHA3-256, SipHash and xxHash, within the context of the Internet of Things (IoT). A series of experiments conducted on the Arduino Uno platform allows for an evaluation of these functions in terms of throughput, memory usage, and avalanche effect. The findings indicate that while SHAKE128 and SHAKE256 demonstrate superior throughput, they require greater memory, particularly with larger input sizes. BLAKE2s exhibits a robust equilibrium between throughput, memory efficiency, and consistent avalanche effects, rendering it a dependable option for 256-bit outputs. Conversely, xxHash and SipHash provide high throughput and minimal memory usage, yet exhibit reduced avalanche effects. The findings of this research provide critical insights for developers and researchers on the selection of appropriate cryptographic solutions, which must be tailored to the constraints and security requirements of IoT devices.
Analysis of Electronic Wallet User Sentiment on Twitter (x) Social Media Using the Naïve Bayes Classifier Algorithm basir, azhar
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8180

Abstract

 Electronic wallets are one of the most popular payment methods in Indonesia with the number of users increasing significantly in recent years, including DANA, GoPay, and LinkAja. Along with the increasing number of users, the need to analyze user opinions and comments on social media, especially on Twitter (X) is also increasing. This study uses an experimental method with data collection from Twitter (X) using data crawling techniques. The dataset used is 1,500 data with 1 attribute. This study aims to analyze user sentiment towards electronic wallets using the Naive Bayes Classifier algorithm with the Python programming language. The results of the study showed that DANA had a negative sentiment of 16.6%, a neutral sentiment of 9.0%, and a positive sentiment of 74.4%. Followed by GoPay with a negative sentiment of 9.4%, a neutral sentiment of 11.4%, and a positive sentiment of 79.2%. Meanwhile, LinkAja had the lowest negative sentiment of 8%, with a neutral sentiment of 12.2% and a positive sentiment of 79.8%. The implementation of the Naive Bayes Classifier algorithm achieved an accuracy rate of 72% for DANA, 88% for GoPay, and 88%, for LinkAja.
Pengembangan Motor IoT untuk Pemantauan Kecepatan dan Pemeliharaan Melalui Telegram Pranata, Rangga; Styawati, Styawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6126

Abstract

This research focuses on developing Motor IoT as an advanced solution for real-time motor speed monitoring and maintenance notifications via Telegram. The research method includes installing a magnetic sensor on the motor to measure wheel rotation and produce accurate speed data. The data is sent to the IoT platform and integrated with Telegram, providing users with speed monitoring information as well as providing timely maintenance notifications. In addition, this system monitors the overall condition of the motorbike. The results of research and testing show that Motor IoT can be used and applied to motorized vehicles with the ability to provide accurate information and efficient maintenance notifications. The use of IoT and Telegram technology provides an effective solution for monitoring motor performance, optimizing maintenance and reducing potential damage. IoT motorbikes not only increase the efficiency of using motorized vehicles, but also contribute to minimizing the risk of damage and increasing the overall service life of motorbikes. In addition, the magnetic sensor was successfully integrated with the motor monitoring and maintenance system via Telegram, providing appropriate responses to predetermined conditions. This system is also able to send notifications to Telegram when the motorbike is started, providing information on the distance traveled by the user, with reminders to change the oil every 1000 kilometers. This research produces innovations that can have a positive impact on the automotive industry and user welfare.
Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024 septiana, ika; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6640

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.
Analisis Sentimen Pembangunan IKN pada Media Sosial X Menggunakan Algoritma SVM, Logistic Regression dan Naïve Bayes Hadi, Nur; Sugiarto, Dedy
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.7106

Abstract

social media X or formerly more familiar with Twitter is one of the familiar social media and has many users in the world whis is a platform for accesing some information and commenting both suggestions and criticsm related to the development of the Capital City of the Archipelago (IKN) which is the center of smart government in East Kalimantan. There are indormation, suggestions and criticisms addressed to the @ikn_id account directly addressed to the Indonesia government as well as public opinions related to IKN by using the IKN hashtag. Public sentiment on the issue is in the form of text on IKN Development. This research aims to analyze public opinion on the government's decision to build the Capital City of Nusatara (IKN) conveyed through X social media using appropriate data analysis methods by comparing the performance of support vector machine, logistic regression, and naïve bayes algorithms and identifying the most effective algorithm in sentiment analysis. The method used in this research to analyze sentiment are support vector machine, logistic regression and naïve bayes. The use of these three algorithms is also to compare the accuracy that is better than other algorithms. The results obtained using the Support Vector Machine algorithm is 80% while using the Logistic Regression and naïve bayes algorithms are 79%.
Analisis Sentimen Inses di Social Media menggunakan Algoritma Naïve Bayes Salsabilla, Tasya; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6611

Abstract

Sexual violence, especially against women and children, is a serious problem in Indonesia. Cases are increasing every year, including incest, which involves sexual relations between close family members. Girls, who are often considered weak and vulnerable, are the main victims. The latest data from the National Commission on Violence Against Women records a decrease in incest cases from 1,210 in 2017 to 215 in 2020. However, attention is still needed, especially because biological fathers are the largest perpetrators. This research uses the Naïve Bayes algorithm for sentiment analysis. This algorithm is an effective classification method based on Bayes' theorem with simple assumptions but is quite effective. Assuming that each feature in the data is independent, Naïve Bayes can work well in text analysis. The research results showed an accuracy rate of 94%. Continued attention to sexual violence, especially incest, is needed to protect vulnerable girls. Protection efforts must continue to be improved, including the application of sentiment analysis methods such as Naïve Bayes for monitoring and early detection. Public awareness and cross-sector cooperation are also key in overcoming this phenomenon.
Perancangan Visualisasi Elektronik Pencegahan Jantung Koroner Berbasis Teknologi Augmented Reality Adrian, Qadhli Jafar; Styawati, Styawati; Prastowo, Agung Tri; Kuswara, Muhammad Shidiq; Anggita, Rizki Devi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8137

Abstract

Augmented reality (AR) merupakan teknologi yang memungkinkan benda maya yang dimasukan secara real time di dalam dunia nyata. Teknologi augmented reality sangat menarik dan mudah untuk diterapkan dan digunakan bagi penggunanya. Pemanfaatan teknologi augmented reality pada saat ini telah meluas ke berbagai aspek termasuk kesehatan. Di dunia kesehatan, penyakit jantung koroner masih menjadi ancaman di Indonesia bahkan di dunia. Penyakit jantung koroner adalah gangguan fungsi jantung, akibat otot jantung kekurangan nutrisi dan oksigen karena adanya penyempitan pada pembuluh darah koroner, namun masyarakat masih sering menilai bahwa penyakit jantung merupakan penyakit yang dapat sembuh dengan sekali pengobatan. Maka di butuhkan suatu media informasi yang kuat, yang dapat meningkatkan pengetahuan masyarakat mengenai penyakit jantung khususnya penyakit jantung koroner karena penyakit jantung jenis ini yang paling populer di masyarakat. Penelitian ini bertujuan untuk mengembangkan sebuah aplikasi berbasis augmented reality (AR) pada platform Android yang dapat digunakan sebagai media visualisasi penyakit jantung, khususnya penyakit jantung coroner. Hal ini untuk meningkatkan pengetahuan masyarakat mengenai penyakit jantung khususnya penyakit jantung coroner. Dalam membangun aplikasi ini, metode yang digunakan adalah metode Multimedia Development Life Cycle (MDLC). Dari hasil pengujian yang dilakukan dengan metode Alpha dan Beta, dapat disimpulkan bahwa aplikasi ini dinilai layak untuk digunakan sebagai media edukasi interaktif tentang penyakit jantung koroner, hal ini dibuktikan dengan hasil pengujian beta test yang telah dilakukan dan menghasilkan nilai rata-rata tingkat persetujuan usability aplikasi secara keseluruhan sebesar 89,3 %.
Analisis Sentimen Terhadap Calon Presiden Indonesia 2024 dengan Metode Extreme Gradient Boosting (XGBOOST) Yulistiani, Yulistiani; Styawati, Styawati
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6127

Abstract

In 2024, Indonesia will implement democracy in the election of the Indonesian head of state. Any political figure who runs for head of state and calculates his popularity based on public opinion. After the General Election Commission (KPU) released the names of the 2024 Indonesian presidential candidates, these names were widely discussed, especially on social networks, one of which was Twitter. Twitter or what is often called X is a platform that provides short, concise and clear information. Twitter users responding to the 2024 presidential candidate have different opinions on Twitter. The sentiments used are positive, negative and neutral. The method used to analyze public opinion with data processed on Twitter social media uses Extreme Gradient Boosting (XGBOOST), classifying tweet test data in the form of classification with prediction output with accurate values. This research takes Twitter data to see public opinion on presidential candidates. The aim of this research is to determine the process of digital text analysis and the application of the XGBOOST method to Twitter user sentiment in two categories (positive and negative) and three categories (positive, negative and neutral) for each candidate, namely Ganjar Pranowo, Anies Baswedan and Prabowo Subianto. The results show an accuracy of 0.96%, precision of 0.96% and recall of 0.97%.

Page 31 of 44 | Total Record : 431

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