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Jurnal Informatika
Published by Universitas Bina Sarana Informatika
ISSN : 23556579     EISSN : 25282247     DOI : https://doi.org/10.31294/informatika
Core Subject : Science,
Computer Science & IT
Jurnal Informatika first publication in 2014 (ISSN: e. 2528-2247 p. 2355-6579) is scientific journal research in Informatics Engineering, Informatics Management, and Information Systems, published by Universitas Bina Sarana Informatika which the articles were never published online or in print. The publication is scheduled twice a year (April and October). The Editor welcomes submissions of manuscripts that relate to the field. Jurnal Informatika respects all researchers Technology and Information field as a part spirit of disseminating science resulting and community service that provides download journal articles for free, both nationally and internationally. The editorial welcomes innovative manuscripts from Technology and Information field. The scopes of this journal are: Expert System, Decision Support System, Data Mining, Artificial Intelligence System, Machine Learning, Genetic Algorithms, Business Intelligence and Knowledge Management, and Big Data.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 25 Documents
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Expert System of Error Tracking Automated Weather Observing System Using Certainty Factor Method Based on Android Application Djibran, Halis M; Purba, Joshua; Saadia, Aprilia Ode; Restele, La Ode; Hasria, Hasria
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12254

Abstract

The limited number of technicians at several BMKG UPT (Task Implementation Units) in Indonesia is the main background of this research. Especially in the field of Aviation Meteorology, which has a significant safety risk for equipment data users. This can be made easier with an expert system. The fault tracking expert system aims to provide information about the symptoms of damage that occur in the Automated Weather Observing System (AWOS) so that it can make it easier for BMKG technicians to repair and handle the equipment. This research stage begins with collecting information data through experts and literature sources regarding AWOS equipment, then calculating the certainty value of the information using the certainty factor method, and produce information that will be displayed through the application. The system uses a Certainty Factor calculation method that presents the calculation of the certainty value of information based on the percentage of information delivery by the source, this method is used in accordance with the type of research that utilizes information from sources or experts in the AWOS field. The resulting system is an android application consisting of several knowledge bases stored in the MySQL database on the server. The results of the data analysis show that the resulting system can be used on the user's smartphone, and users can consult AWOS equipment damage properly. In addition, users can also view the consultation history and damage list. The application user satisfaction questionnaire shows the system has worked and fulfilled the function for users by showing a value of 33.3% Very Good and 66.7% Good.
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Hidayat, Muhamad Taufiq; Kurniawan, Rudi; Suprapti, Tati
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12255

Abstract

Linux, or GNU/Linux, is a widely used open-source operating system built on the Linux kernel that is available for anyone to use, known for its security and privacy advantages. With advancements in information technology, protecting privacy has become increasingly challenging due to data extraction practices done by major tech companies. This has encouraged some Mastodon users to switch to Linux, with many expressing their opinions on using Linux as their main operating system. This research seeks to analyze the sentiments of Mastodon users toward Linux through sentiment analysis to understand whether the trend is predominantly positive, negative, or neutral. The methodology used includes collecting data with the help of the Mastodon.py library which then gets manually labelled with the assistance of a linguistic expert as well as a linguistic rule proposed by previous research. The text mining process includes preprocessing steps which includes feature extraction with n-Gram to gain the most optimized result as well as employing feature selection using TF-IDF. The Naïve Bayes algorithm is employed for text classification. The entire process of data analysis is conducted with the help of AI Studio (RapidMiner) software. The results show that the highest-performing model for sentiment analysis is achieved with an n-gram value of 3, revealing user sentiment polarity towards Linux on Mastodon as follows: 42% positive, 28% negative, and 30% neutral. The sentiment analysis model has an accuracy of 63%, with a precision of 70%, recall of 80%, and an f1-score of 74% which shows that this method is able to optimize the sentiment analysis process. 
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods Pirsingki, Nisa; Wandri, Rizky
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12256

Abstract

Social media facilitates user communication, both in positive, negative and neutral aspects. Tiktok is a popular platform that allows users to stay up to date on the latest news, including the major conflict between Palestine and Israel. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. The objective of this study is to evaluate public sentiment regarding the news of Palestinian refugees en route to Rafah. To achieve this purpose, we will examine 2982 comments on TikTok relating to the hashtag #SaveRafah, which will be the data to be trained. Prior to classification, the data will undergo a preprocessing process and TF-IDF weighting. The two classification methods will be compared to ascertain the most accurate approach. Because the data at the labeling stage has a larger percentage of positive data 90.7%, this study will employ the technique SMOTE to address class imbalance in the data set. The results showed that the Naive Bayes Multinomial method with the application of SMOTE produced an accuracy of 85.43%, a precision of 86.22%, a recall of 85.43%, and an f1-score of 85.53%. Meanwhile, the Decision Tree C4.5 method with the application of SMOTE produced an accuracy of 94.23%, a precision of 94.58%, a recall of 94.23%, and an f1-score of 94.22%. Based on the evaluation results, the best method for sentiment analysis of the hashtag #SaveRafah is Decision Tree C4.5.
Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm AlFauzi, Ihsan; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12257

Abstract

Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotional attributes on TV, radio, and social media. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is used. Algorithms tested include Naive Bayes, K-Nearest Neighbor, Support Vector Machine (SVM), Random Forest, and XGBoost. The results showed that SVM had the best performance with 80% accuracy, followed by KNN (79%), Naive Bayes (77%), XGBoost (77%), and Random Forest (76%). SVM provided the most accurate and consistent predictions in marketing promotion classification. This research concludes that the optimal utilisation of ICT and the application of appropriate classification algorithms can increase the effectiveness of marketing promotions in the digital era.
Implementation of Image Data Security Using the AES-256 Algorithm in the Work Accident Recording System Dzulfiqar Alang Setiawan; Bambang Agus Herlambang; Ramadhan Renaldy
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.12372

Abstract

This study aims to implement an image data security mechanism in a web-based work accident recording system in an industrial environment by applying the Advanced Encryption Standard (AES) 256-bit cryptographic algorithm in Cipher Block Chaining (CBC) mode. The problem faced is the lack of an adequate protection mechanism for sensitive work accident photo files so that they have the potential to be accessed, copied, or modified by unauthorized parties. This study uses the Software-Oriented Prototyping method which allows system development to be carried out iteratively based on user needs and evaluation results at each development stage. The encryption process is carried out when the image file is uploaded into the system by generating a random Initialization Vector (IV) of 16 bytes, then the image data is encrypted using the AES-256-CBC algorithm and stored in ciphertext form with the file extension .enc. The decryption process is carried out when the file will be displayed again using the appropriate secret key and IV without storing the file in plaintext form on the server. The test results show that the encryption process has an average execution time of around 0.002–0.008 seconds, while the decryption process takes around 0.00004–0.001 seconds. In addition, the size of the encrypted file relatively follows the size of the original image file with an additional size of around 16–32 bytes due to the padding process and the use of initialization vectors. The results of the study indicate that the application of the AES-256-CBC algorithm is able to maintain the confidentiality and integrity of image data without having a significant impact on system performance. Thus, the developed system can improve the security of digital file storage and support a more structured, secure, and efficient management of work accident data.

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