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Alam Rahmatulloh
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Program Studi Informatika Fakultas Teknik Universitas Siliwangi Jl. Siliwangi No. 24 Tasikmalaya, Jawa Barat
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INDONESIA
Innovation in Research of Informatics (INNOVATICS)
Published by Universitas Siliwangi
ISSN : -     EISSN : 26568993     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Innovation in Research of Informatics (Innovatics) merupakan Jurnal Informatika yang bertujuan untuk mengembangkan penelitian di bidang: Machine Learning Computer Vision Internet of Things Information System and Technology Natural Language Processing Image Processing Network Security Geographic Information System Knowledge based Computer Graphic Cyber Security IT Governance Data Mining Game Development Digital Forensic Business Intelligence Pattern Recognization Virtual & Augmented Reality Virtualization Enterprise Application Self-Adaptive Systems Human Computer Interaction Cloud Computing Mobile Application Innovatics adalah jurnal peer-review yang ditulis dalam bahasa Indonesia yang diterbitkan dua kali dalam setahun mulai dari Vol. 1 No.1 Maret 2019 (Maret, dan September) dengan proses peninjauan menggunakan double-blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 10 Documents
 1 
Search results for , issue "Vol 6, No 2 (2024): September 2024" : 10 Documents clear
An Analysis of Classification Method Performance on Handwritten Lontara Numerals Bustam, Faida Daeng; Purnawansyah, Purnawansyah; Azis, Huzain
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11999

Abstract

The research investigates the performance of various classification methods on handwritten Lontara digits, a script used by the Bugis and Makassar communities in South Sulawesi, Indonesia. The dataset comprises 10,890 samples from 99 individuals, categorized into 10 classes (digits 0-9). The study employs the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Nu-Support Vector Classifier (NuSVC) algorithms, implementing cross-validation to assess accuracy, precision, recall, and F1 score. The results indicate varying performance across classifiers, with GNB showing the highest recall, while KNN and NuSVC display moderate effectiveness. The study concludes with recommendations for further improving classification accuracy through enhanced feature extraction and algorithm optimization.
Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNN Architectures Pawestri, Sheraton; Murinto, Murinto; Auzan, Muhammad
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11921

Abstract

Increasingly advanced technology and the creation of social media and the internet can become a forum for people to express things or opinions. However, comments or views from users sometimes contain sarcasm making it more difficult to understand. News headlines, sometimes contain sarcasm which makes readers confused about the content of the news. Therefore, in this research, a model was created for sarcasm detection. Many methods are used for sarcasm detection, but performance still needs to be improved. So this research aims to compare the performance of two text classification methods, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in detecting sarcasm in English news headlines using RoBERTa text transformation.  RoBERTa produces a fixed-size vector of numbers 1x768. The research results show that CNN has better performance than RNN. CNN achieved the highest average accuracy of 0.891, precision of 0.878, recall of 0.874, and f1-score of 0.876, with a loss of 0.260 and a processing time of 508.1 milliseconds per epoch. On the contrary, RNN shows an accuracy of 0.711, precision of 0.692, recall of 0.620, f1-score 0.654, and loss of 0.564, with a longer processing time of 116500 milliseconds per epoch. The 10-fold cross-validation evaluation method ensures the model performs well and avoids overfitting. So it is recommended to use the combination of RoBERTa and CNN in other text classification applications that require high speed and accuracy. Further research is recommended to explore deeper CNN architectures or other architectural variations such as Transformer-based models for performance improvements.
Development of Network Security Using A Suricata-Based Intrusion Prevention System Againts Distributed Denial of Service Tahir, Muhlis; Wahyuningsih, Umami; Putra Pratama, Muhammad Iyan; Effindi, Muhamad Afif
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11187

Abstract

Network security is essential in today's rapid technological developments, especially to avoid undesirable things such as attacks carried out by irresponsible parties. An intrusion prevention system is one of the methods used in a network security system. One attack that causes weak server services is Distributed Denial of Service (DDoS). This research aims to develop a Suricata-based Intrusion Prevention System for network security at the research location and to carry out tests to prevent attacks on the network at the research location. This research uses a waterfall model consisting of 5 stages: Analysis, Design, Implementation, Testing and Maintenance. The results of the research carried out on the development of a Suricata-based Intrusion Prevention System were able to detect DDoS attacks (Syn Flood and Ping of Death) and block access to these attacks so that network traffic was stable by utilizing the firewall feature, namely Iptables. The Suricata-based Intrusion Prevention System (IPS) demonstrated strong performance in detecting DDoS attacks, with a 98% detection rate for Syn Flood attacks and a 95% detection rate for Ping of Death attacks. The system maintained an overall average detection rate of 96.5% across both attack types, while keeping false positives low, at 2% for Syn Flood and 3% for Ping of Death. This resulted in an overall false positive rate of 2.5%, indicating the IPS's effectiveness in accurately identifying threats with minimal erroneous alerts, thereby providing robust network security.
Enhancing Maintenance Efficiency Through K-Means Clustering at PT Semen Indonesia Alviano, Muhammad Fadhil; Alifah, Amalia Nur; Ardhani, Calista Ghea; Raditya, Helga Fadhil; Larasati, Harashta Tatimma
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12520

Abstract

PT Semen Indonesia, an industrial company based in Gresik, East Java, is committed to enhancing operational efficiency and managing maintenance costs effectively. By analyzing patterns in maintenance frequency, total costs, and maintenance duration across their various plants, the company can identify work units that require more intensive attention or that can be optimized for greater efficiency. To achieve this, PT Semen Indonesia employs K-Means clustering analysis to gain deeper insights into the maintenance data, identifying patterns that can help improve operational efficiency and develop more targeted maintenance strategies based on the identified clusters. The clustering of planner groups is carried out using variables such as the number of maintenance activities, total costs, and duration of maintenance tasks. As a result of the K-Means clustering, the planner groups have been divided into two clusters: Cluster 1, which consists of planner groups that perform more efficiently, and Cluster 2, which includes those with less efficient performance. Based on these clustering results, PT Semen Indonesia should conduct further evaluation or review of the planner groups in Cluster 2.
Comparative Performance Evaluation of Classification Methods for Arabic Numeral Handwritten Recognition Saly, Intan Novita; Purnawansyah, Purnawansyah; Azis, Huzain
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11998

Abstract

This study aims to evaluate the performance of various classification methods in recognizing handwritten Arabic numerals, particularly the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and NU Support Vector Classifier (NU SVC) algorithms. In this study, a dataset of handwritten Arabic numerals consisting of 9,350 samples with 10 different classes was used. The research process involved data collection, data labeling, dividing the dataset into training and testing data, implementing classification algorithms, and performance testing using cross-validation methods. The results showed that NU SVC had more stable performance with accuracy close to KNN, while GNB showed the lowest performance. The conclusion of this study emphasizes that the selection of algorithms and parameter optimization is crucial to improve the accuracy and efficiency of handwriting recognition systems. Support Vector Machine (SVM) based algorithms proved to be superior in handling complex classification tasks compared to GNB. This study provides significant contributions to the field of handwriting recognition, particularly in the context of Arabic numeral handwriting, and can serve as a reference for developers of optical character recognition (OCR) systems in the future. Future research is recommended to increase the variety of datasets and further explore parameter optimization and data preprocessing techniques to improve system accuracy.
Cyber Threat Detection Using an Ensemble Model Approach for Phishing Website Identification Rofianto, Dani; Safitri, Egi; Amaliah, Khusnatul; Fitra, Jaka; Hijriani, Astria
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12530

Abstract

The development of digital technology has had a significant impact on various aspects of life, including an increase in cybersecurity threats, especially phishing attacks. Phishing is a method of cyber fraud that manipulates victims to provide sensitive information by posing as a trusted entity. This research aims to develop and evaluate the effectiveness of several machine learning algorithms in detecting phishing websites. The methods used in this research include the application of Random Forest, Extra Trees, Multiple Layer Perceptron, Ada Boost, and Decision Tree algorithms on website datasets containing the characteristics of phishing and non-phishing sites. Performance evaluation is performed by measuring the accuracy, precision, recall, and F1 value of each algorithm. In addition, a voting technique is applied to combine the results of the best-performing algorithms with the aim of improving the overall detection accuracy. The results showed that the voting technique was able to provide superior results compared to the use of a single algorithm, with significant improvements in accuracy and recall values. These findings reinforce the importance of ensemble approaches in machine learning to improve phishing detection capabilities, which in turn contributes to improved cybersecurity.
IoT-based Water Quality Control in Tilapia Aquaculture Using Fuzzy Logic Prafanto, Anton; Septiarini, Anindita; Puspitasari, Novianti; Taruk, Medi; Mahendra, Dicky Alvian
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11271

Abstract

Tilapia (Oreochromis niloticus) is a prominent species in freshwater aquaculture due to its high protein content and economic value. Maintaining optimal water quality is crucial for the health and growth of tilapia, particularly in terms of pH levels. Deviations in pH, whether too acidic or too alkaline, can lead to decreased appetite and increased mortality rates in tilapia. The objective of this study is to design an intelligent control system to monitor and regulate the pH and temperature of tilapia aquaculture ponds using the Sugeno Fuzzy method integrated with Internet of Things (IoT) technology. The system employs DS18B20 temperature sensors and E-201-C pH sensors to collect real-time data on pond conditions. The data are then processed by an ESP32 microcontroller, which employs Sugeno Fuzzy logic to determine the appropriate adjustments to be made. The system administers pH buffers to maintain the water within the optimal pH range. Furthermore, the collected data are transmitted to a web server, enabling real-time monitoring and analysis. The findings indicate that the proposed IoT-based system is effective in maintaining water quality, ensuring that the pH and temperature levels remain within the ideal range for tilapia. This study demonstrates the potential of integrating IoT and Sugeno Fuzzy logic to provide a robust solution for managing water quality in aquaculture settings, enhancing the sustainability and productivity of tilapia farming.
A Comparative Analysis of Content-Based Filtering and TF-IDF Approaches for Enhancing Sports Recommendation Systems Herimanto, Herimanto; Samosir, Kevin; Ginting, Fastoria
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12404

Abstract

Sport is one of the important factors for someone to maintain or improve their health. There are many purposes for a person to exercise. However, many people still find it difficult to determine the relevant type of exercise according to their preferences. Recommendation systems are now an important element in human life to provide relevant recommendations to users. The purpose of this research is to develop a sports recommendation system that can provide accurate types of sports recommendations to users and describe how the recommendation system can work in providing recommendations when cold-start problems and non-cold-start problems occur. The method used in this research is content-based filtering by applying Term Frequency - Inverse Document Frequency (TF-IDF) vectorization matrix and cosine similarity algorithm. When a new user logs in, the system first checks the user's preferences to determine whether a cold-start problem or non-cold-start problem occurs. When a cold-start problem occurs, TF-IDF will be used in providing recommendations to the user. Conversely, when a non-cold-start problem occurs, cosine similarity will be used. The results show that by using TF-IDF and cosine similarity, the system successfully provides relevant sports recommendations to users in both cold-start problem and non cold-start problem situations with an accuracy rate of 86.90%. The novelty of this research lies in the understanding of sports provided to users through sports-related journals. Through these journals, it can increase user satisfaction, trust, compliance, and educate users in running sports
Hybrid Cryptosystem Using RC5 and SHA-3 with LSB Steganography for Image Protection Susanti, Adisti Dwi; Hadiana, Asep Id; Umbara, Fajri Rakhmat; Himawan, Hidayatulah
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11515

Abstract

The rapid development of internet technology has been accompanied by a significant increase in information security threats. Ensuring the security of confidential information transmission is crucial. Cryptography and steganography are among the most efficient techniques for safeguarding data. Both fields focus on information concealment. This paper proposes a hybrid approach to protect confidential multimedia data, specifically image media, by using LSB steganography techniques in combination with the RC5 encryption algorithm and the SHA3 hashing algorithm to provide dual-layer protection for information. In the proposed method, image data is first encrypted using the RC5 encryption algorithm with a specified key. Subsequently, a hashing function using SHA3 is applied for dual protection, ensuring data authenticity and integrity. Finally, steganography is performed using the LSB technique to embed the hashed information into the image media. This study aims to enhance the security of information in digital image media, providing a reliable solution to address security challenges. The results indicate that data confidentiality was successfully achieved, with an average PSNR of 52.509 dB and an MSE of 0.3829. Tests were conducted using a dataset of images with various dimensions.
Software Testing in the Indonesian Industry: Survey of Methods, Tools, and Documentation Maspupah, Asri; Rahmani, Ani; Min, Joe Lian; Roshinta, Trisna Ari
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12636

Abstract

Software testing plays a crucial role in the software development by ensuring that software is accurate and of high quality. Many software companies neglect software testing, which can lead to unprofitable business outcomes. For example, ineffective software testing may fail to identify all defects, resulting in increased development costs. A key factor determining the success of software testing is the strategy for implementing the testing process, the selection of testing tools, and the documentation of testing activities. This article examines the current state of software testing processes in the Indonesian software industry. The research objective is to analyze the software testing implementation strategy within the software development context, focusing on three main areas: software testing methodology, software testing tools, and software testing documentation. The research employs a survey method, collecting data from several respondents, Indonesian software companies, via an online questionnaire. The research findings indicate that testing is still predominantly manual. However, some software companies have begun to adopt a combination of manual and automated testing. Most companies utilize software testing documentation for reporting purposes during the execution of tests. Nevertheless, documenting test cases as a guide for testing execution is not prioritized as highly as bug reporting. Conversely, many Indonesian software companies have adopted testing tools and conducted performance testing to ensure software quality. Consequently, the software testing process in the Indonesian software industry tends to adhere to formal methods in accordance with the ISO/IEC/IEEE 29119 software testing standards

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