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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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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 94 Documents
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Performance Comparison of Response Time Native, Mobile and Progressive Web Application Technology Rochim, Rachma Verina; Rahmatulloh, Alam; El-Akbar, R Reza; Rizal, Randi
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 1 (2023): Maret 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i1.7045

Abstract

The development of technology in web-based applications is growing, this creates new problems. The web technology that is currently being discussed is Progressive Web Application (PWA) but is the PWA's performance better than the previous technology. This research is about measuring the performance of the Native Web, Mobile Web and PWA using three testing tools, namely GTMetrix, Lighthouse, and Chrome DevTool. The results of this study show how to measure the performance of a Progressive Web Application (PWA), where PWA can beat the performance of Native Web and Mobile Web if a web page is tested more than once. Test results on the Progressive Web Application (PWA), the minimum number of page files (home) is 217 kB with page loading time of 638 ms, while the medium page (about) is 431 kB with page loading time of 646 ms, and when accessing heavy pages (news) with a size of 41700 kB the page load time is 532 ms.
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.
Implementation of Data Mining at Laboratory Vocational High School Using The C4.5 Algorithm to Predict Students Major Preferences Suherman, Nurisya Rahma; Ruuhwan, Ruuhwan; Sudiarjo, Aso
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8479

Abstract

Education or the learning process is the primary thing for human life. Therefore, a place for acquiring knowledge is established, which is called a school. Schools have their own levels, ranging from early childhood education to higher education institutions. When students enter high school, they are required to make decisions in choosing their majors. Accompanied by technological advancements, the issues in high school major selection can be effectively and efficiently addressed using data mining. Common issues that usually arise include lack of accuracy, precision, and requiring a significant amount of time. Hence, the issues within major selection necessitate the use of data mining, employing the C4.5 algorithm method, to determine the accuracy and precision of large datasets. This research achieved with RapidMiner the result is accuracy score of 94.44%, precision of 81.37%, and sensitivity of 74.00%. Additionally, it also generated a decision tree and with Python has an accuracy of 93% because it automatically rounds the values, so there is no significant difference between the two tools. This proves that the C4.5 algorithm produces fairly accurate performance.
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.
Comparison of Naïve Bayes and Random Forest Algorithm in Webtoon Application Sentiment Analysis Admojo, Fadhila Tangguh; Risnanto, Slamet; Windiawati, Ai Wulan; Innuddin, Muhammad; Mualfah, Desti
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10636

Abstract

The Webtoon application has become one of the popular platforms for reading comics digitally. Webtoons, as a form of digital comics, present various types of comic content. The success of a Webtoon application depends greatly on understanding the preferences and views of its users. User evaluations of Webtoon applications can provide valuable insight into user satisfaction levels, as well as identify problems that need to be fixed by developers. In this research, Sentiment Analysis was applied to user reviews of the Webtoon Application on the Google Play Store. This research uses two different classification algorithms, namely Naïve Bayes and Random Forest, with the aim of comparing their performance in the context of sentiment analysis of user reviews of Webtoon applications. The results of this research are expected to provide an overview of the most suitable algorithm for conducting sentiment analysis classification in Webtoon applications. In collecting the dataset, we involved webtoon user reviews covering various sentiments, such as positive, negative, and neutral. However, in this analysis, the focus is given to two types of sentiment, namely positive and negative. We apply Naïve Bayes and Random Forest algorithms to perform sentiment classification on the reviews. Performance evaluation is carried out by considering metrics such as accuracy, precision, recall, and F1-score. The results of implementing these two algorithms are an accuracy of 74% Naïve Bayes, and 88% Random Forest. It can be concluded that the Random Forest algorithm is superior to the Naïve Bayes algorithm. With this, the Random Forest algorithm becomes a recommendation for classifying sentiment analysis for Webtoon applications with greater accuracy.
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.
Unveiling Culinary Patterns: Implementation Of K-Means Clustering Algorithm on Food Products in Cafes Gumelar, Lasmi Lasmini; Ruuhwan, Ruuhwan; Hikmatyar, Missi
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8665

Abstract

Barcode Se'i and Coffee is one of the cafes on JL. Major Utarya, No. 48, Empangsari, District. Tawang, Tasikmalaya. Barcode Se'i and Coffee is quite famous because the concept of the place is nice, comfortable, and instagrammable. Not only that, but the Barcode café was also the first to create cow sei in Tasikmalaya. By analyzing the cafe menu groupings, information can be found regarding the level of menu sales. This type of analysis, capable of assessing sales levels, involves the use of data mining techniques such as clustering. Data mining is a data processing stage that aims to identify and extract patterns from a certain set of data. One of the methods included in data mining is the clustering technique. Reclassification techniques are used to group objects into several groups based on observed indicators, ensuring that all objects have a significant level of similarity compared to objects placed in different groups. With Rapidminer software and using the k-means algorithm with sales data for 11 months with the calculations carried out producing 5 clusters. Based on the comparison results of 3 K-Means algorithms with different K values, namely 3, 4, 5, the result from Davies Bouldin with a value close to 0 is a value with K 5, with the result from Davies Bouldin being - 0.912.
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
Development Prediction Model to Optimize Cooperative Loans Based on Machine Learning Algorithms Himawan, Hidayatulloh; Pinandita, Tito; Ridwan, Rizky; Aziz, Hilmi
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10677

Abstract

Default on loans by borrowers to the cooperative to optimize the cooperative's business performance. In this research, a default prediction model was developed using several quite popular machine learning algorithms, namely decision tree, K-NN, logistic regression, and random forest, then all models with each of these algorithms were compared and evaluated. to find out which algorithm model is the most effective and accurate in predicting loan defaults in cooperatives. Model evaluation is carried out using metrics such as accuracy, precision, recall, and f1-score. The dataset used in this research was obtained from the loan list at one of the Savings and Loans Cooperatives in Tasikmalaya Regency, the contents of which include attributes such as borrower profile, loan amount, number of installments, and others. This dataset is divided into training data and test data to train and evaluate the model. These machine learning algorithms were chosen because they are quite well known among other algorithms for prediction and have been proven in several financial studies. The results of this prediction model can be used by cooperatives to support decisions in providing appropriate loans.
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.

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