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All Journal Jupiter Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informasi dan Komputer Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Soeropati: Journal of Community Service Journal of Applied Data Sciences TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Publika Pengabdian Masyarakat Jurnal INFOTEL Jurnal Ilmu Siber dan Teknologi Digital Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) IJISCS (International Journal of Information System and Computer Science)
Triloka, Joko
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Detection of SQL Injection Attack Using Machine Learning Based On Natural Language Processing Triloka, Joko; Hartono, Hartono; Sutedi, Sutedi
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.401 KB) | DOI: 10.29099/ijair.v6i2.355

Abstract

There has been a significant increase in the number of cyberattacks. This is not only happening in Indonesia, but also in many countries.  Thus, the issue of cyber attacks should receive attention and be interesting to study.  Regarding the explored security vulnerabilities, the Open Web Application Security Project has published the Top-10 website vulnerabilities. SQL Injection is still become one of the website vulnerabiliteis which is often exploited by attacker. This research has implemented and tested five algorithms. They are Naïve Bayes, Logistic Regression, Gradient Boosting, K-Nearest Neighbor, and Support Vector Machine. In addition, this study also uses natural language processing to increase the level of detection accuracy, as a part of text processing. Therefore, the main dataset was converted to corpus to make it easier to be analyzed. This process was carried out on feature enginering stage. This study used two datasets of SQL Injection. The first dataset was used to train the classifier, and the second dataset was used to test the performance of classifier. Based on the tests that have been carried out, the Support Vector Machine get the highest level of accuracy detection. The accuracy of detection is 0.9977 with 0,00100 micro seconds per query time of process. In performance testing, Support Vector Machine classifier can detect 99,37% of second dataset. Not only Support Vector Machine, the study have also revealed the detection accuracy level of further tested algorithms: K-Nearest Neighbor (0,9970), Logistic Refression (0,9960), Gradient Boosting (0,99477), and Naïve Bayes (0,9754).
Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction Andani, Mia; Triloka, Joko; Irianto, Suhendro Yusuf; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14478

Abstract

Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
Menu Sales Prediction at Kiyo Café Using Machine Learning Fitriana, Jesi; Triloka, Joko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3556

Abstract

This research evaluates the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting raw material stock for Café Kiyo. The study encompasses six key stages, including preparation, literature review, data collection, data mining processing, results and discussion, and conclusion with recommendations. The data mining process adheres to the Knowledge Discovery in Databases (KDD) framework, involving data selection, preprocessing, transformation, data mining, and interpretation and evaluation. The evaluation metrics reveal that KNN boasts a marginally higher accuracy of 98.71% compared to Naïve Bayes with 98.21%. KNN also demonstrates superior precision (81.25%) in identifying true positives, outperforming Naïve Bayes (72.59%). However, Naïve Bayes excels in recall, achieving 95.15% compared to KNN's 50.00%. The Area Under the Curve (AUC) analysis further confirms Naïve Bayes' superiority, with an AUC value of 0.995, indicating better performance in distinguishing between positive and negative classes.
Estimasi Jarak Pandang Meteorologi di Bandar Udara Menggunakan Metode Back Propagation dan CNN Maesaroh, Siti; Muludi, Kurnia; Triloka, Joko
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7138

Abstract

Airports in Indonesia often face bad weather problems that affect visibility and impact flight operations. Historical data shows several incidents caused by decreased visibility due to fog or rain that resulted in flight delays and cancellations. It can be said that the importance of more accurate visibility estimates to improve safety and operational efficiency at airports. The purpose of this study was to determine the performance of the Back Propagation and Convolutional Neural Network (CNN) models in estimating meteorological visibility at airports because accurate visibility is very important in determining operational decisions, especially during bad weather conditions. The selection of the Back Propagation method is based on its advantages in handling various types of data dynamically and in a directed manner so that it is more precise in predicting visibility based on interrelated meteorological variables. While Convolutional Neural Network (CNN) is very effective in handling problems involving image data. However, currently there are quite a lot of studies that use CNN for text processing because the results are quite promising. The data used is meteorological data that includes temperature, humidity, air pressure, wind speed and other parameters at Radin Inten II Airport. From the results of this study, the Backpropagation model is better in ROC AUC (85%) compared to CNN (84%), this shows a slight advantage in distinguishing classes. The CNN model is better in Precision by 71% compared to Back Propagation 70%, which means it is slightly better at avoiding false positive predictions. CNN has a higher correlation on the test data (0.20) compared to Back Propagation (0.18) indicating its predictions are slightly more in line with the actual data. The larger correlation difference in CNN (0.18) compared to Back Propagation (0.10) indicates a higher possibility of CNN overfitting compared to BP. Since both models show almost the same performance and the difference is not too significant, the choice of model can depend on the specific needs in the implementation. If the goal is to get a more stable model, then Backpropagation is more recommended because it has a smaller correlation difference and higher ROC AUC. However, if what is sought is a model with more accurate predictions in real scenarios, then CNN can be a better choice because it has higher Precision and better test correlation.
"Perbandingan Kinerja Rendering EEVEE dan Siklus di Blender 3.5 dalam Konteks Visual Interaktif untuk Animasi 3D" Yulia, Aviv Fitria; Zulkifli; Bintoro, Panji; Andini, Dwi Yana Ayu; Triloka, Joko
Jurnal Penelitian Pendidikan IPA Vol 10 No 7 (2024): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i7.5910

Abstract

This research aims to compare the performance of two rendering engines, namely EEVEE and Cycles, available in Blender 3.5, in the context of developing interactive visual 3D animation. The rendering engine is a key element in 3D animation production, and the right choice between EEVEE and Cycles can have a significant impact on the final animation result. In this research, we conducted a series of experiments and analyzes to evaluate rendering speed, the quality of the resulting images, and the ability to achieve the visual effects desired by the animator. The results of this research provide deep insight into the strengths and limitations of each rendering engine in interactive 3D animation scenarios. These findings can help animators, game developers, and similar creative professionals make more informed choices when choosing a rendering engine that suits their project needs. Thus, this research contributes to the development of rendering techniques in the growing 3D animation industry.
Implementation of Mikrotik Firewall and QoS for Secure and Efficient Internet Networking Seyhan; Triloka, Joko
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.1056

Abstract

Effective network management in school environments is an ongoing challenge as the demand for internet access among students, teachers, and staff continues to grow. This study aims to design and implement a Mikrotik-based network management solution. The research was conducted at SMK Negeri 1 Bandar Lampung, with the objectives of improving network service quality, optimizing bandwidth allocation, and ensuring network security. Mikrotik was chosen due to its flexibility in network management at a relatively affordable cost. The research methods included analyzing the school’s network requirements, designing the network topology, and configuring Mikrotik devices based on Quality of Service (QoS), firewall, and user access control. The results show that the implementation of Mikrotik successfully enhanced network stability, reduced latency, and restricted access to non-relevant websites. These findings highlight that Mikrotik provides an effective and efficient solution for network management, particularly in small to medium-scale settings. This study is expected to serve as a reference for other schools facing similar challenges in network management.
Co-Authors Andani, Mia Andika, Tahta Herdian Andini, Dwi Yana Ayu Aras, Irsan Arie Ardiansyah, Arie Aris Kurniawan Assidik, Reksa Qodri Aviv Fitria Yulia Aziz, RZ. Abdul Badri, Rico Elhando Bintoro, Panji Chairani Chairani Desi Susanti Dewi, Deshinta Arrova Effendi, Mukhammad Khoirul Fadila, Kurnia Fitriana, Jesi Goesderilidar handoyo widi nugroho Hartono Hartono Indera Indera Kurnia Muludi Kurniawan Kurniawan Kurniawan, Tri Basuki Kurniawan, Wildan Indra M Said Hasibuan Maizary, Ary Mutiara, Suci Nouvan, M. Ahadi Novita Sari Pramodiyo Wilantara, Rangga Priyatna, Aziz R Rizal Isnanto Rachmadi, Rachmadi Rintiana, Rintiana Rio Kurniawan Saputra, Muhammad Setiawan, Agustinus Eko Seyhan Siti Maesaroh Sriyanto Sriyanto Sugianto, Dian Sunarso Sunarso Sutedi Sutedi Tarjono Wasilah Wasilah Wibaselppa, Anggawidia Y. Suhendro Yusuf Irianto, Suhendro Zulkifli