Garuda - Garba Rujukan Digital

Designing a Posyandu Scheduling System using a Data-base to Improve Patient Service [Perancangan Sistem Penjadwalan Posyandu Menggunakan Basis Data untuk Meningkatkan Layanan Pasien] Indra Astutik, Ika Ratna; Imanudin, Giri Fajar; Rosid, Mochamad Alfan; Aji, Sukma
Indonesian Journal of Applied Technology Vol. 1 No. 1 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijat.v1i1.2109

Posyandu is a form of community-based health effort (UKBM) that is menaged and organized frm, by, and with the community in implementing healh development in order to empower the community and provide convenience to the obtaining basis heal/social service to accelerate the reduction in maternal and infant mortality. The Posyandu Scheduling System has a very important role to support the services of this public health program. Over time, problems arise regarding the system, in terms of preparing service schedules, still using the manual method, namely aliases, which are still handwritten to the recorder the Posyandu schedule in Kebonagung Village. This research is to design a website-based scheduling system that is useful for making it easier for cadres to disable and process Posyandu scheduling data in order to improve patient service. This system design uses the Hypertext Preprocessor (PHP) programming language, with a MySQL database, and uses Black Box testing to find out if the program is running as expected.

A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Imam Riadi; Abdul Fadlil; Muhammad Nur Faiz; Arif Wirawan Muhammad; Santi Purwaningrum; Laura Sari
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.

Website Security Analysis Using Vulnerability Assessment Method : Case Study: Universitas Internasional Batam Haeruddin; Gautama Wijaya; Hendra Winata; Sukma Aji; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2476

In today’s digital era, ensuring website security is crucial, especially in the education sector which is frequently targeted by cyber attacks. This research aims to test security of the Universitas Internasional Batam (UIB) website using OWASP ZAP and Nessus. The method will be used in this research was vulnerability assessment. It will involve gathering information with the tools such as, Nmap, whois and nslookup. OWASP ZAP detected 11 vulnerabilities, categorized into 6 medium level and 5 low level, including Content Security Policies (CSP) and anti-clickjacking headers. Otherwise, Nessus only detected one medium level vulnerability, the absence of HTTP Strict Transport Security (HSTS). The difference in detection results from the tools that OWASP ZAP is better at finding web application weakness that are consistent with the OWASP Top Ten 2021, while Nessus specifically targets server and network configuration. For educational institutions, these results emphasize the importance of conducting regular vulnerability assessment to protect sensitive data. Recommended action include implementing CSP to prevent Cross-site scripting (XSS) and other injection attacks, enforcing HSTS to secure communication, and its recommend to updating software to mitigate the unknown vulnerabilities. By adopting these measures, institutions can reduce their exposure to cyber attacks, its also can maintain user trust, and strengthen overall security. This research provides a pratical framework for stregthening the security of educational websites against evolving threats. These findings highlight that the importance of using multiple tools can provide a more comprehensive view of security gaps.

A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Riadi, Imam; Fadlil , Abdul; Faiz , Muhammad Nur; Arif Wirawan Muhammad; Purwaningrum, Santi; Sari, Laura
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.

STUDI ANALISA SERANGAN SQL INJECTION Nursapdahi, Nursapdahi; Fitrani, Arif Senja; Rosid, Mochamad Alfan; Aji, Sukma
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 6 No. 1 (2022): PROSIDING SEMINAR NASIONAL INOVASI TEKNOLOGI TAHUN 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v6i1.2474

At this time the website has become one of the modern information media that is growing very quickly. In making a website, not only the design and information are important, but the security aspect of the website itself has a very important role in a website. The need for website security arises from the need to protect data. First, from data loss and corruption. Second, there are irresponsible parties who want to access and change data. Other problems include self-data protection, excessive delays when accessing or using data. The method used in this test will use tools in the form of software and certain methods used to test the security of a website. To analyze website security, the software used is Snort IDS (Intrusion Detection System) and Wireshark. SQL Injection is actually not a new thing in the world of hacking as a web hacking technique, SQL Injection can damage the database of a website. The technique used in SQL Injection is to input basic SQL commands such as create, insert, update, drop, alter, union and select along with other commands.

Website Security Analysis Using Vulnerability Assessment Method : Case Study: Universitas Internasional Batam Haeruddin; Gautama Wijaya; Hendra Winata; Sukma Aji; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2476

In todayâ€™s digital era, ensuring website security is crucial, especially in the education sector which is frequently targeted by cyber attacks. This research aims to test security of the Universitas Internasional Batam (UIB) website using OWASP ZAP and Nessus. The method will be used in this research was vulnerability assessment. It will involve gathering information with the tools such as, Nmap, whois and nslookup. OWASP ZAP detected 11 vulnerabilities, categorized into 6 medium level and 5 low level, including Content Security Policies (CSP) and anti-clickjacking headers. Otherwise, Nessus only detected one medium level vulnerability, the absence of HTTP Strict Transport Security (HSTS). The difference in detection results from the tools that OWASP ZAP is better at finding web application weakness that are consistent with the OWASP Top Ten 2021, while Nessus specifically targets server and network configuration. For educational institutions, these results emphasize the importance of conducting regular vulnerability assessment to protect sensitive data. Recommended action include implementing CSP to prevent Cross-site scripting (XSS) and other injection attacks, enforcing HSTS to secure communication, and its recommend to updating software to mitigate the unknown vulnerabilities. By adopting these measures, institutions can reduce their exposure to cyber attacks, its also can maintain user trust, and strengthen overall security. This research provides a pratical framework for stregthening the security of educational websites against evolving threats. These findings highlight that the importance of using multiple tools can provide a more comprehensive view of security gaps.

Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results Andy Victor Pakpahan; Fahmi Reza Ferdiansyah; Robby Gustian; Muhammad Nur Faiz; Sukma Aji
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2724

Sentiment analysis is a text processing technique aimed at identifying opinions and emotions within a sentence. Machine learning is commonly applied in this area, with algorithms such as NaÃ¯ve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. However, achieving optimal accuracy remains a challenge, particularly when dealing with unstructured text data, such as content from social media platforms. This research seeks to improve sentiment analysis performance by implementing a stacking ensemble learning approach, which combines the predictive strengths of several base models. The base models selected for this study are NaÃ¯ve Bayes, SVM, and Random Forest, while Random Forest also serves as the meta-model to generate final predictions. The study focuses on sentiment analysis in a specific contextâ€”public opinion following the announcement of the Indonesian presidential election results in 2024. The dataset comprises 6,737 tweets collected from the X platform using web scraping techniques in 2024. Results show that individual models achieved varying levels of accuracy: NaÃ¯ve Bayes at 66.84%, SVM at 77.74%, and Random Forest at 74.78%. In contrast, the stacking ensemble model achieved a significantly higher accuracy of 81.53%. This improvement highlights the effectiveness of ensemble learning in integrating different algorithmic perspectives to enhance predictive performance. By leveraging the complementary strengths of each base model, stacking not only boosts accuracy but also increases model robustness, making it highly suitable for real-world sentiment analysis applications that involve noisy and informal textual data from social media.

Eksplorasi Teknik Pre-Processing Berbasis eXtreme Gradient Boosting (XGBoost) pada Serangan DDoS Nur Faiz, Muhammad; Sari, Laura; Imam Riadi; Arif Wirawan Muhammad; Sukma Aji
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9380

Distributed Denial of Service (DDoS) attacks represent a critical threat to modern network security, particularly within Internet of Things (IoT) environments characterized by large-scale and heterogeneous traffic patterns. The primary challenges in detecting such attacks involve class imbalance, irrelevant features, and noise within the data, all of which can degrade the performance of machine learning-based detection models. This study evaluates the impact of a pre-processing pipeline—comprising the Synthetic Minority Over-sampling Technique (SMOTE), correlation-based feature selection, and advanced feature selection methods—on the performance of the XGBoost algorithm in detecting DDoS attacks using the CIC-IoT2023 dataset. Experimental results indicate that the XGBoost model trained on RAW data achieves exceptionally high performance, with an accuracy of 0.999983, precision of 0.985531, recall of 0.961390, and an F1-score of 0.999983. However, after applying the pre-processing techniques, all metrics experienced a decline, with accuracy decreasing to 0.958899, precision to 0.865729, recall to 0.748332, and the F1-score to 0.959158. The reduction in recall suggests a higher number of undetected attacks, whereas the drop in precision indicates an increase in false alarms. Nevertheless, the F1-score remaining above 0.95 demonstrates that the model continues to perform effectively overall. These findings reveal that pre-processing does not always lead to performance improvements, especially when the raw dataset is already relatively clean and balanced. This study provides deeper insights into how SMOTE, feature selection, and noise injection influence the generalization of XGBoost on IoT traffic, and emphasizes that the effectiveness of pre-processing is highly dependent on dataset characteristics and the intended application context of intrusion detection systems.

Hybrid CNN-LSTM for Indonesian Cyberbullying Detection on Social Media X Muhammad Hafizh Fattah; Rosid, Mochamad Alfan; Sukma Aji; Suprianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16938

Cyberbullying on social media platform X has become a critical digital threat and requires automatic detection mechanisms to mitigate psychological impacts on victims. This study proposes a hybrid deep learning architecture that combines Convolutional Neural Network (CNN) for local feature extraction and Long Short-Term Memory (LSTM) for sequential context understanding in classifying Indonesian language cyberbullying comments. This study evaluates model performance using a dataset of 13,677 comments from social media X through a series of systematic testing scenarios, including the impact of regularization, utilization of FastText embeddings, and comparative studies against state-of-the-art models. Experimental results demonstrate that the Early Stopping mechanism is a critical factor in this architecture, where without this mechanism the model experiences accuracy degradation of up to 32%. The proposed CNN-LSTM model achieves 88.38% accuracy and 88.00% F1-Score, improving to 0.9559 AUC with FastText integration. This model achieves over 97% of IndoBERTweet's performance with 22 times lower computational complexity (4.97 million versus 110.88 million parameters) and outperforms machine learning methods such as SVM with an accuracy margin of more than 10 percentage points. This study concludes that the CNN-LSTM architecture offers a robust and efficient solution for cyberbullying detection, particularly for resource-constrained environments

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