Garuda - Garba Rujukan Digital

Pengembangan Kebijakan Keamanan Adaptif Berbasis Machine Learning pada Firewall SDN Surojudin, Nurhadi; Turmudi Zy, Ahmad; Maulana, Donny; Halim Anshor, Abdul
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 1 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i1.919

Dalam era digital yang semakin kompleks, serangan siber seperti Distributed Denial of Service (DDoS) menjadi tantangan besar dalam pengelolaan keamanan jaringan. Penelitian ini mengusulkan pengembangan kebijakan keamanan adaptif berbasis machine learning untuk firewall pada arsitektur Software-Defined Networking (SDN). Dengan menggunakan algoritma Random Forest dan dataset CICIDS2017, sistem mampu mendeteksi serangan DDoS secara otomatis dan akurat. Data diuji melalui metode stratified split agar proporsi label tetap seimbang, serta dilakukan pembersihan nilai tak valid. Model menunjukkan performa sangat tinggi dengan akurasi 99,9978%, precision dan recall 99,996%, serta f1-score 99,996%. Evaluasi melalui confusion matrix mengindikasikan hanya dua kesalahan klasifikasi dari total 45.149 data uji. Hasil ini membuktikan bahwa integrasi machine learning dalam firewall SDN dapat memperkuat deteksi ancaman dan menghasilkan kebijakan keamanan yang dinamis, efisien, serta dapat beradaptasi terhadap serangan baru. Rencana pengembangan ke depan mencakup penerapan pada data real-time dan perluasan cakupan deteksi terhadap jenis serangan lainnya. Temuan ini memberikan kontribusi signifikan dalam pengembangan solusi keamanan jaringan berbasis SDN yang cerdas.

Analisis Sentimen Aplikasi Mobile JKN di Google Play Store Menggunakan Algoritma Naive Bayes Naufal, Luthfi Eka; Surojudin, Nurhadi; Afriantoro, Irfan
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7846

Health is a human right that must be fulfilled by the state, one of which is through the National Health Insurance-Kartu Indonesia Sehat (JKN-KIS) program managed by BPJS Kesehatan. To support the service, BPJS Kesehatan launched the Mobile JKN application in 2017. However, in its implementation, this application still faces various technical issues that affect user satisfaction and experience. This study aims to analyze user sentiment towards the Mobile JKN application by applying the Naive Bayes classification method. The data used comes from 10,000 user reviews on the Google Play Store in the period April to June 2024. The analysis results show that most reviews are positive (64%), followed by negative reviews (32.61%), and neutral (3.39%). The Naive Bayes model used showed excellent performance with an accuracy of 91.3%, an Area Under Curve (AUC) value of 0.985, and balanced precision and recall. However, the classification of neutral reviews is still not optimal due to their ambiguous nature. This research provides useful input for BPJS Kesehatan to improve the quality of JKN Mobile application services and increase user satisfaction.

Penerapan E-Health: Meningkatkan Akses Informasi Kesehatan Melalui Teknologi Rilvani, Elkin; Romli, Ikhsan; Surojudin, Nurhadi; Asmoro, Fachrial Banyu
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.116

Pelatihan bertajuk "Penerapan E-Health: Meningkatkan Akses Informasi Kesehatan melalui Teknologi" ini diselenggarakan sebagai bentuk pengabdian kepada masyarakat dalam upaya memperkuat literasi digital di bidang kesehatan. Tujuan utama dari kegiatan ini adalah untuk memberikan pemahaman dan keterampilan praktis kepada peserta mengenai penggunaan teknologi informasi dalam mengakses, menyimpan, dan berbagi informasi kesehatan secara efektif dan aman. Melalui pendekatan partisipatif dan metode pembelajaran interaktif, peserta dilatih untuk memanfaatkan platform e-health seperti aplikasi mobile kesehatan, sistem rekam medis elektronik, serta layanan konsultasi daring. Hasil dari pelatihan ini menunjukkan peningkatan pengetahuan peserta tentang e-health, kesadaran akan pentingnya keamanan data kesehatan, dan kemampuan dasar dalam menggunakan aplikasi kesehatan digital. Pelatihan ini diharapkan dapat menjadi langkah awal dalam memperluas adopsi teknologi e-health di masyarakat, sehingga akses terhadap layanan dan informasi kesehatan menjadi lebih inklusif, cepat, dan efisien.

Pendampingan Pembuatan Portofolio Event Organizer di Seirah Wisata Ali Takrim Surojudin, Nurhadi; Putra, Fibi Eko; Effendi, M. Makmun; Suryadi
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.118

This community service activity aims to assist Seirah Wisata Ali Takrim in developing a professional portfolio as an event organizer (EO) to enhance its image and competitiveness in the event management services sector. As the tourism and entertainment industry continues to grow, the need for work documentation and digital portfolios has become crucial for expanding networks and attracting potential clients. The implementation method included initial observation, needs interviews, portfolio design training, and technical assistance in developing effective and communicative portfolio content and layout. The outcome of this activity was the creation of a digital portfolio for Seirah Wisata Ali Takrim, which includes documentation of activities, client testimonials, services, and a business identity that bolsters its credibility as an EO service provider. Keywords: mentoring, portfolio, event organizer, tourism, UMKM.

Pelatihan Pengembangan Jiwa Edupreneurship Tour Leader Melalui Event Organizer Surojudin, Nurhadi; Putra, Fibi Eko; Effendi, M. Makmun; Suryadi
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 2 (2024): Desember 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i2.108

Pelatihan pengembangan jiwa edupreneurship bagi tour leader melalui event organizer menjadi aspek penting dalam meningkatkan keterampilan dan daya saing di industri pariwisata. PT. Seirah Wisata Ali Takrim sebagai penyedia layanan perjalanan wisata berupaya menciptakan program pelatihan yang tidak hanya berfokus pada aspek kepemimpinan dan manajemen perjalanan, tetapi juga membangun jiwa kewirausahaan (edupreneurship) bagi para tour leader. Penelitian ini bertujuan untuk menganalisis efektivitas pelatihan dalam membentuk kompetensi tour leader yang profesional, inovatif, dan memiliki keterampilan mengelola event wisata secara mandiri. Metode yang digunakan dalam penelitian ini adalah pendekatan kualitatif dengan observasi, wawancara, serta analisis studi kasus pada peserta pelatihan. Hasil penelitian menunjukkan bahwa pelatihan ini memberikan dampak positif terhadap peningkatan keterampilan komunikasi, manajemen acara, dan inovasi dalam penyelenggaraan tur edukatif. Dengan adanya pelatihan ini, peserta diharapkan mampu mengembangkan konsep perjalanan wisata berbasis edukasi yang bernilai ekonomi serta berkontribusi dalam pengembangan industri pariwisata yang berkelanjutan(Nanlohy et al., 2024).

Pengembangan Website Sekolah sebagai Media Promosi dan Informasi Berbasis Digital Nugroho, Agung; Surojudin, Nurhadi; Maulana, Donny; Romli, Ikhsan
Cahaya Pengabdian Vol. 2 No. 1 (2025): Juni 2025
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/cp.v2i1.235

This community service program aims to overcome the limitations of promotional and informational media at SMP Negeri 1 Cikarang Timur through the development of a digital-based school website. Until now, the dissemination of information and promotion has relied on conventional media, which are ineffective and inefficient, thereby hindering accessibility and the school's image. Using a qualitative-participatory approach, the community service team conducted a needs analysis, design, development, and implementation of the website using the WordPress Content Management System (CMS). The results of this activity show that the developed website successfully functions as an integrated information center and effective promotional media. It is hoped that this website will continue to provide long-term benefits in supporting information transparency and increasing the school's competitiveness in the digital era

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner Surojudin, Nurhadi; Ermanto, Ermanto; Danny, Muhtajuddin; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4136

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.

Recruitment Classification of Security Unit PT. Satria Kencana Abadi Using Naïve Bayes Method Rilvani, Elkin; Surojudin, Nurhadi; Danny, Muhtajuddin; Yoga Pratama, Evan
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4138

To get human resources according to company standards, the problem faced in the company is the difficulty of the selection process with a short time and the complexity of the decision making process resulting in subjective decision making. The purpose of this research is to assist the assessment process in making decisions for determining the selection of security units (SATPAM) to be more targeted so that it can help the company. In this study the data used were 697 data with 558 training data and 139 testing data. This test data was carried out using the Naïve Bayes algorithm method to classify so that it can determine accurate and efficient decision making, using Rapidminer tools which have 82 accuracy, 01%, 81.61% Precision, and 88.75% recall. This shows that the Naïve Bayes algorithm method has a good performance in determining decision making during the selection of security forces (SATPAM) at PT. Satria Kencana Abadi.

Perbandingan Metode Klasifikasi dalam Memprediksi Penyakit Ginjal Kronis Ermanto; Surojudin, Nurhadi
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1263

Chronic Kidney Disease (CKD) is a global health issue with an increasing prevalence that poses a significant economic burden on healthcare systems. Early detection of CKD is crucial to provide proper treatment before the disease progresses to end-stage renal failure. With technological advancements, machine learning methods have been widely utilized to support medical diagnosis with greater speed and accuracy. This study aims to compare the performance of two popular classification algorithms, Decision Tree C4.5 and Naïve Bayes, in predicting CKD using a public dataset from the UCI Machine Learning Repository consisting of 400 patient records with 24 clinical attributes. The research process involved systematic preprocessing steps, including handling missing values, transforming categorical data into numerical form, and selecting relevant attributes. Model evaluation was conducted using 10-Fold Cross Validation with performance metrics such as accuracy, precision, recall, Area Under the Curve (AUC), and statistical T-Test. The results show that Decision Tree C4.5 achieved an accuracy of 93.00%, precision of 84.27%, recall of 100%, and an AUC of 0.944, while Naïve Bayes obtained an accuracy of 93.50%, precision of 85.23%, recall of 100%, and an AUC of 0.948. Although the performance differences between both algorithms are relatively small and statistically insignificant, Naïve Bayes demonstrated slightly better results in terms of accuracy and AUC, while Decision Tree C4.5 offers advantages in interpretability through its classification rules. In conclusion, both algorithms are effective for early CKD diagnosis, and the choice may depend on practical needs, whether emphasizing interpretability or computational efficiency. This study contributes to the development of more accurate and efficient clinical decision support systems for improving healthcare services in CKD management.

Perbandingan Metode Klasifikasi dalam Memprediksi Penyakit Ginjal Kronis Ermanto; Surojudin, Nurhadi
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1263

Chronic Kidney Disease (CKD) is a global health issue with an increasing prevalence that poses a significant economic burden on healthcare systems. Early detection of CKD is crucial to provide proper treatment before the disease progresses to end-stage renal failure. With technological advancements, machine learning methods have been widely utilized to support medical diagnosis with greater speed and accuracy. This study aims to compare the performance of two popular classification algorithms, Decision Tree C4.5 and Naïve Bayes, in predicting CKD using a public dataset from the UCI Machine Learning Repository consisting of 400 patient records with 24 clinical attributes. The research process involved systematic preprocessing steps, including handling missing values, transforming categorical data into numerical form, and selecting relevant attributes. Model evaluation was conducted using 10-Fold Cross Validation with performance metrics such as accuracy, precision, recall, Area Under the Curve (AUC), and statistical T-Test. The results show that Decision Tree C4.5 achieved an accuracy of 93.00%, precision of 84.27%, recall of 100%, and an AUC of 0.944, while Naïve Bayes obtained an accuracy of 93.50%, precision of 85.23%, recall of 100%, and an AUC of 0.948. Although the performance differences between both algorithms are relatively small and statistically insignificant, Naïve Bayes demonstrated slightly better results in terms of accuracy and AUC, while Decision Tree C4.5 offers advantages in interpretability through its classification rules. In conclusion, both algorithms are effective for early CKD diagnosis, and the choice may depend on practical needs, whether emphasizing interpretability or computational efficiency. This study contributes to the development of more accurate and efficient clinical decision support systems for improving healthcare services in CKD management.

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