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All Journal JUPIIS: Jurnal Pendidikan Ilmu-ilmu Sosial JAIS (Journal of Applied Intelligent System) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of New Media Technology JATI (Jurnal Mahasiswa Teknik Informatika) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Komputer, Informasi dan Teknologi Brilliance: Research of Artificial Intelligence International Journal Software Engineering and Computer Science (IJSECS) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Practical Computer Science (JPCS) SABAJAYA Jurnal Pengabdian Kepada Masyarakat Jurnal Lentera Pengabdian Proceeding International Pelita Bangsa Jurnal Pelita Pengabdian SAINTEK JPMAS : Jurnal Pengabdian Masyarakat Inaba of Community Services Journal Jurnal Pengabdian Nasional (JPN) Indonesia Cahaya Pengabdian
Maulana, Donny
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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ANALISIS EFEKTIVITAS SISTEM DETEKSI INTRUSI TERHADAP SERANGAN DDOS: INVESTIGASI BERBASIS SIMULASI Isarianto, Isarianto; Turmudi Zy, Ahmad; Maulana, Donny; Susilo, Arif
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14359

Abstract

Serangan Distributed Denial of Service (DDoS) merupakan ancaman serius bagi keamanan jaringan modern karena mampu melumpuhkan layanan digital secara masif. Penelitian ini menyoroti pentingnya sistem deteksi intrusi (Intrusion Detection System/IDS) yang tangguh dalam menghadapi serangan tersebut, terutama dengan pendekatan pembelajaran mesin. Permasalahan utama yang diangkat adalah bagaimana meningkatkan akurasi deteksi serangan dalam kondisi distribusi data yang tidak seimbang. Penelitian ini bertujuan untuk mengevaluasi efektivitas IDS berbasis algoritma XGBoost dalam mengidentifikasi lalu lintas jaringan berbahaya, khususnya serangan DDoS, dengan memanfaatkan dataset CICIDS2017. Metode yang digunakan meliputi pra-pemrosesan data, penyeimbangan kelas menggunakan undersampling, normalisasi fitur, pelatihan model dengan XGBoost, serta optimasi hyperparameter melalui grid search. Evaluasi kinerja dilakukan menggunakan metrik precision, recall, F1-score, confusion matrix, dan ROC-AUC. Hasil menunjukkan bahwa model mencapai nilai di atas 99% untuk seluruh metrik evaluasi, menandakan performa deteksi yang sangat baik. Penelitian ini menyimpulkan bahwa kombinasi balancing data dan optimasi XGBoost mampu menghasilkan IDS yang andal dalam skenario simulasi serangan DDoS, serta menyoroti pentingnya pengujian lanjutan pada data nyata untuk mengukur kemampuan generalisasi sistem.
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

Abstract

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
The Sentiment Analysis of Bekasi Floods Using SVM and Naive Bayes with Advanced Feature Selection Amali, Amali; Maulana, Donny; Widodo, Edy; Firmansyah, Andri; Danny, Muhtajuddin
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.v4i1.4268

Abstract

Flood management in Bekasi City poses significant challenges, necessitating strategies grounded in an understanding of community sentiment. This study aims to develop and optimize sentiment analysis of social media data related to flooding using Support Vector Machine (SVM) and advanced feature selection techniques. The primary goal is to enhance the accuracy of classifying public sentiment toward flood management efforts in Bekasi City. Data is collected from various social media platforms, preprocessed, and analyzed using SVM with feature selection techniques like Information Gain and Analysis of Variance (ANOVA). (Thoriq et al., 2023) Our findings indicate that using SVM with advanced feature selection significantly improves sentiment classification accuracy compared to standard methods. These results offer insights into public perceptions, helping policymakers improve management strategies and communication for flood events. This method assists in understanding community responses and pinpointing critical areas needing attention. Moreover, this study contributes to disaster management in urban flood-prone areas by presenting a methodological approach applicable to other disaster contexts. Integrating social media sentiment analysis with advanced machine learning techniques offers a robust framework for real-time public sentiment assessment, enhancing disaster response strategies. Furthermore, these techniques help create a more resilient urban environment by improving the efficiency and effectiveness of flood management practices. This comprehensive tool is essential for better preparedness, response, and recovery from flood events, ultimately enhancing community resilience and safety in Bekasi City. This research is part of machine learning in disaster management and a valuable asset for city planners and disaster professionals around the world.
Rancang Bangun Sistem Informasi Inventaris Barang Berbasis Web Pada Cv. Kembar Jaya Mandiri Hardianti, Fazrin Putri; Maulana, Donny; Afriantoro, Irfan; Suratman, Suratman; Anwar, M. Syaibani
Jurnal SIGMA Vol 15 No 3 (2024): Desember 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i3.6040

Abstract

Sistem manajemen inventaris berbasis web ini dirancang untuk meningkatkan efisiensi dan akurasi dalam manajemen inventaris. Penelitian ini menggunakan kajian pustaka, observasi langsung, dan analisis kebutuhan sistem untuk merancang sistem yang mencakup fitur-fitur seperti manajemen data inventaris, pelacakan barang masuk dan keluar, dan pelaporan inventaris secara real-time. Sistem ini dibangun menggunakan teknologi seperti HTML, CSS, PHP, MySQL, dan UML, dengan fitur keamanan termasuk enkripsi data dan manajemen kontrol akses. Hasil pengujian menunjukkan bahwa sistem ini secara efektif mengurangi kesalahan manusia, meningkatkan efisiensi, dan menyediakan informasi inventaris yang akurat dan real-time, sehingga menjadikannya solusi yang efektif bagi organisasi.
Data Mining Association Rule Untuk Menentukan Produk Paling Diminati Pada Bengkel Namura Sihombing, Veronika Rustiani Dame; Maulana, Donny; Miharja, Muhammad Najamuddin Dwi; Suwarno, Agus; Putri, Isria Miharti Mahaerni
Jurnal SIGMA Vol 15 No 3 (2024): Desember 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i3.6041

Abstract

Bengkel Namura merupakan sebuah retail yang bergerak di bidang penjualan suku cadang sepeda motor, seperti busi, aki, oli, bengkel Namura setiap hari banyak terjadi transaksi pada penjualannya namun data tersebut tidak dimanfaatkan untuk informasi bisnis dan strategi bisnis, bengkel Namura juga memiliki kendala dalam menentukan promosi produk dan tidak mengetahui pola pembelian produk yang paling diminati, hal ini apabila dibiarkan akan mengalami kendala pada produsen bengkel karena tidak memanfaatkan sumber data yang ada sebagai informasi atau detail yang baru, Menemukan pola asosiasi produk yang paling umum untuk dijadikan panduan dalam bundling promosi dan mengidentifikasi tren pembelian merupakan tujuan dari penelitian ini. Algoritma apriori yang menggunakan teknik asosiasi untuk mengidentifikasi pola pada data transaksi untuk digunakan sebagai aturan menghasilkan tiga aturan tertinggi yaitu sebagai berikut: jika anda membeli kampas rem, anda akan membeli oli Mpx dengan lift ratio sebesar 2.110874 dan nilai support sebesar 0.091667 serta confidence 0.492537. Jika Anda membeli oli Mpx, Anda juga akan membeli oli gardan dengan rasio daya angkat sebesar 2,542373, tetapi jika Anda membeli oli gardan, Anda akan membeli oli Mpx dengan nilai dukungan sebesar 0,097222 dan keyakinan 0,593220 serta rasio daya angkat sebesar 2,542373.
Convolutional Neural Network Implementation in BISINDO Alphabet Sign Language Recognition System Kinanti, Aning Aning; Maulana, Donny; Edora, Edora
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3629

Abstract

This research develops a system for recognizing finger spelling gestures in Indonesian Sign Language (BISINDO) using Convolutional Neural Network (CNN). The objective of this research is to apply the Convolutional Neural Network (CNN) method to the BISINDO finger spelling gesture recognition system to improve its accuracy. The method employed is Convolutional Neural Network (CNN), an effective method for processing image data for pattern recognition. Based on the test results, the system demonstrates that the developed CNN model is capable of recognizing BISINDO finger spelling gestures with an accuracy of 97.5%. This indicates that the BISINDO finger spelling gesture recognition system performs well in pattern recognition. The implementation of the system for real-time prediction via a web interface using Flask also enhances its accessibility. However, there is still room for improvement, particularly in recognizing one of the 26 letters that has not been predicted accurately. For further development, it is recommended to consider collecting a larger dataset and incorporating more complex gesture variations to improve recognition accuracy.
PREDICTION OF 2024 PRESIDENTIAL ELECTION USING K-NN WITH METRIC APPROACHES CHEBYSHEV AND EUCLIDEAN BASED ON TWITTER DATA INVESTIGATION Darmawan, Steven Ryan; Fatchan, Muhamad; Maulana, Donny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1720

Abstract

The potential difference between the popularity of presidential candidates on social media and in the general public poses a serious challenge in predicting the outcome of the 2024 presidential election. Technical constraints in collecting, cleaning and analyzing dynamic and large-scale social media data can threaten the accuracy and validity of predictions. To overcome this problem, careful steps and in-depth understanding are needed. Therefore, this study aims to predict the winner of the 2024 presidential election from the popularity of presidential candidates Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto on Twitter. The K-Nearest Neighbor (K-NN) method with the Both Metric approach (Euclidean and Chebyshev) was used to analyze 51,192 tweet data through the Knowledge Discovery in Database (KDD) stage using Orange software. The evaluation results show almost the same performance, with AUC values of 0.725 for Euclidean and 0.720 for Chebyshev. The CA result was 55.6% for Euclidean and 55.4% for Chebyshev. Although F1, precision, and recall were almost the same, overall, the Euclidean metric was better. The prediction shows Prabowo Subianto as the most popular candidate on Twitter. Nonetheless, these results need to be interpreted with caution and strengthened with further analysis and additional data to get a more comprehensive conclusion. This research shows that K-NN with both metrics can provide predictions above 50%, reliable enough to be able to predict the most popular candidates on Twitter.
Pengembangan Sistem Aplikasi E-Kaizen Berbasis Website Menggunakan Metode Agile (Studi Kasus PT Cataler Indonesia) Maulana, Donny; Surojudin, Nurhadi; Juluw, Sephia Maharani Niki
Journal of Practical Computer Science Vol. 4 No. 2 (2024): November 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v4i2.5233

Abstract

Technological developments are increasingly developing with technological developments, the activities carried out benefit many parties, one of which is in the manufacturing industry. PT Cataler Indonesia is still experiencing difficulties in the keizen application process because it still uses manual, namely using Microsoft Excel, data collection is in the form of paper, this is prone to data loss or document damage. Therefore, it is necessary to carry out research to develop a website-based e-kaizen application system. The aim of this research is to make it easier for employees to submit kaizen applications and process kaizen data. The method used is the Agile Method, a software development that emphasizes flexibility and responsiveness to change. In implementing the e-kaizen system, it uses the Javascript programming language and Firebase as the database. Based on the research, it can be concluded that the development of a website-based e-kaizen application system that replaces manual processes really supports the fulfillment of needs quickly, accurately and with more updates.
Pelatihan Pengelolaan Sdm Untuk Meningkatkan Kualitas Umkm Dalam Produktivitas Penjualan Rismawati; Purwanti; Anshor, Abdul Halim; Maulana, Donny; Huda, Miftahul
SABAJAYA Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 01 (2025): SABAJAYA : Jurnal Pengabdian Kepada Masyarakat
Publisher : SABA JAYA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59561/sabajaya.v3i01.541

Abstract

Pelatihan "Pengelolaan SDM untuk Meningkatkan Kualitas UMKM dalam Produktivitas Penjualan" berhasil menunjukkan bahwa peningkatan pengelolaan Sumber Daya Manusia (SDM) memiliki peran krusial dalam memperkuat kinerja dan produktivitas UMKM di Bandung Barat. Melalui pelatihan ini, peserta memperoleh pemahaman strategis dan keterampilan praktis dalam aspek rekrutmen, pengembangan karyawan, motivasi, dan penilaian kinerja. Hasil pelatihan menunjukkan peningkatan signifikan dalam pengelolaan SDM, yang mendukung pertumbuhan usaha dan meningkatkan daya saing UMKM. Pendampingan pasca-pelatihan menjadi elemen vital untuk penerapan keterampilan yang diperoleh secara efektif dan berkelanjutan. Diharapkan, peserta dapat menyebarluaskan pengetahuan yang didapat kepada UMKM lain, sehingga dampak positif dari pelatihan ini dapat meluas dan berkontribusi pada pengembangan ekonomi daerah
COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING APPROACH Nugroho, Agung; Wiyanto; Maulana, Donny
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6956

Abstract

Most machine learning algorithms tend to yield optimal results when trained on datasets with balanced class proportions. However, their performance usually declines when applied to data with significant class imbalance. To address this issue, this study utilizes the Synthetic Minority Oversampling Technique (SMOTE) to improve class distribution before model training. Several classification algorithms were employed, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Random Forest. Experimental results reveal that the Random Forest model produced the highest accuracy (95.70%) and the best F1-score, demonstrating a well-balanced trade-off between precision and recall. In contrast, the Logistic Regression algorithm achieved the highest recall (74.20%), indicating better sensitivity in identifying positive instances despite a lower F1-score. These outcomes highlight the importance of choosing appropriate classification methods based on the specific evaluation goals whether prioritizing accuracy, recall, or overall model balance.
Co-Authors Abdul Halim Anshor Abdurrafi, Alfin ADI FITRA Aditiya, Febri Aditya Nugroho Widiadi Afriantoro, Irfan Agung Nugroho Agus Suwarno, Agus Amali Amali, Amali Aminurdin, Majid Andri Firmansyah Anggi Muhammad Rifa’i Aning Risky Montana Anwar, M. Syaibani ARIF SUSILO Asep Muhidin Auliyah, Risma Barokah, Awalina Budiarto, Eko Darmawan, Steven Ryan Daspar Daspar Edora Edora, Edora Edy Widodo Ferawati, Eva Fitria Hermiati, Novi Gatot Tri Pranoto Halim Anshor, Abdul Hardianti, Fazrin Putri Hariyono Hartati, Nani Huda, Miftakul Huda, Miftakul Huda Ikhsan Romli Indah Wahyu Puji Utami, Indah Wahyu Indradewa, Rhian Ira Restu Kurnia Isarianto, Isarianto Ismamudi Ismamudi Ismamudi, Ismamudi Juluw, Sephia Maharani Niki Karsito Kinanti, Aning Aning Kurniadi, Nanang Tedi Lisa Kustina Majid, Annisa Maulana Maulana Majid, Annisa Miftahul Huda Miftakul Huda Miharja, Muhammad Najamuddin Dwi Muhamad Fatchan Muhtajuddin Danny Nawangsih, Ismasari Nisawati, Inna Nugroho, Agung Nur Ilman Ilyas Nurhidayanti, Nisa Pupung Purnamasari Purwanti purwanti, purwanti Putra, Fibi Eko Putra, Maha Putri, Isria Miharti Mahaerni Rahardjo, Sugeng Budi Retno Purwani Setyaningrum Rismawati Rismawati Rismawati sanjay, sanjay Sellina, Sesri Sihombing, Veronika Rustiani Dame Simanjuntak, Antonius Suratman Suratman Suriyanti Surojudin, Nurhadi Tedi, Nanang Tri Ngudi Wiyatno Tri Sasongko, Ananto Turmudi Zy, Ahmad Umah, Rani Nur Wiyanto Wulandari , Anna Yansyah, Hamdi