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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia Progresif: Jurnal Ilmiah Komputer Buletin Ilmiah Sarjana Teknik Elektro Jurnal Abdi Insani Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Algoritma SmartComp The Indonesian Journal of Computer Science Tanggap Masyarakat untuk Aksi dan Sinergi
Ashari, Wahid Miftahul
Universitas AMIKOM Yogyakarta
Agriculture, Biological Sciences & Forestry Chemistry 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 Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics

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Text Data Security Using LCG and CBC with Steganography Technique on Digital Image Wildan, Muhammad; Ashari, Wahid Miftahul
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8457

Abstract

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.
Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks Ramadhan, Rafiq Fajar; Ashari, Wahid Miftahul
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8492

Abstract

The increase in cyber attacks has made network security a very important focus in this digital era. This research compares the performance of two machine learning algorithms, that is Random Forest and Decision Tree for detecting anomalies in networks using the UNSW-NB15 datasets, which include various types of attacks such as DoS, Backdoor, Exploits and others which will be used to train and test both models. The data collection method, pre-processing, data splitting and modelling using SMOTE method to handle data imbalanced were applied in both algorithms and then evaluated using accuracy, precision, recall and f1-score metrics. From the study result, it can be conclude that the Decision Tree algorithm performs better in detecting anomalies in binary data with an accuracy of 99,71%. However, in multi-class data, Random Forest showed slightly better performance, though it required significantly more time for training and prediction. Despite the small difference in accuracy, Decision Tree demonstrated faster prediction times, making it more efficient for time-sensitive applications. This research concludes that while Random Forest provides higher accuracy for complex datasets, Decision Tree offers a more time-efficient solution with comparable accuracy.
Sistem Monitoring Kadar pH Kolam Udang Secara Real-Time Dengan Algoritma Regresi Linier Fatimah, Fajar Nur Aini Dwi; Ashari, Wahid Miftahul
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3215

Abstract

Budidaya udang merupakan salah satu komoditas utama yang sedang digencarkan oleh pemerintah. Kurangnya implementasi teknologi membuat budidaya menghasilkan hasil yang kurang maksimal. Mahalnya alat ukur dan tidak akurasinya alat ukur membuat para pengelola tambak budidaya udang tidak menerapkan teknologi pada proses budidaya. Penelitian ini nantinya akan mengembangkan sebuah algoritme yang dapat menghasilkan estimasi PH akurat dari kondisi air pada tambak udang. Sehingga dengan menggunakan alat ukur yang sederhana para pengelola tambak udang dapat menghasilkan kondisi PH yang akurat. Pengumpulan data dilakukan dengan cara mengembangkan alat ukur atau PH meter yang akan diterapkan kolam udang, proses pengukuran akan dilakukan dengan menggunakan sensor PH yang akan diproses dengan menggunakan microcontroller yaitu arduino. Pengembangkan algoritme estimasi akan menggunakan algoritme regresi linier yang dapat menghasilkan estimasi PH akurat. Diharapkan dari algoritme yang dihasilkan dari penelitian ini dapat membantu para pengelola tambak udang untuk dapat menghasilkan hasil yang lebih maksimal karena dapat mengantisipasi kondisi-kondisi yang tidak diinginkan sejak dini.
Enhancing Website Security Using Vulnerability Assessment and Penetration Testing (VAPT) Based on OWASP Top Ten Rohmaniah, Diana; Ashari, Wahid Miftahul; Lukman, Lukman; Putra, Andriyan Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9069

Abstract

Website security is one of the main concerns in the digital era, given the increasing potential for cyber threats. This research aims to improve website security by using the Vulnerability Assessment and Penetration Testing (VAPT) method that refers to the OWASP Top Ten standard. The applied method includes four main stages: information gathering, vulnerability scanning, exploitation, and reporting. The results showed that there were several successfully exploited vulnerabilities, such as Clickjacking, Improper HTTP to HTTPS Redirection, Directory Listing, and Sensitive Information Disclosure, which were classified based on the OWASP Top Ten. The severity of the vulnerabilities was analyzed using Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Vulnerability Scoring System (CVSS). The analysis results show that some vulnerabilities have high severity after considering the factual conditions of the system. This research provides specific remediation recommendations to address these vulnerabilities, such as the implementation of security headers, deletion of sensitive configuration files, and dependency updates. With this approach, the research is expected to contribute to improving website security and provide effective mitigation guidelines.
Evaluation of the Effectiveness of Lightweight Encryption Algorithms on Data Performance and Security on IoT Devices Indrajati, Damar; Ashari, Wahid Miftahul
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9256

Abstract

Data security remains a major concern in the Internet of Things (IoT) landscape due to the inherent limitations in computational power, memory capacity, and energy availability of IoT devices. To address these challenges, lightweight encryption algorithms have emerged as alternatives to conventional cryptographic methods, aiming to balance performance and security. This study evaluates the effectiveness of five encryption algorithms—SIMON64/128, SPECK64/128, XTEA64/128, PRESENT64/128, and AES128—on IoT devices through experimental analysis of their security strength, execution time, CPU utilization, memory usage, and power efficiency. The experiments were conducted on a Raspberry Pi 3B+ using C-based implementations to emulate realistic IoT scenarios. The findings reveal that AES128 offers the strongest security characteristics, including the highest Avalanche Effect (39.29%) and Differential Resistance Score (6.76/10), but at the expense of significant resource consumption. In contrast, SIMON64/128 and SPECK64/128 deliver superior performance in terms of speed and resource efficiency, making them ideal for low-power environments, albeit with concerns about potential cryptographic backdoors. XTEA64/128 emerges as a practical compromise, delivering moderate security and low power consumption without known vulnerabilities. Based on these results, AES128 is suitable for high-capacity IoT platforms prioritizing strong encryption, while SIMON and SPECK are preferable for resource-constrained devices, with XTEA serving as a balanced alternative. This research contributes a comparative framework to guide the selection of encryption algorithms for IoT systems, ensuring an optimal trade-off between security and operational efficiency.
Pengembangan Keterampilan Dasar Pemrograman bagi Siswa SMA N 2 Bantul sebagai Persiapan di Era Digital Putra, Andriyan Dwi; Nur'aini; Ashari, Wahid Miftahul; Sismoro, Heri; Kuswanto, Jeki
Tanggap Masyarakat untuk Aksi dan Sinergi Vol. 1 No. 02 (2025): Volume 01 Nomor 02 - Juni 2025
Publisher : TAM Global Insights - PT. Treeta Amanah Mandiri Group

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pelatihan Internet of Things (IoT) di SMA Negeri 2 Bantul dilaksanakan sebagai respons terhadap rendahnya literasi teknologi dan keterampilan digital siswa, serta keterbatasan tenaga pengajar dan sumber belajar di bidang IoT. Kegiatan ini bertujuan untuk meningkatkan kompetensi dasar siswa dalam memahami dan mengaplikasikan teknologi IoT melalui pendekatan pembelajaran interaktif, praktik langsung, serta pengembangan proyek mini. Metode pelaksanaan meliputi tahap persiapan, pelaksanaan inti, dan evaluasi, dengan dukungan modul pembelajaran cetak dan digital, video tutorial, serta sesi pendampingan intensif. Hasil evaluasi menunjukkan bahwa 68,3% siswa merasa puas dan 25,5% cukup puas terhadap pelatihan yang diberikan. Pelatihan ini terbukti mampu meningkatkan minat dan pemahaman siswa terhadap IoT, serta memperlihatkan pentingnya sinergi antara sekolah dan perguruan tinggi dalam mendukung transformasi pendidikan berbasis teknologi. Kegiatan ini diharapkan menjadi model pelatihan yang dapat direplikasi di sekolah lain untuk mendorong literasi teknologi sejak dini.
Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic Irawan, Yoki; Pramitasari, Rina; Ashari, Wahid Miftahul; Yansyah, Aiko Nur Hendry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10003

Abstract

Distributed Denial of Service (DDoS) attacks represent a significant danger in network security because they can lead to extensive service interruptions. With these attacks increasingly mirroring regular traffic, smart and effective detection systems are essential. This research seeks to assess the efficacy of the Support Vector Machine (SVM) classification algorithm in identifying DDoS attacks in network traffic. The data utilized is CICIDS2017, focusing on the subset Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv, which contains both legitimate traffic and DDoS attacks like DoS-Hulk, DoS-GoldenEye, and DDoS. The preprocessing stage included eliminating duplicates and null entries, label binary encoding, normalization through Min-Max Scaler, and feature selection applying the Chi-Square technique. The data was divided into 80% for training and 20% for testing purposes. The Radial Basis Function (RBF) kernel was utilized to train the SVM model, and hyperparameter optimization was performed with GridSearchCV. The evaluation of the model's performance was conducted through accuracy, precision, recall, F1-score, confusion matrix, and visual representations including ROC and Precision-Recall Curves. The findings indicate that prior to tuning, the model reached an accuracy of 97%, which increased to 99% post-tuning, accompanied by an F1-score of 0.99. This shows that the SVM algorithm, when paired with appropriate preprocessing and optimization, is very efficient in identifying DDoS attacks within network traffic.
Pengaruh Load Balancing Pada Ser Pengaruh Load Balancing Pada Serangan DDoS Menggunakan Nginx: Pengaruh Load Balancing Pada Serangan DDoS Menggunakan Nginx Satrya Bhayangkara, Dimas; Miftahul Ashari, Wahid
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4118

Abstract

DDoS (Distributed Denial of Service) attacks are one of the most common cyberattacks. This attack can make a server experience an error. Various methods have been used to overcome this attack, one of which is load balancing. Load balancing is responsible for dividing the workload among various servers evenly. In this study, we used Nginx load balancing. The research was conducted by sending 100000, 300000, 400000, and 500000 requests. Throughput after using load balancing shows superiority, with an average of 9,581 kb/s compared to not using load balancing. Response time using load balancing is also better than not using load balancing, with an average of 4507.23 ms. However, the packet loss shows no packet loss, which is 0% after using load balancing and before using load balancing. The effect of load balancing on Nginx can prevent DDoS attacks with a load balancing algorithm that is still good enough to use.
Analysis of the Performance Comparison between Random Forest and SVM RBF in Detecting Cyberbullying on Imbalanced Data with the SMOTE Approach Amalina, Inna Nur; Norhikmah, Norhikmah; Ashari, Wahid Miftahul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5574

Abstract

Cyberbullying has emerged as a growing threat with the widespread adoption of social media, creating significant risks to online safety. Automatic detection of such behavior remains challenging, particularly when the training dataset is highly imbalanced. This study presents a comparative analysis of Random Forest and Support Vector Machine with Radial Basis Function kernel (SVM RBF) for cyberbullying detection, incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The experiments utilized a publicly available, manually annotated dataset containing 47,693 English-language tweets from global users, labeled as cyberbullying or non-cyberbullying. Performance was evaluated using accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved the highest performance before SMOTE (accuracy = 88.52%, precision = 89.07%, recall = 94.00%, F1-score = 91.49%), while SMOTE improved recall for both algorithms but reduced accuracy and precision. These findings highlight that the choice of algorithm and effective handling of class imbalance are critical for enhancing the reliability of automated cyberbullying detection systems, thereby enabling more effective content moderation and safer online environments.
PEMBERDAYAAN KEMITRAAN MASYARAKAT MELAUI BADAN USAHA MILIK DESA (BUMDESA) SEBAGAI PENGGERAK EKONOMI MASYARAKAT GUNA MENCAPAI SDGs DESA DI ERA DIGITAL Nurussa'adah, Erfina; Astari, Devi Wening; Ashari, Wahid Miftahul
Jurnal Abdi Insani Vol 11 No 1 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i1.1206

Abstract

Badan Usaha Milik Desa (BUMDesa) adalah lembaga usaha desa yang dikelola oleh masyarakat dan pemerintahan desa dalam upaya memperkuat perekonomian desa dan dibentuk berdasarkan kebutuhan dan potensi desa. Pada praktiknya ternyata ditemukan permaslahan terkait aspek pengembangan BUMDesa, yakni kendala promosi, lemahnya jaringan pemasaran, kendala manajemen pengelolaan dan administrasi keuangan,serta masih rendahnya kecakapan sumber daya manusia dalam penguasaan teknologi komunikasi. Untuk itu program pengabdian ini dilakukan dengan tujuan memberikan pendampingan pemasaran digital agar BUMDesa Amarta, Desa Pandowoharjo, Sleman, agar BUMDesa memiliki media digital yang aktif memasarkan produknya melalui konten-konten yang merangsang konsumen untuk melakukan pembelian. Selain itu untuk mendukung pengembangan BUMDesa ditargetkan terbentuk manajemen pengelolaan dengan membantu rebranding, keuangan dan pemasaran yang baik. Metode yang digunakan pengusul untuk merealisasikan target adalah melalui workshop terkait manajemen komunikasi pemasaran, komunikasi pemasaran virtual, manajemen pengelolaan dan keuangan. Selain itu juga dilakukan pembuatan website, media sosial, pendampingan pembuatan konten komunikasi virtual (video profil, kemasan, foto produk, e-katalog, e-pamflet) untuk mendukung kegiatan pemasaran berbasis digital. Hasil pengabdian adalah berupa peningkatan pengetahuan dan ketrampilan dalam penggunaan website, ecommers dan sistem keuangan, serta ketrampilan pembuatan konten foto produk. Kesimpulan dari terlaksananya pengabdian adalah pemberian pelatihan dan pendampingan memberikan kontribusi dalam peningkatan pengetahuan, kecakapan dan kemampuan pengelola BUMDesa Amarta memanfatkan teknologi digital.
Co-Authors Amalina, Inna Nur Andriyan Dwi Putra Arginanta, Dallarizki Asharudin, Firman Astari, Devi Wening Baita, Anna Dony Ariyus Eko Pramono Fajar Nur Aini Dwi Fatimah Fatimah, Fajar Nur Aini Dwi Febrian Putra Pratama Gilang Ramadhan, Zaka Hakim, Muhamad Nauval Hakim, Walidy Rahman Indrajati, Damar Jeki Kuswanto Jeki Kuswanto Koprawi, Muhammad Lukman Lukman Martisa Fiorentina, Rinda Muhammad Koprawi Muhammad Tofa Nurcholis Muhammad Wildan Nenden Ranuma Ratri Nevy Bella Samantha Norhikmah Norhikmah Nugraha, Anggit Ferdita Nur'aini Nurussa'adah, Erfina Putra, Wahyu Sukestyastama Rahim, Abd Mizwar A. Ramadhan, Rafiq Fajar Rina Pramitasari Rohmaniah, Diana Saputra Aji, Dian Satrya Bhayangkara, Dimas Senie Destya Setiawan, Rangga Wahyu Sismoro, Heri Sunu Pamungkas, Hanfazano Rahmadimasti Supriatin Supriatin, Supriatin Yansyah, Aiko Nur Hendry Yoki Irawan