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All Journal 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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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 (IJCS)
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.
Kalibrasi Regresi Linier untuk Peningkatan Akurasi Load Cell pada Kursi Roda Cerdas Hakim, Muhamad Nauval; Miftahul Ashari, Wahid; Kuswanto, Jeki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3023

Abstract

Smart wheelchairs are an innovation designed to facilitate user mobility while monitoring their condition in real time. One of the main features developed is an integrated weight reading system. However, the accuracy of the sensor is still affected by sitting posture, body position, and surrounding environmental conditions. This study aims to improve the accuracy of the weighing system on smart wheelchairs by applying linear regression analysis as a sensor calibration method. Data collection was conducted under four conditions of use, namely sitting upright, sitting tilted, walking while sitting upright, and walking while sitting tilted, which represent variations in user load distribution. The calibration model was constructed using the average sensor reading data and evaluated using the R², MAE, and MAPE parameters. The results showed a significant improvement in accuracy with an R² value of 1.0000, MAE of 0.0687 kg, and MAPE of 0.111%, as well as a decrease in the average error from ±1.2 kg to ±0.07 kg after the calibration process. The linear regression method proved to be effective in improving the accuracy of sensor readings with light computational calculations. This study also demonstrates the potential of linear regression as an efficient lightweight calibration method for IoT-based medical systems, particularly on devices such as ESP32 or Arduino that display real-time, high-precision body weight measurements.
Perbandingan Kinerja Algoritma Machine Learning Deteksi Malware dengan Z-Score Normalization Hasil Terbaik pada Random Forest Gilang Ramadhan, Zaka; Miftahul Ashari, Wahid; Koprawi, Muhammad
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3077

Abstract

Malware detection is a major challenge in the world of cybersecurity, especially with the increasing complexity and variety of attacks. Traditional approaches are often unable to identify these new threats, making machine learning (ML) an effective solution. The purpose of this study is to compare the performance of three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM), in detecting malware in the SOMLAP dataset consisting of Windows executable files. Data processing, the use of the SMOTE technique for class imbalance, and assessment using metrics such as accuracy, precision, recall, and F1 score are all part of the research methodology. This study also applies Z-Score Normalization to reduce the influence of extreme values in the data, which helps the model handle data with different scales. The results show that Random Forest has the best performance with an accuracy of 99.16%, followed by XGBoost and SVM. Random Forest excels in the balance between precision, recall, and accuracy, making it the most effective algorithm for detecting malware. This study suggests further algorithm development using ensemble techniques and other optimizations to improve malware detection accuracy in the future.
Classification of Cat Skin Diseases Using MobileNetV2 Architecture with Transfer Learning Saputra Aji, Dian; Ashari, Wahid Miftahul; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11469

Abstract

Skin diseases in cats often present similar visual symptoms across different conditions, making early and accurate diagnosis challenging for pet owners and veterinarians. This study develops a classification model for cat skin diseases: Fungal Infection, Flea Infestation, Scabies, and Healthy, using the MobileNetV2 architecture with a transfer learning approach. A total of 1,600 RGB images were collected from public datasets and divided into 1,280 training and 320 validation samples. The dataset underwent preprocessing, normalization, and data augmentation techniques such as rotation, shear, zoom, and flipping to enhance model generalization and reduce overfitting. Several experiments were conducted to analyze the impact of input size and learning rate adjustments on model performance. The optimal configuration was achieved using an input size of 224×224 pixels, a learning rate of 0.001, and augmentation applied to the training data. The resulting model achieved a validation accuracy of 91.8%, with an average precision, recall, and F1-score of 91%, demonstrating balanced performance across all classes. These results indicate that the MobileNetV2 architecture, combined with appropriate hyperparameter tuning and augmentation, provides a reliable and computationally efficient method for automatic identification of cat skin diseases. This approach can support early diagnosis, improve animal welfare, and serve as a foundation for the development of practical veterinary diagnostic applications.
Co-Authors Amalina, Inna Nur Andriyan Dwi Putra Arginanta, Dallarizki Asharudin, Firman Astari, Devi Wening Dony Ariyus Fajar Nur Aini Dwi Fatimah Fatimah, Fajar Nur Aini Dwi Febrian Putra Pratama Firman Asharudin Firman Asharudin Gilang Ramadhan, Zaka Hakim, Muhamad Nauval 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, Andriyan Dwi Putra, Wahyu Sukestyastama Ramadhan, Rafiq Fajar Rina Pramitasari Rohmaniah, Diana Saputra Aji, Dian Satrya Bhayangkara, Dimas Senie Destya Setiawan, Rangga Wahyu Sismoro, Heri Sunu Pamungkas, Hanfazano Rahmadimasti Yansyah, Aiko Nur Hendry Yoki Irawan