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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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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.
Optimalisasi Engine Optimalization On-Page untuk Meningkatkan Kinerja Situs Berita Digital Menggunakan Analisis CTR dan UX Martisa Fiorentina, Rinda; 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.22-2.2377

Abstract

This study analyzes the application of Search Engine Optimization (SEO) strategies on the Radar Jogja website to increase its visibility on search engines. With internet penetration in Indonesia reaching 78.19% in 2023, digital platforms have become an important necessity for media companies. Radar Jogja faces the challenge of competing for top positions on Google's search engine results pages (SERPs), where most users only access the first two pages. This study uses a descriptive-analytical method to evaluate key SEO elements such as titles, URLs, internal link structure, and page performance. Test results show that CTR increased from 2% to 4.75% and average position improved from 12th to 6th. In addition, dwell time increased to 2.25 minutes and bounce rate decreased, indicating a significant improvement in user experience. The results of this study contribute to content-based SEO strategies for local media to increase digital competitiveness and gain better visibility on search engines.
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.
Smart Fish Farm Budidaya Ikan Nila Menggunakan NodeMCU Terintegrasi Berbasis Internet Of Things Kuswanto, Jeki; Ashari, Wahid Miftahul; Asharudin, Firman
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 1 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i1.5061

Abstract

Teknologi budidaya ikan yang digabungkan dengan pertanian saat ini berkembang  pesat banyak muncul sistem yang cocok untuk menggabungkan antara media tanam dan juga budidaya ikan salah satunya yaitu media tanam vertikultur  yang mana sistem budidaya pertanian ini dilakukan secara vertikal atau bertingkat  pada ruang lingkup indoor maupun outdoor. beberapa proses yang dilakukan masih secara manual yaitu  melakukan penyiraman tanaman,  pengecekan PH air, memberi makan ikan, pengecekan suhu air dalam kolam,  mengontrol tingkat kelembaban tanah, ini dikerjakan secara manual. Oleh sebab itu Smart fish Farm dibutuhkan sistem  sistem ini dibuat otomatisasi berbasis iot (internet of thing)  dibutuhkan untuk mengatasi beberapa apa permasalahan tersebut, dengan memanfaatkan  NodeMCu sebagai mikrokontroler yang akan dihubungkan dengan sensor kelembaban tanah, sensor suhu, PH meter,  motor DC maka kontrol dan pemantauan  penyiram tanaman, pemberian makan ikan, kondisi air air dapat dilakukan secara otomatis. smart fish Farm budidaya ikan nila dan tanaman  vertikultur  berbasis iot (internet of thing)  dapat menampilkan data yang sesuai melalui  aplikasi mobile yang dapat dilihat oleh pengguna, mulai dari tingkat kelembaban tanah menampilkan hasil kadar pH dari 1 sampai dengan 10 kadar pH dan menampilkan suhu kolam ikan dari rentan 15 sampai dengan 32 derajat Celcius serta Aplikasi mobile  dapat mengontrol sistem pakan ikan.
Stroke prediction using data balancing method and extreme gradient boosting Rahim, Abd Mizwar A.; Baita, Anna; Asharudin, Firman; Ashari, Wahid Miftahul; Hakim, Walidy Rahman; Putra, Andriyan Dwi; Supriatin, Supriatin; Pramono, Eko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp655-671

Abstract

Stroke is one of the leading causes of death worldwide, creating an urgent need for effective early detection systems, particularly because conventional methods often struggle with class imbalance and produce biased evaluations. Previous studies have primarily focused on accuracy while overlooking model consistency, data pre-processing quality, and probability-based evaluation. This study evaluates model performance under three conditions: original data using extreme gradient boosting (XGBoost) with scale_pos_weight, original data using the easy ensemble classifier, and class-balanced data generated using random oversampling (ROS), adaptive synthetic sampling (ADASYN), and synthetic minority over-sampling technique (SMOTE). Each model underwent missing value handling, normalization, feature preparation, and hyperparameter optimization using grid search. Performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), confidence intervals, calibration curves, Shapley additive explanations (SHAP), decision curve analysis (DCA), and external validation. The results demonstrate that data resampling significantly improves performance, with the XGBoost-SMOTE combination achieving the best results, including an accuracy of 0.99, AUROC of 0.998, and AUPRC of 0.986, outperforming the other approaches. This method provides more consistent and balanced predictions, supporting the application of artificial intelligence for early stroke risk identification.
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 Firman Asharudin Firman Asharudin 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, Andriyan Dwi 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