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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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
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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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.
Infiltrasi Union: SQL injection untuk Ekstraksi Kredensial Admin Setiawan, Rangga Wahyu; Ashari, Wahid Miftahul
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3460

Abstract

Eksploitasi web adalah suatu tindakan untuk memanfaatkan celah keamanan pada sebuah situs web untuk mendapatkan akses yang tidak sah ke sistem tersebut, SQL injection adalah jenis kerentanan keamanan yang terjadi dalam aplikasi web berbasis database di mana penyerang melibatkan kode berbahaya ke dalam aplikasi untuk mendapatkan akses tidak sah ke informasi sensitif. Makalah ini bertujuan untuk memberikan wawasan yang komprehensif dan sistematis tentang metode yang ada untuk mendeteksi serangan injeksi SQL. Dalam penelitian ini, penulis mengambil aplikasi web sebagai objek dan mencoba mendemonstrasikan serangan aplikasi web umum yaitu SQL injection. Penggunaan query union memiliki peran penting dalam melakukan SQL injection pada kasus ini, karena jika dilihat dari database tersebut tidak memiliki field name username dan password. Inti serangan ini mencoba untuk menggabungkan hasil dari query asli dengan hasil dari query yang dicuri dari tabel "pengguna".
Prototype Sistem Monitoring Lampu Penerangan Jalan Umum Menggunakan Zigbee dan Esp32: Prototype of Public Street Light Monitoring System Using Zigbee and Esp32 Ashari, Wahid Miftahul; Jeki Kuswanto; Firman Asharudin; Andriyan Dwi Putra; Muhammad Tofa Nurcholis
The Indonesian Journal of Computer Science Vol. 13 No. 1 (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.v13i1.3598

Abstract

Street lighting, or Public Street Lighting (PJU), is crucial infrastructure in an area, particularly for nighttime traffic illumination. Efficiency and monitoring pose significant challenges in PJU management. Most PJUs lack a monitoring system, making control and malfunction detection difficult. Monitoring is expected to efficiently regulate the on-off schedule and detect malfunctions. This research aims to develop a cost-effective PJU monitoring and efficiency system without replacing many existing devices. The method employs Zigbee technology and Esp32. This system is anticipated to enhance efficiency and detect issues more promptly than before.
KERANGKA PENELITIAN: MENUJU PENYELESAIAN STUDI DAN PENELITIAN KEAMANAN SIBER: RESEARCH FRAMEWORK: TOWARDS COMPLETION OF CYBERSECURITY STUDY AND RESEARCH Melwin Syafrizal; Wahid MiftahuL Ashari; Mauludil Asri M Cane
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6715

Abstract

This article presents a research framework in cybersecurity developed through a narrative literature review and conceptual analysis. The research process involved four phases: (1) a literature search using keywords related to "research framework" and "cybersecurity" in leading scientific databases; (2) a literature analysis to identify common and specific components in the research design; (3) a synthesis of these components into a systematic research framework; and (4) an enrichment of the framework by considering the unique needs of cybersecurity research, such as ethical aspects, technical experiments, and attack simulations. The resulting research is a Cybersecurity Research Design Framework (CRDF), which comprises ten core steps: identifying the research problem, identifying gaps, formulating the problem, establishing objectives, research questions, a theoretical framework and literature review, research methodology, data collection instruments and techniques, data analysis, and explaining the significance of the research. The CRDF's uniqueness compared to typical computer science research frameworks lies in its integration of specific cybersecurity needs, such as validation through simulations, technical experiments, and consideration of privacy and ethical issues. Thus, CRDF offers not only conceptual guidance but also practical instruments that can help graduate students design more systematic, applicable, and high-quality cybersecurity research.
ANALISIS PENGARUH VARIASI POSTUR DAN MOBILITAS TERHADAP AKURASI ESTIMASI TINGGI BADAN PADA KURSI RODA CERDAS MENGGUNAKAN PENDEKATAN CHUMLEA Fajri Albar Amri; Wahid MIftahul Ashari; Jeki Kuswanto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7316

Abstract

This study evaluates the accuracy of height estimation on a sensor-based smart wheelchair system, focusing on user posture and mobility variables. Precise height measurement is crucial in medical applications, particularly for pharmacological dose determination and nutritional status evaluation, where non-standard posture or user movement poses a high risk of causing data bias. Testing was conducted using the Chumlea method as a validation standard for the ultrasonic sensors integrated into the wheelchair. The experiment involved four specific conditions: normal/static, non-normal/static, normal/dynamic, and non-normal/dynamic postures, involving 20 subjects (10 men, 10 women). Data analysis utilized Mean Absolute Error (MAE) and ANOVA tests to assess the significance of accuracy differences between conditions. The results indicate that testing conditions significantly affect height estimation accuracy. In the normal/static condition, the average MAE was 0.65 cm for men and 1.12 cm for women. In contrast, in the non-normal/dynamic condition, MAE increased sharply to 2.93 cm for men and 2.88 cm for women, indicating a significant decrease in accuracy. The ANOVA statistical test confirmed a significant difference (p < 0.001) in sensor accuracy among the different testing conditions . These findings conclude that non-ideal posture and dynamic mobility simultaneously exacerbate sensor inaccuracies. This research contributes to the development of smart wheelchair technology by providing insights into how external factors influence sensor performance for more accurate medical decision-making.
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 Fajri Albar Amri 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 Mauludil Asri M Cane Melwin Syafrizal Muhammad Koprawi Muhammad Tofa Nurcholis Muhammad Wildan Nenden Ranuma Ratri Nevy Bella Samantha Norhikmah Norhikmah Nugraha, Anggit Ferdita 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