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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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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Automatic Vegetable Watering System Using Fuzzy Logic with Integration of Soil Moisture, Rain Sensors, and RTC Arginanta, Dallarizki; 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.v8i2.8319

Abstract

Conventional vegetable watering often presents challenges, particularly in ensuring that plants receive adequate water without excessive manual intervention. This research proposes a solution in the form of an automatic watering system using fuzzy logic, which integrates soil moisture sensors, rain sensors, and an RTC (Real-Time Clock) for scheduling. The system is designed to replace manual watering methods with an automated process, thus improving the efficiency and effectiveness of vegetable cultivation. The developed device uses a soil moisture sensor to monitor soil conditions, a rain sensor to detect rainfall, and an RTC to determine the optimal watering times. The Arduino Uno acts as the main controller that activates the water pump via a relay driver based on data received from the sensors. Test results show that the system operates according to the established criteria, with a satisfactory accuracy level. The system successfully waters the plants at 07:00 WIB and 15:00 WIB, based on dry soil conditions and no rain. The trials showed that the device has an average soil moisture measurement error of 5%, and a time discrepancy of about 22 seconds on the RTC module. Each 1% increase in soil moisture requires approximately 1 second of watering duration. Watering times are adjusted to prevent the plants from drying out or dying, with a soil moisture threshold of below 40% set as the condition for requiring watering.
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.
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.
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.
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