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All Journal Jurnal Tecnoscienza G-Tech : Jurnal Teknologi Terapan International Journal Software Engineering and Computer Science (IJSECS) Tech : Journal of Engineering Science Smatika Jurnal : STIKI Informatika Jurnal
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Universitas Duta Bangsa
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Mathematics Other

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Prediksi Serangan Sql Injection Pada Jaringan Komputer Menggunakan Metode Support Vector Machine (SVM) Pramono; Aprilisa Arum Sari
JURNAL TECNOSCIENZA Vol. 8 No. 2 (2024): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v8i2.1184

Abstract

Serangan sql injection merupakan ancaman serius bagi keamanan jaringan komputer dan integritas data. Dalam upaya untuk mengatasi ancaman ini, penelitian telah dilakukan untuk mengembangkan metode deteksi yang efektif. Salah satu pendekatan yang menjanjikan adalah menggunakan metode Support Vector Machine (SVM) dalam Machine Learning. Dalam penelitian ini, kami memperkenalkan pendekatan prediksi serangan SQL injection pada jaringan komputer menggunakan SVM. Langkah-langkah prediksi meliputi, pengelompokan data, pelatihan model SVM, validasi, pengujian, dan evaluasi kinerja model. Diharapkan penelitian ini dapat memberikan kontribusi dalam pengembangan sistem deteksi yang dapat melindungi sistem komputer dari ancaman sql injection. Dataset yang akan digunakan dalam penelitian ini berasal dari sebuah website bernama Kaggle. Penelitian ini menganalisis metode yang dihasilkan dari proses klasifikasi berdasarkan Sekenario percobaan menghasilkan nilai akurasi confusion matrix, precision, recall, dan menghasilkan tingkat akurasi 96, 8412% pada sekenario kedua.
Sistem Monitoring Intensitas Cahaya dan Suhu Air Aquascape Menggunakan Internet of Things Pramudya Aziz Wisnuadi Wisnuadi; Rudi Susanto; Pramono
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4645

Abstract

Menjaga kualitas air dan intensitas cahaya dalam aquascape sering kali menjadi tantangan karena kondisi lingkungan yang berubah-ubah. Permasalahan utama dalam penelitian ini adalah bagaimana memantau dan mengatur intensitas cahaya serta suhu air secara otomatis untuk mendukung keberlangsungan hidup biota dalam aquascape. Penelitian ini bertujuan untuk mengembangkan sistem monitoring intensitas cahaya dan suhu air berbasis Internet of Things (IoT) dengan menggunakan metode prototype yang memiliki tahap-tahap mulai dari Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, dan Deployment Delivery & Feedback. Penelitian ini menggunakan sensor DS18B20 untuk memonitoring temperatur air dan sensor GY-302 BH1750 untuk memonitoring intensitas cahaya. Alat ini juga dilengkapi dengan notifikasi melalui aplikasi Blynk pada smartphone, sehingga pengguna dapat memantau kondisi aquascape secara real-time.  Hasil penelitian menunjukkan bahwa alat ini mampu memantau kualitas air dan lingkungan aquascape dengan akurat serta memberikan notifikasi otomatis, sehingga dapat menjaga kondisi optimal untuk biota di dalamnya.
Pengembangan Prototipe Sistem Pemantauan Suhu dan Kelembaban Tanah Berbasis IoT pada Tanaman Bawang Merah Yuyun Prastiwi; Cantika Risky Ramadhana Pawestri; Otto Santoso Putro; Pramono Pramono
Tech : Journal of Engineering Science Vol 1 No 1 (2025): Pengembangan dan Penerapan Solusi Rekayasa untuk Tantangan Lingkungan, Industri,
Publisher : Yayasan Penelitian dan Pengabdian Masyarakat Sisi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69836/tech.v1i1.442

Abstract

Red onions are widely used by the community and intensively cultivated by farmers despite their relatively short growing period. This plant is susceptible to excessive rainfall, which can cause rot. To address this issue, the greenhouse concept provides a solution for maintaining a stable growing environment. Internet of Things (IoT) technology offers an innovative approach through a Smart Greenhouse system capable of automatically monitoring and controlling environmental conditions. In this study, the device was successfully designed and implemented using several key components, including an ESP32, DHT11 sensor, soil moisture sensor, LDR sensor, relay, DC pump, and 9V battery. Testing over three days demonstrated that the device effectively responded to environmental changes. The water pump will activate automatically when soil moisture levels drop below 49%, while the cooling fan turns on when the ambient temperature exceeds 30°C. This system maintains the stability of the microclimate around the red onion growing medium while addressing the need for automation in temperature and humidity management. As such, the Smart Greenhouse system is expected to assist farmers in addressing climate change challenges, reduce reliance on human labor, and enhance the efficiency of red onion cultivation.
Performance Comparison of Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) Algorithms in Human Face Classification Yusuf Iskandar Royan; Pramono Pramono; Anindhiasti Ayu Kusuma Asri
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7384

Abstract

Facial expression recognition is crucial in fields like mental health monitoring and human-computer interaction. This study compares Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) in classifying facial images into stress and non-stress categories. Using a preprocessed dataset of labeled facial expressions, CNN was employed for its strength in automatic spatial feature extraction, while SVM served as a traditional machine learning benchmark. Both models were trained and tested on the same dataset split. Results showed CNN outperformed SVM in all performance metrics: CNN achieved 88.94% accuracy, 94.42% precision, 93.25% recall, and an F1-score of 89.85%, while SVM recorded 76.53% accuracy, 77.14% precision, 85.72% recall, and an F1-score of 80.67%. Despite its lower performance, SVM had faster training and a simpler structure, making it suitable for resource-limited scenarios. The study emphasizes the superiority of deep learning for complex image classification tasks.
Prototype Monitoring Suhu Dan Kelembapan Pada Ruang Server Di RSU PKU Muhammadiyah Sragen Berbasis Internet Of Things Aldita Kusuma Wardana; Rudi Susanto; Pramono Pramono
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1763

Abstract

The Internet of Things (IoT) is an embedded system designed to expand the utilization of always-active internet connectivity. IoT infrastructure includes existing networks and the internet along with its evolution. This infrastructure supports the process of object identification, sensors, and connectivity capabilities, serving as the foundation for the development of independent cooperative services and applications. IoT is also characterized by a high level of autonomy in data collection, event transfer, network connectivity, and interoperability. A microcontroller is a computer system on a single IC chip, often referred to as a single-chip microcomputer. With the advancement of microcontrollers, IoT has evolved to include modern Ethernet- and Wi-Fi-based modules, one of which is the ESP8266. This research aims to develop a temperature and humidity monitoring system for the server room at RSU PKU Muhammadiyah Sragen based on the Internet of Things. With this system, the temperature and humidity in the server room can be monitored online from anywhere, enabling quick action to be taken in the event of excessive temperature rise. The research method involves several stages, including data collection, needs analysis, design, planning, and testing. The results of this study indicate that the developed design is capable of detecting temperature and humidity in the RSU PKU Muhammadiyah Sragen server room, and this information can be displayed through the Blynk application. The testing of the server room monitoring system shows that the temperature sensor has an accuracy of 92.0%, while the humidity sensor shows a value of 91.03 %.
Website-Based Liquid Selection Recommendation System Using Content-Based Filtering Method at Morevapor Gading Store Rizky Rama Mulyawan; Wijiyanto; Pramono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3082

Abstract

Liquid is a favorite product among various vape lovers. This product provides a variety of unique and refreshing flavors, attracting the attention of vaping lovers to always try new variants. The high cost of purchasing vape liquid makes many people prefer to buy products recommended according to their preferences, making MoreVapor Gading the main choice. This study aims to develop a recommendation system for selecting vape liquid using a content-based filtering mechanism with the TF-IDF approach. The TF-IDF approach was chosen because of its ability to provide more precise weighting to relevant but not too common words, resulting in more accurate recommendations compared to other methods. In practice, the results of this study provide significant benefits for MoreVapor Gading, namely increasing the accuracy of product recommendations that can minimize ordering errors and increase customer satisfaction and loyalty. This research method uses a waterfall model consisting of the analysis, design, implementation, and testing stages. The results of the study show that from 21 datasets, the system can provide five recommendations with the highest similarity values, namely Cair Grape 0.1445, American Winter Grape Candy Magic 0.1243, Paradewa Grape Athena 0.1151, American Winter Magic Fanta Float 0.0923, and Foom Breeze Series Guava 0.0918 based on user preferences. The recommendation system developed aims to provide accurate recommendations and in accordance with user preferences in choosing vape liquid.
Co-Authors Aldita Kusuma Wardana Anindhiasti Ayu Kusuma Asri Aprilisa Arum Sari Cantika Risky Ramadhana Pawestri Otto Santoso Putro Pramudya Aziz Wisnuadi Wisnuadi Rizky Rama Mulyawan Rudi Susanto Wijiyanto Yusuf Iskandar Royan Yuyun Prastiwi