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All Journal International Journal of Electrical and Computer Engineering Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Agroindustri JAIS (Journal of Applied Intelligent System) Jurnal Teknologi Terapan Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin Jurnal Berdaya Mandiri Journal of Innovation Information Technology and Application (JINITA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Santi Purwaningrum
Politeknik Negeri Cilacap
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A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Imam Riadi; Abdul Fadlil; Muhammad Nur Faiz; Arif Wirawan Muhammad; Santi Purwaningrum; Laura Sari
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Abstract

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.
Perbandingan Metode Pembobotan Teks dari Algoritma Winnowing dan TF-IDF dikombinasikan Algoritma Cosine Similarity Santi Purwaningrum; Oman Somantri; Nur Wachid Adi Prasetya
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 12, No 4 (2024): Vol. 12, No 4, Desember 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v12i4.130627

Abstract

Tugas akhir di perguruan tinggi adalah syarat kelulusan untuk mendapatkan gelar sarjana atau ahli madya. Tingginya keinginan mahasiswa untuk segera lulus terkadang membuat mahasiswa melakukan tindakan plagiarisme. Plagiarisme adalah tindakan meniru dan mengutip bahkan menyalin atau mengakui hasil karya orang lain sebagai hasil karya dirinya sendiri. Penelitian ini bertujuan untuk mengembangkan sistem yang mendeteksi kesamaan antar dokumen teks berbahasa Indonesia dengan membandingkan dua metode pembobotan teks. Algoritma Winnowing dan TF-IDF adalah metode pembobotan teks yang dikombinasikan dengan metode Cosine Similarity. Cosine Similarity merupakan algoritma yang berfungsi untuk mencari nilai kesamaan antar dokumen dari hasil pembobotan algoritma winnowing dan TF-IDF. Hasil penelitian menunjukkan bahwa algoritma Winnowing memiliki nilai kesamaan rata-rata 66%, lebih tinggi dibandingkan TF-IDF yang hanya memiliki rata-rata 57%. Performa algoritma diukur menggunakan akurasi dan RMSE. Nilai akurasi pada algoritma Winnowing adalah 90.47% dan algoritma TF-IDF 81.84%. Nilai RMSE pada algoritma Winnowing sebesar 5,44 dan TF-IDF sebesar 5,34.Kata kunci : Winnowing, TF-IDF, Cosine Similarity.The final project at a higher education institution is a graduation requirement to obtain a bachelor's or associate degree. The strong desire of students to graduate quickly sometimes leads them to commit plagiarism. Plagiarism is the act of imitating, quoting, or even copying or acknowledging someone else's work as their own. This research aims to develop a system that detects similarities between Indonesian text documents by comparing two text weighting methods. The Winnowing and TF-IDF algorithms are text weighting methods combined with the cosine similarity method. Cosine similarity is an algorithm used to find the similarity value between documents based on the weighting results of the Winnowing and TF-IDF algorithms. The results of the study showed that the Winnowing algorithm had an average similarity value of 66%, higher than TF-IDF which only had an average of 57%. The performance of the algorithm uses measurements and RMSE. The algorithm's performance was measured using accuracy and RMSE. The accuracy value of the winnowing algorithm is 90.47% and the TF-IDF algorithm is 81.84%. The RMSE value of the Winnowing algorithm is 5.44 and TF-IDF is 5.34.Keywords: Winnowing, TF-IDF, Cosine Similarity. 
Gold Price Forecasting using Time Series Modeling on a Web Platform Dwi Ratna Puspita Sari; Sirli Fahriah; Kurnianingsih; Santi Purwaningrum
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/4p33wz16

Abstract

Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.
Co-Authors Abdul Fadlil Afrizal Abdi Musyafiq Agus Susanto Annas Setiawan Prabowo Ari Kristiningsih Arif Wirawan Muhammad Davito Rasendriya Rizqullah Putra Dwi Ratna Puspita Sari Hera Susanti Hety Dwi Astuti Imam Riadi Kurnianingsih, - Laura Sari Maharrani, Ratih Hafsarah Mardiyana Mardiyana Muhammad Nur Faiz Nur Wachid Adi Prasetya Oman Somantri Prih Diantono Abda`u Rafiq, Arif Ainur Ratih Hafsarah Maharrani Riyanto Riyanto Sari Widya Utami Sirli Fahriah Sukma Aji Wittriansyah, Khoeruddin