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Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5) Hesmi Aria Yanti; Heru Sukoco; Shelvie Nidya Neyman
CESS (Journal of Computer Engineering, System and Science) Vol 6, No 1 (2021): Januari 2021
Publisher : Universitas Negeri Medan

Abstract— BitTorrent is a P2P file sharing software protocol that allows clients to apply data to other clients and can affect network performance. Bittorent client traffic data collection uses secondary data taken from official sources on the link https://unb.ca/cic/datasets/index.html in 2016. Traffic data is used as a model for BitTorrent traffic identification using feature-based correlation selection (CFS) and traffic analysis model analysis using Decision Tree Algorithm (C4.5). Feature selection is done to clean irrelevant features so that they can affect the results of the accuracy value. The results of feature selection obtained 7 features and 1 category with 244,689 records and the system connecting the rule tree data training model selected the four best accuracy values. Furthermore, the model training data is carried out by testing the BitTorrent traffic trial data. The results of data testing obtained the best BitTorrent traffic accuracy value of 98.82% with 73,406 records on the 30% data test. Keywords— BitTorrent, C4.5 algorithm, correlation based feature selection, traffic identification, modeling.

PENGOLAHAN DATA SEDERHANA MENGGUNAKAN R STUDIO Hesmi Aria Yanti
Sienna Vol 2 No 1 (2021): SIENNA Volume 2 Nomor 1 Juli 2021
Publisher : LPPM Universitas Muhammadiyah Kotabumi

Simple data processing using the r studio programming language is a data processing process that needs to be done to convert raw data into information. The data processing method uses data acquisition, data input, data selection, division of study programs. split data using k-fold cross-validation, text word cloud model, and model evaluation. Secondary data was acquired in excel format in May 2021, the number of datasets is 71 records with 5 variables, namely number, name, gender, faculty, and study program (Prodi). Data selection aims to select variables that are needed and variables that are not needed are deleted, so that the results of data selection have 2 variables, namely Name and Study Program and a dataset of 71 records. K-fold cross-validation has training data 54 records and testing data 17. The text mining model is visualized with word cloud data, the results of the word cloud testing data test show that there are 4 most important words, including "STI" with a frequency value of 5, "Teacher" a frequency value of 4 , “Law” frequency value 3, and “Agriculture” frequency value 2.

SIMULASI SISTEM MONITORING OKSIGEN TERLARUT (DO) PADA BUDIDAYA UDANG VANAME BERBASIS INTERNET OF THINGS (IoT) Hesmi Aria Yanti; Diana Ananda Putri; Siti Zahrotul Fajriyah
Journal of Informatics and Communication Technology (JICT) Vol 5 No 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.156

Indonesia has a prospective fishery sector, with an elongated coastline making it potential in the vannamei shrimp farming sector using an Internet of Things (IoT)-based system. Vannamei shrimp (Litopenaeus vannamei) is cultivated with an intensive application of high-density systems with a stocking density of 150 m2, so vannamei shrimp cultivation, namely water quality management to balance the bioenergy metabolism of shrimp, requires water quality such as dissolved oxygen (DO) to increase yields and production. To increase production yields, a system is needed that can monitor dissolved oxygen (DO) levels, an important factor for shrimp growth. The minimum DO level for shrimp growth is 3.0 mg/L, and DO that has the potential to cause mortality is <2.0 mg/L, while the optimal DO value for vannamei shrimp cultivation is >3 mg/L with a tolerance of 2 mg/L. The simulation results of an IoT-based system state that if the green light is on as a sign that the oxygen in the pond has been fulfilled, the results of dissolved oxygen readings are good or normal as seen from the display on the LCD, and the simulation value of the temperature system analysis is seen using ThingsBoard, so that the simulation system can be implemented in real terms.

SIMULASI SISTEM MONITORING OKSIGEN TERLARUT (DO) PADA BUDIDAYA UDANG VANAME BERBASIS INTERNET OF THINGS (IoT) Hesmi Aria Yanti; Diana Ananda Putri; Siti Zahrotul Fajriyah
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.156

Indonesia has a prospective fishery sector, with an elongated coastline making it potential in the vannamei shrimp farming sector using an Internet of Things (IoT)-based system. Vannamei shrimp (Litopenaeus vannamei) is cultivated with an intensive application of high-density systems with a stocking density of 150 m2, so vannamei shrimp cultivation, namely water quality management to balance the bioenergy metabolism of shrimp, requires water quality such as dissolved oxygen (DO) to increase yields and production. To increase production yields, a system is needed that can monitor dissolved oxygen (DO) levels, an important factor for shrimp growth. The minimum DO level for shrimp growth is 3.0 mg/L, and DO that has the potential to cause mortality is <2.0 mg/L, while the optimal DO value for vannamei shrimp cultivation is >3 mg/L with a tolerance of 2 mg/L. The simulation results of an IoT-based system state that if the green light is on as a sign that the oxygen in the pond has been fulfilled, the results of dissolved oxygen readings are good or normal as seen from the display on the LCD, and the simulation value of the temperature system analysis is seen using ThingsBoard, so that the simulation system can be implemented in real terms.

SIMULASI SISTEM MONITORING OKSIGEN TERLARUT (DO) PADA BUDIDAYA UDANG VANAME BERBASIS INTERNET OF THINGS (IoT) Yanti, Hesmi Aria; Putri, Diana Ananda; Fajriyah, Siti Zahrotul
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.156

Indonesia has a prospective fishery sector, with an elongated coastline making it potential in the vannamei shrimp farming sector using an Internet of Things (IoT)-based system. Vannamei shrimp (Litopenaeus vannamei) is cultivated with an intensive application of high-density systems with a stocking density of 150 m2, so vannamei shrimp cultivation, namely water quality management to balance the bioenergy metabolism of shrimp, requires water quality such as dissolved oxygen (DO) to increase yields and production. To increase production yields, a system is needed that can monitor dissolved oxygen (DO) levels, an important factor for shrimp growth. The minimum DO level for shrimp growth is 3.0 mg/L, and DO that has the potential to cause mortality is <2.0 mg/L, while the optimal DO value for vannamei shrimp cultivation is >3 mg/L with a tolerance of 2 mg/L. The simulation results of an IoT-based system state that if the green light is on as a sign that the oxygen in the pond has been fulfilled, the results of dissolved oxygen readings are good or normal as seen from the display on the LCD, and the simulation value of the temperature system analysis is seen using ThingsBoard, so that the simulation system can be implemented in real terms.

Analisis Performa Algoritma Convolutional Neural Network (CNN) sebagai Pendeteksi Serangan DDoS Berbasis Deep Learning Gustav, William Paul; Fajri, Naufalul; Hidayat, Raihan; Yanti, Hesmi Aria
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

DDoS (Distributed Denial of Service) merupakan jenis serangan siber yang membuat sebuah layanan online tidak tersedia dengan membuat server, jaringan, atau aplikasi target dengan lalu lintas internet menjadi overload. Serangan ini biasanya dilakukan dengan menggunakan beberapa sistem untuk mengirimkan sejumlah besar permintaan ke target, menyebabkan layanan menjadi lambat atau bahkan tidak berfungsi. Tujuan dari penelitian ini adalah menganalisis lalu lintas serangan DDoS menggunakan pendekatan deep learning dengan algoritma Convolutional Neural Network (CNN). Penggunaan CNN pada penelitian ini dapat meningkatkan akurasi deteksi dan efisiensi serangan DDoS, dengan memanfaatkan kemampuan mengidentifikasi pola dalam data traffic jaringan. Implementasi dataset, melalui beberapa proses yaitu akuisisi data, pre-processing data, model CNN untuk mengklasifikasikan dan evaluasi terhadap traffic DDoS. Temuan ini menunjukkan bahwa model CNN mencapai akurasi tinggi dalam mendeteksi serangan DDoS dengan nilai akurasi training mencapai 99.75% dan akurasi validasi mencapai 99.65%. Ini berarti model mengklasifikasikan data training dengan benar sebesar 99.75% dan data validasi sebesar 99.65%, lebih baik daripada algoritma DNN , RNN, dan GRU. Penelitian lebih lanjut disarankan untuk mengoptimalkan model dan mengeksplorasi penerapannya dalam sistem deteksi real-time.

Analisis Performa Algoritma Convolutional Neural Network (CNN) sebagai Pendeteksi Serangan DDoS Berbasis Deep Learning Gustav, William Paul; Fajri, Naufalul; Hidayat, Raihan; Yanti, Hesmi Aria
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

DDoS (Distributed Denial of Service) merupakan jenis serangan siber yang membuat sebuah layanan online tidak tersedia dengan membuat server, jaringan, atau aplikasi target dengan lalu lintas internet menjadi overload. Serangan ini biasanya dilakukan dengan menggunakan beberapa sistem untuk mengirimkan sejumlah besar permintaan ke target, menyebabkan layanan menjadi lambat atau bahkan tidak berfungsi. Tujuan dari penelitian ini adalah menganalisis lalu lintas serangan DDoS menggunakan pendekatan deep learning dengan algoritma Convolutional Neural Network (CNN). Penggunaan CNN pada penelitian ini dapat meningkatkan akurasi deteksi dan efisiensi serangan DDoS, dengan memanfaatkan kemampuan mengidentifikasi pola dalam data traffic jaringan. Implementasi dataset, melalui beberapa proses yaitu akuisisi data, pre-processing data, model CNN untuk mengklasifikasikan dan evaluasi terhadap traffic DDoS. Temuan ini menunjukkan bahwa model CNN mencapai akurasi tinggi dalam mendeteksi serangan DDoS dengan nilai akurasi training mencapai 99.75% dan akurasi validasi mencapai 99.65%. Ini berarti model mengklasifikasikan data training dengan benar sebesar 99.75% dan data validasi sebesar 99.65%, lebih baik daripada algoritma DNN , RNN, dan GRU. Penelitian lebih lanjut disarankan untuk mengoptimalkan model dan mengeksplorasi penerapannya dalam sistem deteksi real-time.

Analisis Vulnerabilitas HTTP pada Jaringan Publik Menggunakan Wireshark Arief, Arief Rachman Wicaksana; Wisnu , Muhammad Wisnu Haryanto; Yanti , Hesmi Aria; Fauzi , Ahmad Fauzi Zayandra
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Keamanan data yang ditransmisikan melalui HTTP pada jaringan publik menjadi isu yang sangat penting, terutama dengan berkembangnya serangan sniffing yang dapat mengekspos informasi sensitif. Penelitian ini bertujuan untuk menganalisis kerentanannya HTTP pada jaringan publik dengan menggunakan Wireshark sebagai alat untuk menangkap dan menganalisis paket data. Sniffing pada jaringan Wi-Fi publik dapat dengan mudah mengambil data sensitif seperti username dan password yang dikirimkan tanpa enkripsi. Dengan menggunakan Wireshark, penelitian ini dapat menunjukkan dengan jelas potensi masalah keamanan ini, termasuk terlihatnya kredensial dalam cleartext dan eksposur cookie sesi yang seharusnya terlindungi. Hasil penelitian ini menunjukkan bahwa data yang dikirim menggunakan HTTP sangat rentan terhadap penyadapan, dan penggunaan HTTPS sangat diperlukan untuk mengamankan komunikasi data yang sensitif. Analisis ini mengidentifikasi bahwa sebagian besar sesi HTTP yang dianalisis mengungkapkan informasi sensitif yang seharusnya dilindungi. Oleh karena itu, penelitian ini menekankan pentingnya penggunaan protokol yang lebih aman dalam jaringan publik untuk melindungi data pengguna dari potensi serangan sniffing.

Analisis Vulnerabilitas HTTP pada Jaringan Publik Menggunakan Wireshark Arief, Arief Rachman Wicaksana; Wisnu , Muhammad Wisnu Haryanto; Yanti , Hesmi Aria; Fauzi , Ahmad Fauzi Zayandra
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Keamanan data yang ditransmisikan melalui HTTP pada jaringan publik menjadi isu yang sangat penting, terutama dengan berkembangnya serangan sniffing yang dapat mengekspos informasi sensitif. Penelitian ini bertujuan untuk menganalisis kerentanannya HTTP pada jaringan publik dengan menggunakan Wireshark sebagai alat untuk menangkap dan menganalisis paket data. Sniffing pada jaringan Wi-Fi publik dapat dengan mudah mengambil data sensitif seperti username dan password yang dikirimkan tanpa enkripsi. Dengan menggunakan Wireshark, penelitian ini dapat menunjukkan dengan jelas potensi masalah keamanan ini, termasuk terlihatnya kredensial dalam cleartext dan eksposur cookie sesi yang seharusnya terlindungi. Hasil penelitian ini menunjukkan bahwa data yang dikirim menggunakan HTTP sangat rentan terhadap penyadapan, dan penggunaan HTTPS sangat diperlukan untuk mengamankan komunikasi data yang sensitif. Analisis ini mengidentifikasi bahwa sebagian besar sesi HTTP yang dianalisis mengungkapkan informasi sensitif yang seharusnya dilindungi. Oleh karena itu, penelitian ini menekankan pentingnya penggunaan protokol yang lebih aman dalam jaringan publik untuk melindungi data pengguna dari potensi serangan sniffing.

Alat Pendektesi Kadar NPK (Nitrogen, Phosporus Dan Potassium) pada Pupuk Organik Daun Sawit barbasis Internet of Things (IoT) menggunakan Soil NPK Sensor Yanti, Hesmi_Aria; Erfareta, Muhammad Alief; Anggraeni, Auliya; Maulana, Alfarhad; Hibatullah, Muhammad Ridho
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8481

Significant decline in palm oil production; this is due to the termination of fertilizer subsidies and price increases, especially NPK fertilizers. To test the nitrogen (N), phosphorus (P), and potassium (K) content and water content in organic fertilizers made from oil palm leaves, cow dung, water, and other materials. Laboratory testing using chemical analysis and a composting process for 3 months is required. The results of the analysis of N content were 1.63%, P₂O₅ was 0.16%, K₂O was 0.12%, and water content was 12.71%. These results are in accordance with the quality standards of organic fertilizers. The implementation of the NPK content detection tool in oil palm leaves uses these results as a reference. Then the implementation is carried out directly at the location by taking samples of oil palm leaves, cow dung, and water, and then testing is carried out using an integrated NPK detection tool in real time. Where the test results are N = 97 mg/kg, P = 28 mg/kg, and K = 113 mg/kg, these values have not met the minimum threshold for organic fertilizer quality, so it is necessary to add other materials and incubate composting for 3 months or use EM-4 as a liquid that can help accelerate composting. The tool has an accuracy of ±2% and a resolution of up to 1 mg/kg, so the tool is suitable for use as a tool for detecting NPK levels.

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