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All Journal INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Journal of Information Systems and Informatics Joined Journal (Journal of Informatics Education Jurnal Sistem Informasi dan Sistem Komputer LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Julia Jurnal
Rahmawan Bagus Trianto
Universitas An Nuur
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Other

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Kombinasi Metode K-Nearest Neighbor dengan Cosine Similarity untuk Prediksi Serangan Firewall pada Jaringan Komputer Trianto, Rahmawan Bagus; Triyono, Andri; Arum, Dhika Malita Puspita
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.12680

Abstract

The security of the computer network, especially the internet, is very crucial to note. One of the most effective ways to secure a computer network is to use a firewall. However, making a firewall that is still manual will make it difficult for network administrators to secure their computer network. The automatic detection of attacks on the firewall will further enhance the security of the computer network. Prediction or detection of attacks on the firewall automatically and intelligently can use the K-Nearest Neighbor algorithm by measuring the distance of data similarity using Cosine Similarity. The results of this study managed to achieve a high accuracy, which is 99.71%, precision is 74.70% and recall is 74.85% of predicting traffic that goes to the firewall. The results can be used as a standard of accuracy in predicting the traffic leading to the firewall, or even create an additional firewall so that the security of computer networks, especially the user data is saved.
Penerapan Least Squares Support Vector Machines (LSSVM) dalam Peramalan Indonesia Composite Index Andri Triyono; Rahmawan Bagus Trianto; Dhika Malita Puspita Arum
Jurnal Informatika Universitas Pamulang Vol 6, No 1 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i1.10237

Abstract

In the era of very rapidly advancing technology like today, both internet technology and computerization have made various corporate agencies or investors start thinking about the importance of the stock market in their capital division. Previously there were various purchases by the company's capital, such: gold, land, buildings, production machines, but at this time the purchase of capital shares should also start to attract attention and these purchases are legal investments. Various kinds of company shares that are sold can already be seen through the internet and it is very easy and attractive for companies that will make capital purchases, even the model can be chosen for both long-term and short-term capital purchases. This stock price forecasting system using the Least Squares Support Vector Machines (LSSVM) method will be very popular with investors to help determine conclusions for buying shares because it can reduce losses or even make the right decisions so that it will increase profits for investors or companies. Least Squares Support Vector Machines is a simpler model and has been modified from the previous model, namely: Support Vector Machines (SVM) method. Solving linear equations can be solved in a simpler way using LSSVM compared to using SVM. The variable used in the network is the close price variable. The kernel that used for this study is the RBF kernel. This study consists of three phases or stages. The first stage uses 400 historical data rows, second stage uses 800 historical data rows, and the third stage uses 1200 rows of data. This research obtains the best result of accuracy in the third stage. The third stage has the smallest MSE value: 0.00025248 by using 1200 rows of historical data.
Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes Rahmawan Bagus Trianto; Andri Triyono; Dhika Malita Puspita Arum
Jurnal Informatika Universitas Pamulang Vol 5, No 3 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i3.6110

Abstract

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.
Klasterisasi Menggunakan Algoritma K-Means dan Elbow pada Opini Masyarakat Tentang Kebijakan Sekolah Luring Tahun 2022 Rahmawan Bagus Trianto; Agus Susilo Nugroho; Eko Supriyadi
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 1 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i1.2756

Abstract

The covid-19 pandemic that swept across the globe had adverse effects in many areas. One of the most affected areas is education in Indonesia. The online learning model became the only option at the time, which had a negative impact on the quality of education in Indonesia. As time went on, conditions are getting better, but there was still a threat of covid-19. In early 2022 governments began to adopt face-to-face or offline learning that attracted opinions on social media. The opinions that are widely written on social media need to be prepared because they could be input to the government. Clustering using the k-meansalgorithm with the elbow method as its optimizer in determining the best cluster number is one of the opinions processing options on social media for measuring and accounting. Data is treated with two approaches: with and without stemming. Applying the elbow method to the k-means algorithm produces a performance of the clustering model with a DBI value of 0.003 with 4 clusters, and a value of SSE 0.331, for data without stemming. On data with treatment using stemming, it has 3 cluster numbers with a value of DBI at 0.003 and SSE at 0426.
Perbandingan Efisiensi Memori dan Waktu Komputasi Pada 7 Algoritma Sorting Menggunakan Bahasa Pemrograman Java Pujiono, Imam Prayogo; Trianto, Rahmawan Bagus; Hana, Fida Maisa
Jurnal Sistem Informasi dan Sistem Komputer Vol 9 No 2 (2024): Vol 9 No 2 - 2024
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v9i2.481

Abstract

Perkembangan teknologi informasi telah merubah metode penyimpanan data dari fisik menjadi digital, yang menuntut pengorganisasian data yang baik untuk mempermudah pencarian dan verifikasi. Oleh karena itu, pengurutan data menjadi sangat penting dan berbagai algoritma pengurutan telah dikembangkan, seperti Quick Sort dan Heap Sort. Penelitian ini bertujuan membandingkan kinerja waktu komputasi dan penggunaan memori dari tujuh algoritma sorting: Bubble Sort, Insertion Sort, Selection Sort, Shell Sort, Quick Sort, Merge Sort, dan Heap Sort menggunakan bahasa pemrograman Java. Evaluasi dilakukan pada dataset berisi 100, 1.000, dan 10.000 data numerik acak antara 1-99. Hasil penelitian menunjukkan Shell Sort memberikan waktu komputasi tercepat untuk dataset berisi 100 dan 1.000 data, sementara Heap Sort paling efisien untuk dataset berisi 10.000 data. Dari segi penggunaan memori, ketujuh algoritma menunjukkan konsumsi memori serupa, namun Shell Sort membutuhkan memori lebih rendah pada dataset berisi 1.000 data, dan Merge Sort menggunakan memori lebih banyak pada dataset berisi 10.000 data.
Peringkasan Dokumen Teks Bilingual Sebagai Reduksi Fitur Untuk Klasifikasi Menggunakan Algoritma K-NN Rahmawan Bagus Trianto; Agus Susilo Nugroho
LogicLink Vol. 1 No. 1, June 2024
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v1i1.7801

Abstract

Summarizing text is a step to extract the essence of a text document with no more than half. Summarizing text has an important role in extracting the core information from a document in a more concise form. Summarizing text documents can be used as feature reduction in classifying text documents because it can reduce features that are considered irrelevant. Text documents are summarized using the Term Frequency-Inverse Document Frequency (TF-IDF) method, then classified using the K-Nearest Neighbor (K-NN) algorithm. One of the disadvantages of the K-NN algorithm is that it is not optimal in classification if the k value is not appropriate, as well as the selection of an inappropriate distance calculation method. By testing various k values ​​and using the Euclidean Distance distance measurement method, you can increase the accuracy of text document classification. Text document summarization using the proposed TF-IDF method is proven to increase when classification is carried out with K-NN. From the research results, it was found that the classification accuracy at the compression rate increased by 50% with a k value of 6 to 8 of 95.33%. This shows that text document summarization as feature reduction has a positive role in the classification process using the K-NN algorithm.
COMPUTER NETWORK ANALYSIS USING NETWORK  MANAGEMENT SYSTEM AT AN NUUR UNIVERSITY Achmad Rizki Ramadhani; Muhammad Akbar Mustofa; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.10

Abstract

During the pandemic, teaching and learning activities have changed. Which originally used the offline format to go online and its combinations. Internet bandwidth usage plays an important role in the success of the teaching and learning process on campus, including at An Nuur University. By using Cacti Network Management System it can be used as a monitoring system to monitor the movement of internet bandwidth whether it meets the needs of the online learning process or not. Internet bandwidth usage is influenced by several factors such as logical topology, physical topology and configuration in computer networks.
EARLY DETECTION OF DIABETES MELLITUS USING RANDOM FOREST ALGORITHM Andri Triyono; Rahmawan Bagus Trianto; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.13

Abstract

Diabetes mellitus is a deadly disease. Patients with this disease often do not realize that they are improving their diabetes mellitus. It is necessary to do early prevention in order to reduce the sudden death rate of people with diabetes mellitus. In addition, during the COVID-19 pandemic, which increases the risk of death for people with comorbid diabetes mellitus. A system model for the prediction of diabetes mellitus is needed for early diagnosis of this disease. By using machine learning techniques using the Random Forest algorithm and Information Gain can be used to predict diabetes mellitus. This model has a fairly high level of accuracy, which is 98.27%, precision is 97.69% and recall is 98%. 
PENGGUNAAN ALGORITMA FP-GROWTH UNTUK MENENTUKAN PAKET PENJUALAN PADA TOKO PERLENGKAPAN KONVEKSI SRI BUSANA Andri Triyono; Dhika Malita Puspita Arum; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.16

Abstract

Consumers of the Sri Busana convection shop are mostly tailors, both home and convection tailors, which are pretty large, especially in Grobogan district. The increasing number of fashion businesses or tailors in Grobogan district makes data on goods and sales at the sri busana convection shop increase because the sri busana convection shop always strives to meet the needs of tailors or home convection. In overcoming the problem of finding more efficient consumer patterns, an analysis of buying patterns is carried out. Consumer buying patterns were analyzed using Association rules and FP-Growth methods. With this algorithm, the process of determining consumer purchasing patterns consists of 2 product combinations with a support value of 50% and a confidence value of 100%. 3 product combinations with a support value of 40% and a confidence value of 80%. 4 product combinations with a support value of 40% and a confidence value of 80%. 
OPTIMIZATION OF PARTICLE SWARM OPTIMIZATION IN NAÏVE BAYES FOR CAESAREAN BIRTH PREDICTION Dhika Malita Puspita Arum; Andri Triyono; Eko Supriyadi; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.17

Abstract

The Maternal Mortality Rate (MMR) in 2017 according to the World Health Organization (WHO) is estimated to reach 296,000 women who die during and after pregnancy or childbirth. Caesarean birth is the last alternative in labor if the mother cannot give birth normally due to certain indications with a high risk, both for the mother and the baby. factors of a mother giving birth by caesarean section, such as placenta previa, hypertension, breech baby, fetal distress, narrow hips, and can also experience bleeding in the mother before the delivery stage. It is hoped that delivery by caesarean method can minimize problems for the baby and mother. Accurate prediction of the condition of the mother's pregnancy can enable d octors, health care providers and mothers to make more informed decisions regarding the management of childbirth. To predict caesarean births, data mining techniques using the Naive Bayes algorithm can be used. Naive Bayes is very simple and efficient but very sensitive to features, therefore the selection of appropriate features is very necessary because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with several attribute selection procedures such as Particle Swarm Optimization. In this study, the researcher proposes a Particle Swarm Optimization algorithm for attribute weighting in Naive Bayes so as to increase the accuracy of Caesarean birth prediction results 
Co-Authors Achmad Rizki Ramadhani Addien Anaba Agus Susilo Nugroho Agus Susilo Nugroho Andri Triyono andri triyono Andri Triyono Arum, Dhika Malita Puspita Cahya Vikasari, Cahya Dhika Malita Dwi Novia Prasetyanti Eko Supriyadi Hana, Fida Maisa Imam Prayogo Pujiono Kukuh Muhammad Muhammad Abdul Muin Muhammad Akbar Mustofa Supriyadi, Eko Supriyadi Triyono, Andri Umar Haji Mussa’id