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All Journal Jurnal Transformatika Jurnal Informatika Universitas Pamulang Jurnal Informatika dan Rekayasa Elektronik Transformasi Jurnal Pengabdian Masyarakat Julia Jurnal
Arum, Dhika Malita Puspita
Universitas An Nuur
Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Public Health 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.
ALGORITMA APRIORI UNTUK MENENTUKAN PAKET PENJUALAN BARANG DI UMKM BINAAN DISPERINDAG KABUPATEN GROBOGAN Eko Supriyadi; Adri Tiyono; Agus Susilo Nugroho; Dhika Malita Puspita Arum; Achmad Rizki Ramadhani
Jurnal Informatika dan Rekayasa Elektronik Vol. 6 No. 1 (2023): JIRE April 2023
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v6i1.726

Abstract

Minat beli dari masyarakat di Kab. Grobogan sangat kurang di penjualan online Usaha Mikro Kecil Menengah    (UMKM). Dikarenakan penawaran yang ada di e-commerce (UMKM) tidak adanya paket diskon yang ditawarkan,  Oleh karena itu pengembangan e-commerce (UMKM) sebagai wadah penjualan barang oleh masyarakat sangatlah diperlukan perubahan, perubahan yang harus dilakukan adalah menerapkan algoritma apriori yang ditanam di aplikasi e-commerce yang telah ada. Dengan menggunakan algoritma apriori, dapat menghasilkan aturan asosiasi untuk menunjukkan seberapa kuatnya pengaruh item ke item lain dan pola beli konsumen. Data yang di proses adalah data penjualan yang paling diminati dn juga yang kurang diminati masyarakat dipergunakan sebagai paket diskon penjualan. Dari hasil pengujian aplikasi tersebut dapat membantu pemilihan produk yang akan dipaketkan dengan diskon yang ditawarkan kepada masyarakat guna meningkatkan minat beli masyarakat pada UMKM di Kab Grobogan.
ALGORITMA RANDOM FOREST, DECISION TREE, DAN XGBOOST UNTUK KLASIFIKASI STUNTING PADA BALITA Dhika Malita; DHIKA MALITA PUSPITA ARUM; KARTIKA IMAM SANTOSO; ANDRI TRIYONO; EKO SUPRIYADI; AGUS SUSILO NUGROHO; Widodo, Edi
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12202

Abstract

At the age of toddlers, children need special attention because their brains develop around 80%. Stunting is a form of long-term nutritional deficiency that occurs during the growth and development of children, which are marked with height that is not appropriate or less compared to children their age based on the standard WHO. This condition can adversely affect the cognitive development and health of children. Identifying toddlers who are at risk of experiencing stunting at an early stage is very important to reduce the adverse effects that can affect their quality of life in the future. Traditional methods are less effective in predicting stunting because they often ignore the complex factors that affect the nutritional status of toddlers. This study aims to classify stunting toddlers using Random Forest, Decision Tree, and Extreme Gradient Boost (XGBOOST) algorithms. The results obtained showed that the accuracy of the Random Forest algorithm received the highest accuracy of 99.72 %, Extreme Gradient Boost (XGBOOST) at 99.58 %, and Decision Tree received 98 87 %accuracy.
PENGELOMPOKAN PERMINTAAN DARAH BERDASARKAN GOLONGAN DAN WAKTU DI KABUPATEN GROBOGAN DENGAN ALGORITMA K-MEANS triyono, andri; Santoso, Kartika Imam; Arum, Dhika Malita Puspita; Supriyadi, Eko; Nugroho, Agus Susilo
TRANSFORMASI Vol 21, No 1 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i1.418

Abstract

The availability of adequate blood supplies continues to pose a significant challenge for blood transfusion services such as the Indonesian Red Cross (PMI), particularly due to the fluctuating and uneven nature of demand across various blood groups. Incorrectly estimating blood demand can result in either critical shortages that jeopardize patient safety or an excess of supplies that are wasted due to the limited shelf life of blood. The objective of this research is to examine historical blood demand data in Grobogan Regency by applying the K-Means clustering algorithm to identify trends related to time intervals and blood group classifications. The study draws on secondary data involving blood requests across multiple blood groups over a span of several years. By implementing the K-Means method, the research identifies unique trends in demand, highlighting critical periods between 2013–2016 and 2022–2024, during which nearly all blood types showed elevated levels of demand. These insights are crucial for improving blood stock management, refining donor mobilization strategies, and enhancing distribution planning based on empirical patterns. The K-Means algorithm proves effective in handling extensive and continuous numerical data, offering valuable guidance for strategic decision-making in healthcare logistics.
Implementasi Penerangan Jalan Berbasis Panel Surya Pada Desa Tunggak Toroh Grobogan Susilo Nugroho, Agus; Mika Agustiana; Andri Triyono; Dhika Malita Puspita Arum; Eko Supriyadi
Jurnal Pengabdian Masyarakat - PIMAS Vol. 3 No. 1 (2024): Februari
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v3i1.1373

Abstract

Jalan merupakan sebuah infrastruktur utama penunjang kehidupan manusia. Ketika infrastruktur berupa jalan itu sudah baik, maka segala aktifitas masyarakat, mulai dari perekonomian, transportasi, hingga pemerataan pembangunan dapat terwujud pula dengan baik. Desa Tunggak, Kecamatan Toroh, Kabupaten Grobogan merupakan salah satu desa di Jawa Tengah yang infrastruktur jalannya sudah cukup memadai. Namun ada sebuah jalan yang belum memiliki penerangan maksimal di malam hari. Selain visibilitas yang tidak baik dan meningkatkan resiko kecelakaan, juga beresiko mengundang kejahatan. Karenanya, anggota KKN Universitas An Nuur 2023 membuat lampu penerangan jalan di Desa Tunggak. Lampu penerangan jalan dibuat dengan tenaga surya. Dipilihnya lampu penerangan jalan bertenaga surya ini guna memaksimalkan efisiensi daya. Metode yang digunakan dalam kegiatan tersebut adalah identifikasi, implementasi, serta capaian atau luaran kegiatan. Masyarakat Desa Tunggak sangat mengapresiasi pembuatan lampu panel surya yang dilakukan tim KKN Universitas An Nuur 2023. Aktifitas masyarakat di malam hari ketika melewati jalan yang sudah ada panel suryanya, menjadi lebih maksimal dan produktif.
PENGELOMPOKAN PERMINTAAN DARAH BERDASARKAN GOLONGAN DAN WAKTU DI KABUPATEN GROBOGAN DENGAN ALGORITMA K-MEANS triyono, andri; Santoso, Kartika Imam; Arum, Dhika Malita Puspita; Supriyadi, Eko; Nugroho, Agus Susilo
TRANSFORMASI Vol 21, No 1 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i1.418

Abstract

The availability of adequate blood supplies continues to pose a significant challenge for blood transfusion services such as the Indonesian Red Cross (PMI), particularly due to the fluctuating and uneven nature of demand across various blood groups. Incorrectly estimating blood demand can result in either critical shortages that jeopardize patient safety or an excess of supplies that are wasted due to the limited shelf life of blood. The objective of this research is to examine historical blood demand data in Grobogan Regency by applying the K-Means clustering algorithm to identify trends related to time intervals and blood group classifications. The study draws on secondary data involving blood requests across multiple blood groups over a span of several years. By implementing the K-Means method, the research identifies unique trends in demand, highlighting critical periods between 2013–2016 and 2022–2024, during which nearly all blood types showed elevated levels of demand. These insights are crucial for improving blood stock management, refining donor mobilization strategies, and enhancing distribution planning based on empirical patterns. The K-Means algorithm proves effective in handling extensive and continuous numerical data, offering valuable guidance for strategic decision-making in healthcare logistics.
COMPARISON OF SVM, KNN, AND NAIVE BAYES METHOD WITH N-GRAM IN TRAFFIC ACCIDENT CLASSIFICATION Dhika Malita Puspita Arum; Andri Triyono
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.v1i01.11

Abstract

Traffic accidents that occur in Indonesia are still relatively high, the information can be easily obtained through social media, one of which is Twitter. The amount of traffic accident information can be processed and classified according to certain categories. Traffic accident data classification is done using SVM, KNN and Naïve Bayes methods using n-gram feature extraction. The results of this study indicate the best accuracy is 87.63 using the KNN method.
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%. 
Co-Authors Achmad Rizki Ramadhani Adri Tiyono Agus Condro Wibowo Agus Susilo Nugroho Ahmad Yususf Mufarihin Andri Triyono andri triyono Andri Triyono Dhika Malita Dwi Kurniawan Aprilianto Edi Widodo, Edi Eko Supriyadi Kartika Imam Santoso Mika Agustiana Nugroho, Agus Susilo Rahmawan Bagus Trianto susilo nugroho, agus Triyono, Andri