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Developing Application in Anticipating DDoS Attacks on Server Computer Machines Anthony Anggrawan; Raisul Azhar; Bambang Krismono Triwijoyo; Mayadi Mayadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.410

Abstract

The use of server computer machines in companies is primarily a web hosting server that is very easy to experience threats, especially external security threats such as attempts to infiltrate, hacking, viruses, and other malicious attacks. Having a secure server is indispensable for working online and especially if involved in business-related network transactions. The Server's realization to be safe from threats is to protect the server machine's security on the hardware and software side and pay attention to network security that goes to the server machine. Generally, firewall applications on router devices have configuration limitations in securing the network, namely non-integrated applications. In other words, it is necessary to manage the perfect firewall configuration to anticipate Distributed Daniel attacks of Service (DDoS) attacks. Therefore, this study aims to integrate existing firewall applications for router devices into an integrated program to secure the network. The methodology used is the Network Development Life Cycle (NDLC). The research results on this developed application program can overcome DDoS attacks without setting up a firewall on the router device and can automatically monitor DDoS attack activities from outside the Server. Securing servers from DDoS attacks without setting up a firewall on the router device and automating the monitoring of DDoS attack activity from outside the Server are the novelties of this study that have not been available in previous studies.
Sistem Informasi Geografis Pemetaan Jaringan Irigasi dan Embung di Lombok Tengah Ahmat Adil; Bambang Krismono Triwijoyo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1112

Abstract

Irigasi sebagai alternatif pengairan lahan tadah hujan pada musim kemarau, digunakan untuk meningkatkan produksi hasil pertanian. Pemetaan Jaringan irigasi dan embung pada Kabupaten Lombok Tengah menggunakan sistem data spasial dan non-spasial, yang selama ini masih menggunakan pemetaan secara konvensional. Data Spasial dapat menunjuk posisi geografis dengan setiap karakteristik memiliki satu lokasi yang harus ditentukan dengan cara yang unik. Pemetaan dengan model data spasial, dirancang dengan tahapan seperti analisa kebutuhan perangkat lunak, desain, pembuatan kode program dan pengujian. Metode yang digunakan dalam penelitian ini adalah Analisis spasial, merupakan kumpulan – kumpulan dari teknik yang dapat digunakan untuk melakukan pengolahan data SIG. Hasil dari analisis data spasial sangat bergantung dari lokasi atau tempat di mana objek sedang dianalisis. Manipulasi data, akan menampilkan jendela manipulasi data non-spasial jaringan irigasi, embung, dan kecamatan. Pada jendela manipulasi data, pengguna dapat melakukan penambahan, perubahan dan penghapusan terhadap data tabular yang ada, dan memiliki fasilitas pencarian sesuai dengan data yang sedang diakses.
Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images Bambang Krismono Triwijoyo; Ahmat Adil; Anthony Anggrawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1526

Abstract

Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.
Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
PENENTUAN KEKUATAN SINYAL WIFI UNTUK LAYANAN AKSES INTERNET BAGI KONSUMEN KAFE DI MATARAM Pribadi, Agus; Suriyati, Suriyati; Triwijoyo, Bambang Krismono; Adil, Ahmat
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 6, No 1 (2026): JEIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol6no1.1056

Abstract

Initial survey results showed that 86% of respondents stated that cafes are places to access the internet, interact, and fulfill social media needs. Internet access services in cafes require a smooth connection and coverage limited to cafe customers, with no interference from other illegal connections. The challenge is that cafe managers and owners require only devices provided by the cafe. This study aims to offer a solution by adjusting the WiFi signal strength of wireless access points. The research and design method used for WiFi signal strength management included initial identification, signal analysis, planning, and testing. The study resulted in a WiFi signal strength setting of 67%, which met the needs and limited signal coverage. RSSI values of -45 dBm to -69 dBm were sufficient to provide internet access only to cafe customers. Outside the permitted area, the WiFi signal was not received, and therefore, internet access was not possible.Hasil survey awal menunjukan bahwa 86% responden menyatakan bahwa kafe adalah tempat untuk mengakses internet, berinteraksi dan memenuhi kebutuhan bersosial media. Layanan akses internet di kafe yang dibutuhkan adalah kelancaran sambungan dan akses, jangkauan sinyal terbatas hanya untuk pelanggan kafe dan tidak ada gangguan dari penyambung tidak diijinkan. Problematika pemenuhannya adalah pihak pengelola dan pemilik kafe mengharuskan hanya menggunakan perangkat yang sudah disediakan oleh pihak kafe. Penelitian ini bertujuan menawarkan solusi penyelesaian dengan melakukan pengaturan kekuatan sinyal WiFi perangkat wireless access point. Metode yang digunakan untuk riset dan perancangan pengaturan kekuatan sinyal WiFi dengan tahapan identifikasi awal, analisa sinyal, perencanaan pengaturan dan pengujian. Hasil penelitian menghasilkan pengaturan kekuatan pancar sinyal WiFi sebesar 67% yang memenuhi kebutuhan dan membatasi jangkauan sinyal. Nilai RSSI -45 dBm sampai dengan -69 dBm cukup memberikan layanan akses internet hanya untuk konsumen kafe. Di luar area yang diijinkan terbukti tidak mendapatkan sinyal WiFi sehingga tidak mendapatkan akses layanan internet
Lightweight and Interpretable Coin Recognition and Counting UsingGeometric Detection and Fuzzy Score-Based Classification Dasriani, Ni Gusti Ayu; Triwijoyo, Bambang Krismono; Yasa, I Gede Yoga Sudarma; Priyanto, Dadang; Nguyen, Cong Dai
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6067

Abstract

Deep learning-based coin recognition approaches typically require large, annotated datasets and substantial computational resources, yet offer limited interpretability. Such characteristics limit their applicability in lightweight, resource-constrained vision systems. Therefore, this study aims to develop and systematically evaluate a lightweight, interpretable coin recognition and counting method based on geometric detection and fuzzy-score-based classification. The main contribution of this work lies in integrating the Hough Circle Transform, contour-based circularity validation, and a weighted fuzzy score mechanism that aggregates diameter, circularity, and HSV color features without relying on data-driven model training. The proposed approach prioritizes computational efficiency and decision transparency, while maintaining robustness under varying lighting and object configurations. An experimental evaluation was performed on 40 test images containing 362 coins under both bright and dim lighting conditions, with aligned, scattered, and overlapping arrangements. The system achieved a detection rate of 87% and an object-level classification accuracy of 79%. Although image-level accuracy reached 50% under strict evaluation criteria, detailed error analysis indicates that performance degradation is primarily associated with segmentation limitations in overlapping configurations rather than instability in the fuzzy scoring mechanism. These findings demonstrate that a calibrated geometric and fuzzy-based approach can provide a transparent and computationally efficient alternative for small-scale vision applications without requiring large training datasets.