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All Journal International Journal of Global Operations Research Hipotenusa: Journal of Mathematical Society Indonesian Journal of Statistics and Its Applications J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Yanti, Yusma
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Other

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Robust Anomaly Detection in Network Traffic Using Bagging with Majority Voting Ensemble Sultan Ilham Seftiansyah, Muhammad; Chairunnas, Andi; Yanti, Yusma
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.03

Abstract

Anomaly detection in computer networks is a crucial aspect of ensuring system security and availability. One of the most common and disruptive threats is Distributed Denial of Service (DDoS) attacks, which can overload servers and compromise service continuity. Traditional Intrusion Detection Systems (IDS) often struggle to detect sophisticated and evolving attack patterns, leading to reduced detection performance. This research proposes the use of ensemble learning with Bagging and Majority Voting to enhance anomaly detection. The dataset used in this study was CIC-DDoS2019, consisting of 33,066 rows and 88 features, processed through data cleaning, label encoding, and normalization. Three base classifiers—Decision Tree, Random Forest, and XGBoost—were integrated using Bagging with Majority Voting. Experiments were conducted with different train-test split ratios of 70:30, 75:25, 80:20, and 90:10. The results showed that the 70:30 split achieved the best performance with an accuracy of 93.58%, an F1-score of 90.51%, and the fastest evaluation time of 142.86 seconds. Additional tests on spam and phishing datasets confirmed the robustness of the Bagging approach, achieving accuracy above 96%. These findings demonstrate that Bagging with Majority Voting can effectively improve IDS performance and provide a reliable solution for detecting various types of cyberattacks.
Utilization of Geogebra Application as Learning Media in Learning The Three-Dimensional to Increase Students' Interest in Learning Widyastiti, Maya; Yanti, Yusma; Sumarsa, Amar; Durrotul Faizah, Layla
Hipotenusa: Journal of Mathematical Society Vol. 6 No. 1 (2024): Hipotenusa: Journal of Mathematical Society
Publisher : Program Studi Tadris Matematika Universitas Islam Negeri (UIN) Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/hipotenusa.v6i1.815

Abstract

This activity aims to describe students' interest in learning, learning outcomes, and the benefits of using GeoGebra in learning the three-dimensional. The data collected were analyzed using descriptive analysis. The research subjects were 59 students of SMA Negeri 1 Megamendung. Data collection techniques used tests and questionnaires. This research consists of 2 cycles. In cycle 1, the results obtained based on students' interest in learning showed that on average students strongly agreed 18.6%, agreed 31.1%, moderately 44.3%, and disagreed 5.9%. In cycle 2 after using the GeoGebra application, student interest increased to strongly agree 30.9%, agree 48.7%, moderately 18.6% and disagree 1.7%. Based on the learning outcomes in cycle 1, it shows 42.37% of students can solve problems well, while in cycle 2 there is an increase in the percentage of learning outcomes to 56.59%. Based on the results of the above analysis, it can be concluded that GeoGebra is very useful as a learning media and there is an increase in student interest and learning outcomes in learning the three-dimensional using GeoGebra.
Spatio-temporal Clustering Analysis of Dengue Hemorrhagic Fever Cases in West Java 2016 – 2021: Analisis Penggerombolan Spasio-temporal Kasus DBD di Jawa Barat Tahun 2016 – 2021 Yanti, Yusma; Rahardiantoro, Septian; Dito, Gerry Alfa
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i1p56-63

Abstract

In 2020, WHO included dengue as a global health threat among 10 other diseases. This is also a problem in Indonesia, especially the province of West Java. Based on data from the Ministry of Health for 2022, West Java is the largest contributor to cases of Dengue Hemorrhagic Fever (DHF) in Indonesia. The spread of dengue fever is through mosquitoes, but climate also greatly influences the spread of this disease. The spread of West Java is quite wide, consisting of 27 city districts and a relatively high population density. This greatly influences the increase in the number of dengue fever cases. In this research, we will group years with the same dengue fever cases and identify groups of districts/cities in West Java with the same pattern of dengue fever cases for 2016 to 2021. The results obtained are that 2016 is the group with the highest number of cases. Meanwhile, from 27 city districts in West Java, three groups were obtained. Group 1 is the group with the highest number of cases consisting of Sukabumi City, Bandung City, Cimahi City, Depok City, Tasikmalaya City.
Decision Support System for Indibiz Package Selection Using K-Means Clustering and Analytic Hierarchy Process Martika, Karina; Tosida, Eneng Tita; Yanti, Yusma
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.434

Abstract

The rapid development of digital business in Indonesia has encouraged telecommunication providers to improve their services, particularly for small and medium-sized enterprises (SMEs). PT. Telkom Indonesia, through its Indibiz program, offers a wide variety of internet packages to support business operations. However, the diversity of available packages often leads to decision-making difficulties for both customers and internal stakeholders when determining the most suitable service based on customer needs, business scale, and financial capability. This study proposes a web-based Decision Support System (DSS) for Indibiz package selection by combining K-Means Clustering and the Analytic Hierarchy Process (AHP). K-Means is used to segment customers based on sales and usage behavior, while AHP prioritizes criteria such as speed, price, and call quota to produce recommendations. A dataset containing 6,192 Indibiz sales records from July to November 2023 was analyzed. The hybrid model was then implemented into a web-based application that enables decision-makers to visualize clustering results and determine package recommendations interactively. The experimental results demonstrate that the combination of K-Means and AHP produces more objective and consistent recommendations compared to manual selection. The DSS can help both customers and PT. Telkom Indonesia improve decision efficiency and reduce subjective bias in selecting internet packages.
ALTERNATIF PENGGEROMBOLAN DATA DERET WAKTU DENGAN KONDISI TERDAPAT DATA KOSONG: Studi Kasus Penggerombolan Provinsi di Indonesia Berdasarkan Data Deret Waktu Rasio Gini tahun 2007 – 2017 Yanti, Yusma; Rahardiantoro, Septian
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i1.55

Abstract

Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.
Segmentation and Positioning of Lecturers in the Department of Computer Science at Pakuan University Based on Student Assessment: Segmentasi dan Positioning Dosen Jurusan Ilmu Komputer Universitas Pakuan Berdasarkan Penialian Mahasiswa Yanti, Yusma; Saepulrohman, Asep
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p92-104

Abstract

Determining the segmentation and positioning of the lecturers in selecting the thesis supervisor is very important to do. It is because, with this information, the supervision process in thesis writing can run well. This study intends to analyze the segmentation and positioning of lecturers related to determine the thesis supervisor using the Clusterwise Bilinear Spatial Multidimensional Scaling Model (CBSMSM) method. The data used is survey data for fifth-semester bachelor students of the 2019/2020 academic year of the Department of Computer Science, Pakuan University. One hundred sixty-one student observations provide an assessment of 10 attributes regarding the characteristics of 32 lecturers of the department. Furthermore, the estimation of the segment coordinate parameters, lecturer coordinates, dimensions, and attributes simultaneously uses the alternating least square (ALS) algorithm. The number of segments and dimensions are selected based on the smallest sum square error (SSE) value for combining segments and other dimensions. As a result, we get four segments and four dimensions with an SSE value of 4864.003. Furthermore, the department can use this result to illustrate student assessments of their lecturers' characteristics regarding thesis supervision.
Co-Authors Andi Chairunnas Asep Saepulrohman Dito, Gerry Alfa Durrotul Faizah, Layla Eneng Tita Tosida Martika, Karina Maya Widyastiti, Maya Rahardiantoro, Septian Sultan Ilham Seftiansyah, Muhammad Sumarsa, Amar