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All Journal Media Statistika MATEMATIKA Jurnal Gaussian
Triastuti Wuryandari
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Decision Sciences, Operations Research & Management Mathematics Other

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PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON Ridho, Wahyu Anwar; Wuryandari, Triastuti; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.372-381

Abstract

The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MODEL INTERVENSI FUNGSI PULSE Rosilawati, Elsa Dwi; Tarno, Tarno; Wuryandari, Triastuti
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.382-391

Abstract

The intervention model is one model that is frequently used to explain how interventions from both internal and external sources can lead to dramatic fluctuations in a time series of data. The Composite Stock Price Index, known as the IDX Composite, is an index that tracks all stock price performance. For the Composite Stock Price Index from 2 October 2020 to 6 June 2022, daily close price data are used in this study. The data showed a sharp reduction starting on 9 May 2020 (T=386) and lasting for the following 4 days, which made the pulse function the likely intervention model. Rising interest rates and high inflation figures from the United States are to blame for the drop in IDX Composite close price. In addition, a lot of profit-taking was done because of the Eid holidays and the expectation of a substantial increase in COVID-19. The best intervention model created is ARIMA ([3],1,0) with an intervention order of b=0, r=0, and s=11, which can then be used to forecast Composite Stock Price Index for the following period. This is based on the outcomes and analyses. The sMAPE value in the research utilizing this model was 0.98%, suggesting very strong forecasting capabilities.
ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI DAYA KONSENTRASI BELAJAR MENGGUNAKAN EXTENDED COX REGRESSION Jessica Valenci Soegianto; Triastuti Wuryandari; Agus Rusgiyono
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.166-175

Abstract

Learning concentration plays a major role in the success of teaching and learning activities and is the main asset for students in receiving and mastering the subject matter presented. This study aims to determine the average endurance time of learning concentration power of students in grades 4-6 at SDN 02 Jenarwetan and the factors that influence it. The method used is Cox Extended because there are independent variables that do not meet the Proportional Hazard assumption. Parameter estimation uses the Maximum Partial Likelihood Estimation (MPLE) method with the Efron approach because there is data with co-occurrence. Based on the results of data analysis, the average endurance time of students' learning concentration power is 13.22 minutes. It is also known that the factors that influence the endurance of students' learning concentration power are the level of learning motivation and the level of stress experienced by students. Students with high learning motivation are able to maintain their learning concentration for a long period of time , while students with high stress levels are more at risk of losing learning concentration 4.6294 times higher than students with low stress levels.
PERBANDINGAN METODE HAZARD MULTIPLIKATIF DAN ADITIF PADA LAJU PERBAIKAN KONDISI KLINIS PASIEN STROKE DI RS MH THAMRIN CILEUNGSI TAHUN 2021 Zulfa Luthfiyyah Ayunda; Triastuti Wuryandari; Suparti Suparti
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.378-389

Abstract

Stroke is a condition that can result in permanent brain damage or even death. In Indonesia, the prevalence of stroke increased from 7% in 2013 to 10.9% in 2018. Numerous factors can affect a stroke patient's ability to recover. Survival analysis is a method that can be used to identify the variables that influence stroke patient’s ability to recover. Cox proportional hazard and Lin-ying additive hazard approaches were utilized in this study to analyze stroke patient data from MH Thamrin Cileungsi Hospital. The most widely used regression model for survival data is the Cox proportional hazard which makes the assumption that the ratio between the hazard functions of various people is constant. In contrast, in additive hazard regression there is no assumption of proportionality. Age and cardiac history are the factors that have an impact on how well stroke patients recover, according to the findings. The Lin-Ying additive hazard approach yields the best results since its RMSE value is lower (0.3808777) than that of the cox proportional hazard model (0.9248512).
PERBANDINGAN ALGORITMA NEAREST NEIGHBOR DAN ANT COLONY OPTIMIZATION DALAM OPTIMASI RUTE WISATA SEMARANG BERBASIS MULTI-ATTRIBUTE UTILITY THEORY Ashilah Tsuraya Izzati; hasbi yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 15, No 2 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.2.320-331

Abstract

Tourism route planning is an important aspect of tourism development to improve travel efficiency and destination quality. This study aims to determine priority tourist destinations and to compare the performance of tourism route optimization methods in Semarang. Priority destinations are identified using the Multi-Attribute Utility Theory (MAUT) with criteria weights determined by the Rank Order Centroid (ROC). Tourism destinations with the highest utility values are selected as priority destinations and modelled as Traveling Salesman Problem (TSP). Tourism route optimization is carried out by comparing three optimization problem-solving method, namely exact method, heuristic method, and metaheuristic method. The exact method employs the Branch and Bound (B&B) algorithm as a benchmark to obtain the optimal solution. The heuristic method uses the Nearest Neighbour (NN) algorithm and metaheuristic method uses the Ant Colony Optimization (ACO) algorithm. Algorithm performance is evaluated based on total travel distance, computation time, and relative error (RE) to the optimal solution. The result show that the NN algorithm yields a relative error of 8.66%, while the ACO algorithm achieves a lower relative error of 2.2%. This indicates that ACO algorithm produces routes that are closer to the optimal solution.
Co-Authors Abdul Hoyyi Abdur Rofiq Agung Waluyo Agus Rusgiyono Alin Citra Suardi Amanda Devi Paramitha Anes Desduana Selasakmida Annisa Rahmawati Aprilia Sekar Khinanti Arief Rachman Hakim Ashilah Tsuraya Izzati Damayanti, Sindi Danardono Danardono Desy Ratnaningrum Desy Ratnaningrum, Desy Dewi Setya Kusumawardani Dwi Ispriyanti DWI RAHMAWATI Etik Setyowati, Etik Fahra Pracendi Astrelita, Fahra Pracendi Fahrur Rozzi Iskak Faiqotul Himmah Fitri Juniaty Simatupang, Fitri Juniaty Hanik Rosyidah, Hanik Hasbi Yasin Iut Tri Utami Jessica Valenci Soegianto Moch. Abdul Mukid Muhammad Rizky Yuliyanto Mustafid Mustafid Noor Afifah Oktaviana Prayudhani Pritha Sekar Wijayanti Restu Sri Rahayu, Restu Ridho, Wahyu Anwar Rita Rahmawati Rosilawati, Elsa Dwi Sayekti Dewi Anggraini Sri Haryatmi Kartiko Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tuan Hanni Ulya Tsaniya Viona Alliza Diandra Putri Yogi Isna Hartanto Yuciana Wilandari Zega, Nesty Novita Sari Zulfa Luthfiyyah Ayunda