Article Per Year (5 Year)
p-Index From 2021 - 2026
3.666
P-Index
This Author published in this journals
All Journal Media Statistika BIMASTER BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Sarwahita : Jurnal Pengabdian Kepada Masyarakat Variance : Journal of Statistics and Its Applications Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi CENDEKIA : Jurnal Ilmu Pengetahuan Parameter: Journal of Statistics Jurnal Forum Analisis Statistik
Andani, Wirda
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Variance : Journal of Statistics and Its Applications

 1 
IMPLEMENTATION GRID SEARCH OF RBF AND POLYNOMIAL ON SUPPORT VECTOR REGRESSON FOR CLOSING STOCK PRICES PREDICTION ON PT INDOFARMA (INAF) Salsabilla, Arla; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page133-142

Abstract

Stocks represent evidence of ownership of an asset. The highly volatile nature of stock prices makes it difficult for investors to predict stock prices, necessitating the analysis of stock investments. This research aims to forecast for the next 30 days the closing price of PT Indofarma (INAF) stocks using the best model, and the accuracy level of the employed model was analyzed based on the data from the last seven years. The research used the Support Vector Regression (SVR) method, which is known for its capability to handle nonlinear data through kernel functions. The Radial Basis Function (RBF) and polynomial kernels are used in this case. The challenge with SVR lies in determining the optimal hyperparameter, which can be addressed through hyperparameter tuning using grid search. The research results show that the best model is the SVR kernel RBF model with optimal hyperparameter C=1,γ=0.01, and ε=0.01. Based on the performance evaluation results of the best model, the MAPE, MSE, and MAE values are equal to 1.537%,1483.936, and 23.409.
GROSS PREMIUM VALUATION METHOD IN DETERMINING PREMIUM RESERVES IN LIFE INSURANCE Rivaldo, Rendi; Perdana, Hendra; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page215-222

Abstract

Abstract: Life insurance companies maintain reserve funds to pay insurance policy claims, known as premium reserves. Premium reserves are calculated using two approaches: retrospective and prospective. The prospective approach involves calculating the present value of all future expenses minus the total future income for each policyholder, using the Gross Premium Valuation (GPV) method. The GPV method takes into account initial costs, maintenance costs, and administration costs. The case study results indicate that the premium reserve using the GPV method starts at zero in the first year, increases until the last payment year, and then decreases after the payment period until the end of the coverage period. For policyholders of different genders but the same age, the premium reserve for men is greater than for women. Additionally, for male policyholders of varying ages, the premium reserves required increase with age. Furthermore, for male policyholders of the same age but with different interest rates, a higher interest rate results in a smaller premium reserve requirement.
FORECASTING THE COMBINED STOCK PRICE INDEX (IHSG) USING THE RADIAL BASIS FUNCTION NEURAL NETWORK METHOD Fitriawan, Della; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page83-92

Abstract

The capital market is one of the most critical factors in national economic development in Indonesia, as many industries and companies have previously used the capital market as a medium to absorb investment so that their financial position can be strengthened. The main indicator that can reflect the performance of the capital market is the Composite Stock Price Index (IHSG). The IHSG can be used to assess the general situation occurring in the market. Data IHSG is data obtained from the past and used to predict the future, also called time series data. Predictions on IHSG data need to be made so that investors can easily see capital market movements and know the policies that will be taken in the future. The Radial Basis Function Neural Network (RBFNN) method is used. RBFNN aims to get more efficient results because this method does not need to make the data stationary. The analysis results were carried out on a secondary data sample size of 1114 data, which obtained the highest forecasting price of Rp6157,619 on August 2, 2023. Meanwhile, the lowest forecast price on August 5, 2023, is IDR 5564,828 from August 1, 2023, to August 5, 2023.
COMPARISON OF SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFICATION METHOD AND LEXICON BASED ON JIWA+ BY JANJI JIWA APPLICATION REVIEWS Arti, Reyana Hilda; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page135-146

Abstract

The coffee beverage industry in Indonesia is experiencing significant growth, intensifying competition among businesses striving to maintain quality for customer loyalty. E-commerce applications play a vital role in preserving business standards as they directly engage with consumers. Janji Jiwa is among the coffee brands leveraging an application named Jiwa+ in their operations. Analyzing reviews on this e-commerce platform provides valuable insights for business owners and app developers. In this study, sentiment analysis was conducted by classifying reviews into positive, neutral, and negative sentiments using two methods: Lexicon Based and Naïve Bayes. The Lexicon Based method uses a predefined dictionary as the basis for labeling, while Naïve Bayes relies on training data to provide new insights into how both methods handle this type of data. A total of 597 Jiwa+ application reviews from the Google Play Store were utilized, split into 90% training and 10% testing data sets. The study results indicate that Naïve Bayes produces a better model than the Lexicon-Based method, as shown by its higher accuracy, sensitivity, and specificity. This is because Lexicon-Based relies on labeling words from a dictionary, which may not cover all words in the reviews, leading to labeling errors and misclassification.
Co-Authors Agustono, Hendri Amarrullah, Rido Andini, Syarifah Anggria, Vini Apriliyanti, Rita Arti, Reyana Hilda AYU ASTUTI, AYU Dadan Kusnandar Debataraja, Naomi Nessyana Dwinanda, Maria Welita Evy Sulistianingsih Fitriawan, Della Gunawan, Risky Handayany, Indry Hendra Perdana Hutami, Bintang Ratna Imro'ah, Nurfitri IMRO’AH, NURFITRI Istighfarani, Ridha Maharani, Cinta Priscillia Marola, Geby Martha, Shantika Neva Satyahadewi Nur Asih Kurniawati Oprasianti, Risky Pebriyandi, Rifki Rivaldo, Rendi Rizki, Setyo Wira Salsabila, Salsabila Salsabilla, Arla Sary, Rifkah Alfiyyah Selvia, Eva Tamtama, Ray Umiati, Wiji Vebriyanti, Lo Mei Ly Wulan, Elisabet Yusril, Alqaida Zaria, Della