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All Journal Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications
Pika Silvianti
Department of Statistics, IPB University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Other

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Penggerombolan Hasil Ujian Nasional Menggunakan K-Rataan Samar Nouval Habibie; Akbar Rizki; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1014.777 KB) | DOI: 10.29244/xplore.v10i1.365

Abstract

National examination scores can be a basis for the government to make a mapping of education quality in order to increase it. The mapping can be done by using fuzzy cluster analysis. The objective of this experiment is to cluster districts/cities in Indonesia based on national examination score in natural and social science in 2014/2015 until 2017/2018 school year by using the fuzzy c-means method. The evaluation criteria that will be used are the standard deviation ratio, silhouette coefficient, and Xie Beni index. The best cluster size is two clusters, A and B. The clustering result shows cluster A has a higher mean from each subject than cluster B. Therefore, cluster A will be categorized as good, whereas cluster B as bad. The proportion of districts/cities that belong to cluster A decreased each year. The final cluster result can be determined by the mean of its degree of membership from those four school years. The analysis results show that the distribution of education quality is dominated in Java Island and squatter cities. East Nusa Tenggara, West Sulawesi, Central Sulawesi, and North Kalimantan don’t have any districts/cities belong to cluster A.
Pemodelan Tingkat Kriminalitas di Indonesia Menggunakan Analisis Geographically Weighted Panel Regression Endah Febrianti; Budi Susetyo; Pika Silvianti
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.539 KB) | DOI: 10.29244/xplore.v12i1.950

Abstract

Crime is one of the socio-economic problems that Indonesia has not yet resolved. Although Indonesia is categorized as a safe country to visit, in reality, there are still many Indonesian people who experience crime. The resolution of this socio-economic problem is very important because it involves the safety and comfort of the community. This study aims to identify the factors that influence the crime rate in Indonesia and determine the best model for each province by comparing the panel data regression model and the Geographically Weighted Panel Regression (GWPR) model. This research data consists of 34 provinces in Indonesia from 2016 to 2020. The analysis used is panel data regression analysis and GWPR. The result is that the adaptive kernel gaussian GWPR is the best model with of 69,89% and AIC of 167,4585. The GWPR modeling produces model equations and significant variables for each province. In general, five variables have a significant effect on the crime rate, namely percentage of poor population, open unemployment rate, Gross Regional Domestic Product at the constant price per capita, human development index, and mean years of schooling.
Algoritme Support Vector Machine untuk Analisis Sentimen Berbasis Aspek Ulasan Game Online Mobile Legends: Bang-Bang Mar Atul Aji Tyas Utami; Pika Silvianti; Muhammad Masjkur
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.151 KB) | DOI: 10.29244/xplore.v12i1.1064

Abstract

The presence of the digital technology era is facilitated by an internet connection that is easily accessible and provides many features and entertainment, one of which is online games. Mobile Legends: Bang-Bang is a Multiplayer Online Battle Arena (MOBA)-type online game that has been popular since its launch in 2016. Currently, Mobile Legends: Bang-Bang is still the top free game on the Google Play Store. This popularity is inseparable from user reviews that provide different information and sentiment. This research will identify the sentiment of application user reviews based on aspects of gameplay, performance, visualization, and player. The classification method used in this study is the Support Vector Machine (SVM). The online game application Mobile Legends: Bang-Bang tends to have negative sentiment from aspects of gameplay, performance, and player. However, from the visualization aspect, they tend to have positive sentiment. The results of the evaluation of the model based on the value of accuracy, F1-score, and AUC, it was found that the gameplay, Performance, and Player aspects gave better classification results than the Visualization aspect.
Analysis Of Stock Market, Mining Commodity, Exchange Rate, And Energy Sector Stock Index Using Vector Error Correction Model: Analisis Bursa Saham, Komoditas Pertambangan, Kurs, Dan Indeks Saham Sektor Energi Menggunakan Vector Error Correction Model Melati; Pika Silvianti; Farit Mochamad Afendi
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.v7i1p44-55

Abstract

Energy Sector is one of the sectors that has a significant impact on the overall economic growth of a country. Economic growth is always linked to energy consumption, as increasing economic development leads to higher energy demand. Therefore, this study aims to analyze the factors influencing the energy sector stock index in Indonesia using Vector Error Correction Model (VECM). The data used include the energy sector stock index, crude oil prices, coal prices, gas prices, Nikkei Index, Shanghai Index, Dow Jones Index, and exchange rates from January 2021 to March 2023. VECM analysis results indicate that in the short term, crude oil prices and coal prices have a significant impact on the energy sector stock index. In the long term, significant factors are coal prices, gas prices, Nikkei Index, and exchange rates. The Impulse Response Function (IRF) analysis reveals that shocks to the energy sector stock index, crude oil prices, and coal prices can increase the energy sector stock index. Conversely, shocks to the Nikkei Index can decrease the energy sector stock index. The Forecast Error Variance Decomposition (FEVD) results demonstrate that the contributions of the energy sector stock index, crude oil prices, coal prices, and gas prices are significant in explaining the behavior of changes in the energy sector stock index.
Identification of Prospective Subindustries Ahead of the 2024 Simultaneous General Elections with K-Medoids Clustering: Identifikasi Subindustri Prospektif Menjelang Pemilihan Umum Serentak 2024 dengan K-Medoids Clustering Vera Amelia; Pika Silvianti; La Ode Abdul Rahman
Indonesian Journal of Statistics and Applications Vol 7 No 2 (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.v7i2p64-74

Abstract

Indonesia Stock Exchange (IDX) Composite has grown in each general election year since 1998. This indicates that certain subindustries have benefited positively from the election year momentum. However, analyzing each subindustry was less efficient. This study aimed to identify prospective subindustries leading up to the 2024 Simultaneous Election based on the results of K-Medoids clustering on data from the lead-up to the 2019 Simultaneous Election. Research variables covered long-term price rate of change (indicating trends) and volatility (depicting fluctuations). These were derived from transforming historical stock price data for each issuer on a weekly basis in the two years before the 2019 Simultaneous Election. Four clusters emerged: high positive, low positive, high negative, and low negative. Positivity/negativity signify trends and high/low represent fluctuations. High fluctuations indicate higher risks. Prospective subindustries for the 2024 Simultaneous Election with low risk include household furniture manufacturers, basic chemical producers, construction materials, packaging, tires, household goods retail, life insurance, consumer finance, and financial holding companies. On the other hand, sub-industries with high risks for the 2024 Simultaneous Election include aluminum, paper, and textiles.
Co-Authors Akbar Rizki Budi Susetyo Endah Febrianti Farit Mochamad Afendi La Ode Abdul Rahman Mar Atul Aji Tyas Utami Melati Muhammad Masjkur Nouval Habibie Vera Amelia