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All Journal Jurnal Sains dan Seni ITS Jurnal Penelitian dan Pendidikan IPS Indonesian Journal of Applied Statistics Jambura Journal of Mathematics Jurnal Abdi Insani Jurnal Statistika dan Matematika (Statmat) Jurnal Statistika dan Aplikasinya Multica Science and Technology International Journal of Quantitative Research and Modeling Buletin Sistem Informasi dan Teknologi Islam VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Statistika dan Komputasi (STATKOM) Eksponensial ANDIL Mulawarman Journal of Community Engagement Jurnal Polimesin REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Erlyne Nadhilah Widyaningrum
Universitas Mulawarman
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Penerapan Metode SEM-PLS pada Kepuasan Pengguna Aplikasi Instagram Tamba, Felicia Joy Rotua; Nasywa, Syarifah; Salsabila, Adellia; Khoiruddin, Ahmad Zulfikar; Tandi Kala, Ezra Alfrianto; Sifriyani, Sifriyani; Sari, Nariza Wanti Wulan; Yuniarti, Desi; Nadhilah Widyaningrum, Erlyne; Atarezcha Pangruruk, Thesya
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/a8agna87

Abstract

Social media platforms, particularly Instagram, have emerged as widely utilized channels among diverse user groups, including university students, for information sharing, social interaction, and entertainment purposes. The study seeks to analyze how Instagram quality, perceived benefits, and social interaction contribute to user satisfaction and loyalty within the FMIPA community at Mulawarman University.  The SmartPLS 3.0 software facilitates the use of the Structural Equation Modelling technique based on Partial Least Squares (SEM-PLS) in this investigation.  The results show that each of the three independent factors significantly and favourably affects user pleasure, which in turn significantly boosts customer loyalty. The R-square values of 0.685 for satisfaction and 0.655 for loyalty suggest that the proposed model adequately explains the relationships among the variables. Furthermore, all measurement indicators were confirmed to be both valid and reliable. In conclusion, the study demonstrates that users’ positive perceptions of Instagram’s quality, benefits, and social interaction contribute to enhanced satisfaction and foster greater loyalty toward the platform.
Comparative Analysis of Hierarchical Cluster Methods in Inflationary Cities in Indonesia Based on Sectoral Inflation Patterns Khoirunissa, Husna Afanyn; Safitriani, Nur Rezky; Widyaningrum, Erlyne Nadhilah; Putri, Rizka Amalia; Fathan, Morina A.; Nisa, Nabilla Rida Tri
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35105

Abstract

This study aims to assess the performance of single linkage, complete linkage, and average linkage hierarchical clustering algorithms in grouping cities used as inflation benchmarks in Indonesia into clusters based on sectoral inflation patterns. The data utilized are 150 regencies/cities divided into 11 sectors that drive inflation, identified by BPS Indonesia. Prior to clustering, a distance analysis using Euclidean distances was conducted to measure similarity between regions. Evaluation of the optimal number of clusters was conducted by applying the stability measure approach (APN, AD, ADM, and FOM), which showed that creating five clusters produced the most stable results. The results of the analysis revealed that the single linkage approach had the lowest within-cluster to between-cluster standard deviation ratio compared to the other two approaches, which revealed a greater level of homogeneity between the clusters. From an economic perspective, this clustering pattern revealed impressive differences in sectoral inflation pressures between provinces, even between cities within a province. Consequently, the single linkage method is proposed as the optimal method for identifying spatial variations in sectoral inflation in Indonesia.
Comparison of Poisson and Negative Binomial Regression Models in Identifying Factors Influencing Covid-19 Deaths in Indonesia. Nabilla Rida Tri Nisa; Amanatullah Pandu Zenklinov; Husna Afanyn Khoirunissa; Nur Rezky Safitriani; Erlyne Nadhilah Widyaningrum; Rizka Amalia Putri; Morina A. Fathan
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i4.1126

Abstract

This research compares Poisson Regression and Generalized Negative Binomial (GNB) Regression to underscore the factors that influence the growth of COVID-19 deaths in Indonesia. Count data such as mortality cases often violates the Poisson assumption of equidispersion (null mean equals variance) causing overdispersion. The GNB model is suggested as a remedy for overdispersed data crime prevention has become increasingly necessary for systematic development because secondary data from the Indonesian government has included dependable variables such as mortality rates for people aged over 60, diabetes mellitus, heart disease, lung disease, healthcare worker percentages, referral hospitals, and the population. The Poisson Regression reported R² of 87.67% and experienced overdispersion (θ₁ = 356.27, θ₂ = 417,597). The GNB model, in contrast, with a lower AIC (499.5566), overtook Poisson. Important factors that had significant impact on both models were mortality rates for individuals over 60, diabetes mellitus, healthcare workers, and referral hospitals, whereas heart and lung disease mortality rates were the ones that were not material. The GNB model had a better fit and tackled the issues of overdispersion in the Poisson Regression.
Performance, energy balance, and emission characteristi-cs of a spark ignition engine fueled with gasoline and LPG Marthen Paloboran; Thesya Atarezcha Pangruruk; Ismail Rahim; Juhamri Juhamri; Auliya Rahmatul Ummah; Erlyne Nadhilah Widyaningrum
Jurnal Polimesin Vol 24, No 3 (2026): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i3.8906

Abstract

Efforts to reduce greenhouse gas emissions continue to encourage the transition from liquid fossil fuels to gaseous fuels, as gaseous fuels are expected to provide cleaner combustion and lower hydrocarbon emissions. This study aimed to evaluate the performance of a spark ignition engine with a carburetor fuel system operated using gasoline and Liquefied Petroleum Gas (LPG), and to analyze its energy balance based on the First Law of Thermodynamics. The energy balance consisted of input energy from air and fuel, useful output energy, and energy losses during combustion. The engine was operated at speeds ranging from 2000 to 5000 rpm. Fuel consumption was measured after the engine consumed 50 mL of gasoline and 50 g of LPG. The results showed that emissions of HC, CO, and CO2 from LPG were lower than those from gasoline. The useful energy produced by LPG combustion was lower than that of gasoline; however, LPG showed higher thermal efficiency due to lower Specific Fuel Consumption (SFC) and reduced energy losses. The conversion from gasoline to LPG in a carburetor system reduced emissions by approximately 7–73%, whereas the average reduction in an electronic fuel injection system was reported at 11–15%.
APPLICATION OF THE GUSTAFSON–KESSEL ALGORITHM FOR IDENTIFYING SPATIAL PATTERNS OF NATURAL DISASTERS IN EAST NUSA TENGGARA Mitha Rabiyatul Nufus; Chandrawati; Erlyne Nadhilah Widyaningrum
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09205

Abstract

This study examines spatial patterns of disaster vulnerability across districts and cities in East Nusa Tenggara Province, one of Indonesia’s most disaster-prone regions. Although previous studies have highlighted the province’s exposure to multiple hazards, limited attention has been given to clustering methods capable of capturing non-homogeneous and elliptical data structures. This research aims to classify regional disaster vulnerability based on the characteristics of disaster occurrences and to provide empirical support for more targeted mitigation strategies. Secondary data on floods, forest fires, hurricanes, and landslides recorded in 2023 were analyzed using the adaptive Gustafson–Kessel clustering algorithm. The optimal number of clusters was determined using the Silhouette validity index. The results identify three distinct vulnerability groups: regions highly prone to multiple types of disasters, regions predominantly affected by a single hazard, and regions with relatively low disaster risk. The resulting spatial patterns reveal clear differences in disaster intensity and complexity among regions, emphasizing the need for location-specific disaster management policies. This study contributes to disaster risk analysis by demonstrating the applicability of the Gustafson–Kessel algorithm in capturing complex spatial vulnerability patterns that are often overlooked by conventional clustering approaches.
Comparative Analysis of DES-Brown and DES-Holt Methods in Forecasting the Stock Price of PT Telekomunikasi Indonesia Tbk Dela Juliarsih Rahman; Wiwit Pura Nurmayanti; Thesya Atarezcha Pangruruk; Erlyne Nadhilah Widyaningrum; Siti Hadijah Hasanah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm486

Abstract

This study aims to dermine the best forecasting method for the stock price of PT Telekomunikasi Indonesia Tbk using the Double Exponential Smoothing (DES) Brown and DES-Holt methods. The data used consist of stock prices from January 2019 to September 2025. The DES-Brown method employs a single parameter, while DES-Holt uses two parameters. Forecasting accuracy is evaluated using Mean Absolute Deviation (MAD), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the DES-Brown method with a smoothing parameter produces the smallest forecasting errors compared to the DES-Holt method, with MAD, RMSE , and MAPE . Therefore, it can be concluded that the DES-Brown method is the most suitable approach for forecasting the stock price of PT Telekomunikasi Indonesia Tbk.
Modeling East Java Province Poverty Cases Using Birespon Truncted Spline Regression Rizka Amalia Putri; Nindya Wulandari; Erlyne Nadhilah Widyaningrum; Morina A. Fathan; Nur Rezky Safitriani
Indonesian Journal of Applied Statistics Vol 8, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i1.100915

Abstract

An analytical method for determining the relationship between predictor and response variables is regression. For data that shows unidentified patterns, nonparametric regression is a suitable data analysis technique. A nonparametric regression technique is the truncated spline. Due to the widespread use of truncated spline with a single response variable, this study employs biresponse truncated spline, which uses two response variables to produce a better model than single-response modeling. The purpose of this study is to obtain the best model and to identify which variables influence the poverty case in East Java Province using biresponse truncated spline regression. The best knot points were chosen for this investigation using Generalized Cross Validation (GCV). With three knot points and a model goodness of fit () of 95.83%, GCV gives the best modeling results. Applying this model to the East Java Province case of poverty using data on the poverty depth index and the percentage of the population living in poverty in 2023 reveals that the Labor Force Participation Rate (TPAK), Average Years of Schooling (RLS), and Open Unemployment Rate (TPT) all have a significant effect.Keywords: biresponse truncated spline; nonparametric regression; poverty
Comparison of Support Vector Machine and Random Forest for Sentiment Analysis of Gojek Reviews Erlyne Nadhilah Widyaningrum; Filzah Syakirah; M. Fathurahman
REKADATA Vol. 2 No. 1 (2026): Rekayasa Data dan Kecerdasan Artifisial (REKADATA)
Publisher : CV Mazaya Cahaya Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of digital technology has increased the use of online transportation applications in Indonesia, one of which is Gojek. User reviews on the Gojek application in the Google Play Store reflect the level of user satisfaction and dissatisfaction with the services provided. Therefore, sentiment analysis is needed to classify these reviews into positive and negative categories. This study aims to get the result and performance of Support Vector Machine (SVM) and random forest classification methods in sentiment analysis of Gojek user reviews. The data used are user reviews from January 2026 obtained through a scraping technique. The analysis stages include text processing, word weighting using TF-IDF, and hyperparameter optimization using the random search method. The results show that the SVM method achieved an accuracy of 86,78%, precision of 87,96%, recall of 86,23%, and F1-score of 87,09%. Meanwhile, the random forest method achieved an accuracy of 84,75%, precision of 82,87%, recall of 88,85%, and F1-score of 85,76%. Based on these results, the SVM method demonstrates superior overall performance compared to Random Forest in classifying sentiment of Gojek user reviews.
Pemodelan Support Vector Regression Dengan Optimasi Grid Search Untuk Prediksi Curah Hujan Di Kabupaten Berau Fadilla Fadilla; Nariza Wanti Wulan Sari; Meirinda Fauziyah; Erlyne Nadhilah Widyaningrum; Sri Wahyuningsih
REKADATA Vol. 2 No. 1 (2026): Rekayasa Data dan Kecerdasan Artifisial (REKADATA)
Publisher : CV Mazaya Cahaya Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fluktuasi curah hujan yang terjadi dari waktu ke waktu dapat meningkatkan potensi bencana hidrometeorologi, seperti banjir dan tanah longsor. Kondisi tersebut menunjukkan pentingnya pengembangan model yang dapat memberikan prediksi curah hujan secara akurat. Penelitian ini bertujuan menerapkan Support Vector Regression (SVR) dengan kernel polynomial dan optimasi grid search untuk memprediksi curah hujan di Kabupaten Berau. Data yang digunakan berupa curah hujan bulanan sebagai variabel respon serta suhu udara, kelembapan udara, tekanan udara, kecepatan angin, dan penyinaran matahari sebagai variabel prediktor. Data mencakup periode Januari 2014 hingga Desember 2025 dan dinormalisasi menggunakan metode min-max normalization. Optimasi grid search dilakukan dengan menguji nilai cost sebesar 0,5; 1; 2; dan 4 serta degree sebesar 1 dan 2. Pembentukan model dilakukan pada tiga skenario pembagian data training dan testing, yaitu 70:30, 80:20, dan 90:10. Evaluasi model menggunakan RMSE dan MAPE. Hasil analisis menunjukkan bahwa model dengan performa terbaik diperoleh pada proporsi 90:10, dengan RMSE training sebesar 85,5421 dan testing sebesar 86,8895. Nilai MAPE yang dihasilkan masing-masing sebesar 45,3880 dan 61,4889. Prediksi model mampu menggambarkan kecenderungan umum curah hujan aktual, sehingga SVR dengan kernel polynomial dan optimasi grid search dapat digunakan sebagai alternatif dalam prediksi curah hujan di Kabupaten Berau.
Co-Authors Abda Abda Afif Nurdiansyah, Mochamad Amanatullah Pandu Zenklinov Angeline Seru, Indra Aprillia Elsada Rinindah Atarezcha Pangruruk, Thesya Aulia, Nabila Auliya Rahmatul Ummah Bahriah, Ayu Chandrawati Christian, Diego Dela Juliarsih Rahman Desi Yuniarti Fadilla Fadilla Filzah Syakirah Hariyanto Hariyanto Hasanah, Siti Hadijah Hidayanty, Nurul Ilma Husna Afanyn Khoirunissa Ika Purnamasari Ismail Rahim Juhamri Juhamri Khoiruddin, Ahmad Zulfikar Kosasih, Raditya Arya M. Fathurahman M. Fathurahman Marthen Paloboran Meirinda Fauziyah Mitha Rabiyatul Nufus Morina A. Fathan Muchlashin, Anif Muhammad Azra Firdaus Nabilla Rida Tri Nisa Nariza Wanti Wulan Sari Nasywa, Syarifah Ni Luh Desvita Pratiwi Nindya Wulandari Ningsih, Eva Lestari Nisa, Nabilla Rida Tri Nugraha, Pratama Yuly Nur Rezky Safitriani Nur Rezky Safitriani Nurmayanti, Wiwit Pura Pangruruk , Thesya Atarezcha Pangruruk Patiallo, Monalisa Randang Ratna Kusuma, Ratna Rizka Amalia Putri Safitriani, Nur Rezky Sakila Armayani Salsabila, Adellia Shalihatunnisa, Shalihatunnisa Sholikhati, Adrikni Sifriyani, Sifriyani SITI MAHMUDAH SRI WAHYUNINGSIH Suhud Wahyudi Surya Prangga Suyitno Suyitno Suyitno Tamba, Felicia Joy Rotua Tandi Kala, Ezra Alfrianto Thesya Atarezcha Pangruruk Wiwit Pura Nurmayanti Wiwit Pura Nurmayanti Yuniarti, Desi