Article Per Year (5 Year)
p-Index From 2020 - 2025
3.405
P-Index
This Author published in this journals
All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Gaussian BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Cendekia : Jurnal Pengabdian Masyarakat International Journal of Research in Community Services LOSARI: Jurnal Pengabdian Kepada Masyarakat MATHunesa: Jurnal Ilmiah Matematika Journal Of Human And Education (JAHE) Innovative: Journal Of Social Science Research Journal of Statistics and Data Science Diophantine Journal of Mathematics and Its Applications Limits: Journal of Mathematics and Its Applications
Agwil, Winalia
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education 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 Transportation Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Numerasi Interaktif Berbasis Masalah di SDN 104 Kota Bengkulu untuk Meningkatkan Kemampuan Literasi Matematika Fransiska, Herlin; Nugroho, Sigit; Agwil, Winalia
Cendekia : Jurnal Pengabdian Masyarakat Vol 6 No 1 (2024): Juni
Publisher : LPPM UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/cendekia.v6i1.4480

Abstract

Dampak pandemi COVID-19 sangat besar bagi dunia pendidikan terutama didaerah yang belum maju seperti SDN 104 Kota Bengkulu. Hasil pembelajaran berdasarkan asesmen lapangan menunjukkan hasil yang rendah sehingga butuh kerja keras untuk meningkatkannya. Hasil asesmen tersebut menunjukkan kemapuan numerasi merupakan yang terendah padahal kemampuan ini sangat penting bagi siswa. Kegiatan numerasi iteratif berbasis masalah (NIRA) diyakini menjadi pilihan yang tepat untuk meningkatkan kemampuan siswa. Metode yang dilakukan ialah menyediakan bahan pembelajaran dan buku saku yang lengkap dengan soal dan jawaban serta memanfaatkan teknologi. Pemanfaatan teknologi sangat diperlukan demi kelancaran dan keefektifan pembelajaran. Penerapan teknologi seperti penggunaan software matematika yang mudah dan menarik untuk anak, penggunaan video pembelajaran yang menarik dan buku saku yang efektif dan efisien membuat kegiatan berjalan lancar serta bermanfaat untuk jangka panjang. Kegiatan ini akan membuat siswa menjadi melek literasi numerasi dan membantu sekolah dalam mempersiapkan siswa untuk menghadapi asasmen nasional berikutnya. Hasil wawancara kepada siswa dan guru menunjukkan adanya peningkatan kemampuan numerasi siswa. Selain itu hasil analisis nilai pretest dan postest menunjukkan terdapat perbedaan yang signifikan dengan p-value 0.026 artinya kegiatan ini berhasil meningkatkan kemampuan numerasi siswa.
PENERAPAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (APARCH) TERHADAP HARGA MINYAK MENTAH DUNIA Famuji, Ahmad; Sriliana, Idhia; Agwil, Winalia
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.99-109

Abstract

Heteroscedasticity poses a challenge in ARIMA modeling by causing residual variance to be non-constant, leading to less efficient estimates. This issue often arises in time series data due to volatility, which measures data fluctuation over time. To address heteroscedasticity, models like ARCH and GARCH incorporate variance changes into forecasting. However, they lack the ability to capture asymmetry, the difference in impact between good and bad news on volatility. The APARCH model, on the other hand, addresses this by modeling volatility with asymmetry elements. Daily world crude oil prices, known for high volatility, serve as a case study for this research. By employing the APARCH model, the study aims to forecast these prices accurately. Results indicate that the APARCH(1,1) model outperforms the best GARCH model, ARCH(2), as it yields a smaller Mean Absolute Percentage Error (MAPE) of 6.033487. This highlights the superior accuracy of APARCH in forecasting data with heteroscedasticity issues, particularly in the context of daily crude oil prices.
EVALUATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES ON IMBALANCED DATASET FOR POVERTY CLASSIFICATION IN BENGKULU PROVINCE Sriliana, Idhia; Nugroho, Sigit; Agwil, Winalia; Sihombing, Esther Damayanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1143-1156

Abstract

Classification is a statistical method that aims to predict the class of an object whose class label is unknown. The Multivariate Adaptive Regression Splines (MARS) classification method is a classification model that involves several basis functions with influential predictor variables. The MARS classification model is generally effective in classifying imbalanced data, including poverty data classification. The response variable used is the poverty status of households classified into poor and non-poor households, and the predictor variables consist of several poverty indicators. The problem that often arises in classification methods is a class imbalance in the response variable. Due to the poverty status included in the class imbalance data, the Bootstrap Aggregating (Bagging) and Synthetic Minority Over-sampling Technique (SMOTE) approaches will be used to improve classification accuracy on the MARS model. Bagging works by replicating data to strengthen the stability of classification accuracy, while SMOTE works by synthesizing data from minority data classes. The evaluation results showed that the classification model of poverty in Bengkulu Province using the SMOTE-MARS method provides the best classification accuracy compared to the MARS (25.81%) and Bagging-MARS (32.26%) methods based on the sensitivity value obtained, which is 85.36%.
Forecasting A Weekly Red Chilli Price in Bengkulu City Using Autoregressive Integrated Moving Average (ARIMA) and Singular Spectrum Analysis (SSA) Methods Putriasari, Novi; Nugroho, Sigit; Rachmawati, Ramya; Agwil, Winalia; Sitohang, Yosep O
Journal of Statistics and Data Science Vol. 1 No. 1 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i1.21007

Abstract

Red chili occupies a strategic position in the Indonesian economic structure because its use applies to almost all Indonesian dishes. Therefore, controlling the price of red chili is anecessity to maintain national economic stability. The purpose of this research is to forecast a red chili weekly price using ARIMA and SSA based on the weekly data of chili prices from January 2016 - December 2019 sourced from Statistics Indonseia (BPS) Branch Office of Bengkulu Province. The data have been analyzed using software R. Based on MAPE, ARIMA (2,1,2) provides the best forecasting with value 0.49% while SSA 10.64%.
Sentiment Analysis of Twitter User’s Perceptions of the Campus Merdeka Using Naïve Bayes Classifier and Support Vector Machine Methods Salsabilla, Intan; Alwansyah, Muhammad Arib; Nugroho, Sigit; Agwil, Winalia
Journal of Statistics and Data Science Vol. 2 No. 2 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v2i2.30577

Abstract

The Campus Merdeka program is being implemented by the government to realize autonomous and flexible learning in tertiary institutions to create a learning culture that is innovative, not restrictive, and the needs of students. The Campus Merdeka provides added value and is attractive and provides various responses from the public both directly and on different social media platforms. One of the social media platforms is Twitter. Therefore, research was conducted on the community's response to the Campus Merdeka program on Twitter social media. Twitter documents in the form of community response tweets to the Campus Merdeka program are classified into two categories, namely positive responses and negative responses. The method used in this study is the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) with a Polynomial Degree 2 kernel. The highest level of accuracy resulting from this research is 73.5% with a parameter value of  of 0.5, a constant value  is 0.5, with training data of 309 documents for training data and 132 documents for test data. The accuracy results obtained for the Naïve Bayes Classifier method are 65.9% and for the Support Vector Machine method, an accuracy is 73.5%.
Performa Teknik Regularisasi Dalam Penanganan Masalah Multikolinieritas Fikri, Alin Febianti; Agwil, Winalia; Agustina, Dian
Diophantine Journal of Mathematics and Its Applications Vol. 2 No. 1 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v2i01.28480

Abstract

Multikolinieritas adalah kondisi terdapat hubungan linier antar variabel independen, dimana diantara variabel independen tersebut saling berkorelasi. Akibatnya akan sulit untuk melihat pengaruh variabel independen terhadap variabel dependen. Penanganan multikolinieritas salah satunya dapat dilakukan menggunakan teknik regularisasi yaitu bentuk regresi yang mengatur atau menyusutkan perkiraan koefisien menuju nol. Teknik regularisasi yang akan dibahas pada penelitian adalah regresi ridge, LASSO dan elastic net. Regresi ridge hanya dapat menyusutkan koefisien regresi menuju angka 0, tetapi tidak pernah tepat ke angka 0. Regresi elastic net dapat menyusutkan koefisien regresi tepat nol, melakukan seleksi variabel secara simultan dan dapat memilih kelompok peubah yang berkorelasi. Sedangkan, regresi LASSO hanya dapat menyusutkan koefisien dan menetapkan koefisien ke angka 0. Oleh karena itu, LASSO dapat menghasilkan model dengan variabel terbaik. Namun, LASSO memiliki beberapa kelemahan. Ketika jumlah variabel independent lebih kecil dibanding jumlah amatan, kinerja LASSO lebih didominasi oleh ridge. Ketika jumlah variabel independent lebih besar dibanding jumlah amatan, maka LASSO hanya memilih n variabel yang diikutkan dalam model. Sehingga, untuk mengatasi high dimensional data yang mengandung multikolinieritas dilakukan penelitian menggunakan teknik regularisasi regresi ridge, LASSO dan elastic net untuk dibandingkan kebaikan modelnya berdasarkan nilai MSE terkecil. Data yang digunakan merupakan data simulasi dan studi kasus dari website resmi BPS serta UCI machine learning repository. Disimpulkan bahwa dari 30 pengacakan, model ridge baik memodelkan dataset dengan p = 20, 40, dan 80 atau kondisi dataset dimana jumlah variabel independent lebih kecil dibanding jumlah amatan dan elastic net baik memodelkan dataset dengan p = 100, 160, dan320.
Enhancing Data Visualization Competencies Through Power BI Training Agwil, Winalia; Sunandi, Etis; Rizal, Jose; Faisal, Fachri; Nugroho, Sigit; Syahada, Sri; Hermalia, Hermalia
International Journal of Research in Community Services Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i2.901

Abstract

Vocational High School (SMK) aims to prepare students with the skills and knowledge required to meet industry demands. Recognizing the importance of data analysis and visualization in the workforce, this community service focuses on enhancing these competencies among SMKN 04 Kota Bengkulu students, particularly those in the Software Engineering program. A community service program was conducted to train students in utilizing Power BI for real-time and interactive data visualization. The training program included preparatory surveys, module development, and practical workshops. Students actively participated, demonstrating a greater interest and understanding of data visualization concepts. Evaluation results showed that 89% of participants found the training beneficial, and 84% mastered Power BI’s visualization techniques. The outcomes highlight the program's effectiveness in equipping students with industry-relevant skills, emphasizing the need for similar initiatives targeting broader student groups. This project bridges the gap between vocational education and the digital economy's demands.
Forecasting A Weekly Red Chilli Price in Bengkulu City Using Autoregressive Integrated Moving Average (ARIMA) and Singular Spectrum Analysis (SSA) Methods Putriasari, Novi; Nugroho, Sigit; Rachmawati, Ramya; Agwil, Winalia; Sitohang, Yosep O
Journal of Statistics and Data Science Vol. 1 No. 1 (2022)
Publisher : UNIB Press

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

Abstract

Red chili occupies a strategic position in the Indonesian economic structure because its use applies to almost all Indonesian dishes. Therefore, controlling the price of red chili is a necessity to maintain national economic stability. The purpose of this research is to forecast a red chili weekly price using ARIMA and SSA based on the weekly data of chili prices from January 2016 - December 2019 sourced from Statistics Indonseia (BPS) Branch Office of Bengkulu Province. The data have been analyzed using software R. Based on MAPE, ARIMA K (2,1,2) provides the best forecasting with value 0.49% while SSA 10.64%.
Analisis Perbandingan Metode Hirearchical, K-Means, dan K-Medoids Clustering dalam Pengelompokan Kasus Penyakit Menular di Bengkulu Tengah Tasti, Desi T.; Gumay, Fridz M.; Aysha, Ulfianida; Agwil, Winalia; Pratami, Wingke Y.
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.32048

Abstract

In Indonesia, infectious diseases are still a persistent problem. Experts have accumulated knowledge regarding the emergence of the disease. In the last ten years, Indonesia is still experiencing the problem of triple burden diseases. Where Indonesia is still hit by infectious diseases, non-communicable diseases (NCDs) and diseases that should have been resolved, apart from that, infectious diseases are also still a big problem that must be faced. Researchers are interested in conducting research on infectious diseases in Central Bengkulu Regency. When analyzing infectious diseases, grouping can be done. Cluster analysis is an approach to looking for similarities in data and placing similar data into groups. There are two grouping methods in cluster analysis, hierarchical methods and non-hierarchical methods. One of the cluster analyzes using hierarchical methods is the average linkage method, while non-hierarchical ones are K-Means and K-Medoids. The variables used in this research are TBC and DHF in 2022. The highest rates of TB and DHF occurred in Pondok Kelapa sub-district, namely 29 and 23 cases. Based on the results of the analysis, it consists of 2 clusters, with cluster 1 consisting of 9 sub-districts, while cluster 2 consists of 2 sub-districts. Based on the results of evaluating the best method using the Calinski-Harabasz Index, it was found that the K-medoids method was the best method with a value of 0.
Enhancing Data Visualization Competencies Through Power BI Training Agwil, Winalia; Sunandi, Etis; Rizal, Jose; Faisal, Fachri; Nugroho, Sigit; Syahada, Sri; Hermalia, Hermalia
International Journal of Research in Community Services Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i2.901

Abstract

Vocational High School (SMK) aims to prepare students with the skills and knowledge required to meet industry demands. Recognizing the importance of data analysis and visualization in the workforce, this community service focuses on enhancing these competencies among SMKN 04 Kota Bengkulu students, particularly those in the Software Engineering program. A community service program was conducted to train students in utilizing Power BI for real-time and interactive data visualization. The training program included preparatory surveys, module development, and practical workshops. Students actively participated, demonstrating a greater interest and understanding of data visualization concepts. Evaluation results showed that 89% of participants found the training beneficial, and 84% mastered Power BI’s visualization techniques. The outcomes highlight the program's effectiveness in equipping students with industry-relevant skills, emphasizing the need for similar initiatives targeting broader student groups. This project bridges the gap between vocational education and the digital economy's demands.
Co-Authors Afni, Della Nur Alwansyah, Muhammad Arib Andini, Agita Aysha, Ulfianida Ayu Lestari, Wina Bagus Sartono Damayanti , Septri Dian Agustina Dzakirah, Qanitahudz Fachri Faisal Fairuzinda, Athaya Fajriyanti, Meisya Famuji, Ahmad Fikri, Alin Febianti Firdaus Firdaus Gumay, Fridz M. Gumay, Fridz Meryatdas Hasani, Idam Abdurrohim Herlin Fransiska Hermalia, Hermalia Idhia Sriliana Jose Rizal Meylisah, Eni Nurul Hidayati Oktarina, Cinta Rizki Pandu Winata, Aji Pepi Novianti Popita, Resi Pratami, Wingke Y. Putriasari, Novi Rachamawati , Ramya rachmawati, Ramya Reza Pahlepi, Reza Rini, Dyah Setyo Salsabilla, Intan Saragih, Emiya Surabina Br Setyo Rini, Dyah Sigit Nugroho Sihombing, Esther Damayanti Simanullang, Aditama Rouliber Siregar, Samuel Eurico Sitohang, Yosep O Sunandi, Etis Supianti, Filo Suratmin, Idam Abdurrohim Hasani Syah, Vivi Elvira Saputri Syahada, Sri Tasti, Desi T. Ulfasari Rafflesia Widyan Adha, Febry Yulian Utami, Rahmi