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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang CAUCHY: Jurnal Matematika Murni dan Aplikasi Indonesian Journal of Artificial Intelligence and Data Mining Martabe : Jurnal Pengabdian Kepada Masyarakat FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Proceeding International Conference on Mathematics and Learning Research Indonesian Journal of Applied Mathematics and Statistics
Winalia Agwil
Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Public Health Social Sciences

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PENERAPAN JARINGAN SARAF TIRUAN PADA DATA GEMPA BUMI DI PROVINSI BENGKULU Winalia Agwil
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.152-158

Abstract

Provinsi Bengkulu merupakan wilayah yang sangat dekat dengan subduksi lempeng Eurasia dan Indo-Australia, hal ini mengakibatkan provinsi Bengkulu menjadi daerah yang rawan terjadinya bencana gempa bumi. Prediksi mengenai banyak kejadian dan rata-rata magnitudo gempa sangat menarik untuk di teliti. Penelitian mengenai analisis gempa bumi telah banyak dilakukan salah satunya dengan metode data mining yaitu Jaringan Saraf Tiruan. Tujuan dari penelitian ini adalah memperoleh arsitektur jaringan terbaik yang diterapkan pada data frekuensi kejadian dan rata-rata magnitudo gempa bumi per bulan di Provinsi Bengkulu. Kriteria pemilihan arsitektur jaringan terbaik dilakukan dengan membandingkan nilai RMSE dan MAE setiap kemungkinan arsitektur yang terbentuk. Hasil prediksi rata-rata magnitudo per bulan yang dimodelkan dengan arsitektur 1-3-1 lebih baik dibandingkan dengan arsitektur 12-3-1.
ANALISIS KETEPATAN WAKTU LULUS MAHASISWA DENGAN MENGGUNAKAN BAGGING CART Winalia Agwil; Herlin Fransiska; Nurul Hidayati
FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Vol 6, No 2 (2020): FIBONACCI: Jurnal Pendidikan Matematika dan Matematika
Publisher : Fakultas Ilmu Pendidikan Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/fbc.6.2.155-166

Abstract

Ketepatan waktu lulus mahasiswa menjadi salah satu indikator penilaian kelayakan program studi sebagai unit pelaksana pendidikan pada perguruan tinggi. Mengetahui faktor yang mempengaruhi waktu lulus mahasiswa akan membantu program studi dan dosen dalam mengambil keputusan untuk meningkatkan kuantitas mahasiswa lulus tepat waktu. Tujuan dari penelitian ini adalah untuk mendapatkan gambaran tentang karakteristik mahasiswa yang mempengaruhi ketepatan waktu lulus mahasiswa program studi S1 Matematika dengan menggunakan metode Ensemble Tree. Metode Ensemble tree yang digunakan adalah Bagging CART, dengan harapan dapat menghasilkan performa klasifikasi yang tinggi dan gambaran kharateristik mahasiswa yang baik. Data yang digunakan adalah data mahasiswa program studi S1 Matematika dari tahun 2010 sampai dengan 2019.  Kebaikan klasifikasi dilihat dari nilai Accuracy, Sensitivity dan Specificity. Metode pohon klasifikasi tunggal (CART) memberikan nilai Accuracy sebesar 82.1% , sensitivity sebesar 68.2 % dan specificity sebesar 91.2 %. Sedangkan dengan metode Bagging CART diperoleh Accuracy sebesar 85.7% , sensitivity sebesar 77.3 % dan specificity sebesar 91.2 %. Berdasarkan perbandingan nilai akurasi yang diperoleh dapat disimpulkan bahwa menerapkan metode Bagging pada pohon klasifikasi tunggal CART dapat meningkatkan performa klasifikasi.
Data Sharing Technique for Electronic Health Record (EHR) Classification using Support Vector Machine Algorithm Moh. Erkamim; Said Thaufik Rizaldi; Sepriano Sepriano; Khoirun Nisa; Sulhatun Sulhatun; Zilrahmi Zilrahmi; Winalia Agwil
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.24794

Abstract

The Electronic Health Record (EHR) integrates information about medical history in patients, complications, and history of drug use efficiently, which demands optimality and speed of service for efficiency and effectiveness of services, especially in determining outpatient and inpatient services on accurate patient history data. In efforts to improve data accuracy, this study combined the c, γ, and degree kernels in the Linear, Polynomial, and Radial Basis Function (RBF) kernels as well as data sharing techniques 10-fold cross-validation, k-medoids, and Hold- out (70 % 30%) resulted in superior K-Medoids data sharing techniques for each Polynomial kernel with an accuracy of 75.76% and a Radial Basis Function (RBF) kernel with an accuracy of 75.56% so that it can be said that the combination of K-Medoids and Polynominal kernel in the algorithm Support Vector Machine (SVM) can be used in this research case
PELATIHAN INFOGRAFIS BERBASIS CANVA UNTUK MENINGKATKAN SOFTSKILL DIERA DIGITAL DI KELURAHAN TANAH PATAH KOTA BENGKULU Avrillia Permata Hati; Febry Widyan Adha; Uci Nopita Safitri; Winalia Agwil
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 2 (2024): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i2.744-750

Abstract

Diera digitalisasi membawa banyak sekali perubahan baik dibidang pemerintahan, swasta dan masyarakat. Kebermanfaatan era digitalisasi menjadi tantangan sendiri bagi kita yang hidup dizaman ini, proses alih media cetak, audio dan video menjadi bentuk digital disebut digitalisasi. Untuk melakukan proses alih media cetak menjadi digital diperlukan skill, salah satu softskill yang bermanfaat diera digital yaitu skill desain grafis terutama untuk mengubah media cetak seperti diagram dan foto menjadi digital berupa infografis. Mengatasi tantangan tersebut, dilakukan pelatihan infografis berbasis Canva untuk meningkakan softskill desain grafis untk pembuatan infografis yang diadakan di Kantor kelurahan Tanah Patah. Bertujuan memberikan edukasi betapa pentingnya infografis dalam dunia digital, dapat membuat menghasilkan desain infografis yang bagus, menarik, dan informative. Adapun metode yang dilakukan pada pengabdian masyarakat ini yaitu pendidikan di kelas dengan tahapan persiapan, tahapan pelaksaanaan, dan tahapan evaluasi. Hasil dari pengabdian ini tercetaknya sebuah modul pelatihan infografis berbasis canva dan tercetaknya sebuah infografis Jumlah Penduduk Keluarahan Tanah Patah Kota Bengkulu berdasarkan Rukun Tetangga (RT) dan Jumlah Penduduk Keluarahn Tanah Patah Kota Bengkulu berdasarkan Jenis Kelamin
Comparison Of Methods For Handling Imbalanced Datasets In Improving Classification Algorithm Performance Dyah Setyo Rini; Winalia Agwil; Dian Agustina; Ahmad Famuji
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35780

Abstract

Data availability in large observations and dimensions is known as big data. There are several problems in processing big data, such as imbalanced datasets. In classification modeling, an imbalanced dataset is a common challenge. Data class predictions are more likely to be accurate in the majority class data and inaccurate in the minority class, resulting from the problem of imbalanced data. The data-level, the algorithm-level, and the ensemble method approach are the solutions that have been extensively researched. Some methods with a data-level approach are SMOTE, Undersampling, and Oversampling.  The algorithm-level method is NWKNN. And then, the ensemble approach is UnderBagging, RUSBoosting, SMOTEBoost, and SMOTEBagging. The goal of this study is to determine the best method for handling each case of the imbalanced dataset. There are three cases of imbalance, namely mild, moderate, and extreme. A simulation study was conducted for each imbalanced case to evaluate the accuracy of each method. Based on the AUC value, the SMOTEBagging method is the best for mild imbalance cases with an AUC value of 0.9581. For moderate imbalance cases, the SMOTEBagging method is the best method, with an AUC value of 0.9033. Meanwhile, for extreme imbalance cases, the UnderBagging method provides the best performance.
Comparative Analysis of ARMA and GARMA Models in Predicting the Blooming Dynamics of Rafflesia arnoldii in Bengkulu Province Amelliana Melasarrah; Jose Rizal; Winalia Agwil
Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v3i1.29

Abstract

Bengkulu Province, known as the Land of Rafflesia, is home to Rafflesia arnoldii, a rare and iconic flowering species. Understanding the temporal dynamics of its blooming frequency is essential not only for effective conservation planning but also for strengthening flora-based ecotourism initiatives. Time series forecasting has been widely applied to ecological and environmental data, with the Autoregressive Moving Average (ARMA) model being one of the most commonly used approaches. However, ARMA relies on the white-noise assumption, which is often violated in count data such as the number of Rafflesia arnoldii blooms, leading to reduced accuracy. To address this limitation, this study applies the Generalized Autoregressive Moving Average (GARMA) model, which accommodates non-Gaussian data from the exponential family, including Poisson and Negative Binomial distributions. The dataset consists of monthly records of blooming events collected by the Bengkulu Natural Resources Conservation Agency from January 2015 to December 2023. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Arctangent Absolute Percentage Error (MAAPE). Results show that GARMA(1,0) achieved RMSE = 2.424, MAD = 2.224, and MAAPE = 31%, while GARMA(0,2) achieved RMSE = 2.550, MAD = 1.483, and MAAPE = 26%. In contrast, ARMA(1,0) performed less effectively, with RMSE = 3.694, MAD = 2.676, and MAAPE = 36%. These findings demonstrate that GARMA provides more stable and accurate forecasts, effectively capturing the stochastic properties of count data without depending on residual normality. The study highlights the methodological superiority of GARMA over ARMA, offering both theoretical contributions to time series modeling and practical benefits for biodiversity conservation. By enabling more reliable predictions of Rafflesia arnoldii blooms, GARMA can inform conservation policies and support sustainable tourism strategies in Bengkulu Province.
Comparative Analysis of SARIMA and SARIMAX Models for Rainfall Forecasting: A Case Study of Bandung City with Humidity as an Exogenous Variable Claudia Cantika Bella; Jose Rizal; Winalia Agwil
Proceeding International Conference on Mathematics and Learning Research 2025: Proceeding International Conference on Mathematics and Learning Research
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Accurate rainfall forecasting is crucial in Indonesia, where climate change exacerbates the risks of droughts and floods. This study conducts a comparative analysis of Seasonal Autoregressive Integrated Moving Average (SARIMA) and its extension with exogenous variables (SARIMAX) to evaluate the impact of incorporating air humidity in rainfall prediction for Bandung City. Unlike SARIMA, which relies solely on univariate data, SARIMAX integrates external climatic factors, potentially enhancing predictive accuracy. This study analyzed monthly rainfall and air humidity data from January 2014 to December 2023. The modeling procedure included stationarity testing, seasonal decomposition, model identification using ACF and PACF diagnostics, parameter estimation via Maximum Likelihood Estimation (MLE), and residual diagnostic checks. Forecasting performance was comparatively evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Scaled Error (MASE). The findings indicate that SARIMAX consistently outperforms SARIMA, yielding lower AIC and BIC values and achieving a MASE of 0.690 compared to 0.840 for SARIMA. This demonstrates that exogenous climatic variables play a crucial role in reducing forecasting error, particularly for seasonal and climate-sensitive time series. Beyond methodological contributions, the findings offer practical implications: incorporating humidity into forecasting models provides policymakers and disaster management authorities with more precise information for climate adaptation and risk mitigation strategies.
Co-Authors Ahmad Famuji Amelliana Melasarrah Avrillia Permata Hati Claudia Cantika Bella Dian Agustina Dyah Setyo Rini Febry Widyan Adha Herlin Fransiska Jose Rizal Khoirun Nisa Moh. Erkamim Nurul Hidayati Said Thaufik Rizaldi Sepriano Sepriano Sulhatun Sulhatun Uci Nopita Safitri Zilrahmi, Zilrahmi