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All Journal Jurnal Matematika BIMASTER GRADIEN Jurnal Geocelebes BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengabdian Masyarakat Bumi Raflesia Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Jurnal Ilmiah Matematika Jurnal Eurekamatika MATHunesa: Jurnal Ilmiah Matematika Journal of Mathematics UNP Indonesian Journal of Community Empowerment and Service Aceh International Journal of Science and Technology Proceeding International Conference on Mathematics and Learning Research Indonesian Journal of Applied Mathematics and Statistics
Jose Rizal
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Universitas Bengkulu, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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MODEL DEPENDENSI HARGA-HARGA KOMODITAS EKSPOR UNGGULAN INDONESIA MENGGUNAKAN PENDEKATAN COPULA Riri Sesiati; Jose Rizal; Yulian Fauzi
Journal of Mathematics UNP Vol 8, No 2 (2023): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/unpjomath.v8i2.14860

Abstract

  The export of non-oil and gas leading commodities (palm oil, rubber and cocoa) contributes the largest state revenue. Copula is used to model the dependency between two variables that have different marginal distributions. The data to be used is secondary data from the Commodity Futures Trading Supervisory Agency (BAPPEBTI) starting from January 1, 2018 - February 28, 2020 for the period before the Covid-19 pandemic and March 1, 2020 - November 26, 2021 for the period after the Covid-19 pandemic. The dependency of the two variables to be measured is the price of palm oil, rubber and cocoa before and after the start of the Covid-19 pandemic in Indonesia. The Copula that will be used are Joe, Gumbel, Frank, Gaussian, Clayton, and Student's t. The best Copula model obtained for palm oil and rubber dependencies is the Joe Copula, while palm oil and cocoa and cocoa rubber dependencies are the Clayton Copula.  
Upaya Peningkatan Kemampuan Pengolahan Data Penelitian Bagi Mahasiswa Melalui Pelatihan Program R Hidayati, Nurul; Nugroho, Sigit; Rizal, Jose; Afandi, Nur
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 7 No. 1 (2024): April : Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

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

Abstract

Open Sourse R  adalah suatu software statistika yang popular, lengkap, dan berlisensi gratis sehingga terhindar dari tuntutan hukum saat mengolah dan menganalisis data hasil penelitian serta memiliki visualisasi grafik yang lebih canggih dan menarik dibandingkan software Statistika lainnya. Oleh karena itu, tim pengabdian tertarik untuk melakukan kegiatan pengabdian untuk memberikan pelatihan pengolahan data penelitian melalui program R bagi mahasiswa di Universitas Islam Negeri Fatmawati Soekarno (UIN-FAS), Fakultas Tarbiyah dan Tadris, Prodi Tadris Bahasa Indonesia, Prodi Tadris IPA, dan Prodi Tadris Matematika. dikarenakan mahasiswanya belum mengenal program R dalam pengolahan data. Tujuan dari kegiatan PPM yaitu meningkatkan pengetahuan dan keterampilan mahasiswa tentang teknik pengoperasian program R dalam pengolahan data penelitian dan  meningkatkan kemampuan mahasiswa dalam menginterpretasikan hasil dari pengolahan data statistik berbasis penelitian. Tahapan-tahapan dalam kegiatan ini, yaitu tahap persiapan, pelaksanaan, dan evaluasi (pre test dan post test). Evaluasi kegiatan yaitu pre test dan post test menunjukkan bahwa terdapat perbedaan rata-rata hasil belajar antara pre test dan post test. Hal ini mengindikasikan bahwa ada pengaruh pelatihan terhadap peningkatan pengolahan data dengan menggunakan program R pada mahasiswa. Jadi, dapat ditarik suatu kesimpulan bahwa kegiatan PPM ini dikatakan cukup berhasil dalam menambah wawasan, ilmu pengetahuan, dan keterampilan bagi mahasiswa.   Kata Kunci: statistika, program, data
Pemodelan IPM di Provinsi Bengkulu dengan Pendekatan Metode Geographically Weighted Regression (GWR) dan Geographically Temporally Weighted Regression (GTWR) Oktarina, Cinta Rizki; Rizal, Jose; Faisal, Fachri; Tasyah, Qhiky Lioni; Pratiwi, Stevy Cahya
Jurnal EurekaMatika Vol 12, No 1 (2024): Jurnal EurekaMatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v12i1.66629

Abstract

The Geographically Temporally Weighted Regression (GTWR) method is a development of the Geographically Weighted Regression (GWR) method, namely by considering elements of location and time. This research aims to obtain the best estimation results between the GWR and GTWR methods applied to human development index data in Bengkulu Province for 2018–2022. There are three variables modelled, namely three independent variables: life expectancy, average years of schooling, and open unemployment rate, while the dependent variable is the Human Development Index. The research results show that the three independent variables significantly influence the dependent variable and have spatial heterogeneity in the modelled data. In addition, the coefficient of determination value for GTWR is 99.98%, while for GWR it is 99.74%, so the GTWR method is better for modelling the Human Development Index in Bengkulu Province for 2018–2022.Keywords: Coefficient of Determination, GWR Method, GTWR Method, Human Development Index, Spatial heterogeneity.AbstrakMetode Geographically Temporally Weighted Regression (GTWR) merupakan pengembangan dari metode Geographically Temporally Weighted Regression (GWR), yakni dengan mempertimbangkan unsur lokasi dan waktu. Penelitian ini bertujuan untuk mendapatkan hasil estimasi terbaik antar metode GWR dan GTWR yang diterapkan pada data indeks pembangunan manusia di Provinsi Bengkulu Tahun 2018-2022. Terdapat tiga variabel yang dimodelkan, yakni tiga variabel bebas: angka harapan hidup, rata-rata lama sekolah, dan tingkat pengangguran terbuka, sedangkan variabel takbebas adalah Indeks Pembangunan Manusia. Hasil penelitian menunjukkan bahwa ketiga variabel bebas tersebut mempengaruhi variabel takbebas secara signifikan dan terdapat sifat heterogenitas spasial pada data yang dimodelkan. Sebagai tambahan, nilai koefisien determinasi untuk GTWR sebesar 99.98%, sedangkan untuk GWR sebesar 99.74%, jadi metode GTWR lebih baik untuk memodelkan Indeks Pembangunan Manusia di Provinsi Bengkulu tahun 2018-2022.
Studi Komparatif Model ARIMA, ANN, dan Hybrid ARIMA-ANN untuk Peramalan Laju Inflasi di Indonesia Rizal, Jose; Dzakirah, Qanitahudz; Sunandi, Etis
Jurnal Ilmiah Matematika Vol. 12 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jim.v12i1.30366

Abstract

Model ARIMA merupakan metode populer dalam peramalan data deret waktu. Namun demikian, model ini memiliki keterbatasan dalam mengenali pola nonlinier yang dapat menyebabkan peningkatan kesalahan peramalan. Sebagai solusinya, Artificial Neural Network (ANN) yang mampu memotret pola nonlinier dapat diaplikasikan. Untuk menggabungkan keunggulan dari kedua model, dikembangkan metode Hybrid ARIMA-ANN. Penelitian ini membandingkan performa model ARIMA, ANN, dan Hybrid ARIMA-ANN pada kajian peramalan laju inflasi di Indonesia. Hasil penelitian menunjukkan bahwa pada data training, model Hybrid ARIMA-ANN memberikan performa terbaik dengan nilai MAPE sebesar 18,73% dan MASE sebesar 1,86. Pada data testing, model Hybrid menunjukkan peningkatan akurasi dengan nilai MAPE sebesar 17,95% dan MASE sebesar 1,16. Hasil ini mendukung penerapan metode Hybrid ARIMA-ANN sebagai pendekatan yang lebih andal dan efektif untuk peramalan deret waktu yang memuat pola linier dan nonlinier secara bersamaan.
EARTHQUAKE FREQUENCY DATA MODELING IN MENTAWAI USING FUZZY TIME SERIES LEE AND FUZZY TIME SERIES TSAUR Damayanti, Septri; Rizal, Jose; Yosmar, Siska; Afandi, Nur; Acnesya, Vivin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0281-0294

Abstract

The Fuzzy Time Series (FTS) was first studied by Song and Chissom based on the theory of fuzzy sets and the concept of linguistic variables and their applications discovered by Zadeh. FTS has several models, namely FTS Lee, FTS Tsaur, and so on. In this study, we will model earthquake frequency data in Mentawai using FTS Lee and FTS Tsaur. The seismicity data used in this study is earthquake frequency data in the Mentawai which are calculated from 1960 to 2022. Additionally, the seismicity data source is taken from the U.S. Geological Survey catalog. Based on MAPE and MSE, the results obtained on the FTS Lee and FTS Tsaur models are MAPE values of 37,511% and 27,051%. And the MSE values obtained were 27,073 and 11,671. Thus, the best model used in modeling data on the frequency of earthquake occurrences in the Mentawai Islands is the Ruey Chyn Tsaur Fuzzy Time Series model.
Comparative Analysis of SARIMA, FFNN, and Hybrid Models for Sea Surface Temperature Prediction at Enggano Island (2018–2024) Natisharevi, Raditya Janaloka; Rizal, Jose; Firdaus, Firdaus; Novianti, Pepi; Lestari, Wina Ayu
JURNAL GEOCELEBES Vol. 9 No. 2: October 2025
Publisher : Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/geocelebes.v9i2.46445

Abstract

Sea Surface Temperature (SST) is a key oceanographic variable that influences fish distribution and the livelihoods of coastal communities. On Enggano Island, where most residents rely on fishing, SST is critical for identifying optimal fishing grounds due to limited accessibility and high operational costs. Accurate modeling and forecasting of SST are therefore essential for effective fisheries management and sustainable resource use. This study analyzes and predicts monthly SST patterns in Enggano Island using Seasonal Autoregressive Integrated Moving Average (SARIMA), Feed Forward Neural Network (FFNN), and Hybrid SARIMA-FFNN models. SARIMA effectively captures linear trends and seasonal variations but struggles with nonlinear dynamics and requires statistical assumptions. Conversely, FFNN models nonlinear relationships without such assumptions but is less efficient in representing linear and seasonal structures. The hybrid SARIMA-FFNN combines the strengths of both approaches, integrating linear-seasonal accuracy with nonlinear adaptability. Monthly SST data from January 2018 to December 2024, covering northern, eastern, southern, and western regions of Enggano Island, were analyzed. Results show that all models achieved high predictive accuracy, with MAPE values below 10%. Based on RMSE, FFNN outperformed the other models across all regions (north: 1.173, east: 0.999, south: 1.245, west: 1.049), confirming FFNN as the most accurate model for SST prediction. Predicted SST values across the four regions exhibited only minor differences, offering fishermen flexibility in selecting fishing grounds. Sustainable fishing strategies should also consider species-specific temperature preferences and other ecological factors influencing fish distribution.
Pengenalan Microsoft Excel untuk Meningkatkan Pemahaman Dasar pengolahan dan Analisis Data di SMK Negeri 4 Kota Bengkulu Widayati, Ratna; Rizal, Jose; Rachmawati, Ramya; Faisal, Fahri; Rafflesia, Ulfa; Dwi Kumala, Siska; Septa, Oon; Nooravieta Setiawan, Aisyah
Indonesian Journal of Community Empowerment and Service (ICOMES) Vol. 5 No. 2 (2025): December 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/icomes.v5i2.45338

Abstract

Pengabdian kepada masyarakat merupakan salah satu pilar tridharma perguruan tinggi yang mengimplementasikan ilmu pengetahuan dan teknologi secara langsung untuk memberikan manfaat kepada masyarakat. Hal tersebut merupakan motivasi dilaksanakannya kegiatan pengabdian kepada masyarakat berupa pelatihan penggunaan Microsoft Excel bagi siswa SMK Negeri 4 Kota Bengkulu.  SMK Negeri 4 Kota Bengkulu dipilih sebagai lokasi kegiatan karena kebutuhan peningkatan literasi digital, khususnya dalam pemanfaatan Microsoft Excel untuk mendukung efektivitas pengolahan data dan administrasi sekolahPelatihan ini bertujuan untuk meningkatkan keterampilan pengolahan data siswa, tidak hanya difokuskan pada pemahaman perhitungan dasar, tetapi juga pengenalan antarmuka, penggunaan rumus dan fungsi dasar (SUM, AVERAGE, IF, dll.), pembuatan tabel dan grafik, serta teknik pengolahan data sederhana. Kegiatan dilaksanakan di laboratorium komputer sekolah dengan melibatkan siswa dan guru pendamping sebagai peserta aktif. Melalui tahapan persiapan yang matang, pelaksanaan yang interaktif, serta evaluasi berbasis pre-test dan post-test, pelatihan ini terbukti memberikan dampak positif terhadap kemampuan siswa. Hasil uji Wilcoxon Signed-Rank menunjukkan peningkatan signifikan dalam pemahaman dan keterampilan peserta setelah mengikuti pelatihan. Selain itu, keterlibatan mahasiswa sebagai fasilitator turut membantu dalam proses pembelajaran yang lebih efektif.
The Probability Model of Earthquake Frequency in the Enggano Segment using Poisson Mixture Models Yosmar, Siska; Rachmawati, Ramya; Damayanti, Septri; Rizal, Jose
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.33446

Abstract

An earthquake is a natural disaster that occurs suddenly resulting in numerous casualties, such as loss of life and property. Bengkulu Province is among the provinces affected by severe earthquakes. Studies on probability models for the frequency of earthquake events in Bengkulu Province are still scarce, as outlined in the 2017 book “Map of Sources and Hazards of Indonesian Earthquakes.” This research uses Poisson mixture models to build a probability model for the frequency of earthquake events in the Enggano segment, located in the coastal area of Bengkulu Province.   ..   The phases of model building are the model diagnosis phase, testing the dispersion state relative to the Poisson distribution, testing the dependence of research data on time variables using the Ljung-Box test, and testing the criteria for selecting the best model using the Bayesian Tests Measures of Information Criterion (BIC) and Akaike Information Criterion (AIC). Annual earthquake frequency data from January 1, 1971, to December 31, 2022, were retrieved from the USGS catalog of data on the frequency of major earthquakes with a magnitude of Mw ≥ 4.40, which occurred a total of 633 times. After completing the model building phase, the AIC and BIC values for each model were determined by determining the number of unobserved groups. Both Poisson mixture models and Poisson hidden Markov models produced the same number of unobserved groups of 3 groups with AIC=302.91 and BIC=324.38.
Comparative Analysis of SARIMA and SARIMAX Models for Rainfall Forecasting: A Case Study of Bandung City with Humidity as an Exogenous Variable Bella, Claudia Cantika; Rizal, Jose; Agwil, Winalia
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.
PREDIKSI HARGA SAHAM PT BANK NEGARA INDONESIA (PERSERO) TBK MENGGUNAKAN MODEL STOKASTIK GEOMETRIC BROWNIAN MOTION : (STUDI KASUS: DATA HARGA SAHAM BBNI 2024) Rizal, Jose; Rahma Sholeha, Tari; Hidayati, Nurul; Novianti, Pepi; Sriliana, Idhia
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p227 - 234

Abstract

The Geometric brownian motion (GBM) model is widely used in predicting financial instruments such as stocks, because it can overcome the weakness of Brownian motion (BM) which can produce negative values. This study aims to apply the GBM model to predict the daily stock price of PT Bank Negara Indonesia (Persero) Tbk (BBNI) for the period from January to December 2024. The data used is secondary data on daily closing prices obtained from Investing.com, with a distribution of 95% training data and 5% testing data. Parameter drift and volatility are estimated using the Maximum Likelihood Estimation (MLE) method, while model accuracy is evaluated using MAPE and RMSE. The results show that a data proportion of 95%:5% provides the best prediction performance with a MAPE value of 5.724% and an RMSE of 0.267, indicating a high level of accuracy. Thus, the GBM model is reliable enough to describe the price movements of BBNI shares. Future research could develop models that take external factors into account or compare them with other stochastic models.
Co-Authors , Debora Acnesya, Vivin Adi Irwanto Agwil, Winalia Amelliana Melasarrah Baki Swita Bella, Claudia Cantika Cinta Rizki Oktarina Dwi Kumala, Siska Dzakirah, Qanitahudz Etis Sunandi Fachri Faisal Faisal, Fahri Firdaus Firdaus Idhia Sriliana Lestari, Wina Ayu Natisharevi, Raditya Janaloka Nur Afandi Nurul Hidayati Pepi Novianti Pratiwi, Stevy Cahya Rafflesia, Ulfa Rahma Sholeha, Tari Ramya Rachmawati Ratna Widayati Riki Wahyudi Riri Sesiati Septa, Oon Septri Damayanti Setiawan, Aisyah Nooravieta Sigit Nugroho Sigit Nugroho Siska Yosmar Sunandi, Etis Syahrul Akbar Tasyah, Qhiky Lioni Ulfasari Rafflesia Winalia Agwil Yulian Fauzi