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All Journal International Journal of Electrical and Computer Engineering JURNAL E-DUMATH Science and Technology Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics Spatial : Wahana Komunikasi dan Informasi Geografi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun PELS (Procedia of Engineering and Life Science) Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Mahasiswa Ilmu Komputer SWARNA: Jurnal Pengabdian Kepada Masyarakat Integra: Journal of Integrated Mathematics and Computer Science
Mustofa Usman
Matematika, Universitas Lampung
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Application of GSTARMA Spatial-Temporal Model for Inflation Analysis in South Sulawesi Province Sari, Dede Ratna; Widiarti; Nurvazly, Dina Eka; Usman, Mustofa; Loves, Luvita
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241322

Abstract

The Generalized Space-Time Autoregressive Moving Average (GSTARMA) model is a development of the time series model that can capture both spatial and temporal dynamics simultaneously. This study uses the GSTARMA model to analyze inflation data in five cities in South Sulawesi Province from January 2017 to October 2024. The GSTARMA model obtained is GSTARMA (1,0,1) with a cross-correlation normalization spatial weight matrix. The results of the analysis indicate a spatial influence between locations and temporal relationships in the inflation data.
Multidimensional Log-Linear Modeling (Case Study: Gender, Age, Head Circumference, and Nutritional Status Among Early Childhood Children) Yoka, Ranara Athalla; Usman, Mustofa; Chasanah, Siti Laelatul; Widiarti; Handayani, Vitri Aprilla
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252228

Abstract

Poor nutritional status tends to increase the risk of morbidity and mortality among children in developing countries. Therefore, data on these rates can be an important indicator in describing the condition of undernutrition in a community. Log-linear model analysis can be used to categorize data on nutritional status. Based on data obtained from the Rajabasa Indah Health Center area, Rajabasa Subdistrict, Bandar Lampung City, there are 418 children who have examined at the Posyandu. The analysis model conducted in this study involves four variables, each variable is categorized into several categories according to predetermined criteria. Gender with two categories (male and female), age with two categories (1-12 months and 13-60 months), head circumference with two categories (normal and abnormal), and nutritional status with three categories (undernourished, well-nourished, and overnourished). This study aims to determine the best model using log-linear analysis that can explain the relationship between the four variables. The results obtained are the best model for the data involved in the [UG][LG][J] structure, the structure describes the interaction between age and nutritional status and head circumference and nutritional status.
Integrating VAR and CNN Models for Accurate Forecasting of Money Supply in Indonesia Warsono; Sulandra, Ardelia Maharani; Kurniasari, Dian; Usman, Mustofa; Susetyo, Budi
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252230

Abstract

Economic forecasting serves as a fundamental element in supporting decision-making processes across multiple sectors. One of the main areas of interest in this field is the estimation of the money supply within an economy. The Vector Autoregressive (VAR) model is a commonly applied method for forecasting; however, it often encounters limitations when processing data with nonlinear patterns. Convolutional Neural Networks (CNNs) offer an alternative approach, particularly effective in identifying nonlinear structures that are not adequately captured by VAR models. A hybrid VAR-CNN model is therefore proposed, combining the respective strengths of both techniques to improve the accuracy of predictions. This research applies to the hybrid VAR-CNN model to forecast economic variables for the period from July 2022 to June 2023. The model consists of two main components: the first utilizes forecasted values generated by the VAR model, while the second processes the residuals from the VAR output using a CNN. With 80% of the data allocated for training and 20% for testing, the hybrid VAR-CNN model demonstrates improved performance over alternative forecasting methods. Evaluation based on Mean Absolute Percentage Error (MAPE), supremum (D) values, and p-values confirms the effectiveness of this hybrid approach.
The Kernel Function of Reproducing Kernel Hilbert Space and Its Application on Support Vector Machine Utami, Bernadhita Herindri Samodera; Warsono; Usman, Mustofa; Fitriani
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1096-1108

Abstract

Reproducing Kernel Hilbert Space (RKHS) is a Hilbert space consisting of functions that can be represented or reproduced by a kernel function. The development of data science has made RKHS a method that refers to an approach or technique using the concept of reproducing kernels in certain applications, especially machine learning. Support Vector Machine (SVM) is one of the machine learning methods included in the supervised learning category for classification and regression tasks. This research aims to determine the form of linear kernel functions, polynomial kernel functions, and Gaussian kernel functions in Support Vector Machine analysis and analyze their performance in Support Vector Machine classification and regression. Application of the RKHS method in SVM classification analysis using World Disaster Risk Dataset data published by Institute for International Law of Peace and Armed Conflict (IFHV) from Ruhr-University Bochum in 2022 obtained results that are based on the results by comparing the predictions of training data and testing data using linear kernel functions, polynomial kernels and Gaussian kernels, it is recommended that classification using linear kernels provides the best prediction performance.
Dynamic Modeling of Energy Data: World Crude Oil and Coal Prices 2017-2023 (A State-Space Model Analysis of Multivariate Time Series) Russel, Edwin; Wamiliana; Usman, Mustofa; Elfaki, Faiz AM; Adnan, Arisman; Lindrianasari
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1301-1311

Abstract

The analysis of global crude oil and coal prices has attracted considerable research interest, as these prices significantly affect both society and industry, making the topic highly relevant for governments and policy makers. This study examines the correlation between global coal and crude oil prices from 2017 to 2023. It analyzes the behavior of these price series using a unit root test and develops an optimal model for conducting a Granger-causality analysis. To forecast crude oil and coal prices for the next 30 periods, a state-space modeling approach is applied. The unit root test results reveal that these prices are non-stationary, suggesting that any shocks to prices will have persistent effects. The best-fitting model for the association between coal and crude oil prices is a vector autoregressive model of order two (VAR(2)). The Granger-causality results reveal that current crude oil prices are influenced by both their own past values and previous coal prices, and vice versa. Forecasts using the state-space model suggest a modest upward trend for crude oil prices over the next 30 periods, while coal prices are projected to rise more strongly.
Generalized Space Time Autoregressive (GSTAR) Model for Air Temperature Forecasting in the South Sumatera, Riau, and Jambi Provinces Aprianti, Ayu; Faulina, Naflah; Usman, Mustofa
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.36049

Abstract

AbstractOver the past few years, there has been a significant increase in air temperatures in regions such as South Sumatera, Riau, and Jambi, posing threats of drought, water resource crises, and erratic weather patterns. In response, developing air temperature forecasting techniques becomes imperative for effective climate change management. This study proposes implementing the Generalized Space Time Autoregressive (GSTAR) model as a practical approach for forecasting air temperatures in these regions using two weighting methods, i.e., inverse distance and normalized cross-correlation weighting. The GSTAR model, an extension of the Space Time Autoregressive (STAR) model, offers enhanced complexity by incorporating specific time and location factors, thereby increasing forecasting flexibility. The result reveals that GSTAR(1,1) with normalized cross-correlation weighting is the most optimal model, with a Root Mean Square Error (RMSE) value of 3.135, indicating high forecasting accuracy. The selection of this model is grounded in the geographical proximity and similarity of environmental characteristics of the three regions. This research contributes novel insights into the underlying mechanisms of air temperature dynamics in neighboring areas, providing a robust foundation for formulating effective policy and mitigation strategies in addressing climate change challenges.Keywords: air temperatures, normalized cross-correlation weighting, GSTAR(1,1), inverse distance weighting. AbstrakDalam beberapa tahun terakhir, suhu udara mengalami peningkatan signifikan di wilayah-wilayah seperti Sumatera Selatan, Riau, dan Jambi, yang mengancam kekeringan, krisis sumber daya air, dan perubahan pola cuaca yang tidak terduga. Menghadapi situasi tersebut, pengembangan teknik peramalan suhu udara diperlukan untuk mengantisipasi dan mengelola dampak ekstrem dari perubahan iklim. Studi ini mengusulkan implementasi model Generalized Space Time Autoregressive (GSTAR) sebagai pendekatan praktis untuk meramalkan suhu udara di wilayah-wilayah tersebut menggunakan dua metode pembobotan yaitu pembobotan invers jarak dan normali korelasi silang. Model GSTAR, sebagai perluasan dari model Space Time Autoregressive (STAR), menawarkan kompleksitas yang lebih baik dengan menggabungkan faktor-faktor waktu dan lokasi tertentu, sehingga meningkatkan fleksibilitas dalam ramalan. Hasil analisis menunjukkan bahwa GSTAR(1,1) dengan pemberian bobot normalisasi korelasi silang merupakan model yang paling optimal, dengan nilai Root Mean Square Error (RMSE) sebesar 3.135, menandakan tingkat akurasi yang tinggi. Pemilihan model ini didasarkan pada kedekatan geografis dan kesamaan karakteristik lingkungan dari ketiga wilayah tersebut. Penelitian ini memberikan wawasan baru dalam mekanisme dinamika suhu udara di wilayah-wilayah yang berdekatan, serta memberikan dasar yang kuat bagi perumusan kebijakan dan strategi mitigasi yang efektif dalam menghadapi tantangan perubahan iklim.Kata Kunci: bobot invers jarak, bobot normalisasi korelasi silang, GSTAR(1,1), suhu udara.2020MSC: 62P30
Survey of Estimability Criteria, Connected Design and Testing Testable Hypotheses in Unbalanced Design Elfaki, Faiz AM; Russel, Edwin; Widiarti; Usman, Mustofa; Daoud, Jamal I.
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 3 (2025): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252343

Abstract

In the linear model Y = Xβ+ε with X having a full column rank, all β parameters can be estimated and the estimates are unique. However, in cases where X does not have a full column rank, not all β parameters can be estimated. In this paper, the problem to be discussed is how to determine parameters or parameter functions that are estimable and testable. Applications to the case of unbalanced data will be presented.
Pelatihan Pemahaman Analisis Data Kategori dan Aplikasinya untuk Dosen PTS Bandar Lampung Usman, Mustofa; Wamiliana, Wamiliana; Chasanah, Siti Laelatul; Russel, Edwin; Widiarti, Widiarti
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 7 No. 1 (2026): Maret
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v7i1.257

Abstract

Statistika sebagai salah satu sarana berpikir ilmiah adalah bagian yang penting dalam analisis data penelitian. Dalam bidang ilmu sosial, analisis data kategori berperan sebagai salah satu metode dalam analisis data, terutama dalam pemodelan loglinear untuk tabel kontingensi berdimensi tinggi. Tujuan kegiatan pengabdian ini adalah untuk meningkatkan pemahaman dosen-dosen pada analisis data kategori. Metode pendekatan yang digunakan dalam kegiatan ini adalah metode ceramah, diskusi dan demonstrasi penyususnan SAS program untuk data kategori dan penafsirannya. Hasil dari pelatihan ini telah meningkatkan pemahaman dan pengetahuan seluruh peserta akan analisis data kategori. Analisis data kategori umumnya belum dipahami oleh dosen-dosen bidang ilmu sosial, dengan kegiatan pengabdian ini menimbulkan kesadaran pentingnya memahami alat analisis data kategori untuk riset-riset di bidang ilmu-ilmu sosial.
Co-Authors Aang Nuryaman Akmal Junaidi Anjani, Syarli Dita aprianti, ayu Arisman Adnan Budi Susetyo Daoud, Jamal I. Dian Kurniasari Dian Kurniasari Dina Eka Nurvazly, Dina Eka Elfaki, Faiz A.M Elfaki, Faiz AM Faulina, Naflah Fitriani Handayani, Vitri Aprilla Herindri Samodera Utami, Bernadhita Hestina P., Shelvira Irfan, Miftahul Irma Lusi Nugraheni Jani Master, Jani Khoirin Nisa Kurniawati, Virda Lestari, Windi Lindrianasari Loves, Luvita Lumbanraja, Favorisen R Nisa, Rizki Khoirun Nuning Nurcahyani Nunung Nurhasanah Putri, Almira Rizka Rahman Cahyadi, Rahman Russel, Edwin Salsabila, Anindya Dafa Sari, Dede Ratna Shella, Tiara Pramay Siti Laelatul Chasanah, Siti Laelatul Sulandra, Ardelia Maharani Sutarto Sutarto Teguh Panuju, Achmad Yahya Utami, Bernadhita Herindri Samodera W, Wamiliana W, Warsono Wamiliana Wamiliana Wamiliana Wardhani Utami Dewi Warsono Warsono Warsono Warsono Warsono Widiarti Widiarti Widiarti Yoka, Ranara Athalla