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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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Pemetaan Persebaran Penyakit Malaria di Kecamatan Punduh Pidada, Kabupaten Pesawaran, Provinsi Lampung Nugraheni, Irma Lusi; Usman, Mustofa; Sutarto, Sutarto
Jurnal Spatial Wahana Komunikasi dan Informasi Geografi Vol. 23 No. 1 (2023): Spatial : Wahana Komunikasi dan Informasi Geografi
Publisher : Department Geography Education Faculty of Social Science - Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/spatial.231.2

Abstract

Malaria transmission in Pesawaran District has decreased for three consecutive years, namely 2018-2020 where it reached an API rate of 0.67 per 1000 population. In 2020 Punduh Pidada District has 28 positive cases of malaria. Factors that influence the number of cases of malaria transmission include environmental factors such as rainfall, land use, and altitude. The purpose of this study was to determine the distribution of malaria in terms of transmission factors in the form of environmental factors in the form of land use and topography. The method used in this research is qualitative with descriptive analysis. Data analysis techniques in this study are buffering techniques and map analysis. The results of this study indicate that land use and topography have an influence on the number of cases of malaria transmission in Punduh Pidada District. The land uses that had the highest cases were mixed forest (18 cases), plantations (18 cases), settlements (20 cases), and fish ponds (20 cases). Meanwhile, the altitude/topography of less than 200 meters above sea level covers all sample locations of malaria transmission in Punduh Pidada District in 2016-2021.
Enhancing Weather Forecasting in Bandar Lampung: A Hybrid SARIMA-LSTM Approach Kurniasari, Dian; Salsabila, Anindya Dafa; Usman, Mustofa; Warsono, Warsono
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27188

Abstract

Indonesia’s tropical climate, marked by rainy and dry seasons, is increasingly affected by extreme weather events driven by climate change. Rising temperatures, shifting rainfall patterns, and sea-level rise have intensified health risks such as malaria, dengue hemorrhagic fever (DHF), and gastrointestinal infections. Accurate weather forecasting is essential for mitigating these challenges and informing risk management strategies. This study develops and evaluates a hybrid SARIMA-LSTM model for weather forecasting in Bandar Lampung, integrating time series analysis with deep learning to enhance predictive accuracy. SARIMA captures seasonal variations, while LSTM models nonlinear relationships, offering a robust approach to forecasting complex weather patterns. The SARIMA (6,1,0)(3,1,0)26 model was selected for its effective seasonal representation and combined with LSTM to leverage its capability in modelling nonlinear dependencies. Hyperparameter optimization using grid search further improved model performance. Two data partitioning approaches were tested: 70%-30% and 80%-20% splits for training and testing, respectively. The SARIMA-LSTM hybrid model demonstrated superior performance with the 80%-20% split, achieving MSE, RMSE, and MAPE values of 0.1174, 0.3426, and 0.0104%, respectively. The model accurately forecasted weather conditions over 21 weeks, aligning closely with observed trends and effectively capturing seasonal patterns. These findings underscore the model’s potential to support public health strategies, including disease outbreak mitigation for malaria and DHF, and enhance disaster preparedness in flood-prone areas.
Penerapan Model Geographically Weighted Logistic Regression dengan Fungsi Pembobot Adaptive Gaussian Kernel pada Data Kemiskinan Nurhasanah, Nunung; Widiarti, Widiarti; Nurvazly, Dina Eka; Usman, Mustofa
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26504

Abstract

Regression analysis is one statistical method used to determine the relationship between a dependent variable and one or more independent variables. Dependent variables that are categorical are analyzed using logistic regression analysis. Geographically Weighted Logistic Regression (GWLR) is a method that is a local version of logistic regression, where location factors are considered. This method assumes that the dependent variable data are distributed binomially. In this study, the GWLR method is used to determine the factors influencing the poverty percentage in West Java Province in 2022 using an adaptive Gaussian kernel weighting function. The variables used are per capita expenditure, average length of schooling, Gross Regional Domestic Product (GRDP) per capita, and population density. The results of this study indicate that the variables of per capita expenditure, Gross Regional Domestic Product (GRDP) per capita, and population density significantly influence the poverty percentage in West Java Province in 2022.
Pelatihan Peningkatan Pemahaman Logika dan Aplikasinya pada Pembuktian dalam Matematika untuk Dosen-Dosen Ilmu Komputer di PTS Bandar Lampung Usman, Mustofa; W, Wamiliana; W, Warsono; Russel, Edwin
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.134

Abstract

Logic is a very important knowledge in all fields, for example law, mathematics, language, and computer science. In mathematics, logic plays a role in providing ways or methods or steps in proving a theorem. In computer science, logic is very important because it is the mathematical basis of software: logic is used to formalize the semantics of programming languages and program specifications, and to verify the correctness of programs. However, the weakness is that logic is generally not taught in the curriculum in Computer Science and of course this is a gap that must be overcome if we want to build quality alumni. The problem is that lecturers generally do not understand the basic concepts of logic and their applications in mathematical proof methods. To overcome this weakness, a beginner activity was held, namely training in basic logic concepts and their applications in mathematical proof methods. This community service activity was carried out using lecture and discussion methods and exercises attended by computer science lecturers and students.
IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA Kurniasari, Dian; Kurniawati, Virda; Nuryaman, Aang; Usman, Mustofa; Nisa, Rizki Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1919-1930

Abstract

Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.
Analysis of Food Security Factors in Indonesia using SEM-GSCA with the Alternating Least Squares Method Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20378

Abstract

An economic recession, characterized by prolonged economic decline, increased unemployment, and decreased spending, is projected to occur globally in 2023, potentially impacting production capacity within the food sector. Experts have identified various contributing factors such as shifts in global trade dynamics and geopolitical tensions, highlighting the need to understand the broader global economic context leading to this recession. To achieve this goal, in this research SEM is used to analyze the relationship between variables that influence food security. Furthermore, GSCA is used to handle complex structural models and non-normal data distribution. Special considerations include the use of ALS methods to estimate parameters effectively and consistently. The findings of this research are the important role of availability, access and utilization in shaping food security in Indonesia, with a contribution of 98% of the overall influence shown by the model. These insights help governments design targeted interventions to improve food security, especially amidst challenges posed by a potential global economic downturn. Implementing strategies to increase availability, increase access and optimize utilization is very important in maintaining food security amidst economic uncertainty.
Robust Panel Data Regression Analysis using the Least Trimmed Squares (LTS) Estimator on Poverty Line Data in Lampung Province Lestari, Windi; Widiarti; Utami, Bernadhita Herindri Samodera; Usman, Mustofa; Handayani, Vitri Aprilla
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241210

Abstract

Robust regression is an alternative method in regression analysis designed to produce stable parameter estimates, even when the data contain outliers or deviate from classical assumptions. One of its estimation techniques, the Least Trimmed Square (LTS),works by minimizing the smallest squared residuals, thereby assigning smaller weights to extreme data points. This method serves as a solution when classical approaches, such as Ordinary Least Squares (OLS), fail to meet the assumptions, especially in socio-economic data that are often complex and prone to outliers. This study employs robust regression with the LTS estimator on panel data to examine the impact of population size , population density , and registered job vacancies on poverty lines in Lampung Province. The data cover 15 districts and cities from 2019 to 2023. The analysis results show that the model obtained has a coefficient of determination of R2=0.8909. This means that the three predictor variables can explain 89.09% of the variation in the poverty line.
A Hybrid ARIMA–GRU Model for Forecasting Palm Oil Prices at PT Sawit Sumbermas Sarana in Central Kalimantan Kurniasari, Dian; Shella, Tiara Pramay; Usman, Mustofa; Warsono
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 1 (2025): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252112

Abstract

The palm oil industry plays a strategic role in Indonesia's economic landscape. As one of the world’s largest producers, Indonesia holds substantial potential in marketing both crude palm oil (CPO) and palm kernel oil on domestic and international fronts. Palm oil prices consistently correlate with CPO prices, given that the pricing of palm oil is benchmarked against CPO, resulting in market fluctuations. Forecasting future palm oil prices becomes an essential measure in response to this volatility. The ARIMA (AutoRegressive Integrated Moving Average) model has been widely recognized as a reliable method for time series forecasting. Despite its strengths, ARIMA faces challenges in identifying the non-linear components that are often present in real-world data. The Gated Recurrent Unit (GRU) model, which incorporates an update gate and a reset gate, offers an alternative that effectively captures complex non-linear patterns. A hybrid model integrating ARIMA and GRU has therefore been developed with the aim of improving predictive accuracy. This hybrid approach includes two stages: the ARIMA model for initial predictions and a GRU model that processes the residuals from the ARIMA output. In this study, the ARIMA-GRU hybrid model demonstrated strong performance, yielding a Mean Squared Error (MSE) of 868.4690, a Root Mean Squared Error (RMSE) of 29.4698, a Mean Absolute Percentage Error (MAPE) of 0.0117, and an overall accuracy of 99.9824%.
Comparison of Naïve Bayes and Random Forest Models in Predicting Undergraduate Study Duration Classification at the University of Lampung Hestina P., Shelvira; Widiarti; Nuryaman, Aang; Usman, Mustofa
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.20241317

Abstract

This study aims to compare the performance of the Naïve Bayes and Random Forest classification algorithms in predicting the study duration of undergraduate students in the Mathematics Study Program at the University of Lampung. The dataset consists of 537 graduation records from 2020–2024. The research steps include data preprocessing, data partitioning (train-test split and k-fold cross validation), model building, and evaluation using a confusion matrix. The results show that the Random Forest algorithm achieved the highest accuracy of 94.44%, outperforming Naïve Bayes which reached a maximum accuracy of 92.59%. These findings suggest that Random Forest is more effective for classifying student study durations. These findings suggest that Random Forest is more effective for classifying student study durations.
Georaphically Weighted Ridge Regression Modelling on 2023 Poverty Indicators Data in the Provinces of West Kalimantan and Central Kalimantan Anjani, Syarli Dita; Widiarti; Utami, Bernadhita Herindri Samodera; Usman, Mustofa; Handayani, Vitri Aprilla
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.20241320

Abstract

Regression analysis is a method to explain the relations between independent variables and a dependent variable. Linear regression analysis relies on certain assumptions, one of the assumption is homogeneity. However, there is a situation when the variance at each observation differs or called spatial heterogeneity.This issue can be solved using Geographically Weighted Regression (GWR), a statistical method that can be fixed spatial heterogeneity by adding a local weighted matrix, the result in GWR model is a local model for each observation point. However, GWR has a limitation, it cannot handle multicollinearity. Ridge regression is a method used to solved multicollinearity by adding a bias constant (λ). A GWR model that contains multicollinearity and fixed using ridge regression is known as Geographically Weighted Ridge Regression (GWRR).
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