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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 Journal of Community Service and Empowerment PELS (Procedia of Engineering and Life Science) Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Dedikasi untuk Negeri Jurnal Mahasiswa Ilmu Komputer Sciencestatistics: Journal of Statistics, Probability, and Its Application 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 Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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KARAKTERISTIK FUNGSI DISTRIBUSI FOUR-PARAMETER GENERALIZED-t Cahyadi, Rahman; ., Warsono; Usman, Mustofa; Kurniasari, Dian
JURNAL E-DUMATH Vol 2, No 1 (2016)
Publisher : JURNAL E-DUMATH

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

Abstract

This research is about the characteristic function of four-parameter generalized tdistribution. Four-parameter generalized t distribution has four parameters whichare μ as a location parameter, σ as a scale parameter, p and q as a shapeparameter, and B as a function of beta. The characteristic function are retrievedfrom the expectation of e itx , where i as a imaginary number. Then, the characteristicfunction of four-parameter generalized t distribution was able to be determined byusing definition and trigonometric expansions. Based on those two methods thisstudy got the same results and then will be continued proving the fundamentalproperties of the characteristic function of four-parameter generalized t distribution.Furthermore, it needs graph simulation on a characteristic function of four-parameter generalized t distribution. Graph simulation result on the characteristicfunction of four- parameter generalized t distribution was formed a closed curve(circle) are smooth.Keywords: four-parameter generalized t distribution, characteristic function,graph simulation
IMPELEMENTASI K-NEAREST NEIGHBORS, DECISION TREE DAN SUPPORT VECTOR MECHINE PADA DATA DIABETES Irfan, Miftahul; Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
Jurnal Mahasiswa Ilmu Komputer Vol. 4 No. 2 (2023): Jurnal Mahasiswa Ilmu Komputer October 2023
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v4i2.4007

Abstract

Diabetes merupakan salah satu penyakit yang menjadi penyebab kematian terbesar didunia. Kasus kematiannya pun tercatat lebih dari 4 juta pada tahun 2019. Diabetes juga dapat menyebabkan timbulnya penyakit lainnya. Bahaya diabetes ini menjadi perhatian khusus WHO. Seiring dengan perkembangan teknologi ini, banyak sekali kolaborasi antara bidang kesehatan, statistic dan computer untuk menanggulangi berbagai macam penyakit. Algortima machine learning menjadi popular dalam proses klasifikasi data dan sudah banyak diterapkan pada data kesehatan. Dengan begitu pada artikel ini akan dilakukan perbandingan algoritma machine learning KNN, Decision Tree, dan SVM untuk melihat algortima mana yang paling cocok untuk klasifikasi data diabetes. Hasil menunjukkan bahwa KNN dan SVM memiliki akurasi yang cukup besar yaitu 81,13%. Sehingga kedua algortima tersebut dapat menjadi rekomendasi proses klasifikasi data diabetes sehingga dapat membantu dokter dalam menanggulangi penyakit diabetes. Hasil ini juga menunjukkan bahwa 8 variabel yang digunakan berpengaruh terhadap resiko diabetes
PM2.5 Concentration Pattern in ASEAN Countries Based on Population Density Teguh Panuju, Achmad Yahya; Usman, Mustofa
Procedia of Engineering and Life Science Vol 4 (2023): Proceedings of the 6th Seminar Nasional Sains 2023
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v4i0.1385

Abstract

The concentration of PM2.5 in ambient air is one of the indicators of air quality that affects public health. This pollutant is considered hazardous due to its small size, which allows it to enter the lungs and remain suspended in the air for a considerable amount of time. Identifying the patterns of PM2.5 concentration distribution is important to recognize the influential factors in increasing PM2.5 concentrations, thus enabling better formulation of solutions. This study analyzed the patterns of PM2.5 concentrations in three ASEAN countries: Indonesia, Vietnam, and Thailand. Four randomly selected measurement locations were chosen in each country, with two locations in densely populated areas and two others in low-density areas. The sample data of PM2.5 concentrations were analyzed using nested factor analysis of variance, which allowed the relationship between the taken parameters, namely country, location, and population density classification, to be determined. The results revealed that all parameters had a significant influence on PM2.5 concentrations.
Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model Widiarti; Usman, Mustofa; Putri, Almira Rizka; Russel, Edwin
Science and Technology Indonesia Vol. 9 No. 1 (2024): January
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.2024.9.1.189-197

Abstract

Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between short-term dynamics and deviation, then a threshold vector error correction model (TVECM) can be used. The variables used in this research consist of oil production and Indonesian oil and gas production from January 2019 to March 2021. The research results show that the best model for data on oil production and oil and gas production is the TVECM 2 Regime model. Based on the TVECM 2 Regime model, further analysis, namely Granger causality and Impulse Response Function are discussed.
LSTM-CNN Hybrid Model Performance Improvement with BioWordVec for Biomedical Report Big Data Classification Kurniasari, Dian; Warsono; Usman, Mustofa; Lumbanraja, Favorisen Rosyking; Wamiliana
Science and Technology Indonesia Vol. 9 No. 2 (2024): April
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.2024.9.2.273-283

Abstract

The rise in mortality rates due to leukemia has fueled the swift expansion of publications concerning the disease. The increase in publications has dramatically affected the enhancement of biomedical literature, further complicating the manual extraction of pertinent material on leukemia. Text classification is an approach used to retrieve pertinent and top-notch information from the biomedical literature. This research suggests employing an LSTM-CNN hybrid model to tackle imbalanced data classification in a dataset of PubMed abstracts centred on leukemia. Random Undersampling and Random Oversampling techniques are merged to tackle the data imbalance problem. The classification model’s performance is improved by utilizing a pre trained word embedding created explicitly for the biomedical domain, BioWordVec. Model evaluation indicates that hybrid resampling techniques with domain-specific pre-trained word embeddings can enhance model performance in classification tasks, achieving accuracy, precision, recall, and f1-score of 99.55%, 99%, 100%, and 99%, respectively. The results suggest that this research could be an alternative technique to help obtain information about leukemia.
Comparative analysis of deep Siamese models for medical reports text similarity Kurniasari, Dian; Usman, Mustofa; Warsono, Warsono; Lumbanraja, Favorisen Rosyking
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6969-6980

Abstract

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
Analysis of Linear Log Models on Covid-19 Data in Indonesia Suciati, Indah; Warsono, Warsono; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i1.3163

Abstract

Covid-19 is still a concern of the world, including Indonesia. The transmission of Covid-19 is very fast and has a wide impact on all people around the world, especially Indonesia. In everyday life, we find a lot of data that looks into a certain category. Categorical analysis of data can be done using the log linear model. The log linear model is used to analyze the relationship between categorical variables that form a contingency table of arbitrary dimensions. The analysis used in this study is to make descriptive statistics and three-way contingency tables, then perform the analysis with the help of SPSS 25.0 software where the goodness of fit test is used to see which models can be used or suitable. The purpose of this study is to analyze a log linear model, so that a log linear model is obtained that is suitable for Covid-19 data based on gender, province, and age group. The conclusion of this study is that of the 9 models used, the model is the most suitable model to be used, with a value of 18,885 and the equation of the log linear model is , which means that there is a relationship between the two factors for the variables gender and province, gender and age group and province and age group in Covid-19 cases in Covid-19 in Indonesia by gender, province, and age group.
Goodness Of Fit Test In Structural Equation Modeling with Unweighted Least Square (ULS) Estimation Method Amanathi, Ani; Setiawan, Eri; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5021

Abstract

Structural equation model (SEM) is a multivariate analysis method that is used to describe a linear relationship simultaneously between indicator variables and latent variables. There are several estimation methods in SEM, one of them is Unweighted Least Square (ULS). The method doesn‟t have specific assumptions about the distribution of variables. This study aims to estimate the model using the ULS method and see the influence of employee competency variables and library facilities on the quality of service at the University of Lampung library. Survey of quality of service in the library of Lampung University is used in the research. Based on the results of the study, it is found that from the three suitability tests, namely the overall model test, the structural model test and the measurement model test using ULS estimation give good results in explaining the compatibility between the model and observation results.
Bayesian Structural Time Series Model for Forecasting the Composite Stock Price Index in Indonesia Suciati, Indah; Usman, Mustofa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 1 No. 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v1i2.5023

Abstract

One of the models that can be used to predict time series data is the Bayesian Structural Time Series (BSTS) model. The BSTS model is a more modern model and can handle data movement better. In the BSTS model, the Markov Chain Monte Carlo (MCMC) sampling algorithm is used to simulate the posterior distribution, which smoothes the forecasting results over a large number of potential models using Bayesian averaging models. The purpose of this study was to obtain the best BSTS model for Composite Stock Price Index (CSPI) data in Indonesia based on the state component and the number of MCMC iterations, and obtain forecasting results for CSPI value in Indonesia for the next 24 months, namely the period July 2023 to June 2024. The results obtained are based on a comparison of the R-square values in the model, the BSTS model with local linear trend and seasonal state components, and the number of MCMC iterations n = 5 00 is the best BSTS model that can be used for forecasting the CSPI value in Indonesia with an R-square value of 99.96%. The results of forecasting the CSPI value in Indonesia for the period July 2023 to June 2024 range from 6589 to 6760, with the lowest forecasting value in October 2023 and the highest in March 2023.
Training on statistical applications in structuring village administration in Purworejo Usman, Mustofa; Russel, Edwin; Putri, Lidya Ayuni
Journal of Community Service and Empowerment Vol. 5 No. 2 (2024): August
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jcse.v5i2.29316

Abstract

In the modern era, statistics has become an integral part of various aspects of our lives, serving as a universal language to understand the world around us. The integration of statistical applications can significantly enhance the process of data and information management in village governance. This community service project aims to assess the potential of statistical applications in supporting village administration officials, contributing to improved village governance and self-sufficiency. Through preliminary studies and tailored SPSS training programs, the practical knowledge and skills of village officials and cooperative members are enhanced. Evaluation after the training indicates a significant improvement in understanding and application of statistical tools. The integration of statistical applications is expected to contribute to better-organized village administration and good governance in Purworejo Village. This initiative underscores the role of statistics as a powerful tool in village administration, bridging various fields of knowledge and providing valuable insights into the world. The successful implementation of statistical applications in Purworejo Village serves as a model for other communities seeking to enhance governance through data-driven decision-making.
Co-Authors Aang Nuryaman Akmal Junaidi Amanathi, Ani Anjani, Syarli Dita aprianti, ayu Arisman Adnan Budi Susetyo Dian Kurniasari Dian Kurniasari Dina Eka Nurvazly, Dina Eka Elfaki, Faiz A.M Elfaki, Faiz AM Eri Setiawan Faulina, Naflah Favorisen R. Lumbanraja 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 Nisa, Rizki Khoirun Nuning Nurcahyani Nunung Nurhasanah Putri, Almira Rizka Putri, Lidya Ayuni Ragayu, Noti Rahman Cahyadi, Rahman Russel, Edwin Salsabila, Anindya Dafa Sari, Dede Ratna Shella, Tiara Pramay Siti Laelatul Chasanah, Siti Laelatul Suciati, Indah Sulandra, Ardelia Maharani Sutarto Sutarto Teguh Panuju, Achmad Yahya Utami, Bernadhita Herindri Samodera W, Wamiliana W, Warsono Wamiliana Wardhani Utami Dewi Warsono Warsono Warsono Warsono Warsono Widiarti Widiarti Widiarti Yoka, Ranara Athalla