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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 MATHunesa: Jurnal Ilmiah Matematika 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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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: Pola Konsentrasi PM2.5 di Negara-negara ASEAN Berdasarkan Kepadatan Penduduk 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.
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

Over 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
Pembinaan Desa Cinta Statistik Bagi Perangkat Desa Panutan Sebagai Upaya Penyelenggaraan Statistik Desa Berkesinambungan Warsono, Warsono; Usman, Mustofa; Junaidi, Akmal; Herindri Samodera Utami, Bernadhita
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 9 (2024): SWARNA: Jurnal Pengabdian Kepada Masyarakat, September 2024
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v3i9.1511

Abstract

Sebagai tindak lanjut MoU (Memorandum of Understanding) antara Universitas Lampung dengan Badan Pusat Statistik Provinsi Lampung, khususnya BPS Kabupaten Pringsewu maka perlu dilakukan pembinaan berkesinambungan yang bertujuan agar perangkat desa di Kabupaten Pringsewu lebih memahami pentingnya data statistik serta berpartisipasi aktif dalam penyelenggaraan statistik desa. Berdasarkan analisis situasi di Desa Panutan, Kecamatan Pagelaran, Kabupaten Pringsewu dimana tampilan monografi di website desa (https://panutan-pringsewu.desa.id/) masih monoton, kurang menarik, dan belum merepresentasikan statistik desa secara lengkap. Oleh karena itu, diperlukan pembinaan diseminasi dan website bagi perangkat desa di Kabupaten Pringsewu sebagai upaya penyelenggaraan statistik desa berkesinambungan. Kegiatan pengabdian ini dilaksanakan pada 26 Juni 2024 menggunakan kombinasi metode ceramah, praktik, dan tanya jawab. Berdasarkan hasil kuisioner, 100% peserta menyatakan tertarik menggunakan perangkat lunak Canva dalam menyajikan infografis profil desa. Dalam hal kemudahan dalam menggunakan aplikasi Canva, sebanyak 62% mampu membuat infografis tanpa kendala dan sebanyak 38% masih mengalami kendala dalam mempraktikkan pembuatan infografis yang disebabkan oleh perangkat dan jaringan sinyal yang tidak mendukung.
Konservasi Anggrek Dan Peningkatan Peringkat Greenmetric Melalui Kegiatan Penanaman Anggrek Di Kampus Widiarti; Usman, Mustofa; Wamiliana; Nurcahyani, Nuning; Master, Jani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 2 No. 1 (2023): 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.v2i1.39

Abstract

Upaya pelestarian anggrek, khususnya anggrek spesies merupakan salah satu kegiatan konservasi lingkungan hidup. Dalam rangka pelestarian lingkungan, lingkungan kampus sudah dipenuhi berbagai pohon-pohon rindang dan tinggi, yang sangat sesuai untuk habitat anggrek hutan. Adanya tanaman-tanaman ini merupakan salah satu upaya konservasi lingkungan, keindahan, dan pengurangan gas CO2. Untuk menambah keindahannya, pohon-pohon besar yang ada di lingkungan taman kampus dapat ditempel berbagai jenis anggrek yang sesuai dengan habitatnya seperti amabilis, retusa, bulbophyllum, aphyllum, dan dendrobium. Anggrek, selain indah dan cantik, juga akan mengurangi kadar CO2 di udara sehingga penanaman anggrek di lingkungan kampus akan berdampak baik terhadap peringkat greenmetric. Tujuan kegiatan pengabdian ini adalah untuk: (1) melestarikan anggrek spesies khususnya amabilis yang merupakan spesies asli Lampung, (2) mengurangi CO2 dan meningkatkan peringkat greenmetric. Kegiatan ini melibatkan tim dosen, mahasiswa, dan staff untuk membantu merawat tanaman anggrek. Tingkat keberhasilan hidup anggrek untuk beradaptasi di lingkungan kampus sangat baik (lebih dari 95%). Partisipasi dan antusiasme masyarakat dan civitas akademika di lingkungan kampus juga sangat baik. Hal ini ditandai dengan pertumbuhan anggrek yang baik dan masih utuhnya plant anggrek yang ditanam.
Modeling Vector Error Correction with Exogeneous (VECMX) Variable for Analyzing Nonstationary Variable Energy Used and Gross Domestic Product (GDP) Usman, Mustofa; Wamiliana; Russel, Edwin; Kurniasari, Dian; Widiarti; Elfaki, Faiz A.M
Science and Technology Indonesia Vol. 10 No. 1 (2025): 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.2025.10.1.283-293

Abstract

Analysis of energy used, GDP and population has been carried out in many countries and has become a topic of interest for many researchers and governments. This is because energy used is an important factor for society and industry in a country. In this study, the modeling of the relationship between energy used, GDP and population as an exogenous variable for the cases of Indonesia from 1967-2023 will be discussed. The energy used and GDP data are nonstationary with order one, I(1), and there is cointegration between energy used and GDP. Therefore, the model which will be used is the Vector Error Correction Model with Exogenous variable (VECMX) with population as the exogenous variable. From the results of analysis, the best model is VECMX(3,1) with cointegration rank R=1. Based on this model, the pattern of the relationship among the three variables, Granger-causality between energy used and GDP, exogenous impact on energy used and GDP, and forecasting for the next 10 years will be discussed.
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 Eri Setiawan 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 Wiranto, Ahmad Rizki Yoka, Ranara Athalla