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All Journal Jurnal Teknik Jurnal Sains Matematika dan Statistika Indonesian Journal of Artificial Intelligence and Data Mining JURNAL ABDI MASYARAKAT INDONESIA (JAMIN)
Christina Eni Pujiastuti
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Other

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Forecasting Laju Inflasi Indonesia Menggunakan Rantai Markov Joko Riyono; Christina Eni Pujiastuti; Aina Latifa Riyana Putri
Jurnal Sains Matematika dan Statistika Vol 8, No 1 (2022): JSMS Januari 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v8i1.14767

Abstract

Di tahun 2020, pandemi Covid-19 yang melanda dihampir seluruh penjuru dunia telah mengakibatkan kemerosotan perekonomian di beberapa negara. Inflasi sebagai salah satu indikator petunjuk perekonomian suatu negara dapat digunakan untuk mengukur kemerosotan perekonomian. Inflasi merupakan suatu proses kecenderungan atau trend naiknya harga barang dan jasa yang berlangsung secara terus menerus selama beberapa periode waktu. Inflasi yang terkendali dan sangat rendah merupakan salah satu keinginan pemerintah sehingga dapat mendukung terpeliharanya daya beli masyarakat. Sebaliknya inflasi yang tinggi akan mempersulit berkembangnya dunia usaha dalam perencanaan dunia bisnis yang meliputi kegiatan produksi, investasi maupun dalam penentuan harga barang dan jasa yang diproduksi. Pengetahuan prediksi laju inflasi yang akan datang sangat berguna untuk menyiapkan strategi yang tepat dalam menyusun kebijakan di sektor ekonomi. Data laju inflasi merupakan data runtun waktu, yang bersifat acak. Di dalamnya merupakan data perpindahan dari satu waktu ke waktu lainnya yang dapat diklasifikasikan sebagai inflasi ringan, sedang, tinggi dan hiperinflasi. Proses stokastik merupakan suatu proses matematika yang dapat digunakan untuk memodelkan fenomena data yang bergantung pada waktu. Rantai Markov merupakan proses stokastik dengan parameter diskrit yang memenuhi sifat Markov, kejadian yang akan datang hanya bergantung kepada keadaan saat ini. Pada paper ini berdasarkan data laju inflasi di Indonesia dari tahun 1969 sampai tahun 2020 akan dipakai untuk memprediksi tingkat inflasi di masa datang menggunakan analisis distribusi stasioner rantai Markov dan aplikasi software maple dan diperoleh bahwasanya laju inflasi tahun kedepan masih dalam taraf akan rendah sebesar 67.3%, kemungkinan akan sedang sebesar 28.3%, dan kemungkinan akan tinggi sebesar 4.4%.  Kata Kunci: Laju Inflasi, Data Time Series, Proses Stokastik, Rantai Markov.
Early Detection of COVID-19 Disease Based on Behavioral Parameters and Symptoms Using Algorithm-C5.0 Joko Riyono; Aina Latifa Riyana Putri; Christina Eni Pujiastuti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.22074

Abstract

The spread of COVID-19 disease has continued since it was first discovered at the end of 2019 until now. Transmission of COVID-19 is very fast, including through close contact through droplets and through the air. Therefore, early detection of COVID-19 is very important for patients and also those around them to be able to fight the COVID-19 pandemic because if patients get proper and fast treatment, then other people around them will be protected. In this study, an analysis of the classification of decision making for COVID-19 detection was carried out based on behavioral parameters and symptoms that could trigger exposure to COVID-19 using the C5.0 algorithm, followed by measuring the performance of the model using the Confusion Matrix. The C5.0 algorithm is a decision tree-based data mining method. The results of the C5.0 algorithm use a comparison of training data and test data of 70:30. After going through the Confusion Matrix test, an accuracy value of 98% is obtained which indicates that the resulting classification is very good, so that the resulting model can be used for early detection of COVID-19 patients.
Analysis of Preventive Maintenance on Heavy Dump Suspension Using Reliability-Centered Maintenance Method Puspa, Sofia Debi; Tono Sukarnoto; Dany Nugraha Tantra; Christina Eni Pujiastuti; Joseph Andrew Leo
Jurnal Teknik Vol 23 No 1 (2025): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v23i1.512

Abstract

The suspension system on Heavy Dump (HD) vehicles is crucial in maintaining stability, comfort, and reliability when used in harsh environments. HD vehicles have a hydraulic-pneumatic suspension that uses nitrogen gas and oil to overcome the load and vibrations from the road. The suspension maintains vehicle components and protects the load, especially when crossing damaged or bumpy roads at high speed. This study aims to optimize preventive maintenance planning using the Reliability-Centered Maintenance (RCM) method and determine the suspension system's preventive activities to reduce breakdowns. In designing effective preventive maintenance, the RCM approach and related methods, such as FMEA, are used to identify critical machines as the focus of analysis. In addition, a statistical distribution approach is used to determine the optimal maintenance activity interval. Based on the analysis results, it was obtained that the data follows a lognormal distribution where the optimization of preventive maintenance on the suspension component is every 370-hour time interval for each machine working. Changing the time interval increased the reliability value from 34.09% to 93.60% before and after preventive maintenance. Preventive maintenance activities with a time interval of 370 hours to reduce unscheduled breakdowns in the form of adjusting suspension components
PELATIHAN ANALISIS KORELASI DAN REGRESI DENGAN MENGGUNAKAN PERANGKAT LUNAK “R” UNTUK MENINGKATKAN KETERAMPILAN PENGOLAHAN DATA BAGI GURU Puspa, Sofia Debi; Joko Riyono; Fani Puspitasari; Christina Eni Pujiastuti
Jurnal Abdi Masyarakat Indonesia (JAMIN) Vol 6 No 1 (2024): JURNAL ABDI MASYARAKAT INDONESIA (JAMIN)
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jamin.v6i1.17408

Abstract

Regression analysis is a statistical method that aims to determine the relationship between one or more independent variables and the dependent variable. Correlation expresses the degree of linear relationship between the two variables. The application of regression and correlation analysis is needed, especially for teachers, to determine what factors influence students' understanding of learning and it can be seen how strongly a variable influences other variables. This Community Service Activity is motivated by the need for educators to analyze the causes of factors that influence students's understanding of learning through data. This Community Service for partners aims to provide understanding related to Correlation and regression Analysis with" R" software. It is hoped that this Community service will make it easier for educators to decide on suitable learning models in class after knowing what factors influence student understanding in learning. Fifty-one participants attended this activity, and the training was carried out online. Based on the comparison of pre-test and post-test scores, there is an increased understanding of Correlation and Regression Analysis. The average increase in the trainees' ability was 34.14, 83.67% of the average before the training.
Co-Authors Dany Nugraha Tantra Fani Puspitasari Joko Riyono Joko Riyono Joseph Andrew Leo Puspa, Sofia Debi Putri, Aina Latifa Riyana Tono Sukarnoto