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Unisda Journal of Mathematics and Computer Science (UJMC)
Published by Universitas Islam Darul Ulum
ISSN : 24603333     EISSN : 2579907X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education Mathematics
Unisda Journal of Mathematics and Computational Science (UJMC) is a research journal published by Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan with the scope of pure mathematics, applied science, education, statistics
Arjuna Subject : -
Articles 6 Documents
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Search results for , issue "Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science" : 6 Documents clear
Sebuah Karakteristik dari Modul Uniserial dan Gelanggang Uniserial I Gede Adhitya Wisnu Wisnu Wardhana; Fariz Maulana
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2674

Abstract

The module is a generalization of the vector space. The module that will be discussed is a uniserial module, which is a module that only has one composition series. A uniserial ring is a ring whose module over itself is uniserial. The uniserial ring is an Artin and local ring, but the converse is not necessarily true. In this paper, we will discuss Artin and local ring with additional properties so that it is characteristics of the uniserial ring.
Optimalisasi Jadwal Kegiatan Belajar Mengajar di MI NW I Talun Selama Pandemi Covid-19 Bulqis Nebulla Syechah
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2714

Abstract

The Covid-19 pandemic has impacted many areas of life, including education in Indonesia. After the Indonesian government implemented a lockdown in a few months, now we are in a new normal era where everyone can do any activity based on the covid-19 protocol. In the new normal era, students and teachers are allowed to do teaching and learning activities with an offline system based on the Covid-19 protocol, this makes the schedule ineffective, such as in MI NW 1 Talun. At this school, each class has a schedule to do learning activities three times a week. Based on this, in this study, the authors will offer a schedule formulated based on variables of teachers, students, classes, classrooms, days and times, and covid-19 protocols. This research method is a genetic algorithm that is a method of optimization based on natural selection. The results of this study show that the new schedule is more effective than before and every student or teacher can do teaching and learning activities to the maximum and avoid covid-19.
Pemodelan Kasus Pasien Terkonfirmasi Positif Covid-19 Per-Hari Di Indonesia dengan Metode SARIMA Wigid Hariadi; Sulantari Sulantari
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2743

Abstract

The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular method for forecasting univariate time series data for data containing seasonality. This method consists of several stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the SARIMA (p,d,q)(P,D,Q)S model. The SARIMA model can be applied in various fields, one of which is the medical field. The number of patients infected with the CoVID-19 virus continues to grow every day. Indonesia is one of the countries experiencing the impact of the COVID-19 virus. On December 28, 2021, the number of positive Covid-19 patients in Indonesia was 4,262,157, with 4,113,472 patients recovering and 144,071 patients dying. Seeing the high number of positive cases of Covid-19 in Indonesia, the author wants to conduct research on modeling cases of patients who are confirmed to be positive for Covid-19 per day in Indonesia and then from this model, data forecasting will be carried out for the next 28 periods. The data collection period is from November 1, 2021 to December 28, 2021. And the results of a good model for predicting cases of confirmed positive COVID-19 patients per day in Indonesia are the SARIMA (2,1,2)(2,1,1)7 model, with The seasonal length is 7 periods, and the sum squared resid is 0.927619. Abstrak Model Seasonal Autoregressive Integrated Moving Average (SARIMA) adalah metode populer untuk meramalkan data deret waktu univariat untuk data yang mengadung musiman. Metode ini terdiri dari beberapa tahapan, yaitu: identifikasi, penilaian parameter, pemeriksaan diagnostik, dan peramalan menggunakan model SARIMA (p,d,q)(P,D,Q)S. Model SARIMA dapat diterapkan di berbagai bidang, salah satunya bidang medis. Jumlah pasien yang terinfeksi virus CoVID-19 terus bertambah setiap harinya. Negara Indonesia merupakan salah satu Negara yang mengalami dampak virus covid-19. pada 28 Desember 2021, jumlah pasien positif Covid-19 di Indonesia sebanyak 4.262.157 pasien, dengan 4.113.472 pasien sembuh dan 144.071 pasien meninggal dunia. Melihat tingginya kasus positif Covid-19 di Indonesia, maka penulis ingin melakukan penelitian tentang pemodelan kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia untuk kemudian dari model tersebut akan dilakuakn peramalan data untuk 28 periode kedepan. Periode pendataan dari tanggal 1 November 2021 sampai dengan 28 Desember 2021. Dan hasil model yang baik untuk memprediksi kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia adalah model SARIMA (2,1,2)(2,1,1)7, dengan panjang musiman nya 7 periode, dan nilai sum squared resid sebesar 0.927619.
Penerapan Double Exponential Smoothing Holt dan ARIMA pada Jumlah Kebutuhan Gabah UD Lancar Ericha Dwi Ayu Prihastini; Novita Eka Chandra; Awawin Mustana Rohmah
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2761

Abstract

Abstract. Since come the rice thresher (combi) machine effective than the manual process, the rice milling industries such us UD Lancar, only receiving the grain which produced from it so the supplay of rice is decreasing so resulting in the risk of loss for themselves. The forecasting activity in here used for to assist UD Lancar in estimating the demand for rice in the next period, so can anticipate looking for other grain supplier for to fulfill the demand of market. The data will be analyzed using the Double Exponential Smoothing Holt and ARIMA method. The result of the data processing is show the Double exponential smoothing holt method has MSE error value of 413.445.841,75,while in the ARIMA (2,1,1) method has MSE value was 64.826.353,94404. The Arima (2,1,1) method is better than the double exponential smoothing Holt method because it has a smaller MSE value, so it can be used in the forecasting. Keywords: Forecasting, Double Exponential Smoothing Holt, ARIMA. Abstrak. Sejak adanya mesin perontok padi (combi) yang memiliki tingkat efektifitas lebih baik dibandingkan proses manual, para pemilik industri penggilingan padi seperti UD Lancar kini hanya menerima gabah hasil proses mesin combi yang mengakibatkan persediaan beras mengalami penurunan sehingga dapat mengakibatkan permintaan konsumen tidak terpenuhi dan berujung pada resiko kerugian. Kegiatan peramalan ini bertujuan untuk memperkirakan permintaan beras UD Lancar pada periode selanjutnya, sehingga UD Lancar dapat mengantisipasi dengan cara mencari pemasok gabah lain untuk memenuhi permintaan pasar. Analisis data menggunakan metode Double Exponential Smoothing Holt dan ARIMA. Berdasarkan hasil analisis, pada metode Double Exponential Smoothing Holt memiliki nilai kesalahan MSE sebesar 413.445.841,75, sedangkan metode ARIMA (2,1,1) memiliki nilai kesalahan MSE sebesar 64.826.353,94404. Metode ARIMA (2,1,1) memiliki nilai kesalahan MSE lebih kecil dibandingkan metode Double Exponential Smoothing Holt, sehingga dapat digunakan dalam proses peramalan. Kata Kunci: Peramalan, Double Exponential Smoothing Holt, ARIMA.
Analisis Sistem Antrian Pasien Rawat Jalan Menggunakan Distribusi Poisson dan Distribusi Erlang Siti Alfiatur Rohmaniah; Siti Masnikafah; Mohammad Syaiful Pradana
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2768

Abstract

Antrian di Puskesmas merupakan proses menunggu pasien untuk mendapat pelayanan. Fenomena antrian yang panjang dan lama terjadi pada Puskesmas Turi Kabupaten Lamongan terlebih pada saat kondisi yang ramai.Tujuan penelitian ini untuk mengetahui jumlah pelayan optimum Puskesmas Turi dalam kondisi ramai pasien. Penentuan jumlah pelayan berdasarkan tingkat kedatangan yang terwakilkan dengan Distribusi Poisson, sedangkan waktu pelayanan diwakili oleh Distribusi Erlang. Pada penelitian ini terdapat tiga fase pelayanan yaitu pendaftaran, pelayanan dokter, dan pelayanan apotek. Dalam penentuan jumlah pelayan optimal melihat dari nilai ultilitas. Hasil penelitian pada kondisi ramai pasien di Puskesmas Turi terjadi pada hari Senin dengan laju kedatangan 4 pasien per menit dan laju pelayanan selama 10 menit per pasien. Rata-rata waktu menunggu dalam antrian sebesar 0,035 menit, rata-rata waktu menunggu dalam sistem selama 0,04 menit dan rata-rata banyaknya pasien dalam antrian maupun sistem tidak ada pasien per menitnya. Nilai ultilitas yang diperoleh sebesar 0,4, sehingga jumlah pelayan pada saat kondisi ramai pasien sudah sesuai sebanyak satu pelayan.
Spasial Data Panel Dalam Menentukan Faktor-Faktor Yang Berpengaruh Terhadap Jumlah Kasus Demam Berdarah Dengue (DBD) Anisa Nabila; Rahmadi Yotenka
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v7i2.2845

Abstract

Dengue Fever (DF) is an infection caused by the dengue virus, which several types of mosquitoes can spread. Indonesia has become a dengue-endemic area since 1968 and has spread in 34 provinces with 416 districts and 98 cities. In 2015 there were 126,675 cases of dengue fever in Indonesia, an increase in 2016 to 200,830 cases; the following year, it decreased to 59,047 cases. Then the cases have fluctuated every year. This study aims to look at the factors that influence dengue cases in Indonesia, especially on the islands of Java and Bali. This is because during the last five years (2015 – 2019) the highest dengue cases in Java & Bali were in Indonesia. The method used in this research is spatial analysis of panel data with the best model of SAR (spatial autoregressive models). The results of this study are the percentage of districts/cities that implement policies for healthy areas, the percentage of poor people, and health facilities have a significant effect on the number of dengue cases in Java & Bali.

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