Peramalan Jumlah Siswa Baru Madrasah Aliyah (MA) Manhalul Ma’arif Darek-Lombok Tengah

This study aimed to forecast the new student number at Madrasah Aliyah (MA) Manhalul Ma'arif Darek. The data used in this study was the annual time series data of new students who enrolled in the school, from the 1998/1999 academic year to 2016/2017. Based on the data obtained, it shows that the number of new students who enroll in Madrasah Aliyah (MA) Manhalul Ma'arif Darek tends to fluctuate. This fluctuating pattern is a problem faced by Madrasah Aliyah (MA) Manhalul Ma'arif Darek in determining strategic and policy steps related to planning the provision of school facilities / infrastructure. Therefore we need a forecasting method in accordance with the data pattern. The forecasting method used is the Fuzzy Time Series Cheng method. This method uses fuzzy principles as the basis of the forecasting process. The forecasting process results obtained the Mean Square Error (MSE) value of 101.5009 and the Mean Absolute Percentage Error (MAPE) value of 18.49%. The results showed that the Fuzzy Time Series Cheng method performed well in predicting the number of new students at Madrasah Aliyah (MA) Manhalul Ma'arif Darek

Klasifikasi Status Penerima Bantuan Program Keluarga Harapan Menggunakan Metode Analisis Diskriminan

Abstract The Problem that often occurs in the distribution of PKH is that the assistance provided is not targeted correctly. Efforts that can be made to solve these problems are by ensuring that the criteria for receiving PKH are accurate and in accordance with the applicable criteria. Based on these criteria, there needs to be a classification of household status that receives PKH assistanceand those who do not. This classification process can be done using Discriminant Analysis. The result of classification using discriminant analysis for the case of PKH assistance recipients in NTB Province obtained an APER value of 0.2450, which means a classification error rate of 25.4% or the classification result is considered accurate Keywords: Discriminant Analysis; APER; Classification; PKH.Abstrak Permasalahan yang sering terjadi dalam penbagian bantuan PKH adalah bantuan yang diberikan tidak tepat sasaran Upaya yang dapat dilakukan untuk menyelesaiakan permasalahan tersebut adalah dengan memastikan kriteria penerima bantuan PKH sudah tepat dan sesuai dengan ketentuan kriteria yang berlaku. Berdasarkan kriteria-kriteria tersebut, maka perlu adanya klasifikasi status rumah tangga yang menerima bantuan PKH dan tidak. Proses pengklasifikasian ini dapat dilakukan menggunakan Analisis Diskriminan. Hasil pengklasifikasian menggunakan analisis diskriminan untuk kasus status penerima bantuan PKH di Provinsi NTB diperoleh nilai APER sebesar 0.2450 yang artinya tingkat kesalahan klasifikasi sebesar 24.5 % atau hasil pengklasifikasian tergolong akurat. &nbsp

Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline

Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated.  Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data,  The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8).  Based on the results obtained, the government can consider these factors for socializatio

Pelatihan Metode Statistika untuk Meningkatkan Kompetensi Guru dalam Penulisan Karya Tulis Ilmiah (KTI) di SMAN 1 Selong

Sejak Permenpan Nomor 16 Tahun 2009 yang mengatur persyaratan kenaikan pangkat bagi guru, banyak guru yang mengalami stagnasi dalam perkembangan karir mereka karena kesulitan dalam menulis Karya Tulis Ilmiah (KTI). Salah satu faktor penyebabnya adalah kurangnya pengetahuan tentang analisis statistika yang diperlukan untuk mengolah dan menginterpretasikan hasil penelitian mereka. Sebagian besar analisis yang dilakukan oleh guru terbatas pada analisis deskriptif sederhana, hal ini menyebabkan analisis yang dilakukan masih kurang mendalam dan belum mampu menyimpulkan secara komprehensif. Program ini pengabdian kepada Masyarakat ini bertujuan untuk Meningkatkan Kompetensi Guru dalam Penulisan Karya Tulis Ilmiah (KTI) dengan melakukan pelatihan metode statistika dan bagaimana mengaplikasikannya. Hasil program menunjukkan peningkatan signifikan dalam pemahaman guru tentang statistika, beberapa metode statistika yang relevan, serta penggunaan perangkat lunak SPSS. Program ini tidak hanya meningkatkan kompetensi statistika guru, tetapi juga berpotensi meningkatkan kualitas pendidikan di sekolah dan menjadi contoh inspiratif bagi sekolah lain yang menghadapi masalah serupa dalam pemahaman statistik

Pemodelan Tingkat Pengangguran Terbuka di Indonesia Menggunakan Analisis Regresi Data Panel

Indonesia has entered the peak of the demographic bonus which can provide positive and negative impacts for various fields. One of them is in the economic field, namely the increasing number of productive population who are unabsorbed in the world of work and is referred to as an open unemployment. This research was conducted to build a model and to analyze the Open Unemployment Rate, Economic Growth, Provincial Minimum Wage, Level of education, Population growth, Labor Force Participation Rate, Employment, Human Development Index, Poor Residents, Illiterate Population, Average Length of School, Domestic Investment, Foreign Investment, and School Participation Rate, that influence the open unemployment rate in Indonesia using panel data regression analysis with data 2015-2021 from 34 provinces. A fixed effect model with different intercept values for every participant is the best panel data regression model (Fixed Effect Model) that could be found. Based on simultaneously research, it was discovered that every component of the model significantly effect the open unemployment rate. Partially, it was discovered that the following factors significantly effect the open unemployment rate in Indonesia: Employment, Labor Force Participation Rate, Economic Growth, Population Growth, Human Development Index, Poor Population, and Average years of Schooling.Indonesia telah memasuki puncak bonus demografi yang dapat memberikan dampak positif maupun dampak negatif terhadap berbagai bidang. Salah satunya dalam bidang ekonomi yakni semakin banyak populasi penduduk usia produktif yang tidak terserap dalam dunia kerja dan disebut sebagai pengangguran terbuka. Penelitian ini dilakukan untuk membangun model dan menganalisis faktor-faktor yang mempengaruhi tingkat pengangguran terbuka di Indonesia menggunakan analisis regresi data panel. Model regresi data panel terbaik yang diperoleh yakni model  pengaruh tetap dengan nilai intersep pada setiap individu berbeda (Fixed Effect Model). Berdasarkan penelitian diperoleh faktor-faktor yang signifikan mempengaruhi Tingkat Pengangguran Terbuka (TPT) di Indonesia yakni Pertumbuhan Ekonomi (), Pertumbuhan Penduduk (), Tingkat Partisipasi Angkatan Kerja (), Penyerapan Tenaga Kerja (), Indeks Pembangunan Manusia (), Penduduk Miskin (), dan Rata-rata Lama Sekolah () dengan koefisien determinasi () sebesar 44,81%

Peramalan Harga Beras dengan Metode Double Exponential Smoothing dan Fuzzy Time Series (Study Kasus : Harga Beras di Kota Mataram)

Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 627400.307 and based on the MAPE value of 7.39%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method.Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 566312.340 and based on the MAPE value of 6.75%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method. Keywords: Double Exponential Smoothing Holt, Fuzzy Time Series Cheng, Rice Price, MAPE, MS

Analisis Pengendalian Kualitas Air Minum dalam Kemasan Menggunakan Metode FMEA dan Penerapan Kaizen (Study Kasus di PT.Lombok Pusaka Adam, Jelantik Lombok Tengah)

Clean water is one of the basic needs with unlimited use, even in the economic field. The opportunities provided can be utilized by companies that produce bottled drinking water. The existence of defective products is obtained in production so that the need for quality analysis of the product is still within the control limits on the P chart. This is done by knowing the highest value in the influential failure mode. So that suggestions for improvement with Kaizen can be given. Based on the control P chart obtained, all points of defective products in the production process are within control limits with a UCL limit of 0.00804 and an LCL limit of 0.00602. This indicates that the defective product is statistically controlled. The Failure Mode and Effect Analysis (FMEA) method assigns a priority value to each failure mode, and the value is the Risk Priority Number (RPN). The biggest RPN is that the cover does not stick to the surface of the cup, with an RPN value of 240. The proposed improvement using the Kaizen method is to increase inspections and routine repairs on the machine.The clean water is one of the basic needs with unlimited use even in the economic field. The opportunities provided can be utilized by companies that produce bottled drinking water (AMDK). The existence of defective products is obtained in production so that the need for quality analysis of the product is still within the control limits on the P chart. This is done by knowing the highest value in the influential failure mode. So that suggestions for improvement with kaizen can be given. Based on the control P chart obtained, all points of defective products in the production process are within control limits with a UCL limit of 0.00804 and an LCL limit of 0.00602. This indicates that the defective product is statistically controlled. The FMEA method assigns a priority value to each failure mode. The value is the Risk Priority Number (RPN). The biggest RPN is that the cover does not stick to the surface of the cup, with an RPN value of 240. The proposed improvement using the Kaizen method is to increase inspections and routine repairs on the machine. Keywords: Failure Mode and Effect Analysis (FMEA), Kaizen, Quality Control, Statistical Process Control (SPC

ANALISIS LULUSAN MATEMATIKA FMIPA UNIVERSITAS MATARAM MENGGUNAKAN DIAGRAM KONTROL MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA

Quality control of Mathematics graduates of FMIPA UNRAM in 2012-2018 with a control chart of the Multivariate Exponentially Weighted Moving Average (MEWMA), is carried out to determine the quality of graduates of the Mathematics Study Program FMIPA UNRAM and to compare the quality of Mathematics graduates FMIPA UNRAM in 2012-2018 using MEWMA and T2 Hotelling control charts. In this test, control was carried out using three control variables, namely the Grade Point Average , length of study , and the number of Semester Credit Units . This study used the weighting value  with the Upper Control Limit (UCL) of 10,685. The results obtained that the control chart formed to show that 42 data are outside the control limits (out of control), which causes the quality of graduates to be out of control. Using the EWMA univariate control chart, it is known that the length of study variable  causes the data to be out of control. Therefore, a revision was made to the MEWMA control chart so that the quality control process for Mathematics FMIPA Mataram University graduates was within the control limits after the second revision. This is indicated by the absence of observation points  outside the control limits so that the quality of mathematics graduates can be said to be good. Furthermore, a comparison of the results of observations with the quality of Mathematics graduates of Mataram University using the T2 Hotelling control chart in the previous study was carried out with the results that there was one data subgroup that was outside the control limit with = 14.5249 which led to the need for one revision of the T2 Hotelling control chart to obtain statistically controlled diagram. Thus, it can be said that the MEWMA control chart is more sensitive than the Hotelling T2 control chart.Pengendalian kualitas lulusan Matematika FMIPA UNRAM tahun 2012-2018 dengan diagram kontrol Multivariate Exponentially Weighted Moving Average (MEWMA), merupakan bentuk uji yang digunakan untuk mengontrol kualitas lulusan dan mengetahui penyebab kurangnya kualitas lulusan mahasiswa Matematika.  Dalam uji ini, dilakukan pengontrolan dengan menggunakan tiga variabel kontrol yaitu Indeks Prestasi Kumulatif , lama studi , dan jumlah Satuan Kredit Semester . Pada penelitian ini digunakan nilai pembobot  dengan Batas Kontrol Atas (BKA) senilai 10,685. Diperoleh hasil bahwa diagram kontrol yang terbentuk menunjukkan terdapat 42 data yang berada diluar batas kontrol (out of control), hal ini menyebabkan kualitas lulusan menjadi tidak terkontrol. Dengan menggunakan diagram kontrol univariat EWMA, diketahui bahwa variabel lama studi  menjadi penyebab data out of control. Oleh karena itu, dilakukan revisi pada diagram kontrol MEWMA sehingga proses pengendalian kualitas lulusan Matematika FMIPA Universitas Mataram berada dalam batas kontrol setelah revisi kedua. Hal tersebut ditunjukkan dengan tidak adanya titik pengamatan  yang berada di luar batas kontrol sehingga kualitas lulusan matematika dapat dikatakan baik. Selanjutnya, dilakukan perbandingan hasil pengamatan dengan kualitas lulusan Matematika Universitas Mataram menggunakan diagram kontrol T2 Hotelling pada penelitian sebelumnya dengan hasil terdapat satu subgrup data yang keluar batas kontrol dengan = 14,5249 yang menyebabkan perlunya revisi diagram kontrol T2 Hotelling sebanyak satu kali revisi sehingga mendapatkan diagram yang terkontrol secara statistik. Dengan demikian, dapat dikatakan bahwa diagram kontrol MEWMA lebih sensitif dibandingkan dengan diagram kontrol T2 Hotelling

Analisis Faktor Untuk Pemetaan Karakteristik pada Percobaan Dekafeinasi Kopi Robusta

In recent years, there has been a positive trend in coffee consumption in Indonesia. Coffee that was initially identical to older man's drinks is starting to be liked by teenagers and children because coffee contains caffeine which can have an addictive effect. Coffee has various benefits, such as preventing drowsiness, antioxidants, improving brain performance, and reducing fatigue. However, drinking a lot of coffee than your body can tolerate will cause symptoms of insomnia, excessive anxiety, and increased blood pressure. Various experiments have been made to reduce the caffeine content in coffee (decaffeination), one of which is mixing coffee with chayote juice (Sechium edule). Furthermore, this article classified the characteristics of decaffeinated products, caffeine content, moisture content, total acid titration, ash content, hue color, and L value. Using factor analysis, it is known that the characteristics can be mapped into three principal components. The first principal component consists of variables of caffeine content, water content, and hue color value. The second principal component consists of ash content and total acid content titration variables, and the third principal component, this factor, consists only of the characteristic L. It is also known that these three main components can explain 74.2% of the diversity of origin.In recent years, there has been a positive trend in coffee consumption in Indonesia. Dringking coffee which were originally identical as oldmans drinks, is starting to be liked by teenagers to children. This is because coffee contains caffeine which can have an addictive effect. Drinking coffee in the right dose can have positive effects for the drinker, such as stimulating the ability of brain function and also as an antioxidant. However, if you drink more coffee than your body can tolerate, it will cause symptoms of insomnia, excessive anxiety and increased blood pressure. Various attempts have been made to reduce the caffeine content in coffee (decaffeination), one of which is by mixing coffee with chayote juice (Sechium edule) as has been done by Paramartha (2021). Furthermore, this article classifies the characteristics of decaffeinated products, caffeine content, moisture content, total acid content of titration, ash content, hue color, and L value. By using factor analysis, it is known that the characteristics can be mapped into three main factors, where the first main factor consists of variables of caffeine content, water content, and hue color value. The second main factor consists of variables of ash content, and total acid content titration, and the third major factor, this factor consists only of the characteristic L. It is also known that 74.2% of the diversity of origin can be explained by the three main factors