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Analisis time series untuk memprediksi jumlah penduduk miskin di Cilacap Riski Aspriyani; Najmah Istikaanah
Delta-Pi: Jurnal Matematika dan Pendidikan Matematika Vol 12, No 2 (2023)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/dpi.v12i2.6707

Kemiskinan merupakan salah satu aspek yang harus ditanggulangi pemerintah agar setiap warga negara mendapatkan kesejahteraan yang sama dan perekonomian yang merata. Pentingnya kebijakan untuk memberantas kemiskinan telah dilakukan pemerintah indonesia dan khsususnya pemerintah daerah cilacap telah berupaya besar untuk dapat mengurangi tingkat kemiskinan. Diketahui bahwa di Cilacap tercatat tingkat kemiskinan pada tahun 2022 sebesar 11.02%. Untuk itu, sebagai upaya untuk dapat mengurangi jumlah kemiskinan di cilacap perlu adanya data prediksi yang diharapkan dapat menjadi referensi ilmiah untuk menyusun kebijakan dan strategi penanganan yang tepat. Hal tersebut menjadi dasar adanya penelitian ini yang bertujuan untuk melakukan prediksi jumlah penduduk miskin di cilacap dengan analisis time series berbantuan software POM-QM untuk komputasinya. Metode time series yang digunakan ialah moving average, weighted moving average, dan exponential smoothing. Data jumlah penduduk miskin di Cilacap yang digunakan ialah data pada tahun 2005 sampai dengan 2022 berdasarkan data BPS. Diperoleh hasil bahwa, metode exponential smoothing merupakan metode terbaik time series untuk peramalan jumlah penduduk miskin di cilacap dengan tingkat kesalahan sebesar 6.77% atau akurasi sebesar 93.23% yang berarti sangat akurat. Selanjutnya, hasil prediksi untuk periode 2023 didapatkan bahwa jumlah penduduk miskin di cilacap sebesar 191668 jiwa.Kata kunci:Time Series, Jumlah Penduduk Miskin, Exponential Smoothing, Moving Average

SHORTEST ROUTE FOR DISTRIBUTION OF ELECTION LOGISTICS IN CILACAP REGENCY USING THE BRANCH AND BOUND ALGORITHM Mizan Ahmad; Riski Aspriyani; Nadzifah; Umi Ma’rifah
PROCEEDING AL GHAZALI International Conference Vol. 1 (2023): INCLUSIVENESS, DIGITAL TRANSFORMATION, AND RENEWABLE ENERGY FOR A BETTER FUTURE
Publisher : Universitas Nahdlatul Ulama Al Ghazali Cilacap

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

Distribution of election logistics requires a large budget. Therefore, distribution planning is needed to streamline the budget. One form of planning is in the form of planning the shortest distribution route. This research aims to determine the shortest route for distribution of election logistics in Cilacap Regency using the Branch and Bound Algorithm. Determining the distance between sub-districts in Cilacap Regency using the help of GoogleMaps. Based on the results of this research, the shortest route for distribution of election logistics in Cilacap Regency is 149.1 km for the first route and 335 km for the second route.

Ruang Barisan Selisih Diperumum Tipe Cesaro pada Ruang Bernorma-n Ahmad, Mizan; Aspriyani, Riski
Journal of Fundamental Mathematics and Applications (JFMA) Vol 5, No 2 (2022)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v5i2.15747

Pada tulisan ini dibahas mengenai beberapa kelas ruang barisan selisih diperumum Cesaro pada ruang bernorma-n. Diselidiki kelengkapan masing-masing kelas dan hubungan antar kelas. Pada akhir tulisan ini, dikonstruksikan dual Kothe-Toeplitz dari beberapa ruang barisan selisih diperumum Cesaro pada ruang bernorma-n.

Analisis Regresi Liner untuk Meramalkan Jumlah Siswa Sekolah Dasar di Cilacap Aspriyani, Riski; Muhassanah, Nur'aini
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 2 (2024): Jurnal Derivat (Agustus 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.6474

This research aims to determine a prediction model using Simple Linear Regression for time series data on the number of elementary school students in Cilacap from 2010 to 2023 and to obtain predicted results on the number of elementary school students in Cilacap for the following year. The data pattern of the number of elementary school students in Cilacap is known to have a decreasing trend. The time series data was subjected to the Durbin-Watson test to see whether there was autocorrelation. It was found that data on the number of elementary school students in Cilacap from 2010 to 2023 did not have autocorrelation with the Durbin-Watson (d) computing value of 1.385. The requirements for time series data have been met, so that forecasting analysis can be carried out using Simple Linear Regression and it is found that the regression equation is y ̂=168698.604-1600.519x. This regression equation is used to predict the value of the number of elementary school students in Cilacap for the next year. The forecasting accuracy level is 97.303% or with a MAPE error value of 2.697%, which means that the ability of the regression model to predict is very accurate. Thus, the predicted data on the number of elementary school students in Cilacap for the next period in 2024 is 144690 students. Keywords: Forecasting, Time Series, Linear Regression

IMPLEMENTASI SPSS DALAM ANALISIS DATA BAGI MAHASISWA DI CILACAP Aspriyani, Riski; Hartono, Bryan Pudji; Ahmad, Mizan; Susilowati, Eka
Jurnal Terapan Abdimas Vol 7, No 2 (2022)
Publisher : UNIVERSITAS PGRI MADIUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/jta.v7i2.12717

Abstract. Statistics is a field of science that can be used as a theory to assist in making research decisions, especially quantitative research. Appropriate statistical tests are needed to analyze research data based on the formulated hypotheses. For this reason, a good ability in analyzing data is needed. The process of data analysis in quantitative research can be assisted using the SPSS statistical program. Most of the students in Cilacap who have an interest in quantitative research still need additional knowledge about the statistical tests used such as the T-test, Analysis of Variance and Regression, and Linear Correlation. In addition, many students have difficulty analyzing data using the SPSS program. Misunderstanding in reading SPSS output results caused many errors to occur. On the other hand, many students do not understand the prerequisites for hypothesis analysis and do not know the procedures or steps for data processing using SPSS. For this reason, a deeper understanding and training are needed to process data using SPSS. The SPSS training was conducted using lectures, question and answer methods, and practicals. In the process, they were very active in asking questions and actively following all the material given by the resource persons. The instrument is given as evaluation material for further service. it was obtained that the achievement of all aspects was classified as good with details on the suitability aspect of statistical tests with problems of 83%, the achievement of variable research aspects of 81%, the achievement of data processing aspects using SPSS of 86%, the achievement of SPSS output interpretation of 89%. Abstrak. Statistika merupakan bidang ilmu yang dapat menjadi teori untuk membantu dalam pengambilan keputusan suatu penelitian khususnya penelitian kuantitatif. Dalam menganalisis data penelitian dibutuhkan uji statistik yang sesuai berdasarkan hipotesis yang dirumuskan. Untuk itu, diperlukan kemampuan yang baik dalam menganalisis data. Proses analisis data dalam penelitian kuantitatif dapat dibantu menggunakan program statistika SPSS. Mahasiswa di Cilacap yang memiliki minat dalam penelitian kuantitatif sebagian besar masih membutuhkan tambahan pengetahuan mengenai uji statistik yang digunakan seperti uji T, Analisis Variansi serta Regresi dan Korelasi linear. Selain itu, banyak mahasiswa yang kesulitan dalam analisis data menggunakan program SPSS. Ketidakpahaman dalam membaca hasil output SPSS menyebabkan tidak sedikit kekeliruan terjadi. Di sisi lain, banyak mahasiswa yang tidak paham akan prasyarat analisis hipotesis, serta ketidaktahuan prosedur atau langkah pengolahan data menggunakan SPSS. Untuk itu, tujuan kegiatan ini dilakukan ialah untuk memberikan tambahan pengetahuan, pemahaman dan keterampilan dalam mengolah data menggunakan SPSS. Pelatihan SPSS ini dilakukan dengan metode ceramah, tanya jawab serta praktikkum. Dalam prosesnya mereka sangat aktif bertanya dan aktif mengikuti semua materi yang diberikan oleh narasumber. Instrumen diberikan sebagai bahan evaluasi untuk pengabdian selanjutnya. Diperoleh bahwa ketercapaian seluruh aspek tergolong kategori baik dengan rincian pada aspek kesuaian uji statistik dengan permasalahan penelitian sebesar 83%, ketercapaian aspek identifikasi variabel penelitian sebesar 81%, ketercapaian aspek pengolahan data menggunakan SPSS sebesar 86%, serta ketercapaian intepertasi output SPSS sebesar 89%.

Prediksi Banyaknya Gangguan Keamanan Ketertiban Masyarakat Menggunakan Model ARIMA Riski Aspriyani; Fadhilla, Widya Rizky
MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 1 (2025): Vol. 8 No. 1 Maret 2025
Publisher : Prodi Pendidikan matematika Universitas Islam Majapahit (UNIM), Mojokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/majamath.v8i1.3822

Abstrak Prediksi data sangat penting dalam mengantisipasi terjadinya gangguan keamanan dan ketertiban masyarakat. Dengan adanya prediksi data yang dilakukan dapat mendeteksi gangguan yang akan muncul dan data prediksi yang diperolah dapat dijadikan bahan pertimbangan pemerintah dalam pengambilan keputusan kebijakan serta penetapan strategi pencegahan. Untuk itu, urgensi penelitian ini menjadi penting dalam upaya mendapatkan data prediksi gangguan keamanan dan ketertiban masyarakat sehingga pemerintah dapat bertindak lebih proaktif dalam pencegahannya. Model yang digunakan dalam prediksi banyakanya gangguan keamanan dan ketertiban masyarakat adalah model ARIMA yang berbentuk ARIMA (p,d,q) dengan p menyatakan ordo dari unsur Autoregressive (AR), d ialah ordo dari unsur Integrated (I), dan q dari ordo Moving Average (MA). Model terbaik dipilih jika memenuhi uji signifikansi parameter, uji white noise, uji normalitas dan melihat nilai error RMSE, MAPE. Pengujian dilakukan dengan bantuan SPSS, diperoleh bahwa model ARIMA terbaik adalah Model ARIMA (0,1,1) dengan nilai RMSE 4.938 dan MAPE sebesar 37.141. ARIMA (0,1,1) merupakan model yang mampu meramalkan dengan baik untuk dapat digunakan selanjutnya pada prediksi atau peramalan beberapa periode ke depan. Dihasilkan bahwa, banyaknya gangguan keamanan dan ketertiban masyarakat di wilayah Batang dari bulan April 2025 sampai dengan Desember 2025 yaitu sebanyak 17.89 , 17.92, 17.96, 17.99, 18.02, 18.05, 18.09, 18.12, 18.15. Kata Kunci: Peramalan, ARIMA (p,d,q), Gangguan Keamanan Abstract Data prediction is very important in anticipating the occurrence of disturbances in public order and security. With the data prediction that is carried out, disturbances that will arise can be detected and the prediction data obtained can be used as a consideration by the government in making policy decisions and determining prevention strategies. For this reason, the urgency of this research is essential to obtain data on predictions of disturbances in public order and security so that the government can act more proactively in preventing them. The model used in predicting the number of disturbances in public order and security is the ARIMA model in the form of ARIMA (p,d,q) with p stating the order of the Autoregressive (AR) element, d being the order of the Integrated (I) element, and q from the Moving Average (MA) order. The best model is chosen if it meets the parameter significance test, white noise test, and normality test and sees the error values ??RMSE, and MAPE. Testing was carried out with the help of SPSS, it was obtained that the best ARIMA model was the ARIMA Model (0,1,1) with an RMSE value of 4,938 and a MAPE of 37,141. ARIMA (0,1,1) is a model that can predict well and can be used further in predictions or forecasts for several periods ahead. It was found that the number of disturbances to public order and security in the Batang area from April 2025 to December 2025 was 17.89, 17.92, 17.96, 17.99, 18.02, 18.05, 18.09, 18.12, and 18.15. Keywords: Forecasting, ARIMA (p,d,q), Disturbance of Public

Analisis Regresi Liner untuk Meramalkan Jumlah Siswa Sekolah Dasar di Cilacap Aspriyani, Riski; Muhassanah, Nur'aini
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 2 (2024): Jurnal Derivat (Agustus 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.6474

This research aims to determine a prediction model using Simple Linear Regression for time series data on the number of elementary school students in Cilacap from 2010 to 2023 and to obtain predicted results on the number of elementary school students in Cilacap for the following year. The data pattern of the number of elementary school students in Cilacap is known to have a decreasing trend. The time series data was subjected to the Durbin-Watson test to see whether there was autocorrelation. It was found that data on the number of elementary school students in Cilacap from 2010 to 2023 did not have autocorrelation with the Durbin-Watson (d) computing value of 1.385. The requirements for time series data have been met, so that forecasting analysis can be carried out using Simple Linear Regression and it is found that the regression equation is y ̂=168698.604-1600.519x. This regression equation is used to predict the value of the number of elementary school students in Cilacap for the next year. The forecasting accuracy level is 97.303% or with a MAPE error value of 2.697%, which means that the ability of the regression model to predict is very accurate. Thus, the predicted data on the number of elementary school students in Cilacap for the next period in 2024 is 144690 students. Keywords: Forecasting, Time Series, Linear Regression

Emotional Intelligence and Numerical Abilities: How are They Related? Aspriyani, Riski; Hartono, Bryan Pudji
Jurnal Pendidikan MIPA Vol 23, No 3 (2022): Jurnal Pendidikan MIPA
Publisher : FKIP Universitas Lampung

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

This Ex Post Facto research aims to determine the correlation between emotional intelligence and students' numerical abilities and the strength of the correlation between the two variables. Data collection was carried out on seventh-grade students at MTS N 7 Sleman Yogyakarta with a total of 35 respondents. The sampling technique used was simple random sampling. Respondents were given an emotional intelligence questionnaire and a numerical ability test whereas previously the instruments were tested for validity and reliability. The data analysis technique in this study uses Simple Linear Correlation. Prerequisites that must be met before the significance test is a linearity test and a normality test. Using the product moment/Pearson correlation, the result is that the rxy value is 0.547. Also, obtained the value of sig. of 0.001 < 0.05 or the value of F = 12.824 > 4.17 as a result H0 is rejected. This study concludes that there is a positive correlation between emotional intelligence and students' numerical ability with a strength of 0.547. This means that the higher the emotional intelligence possessed, the higher the student's numerical ability. The value of the coefficient of determination (R2) is 29.90%, which means that the emotional intelligence variable affects students' numerical abilities by 29.90%, thus 71.10% is influenced by other variables.Keywords: emotional intelligence, numerical abilities, Pearson correlation.DOI: http://dx.doi.org/10.23960/jpmipa/v23i3.pp918-929

Analisis Kemampuan Komunikasi Matematika Siswa Ditinjau dari Motivasi Berprestasi Aspriyani, Riski; Hartono, Bryan Pudji
Edumatica : Jurnal Pendidikan Matematika Vol 11 No 3 (2021): Edumatica: Jurnal Pendidikan Matematika (Desember 2022)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Jambi

The achievement motivation that exists in students needs to be stimulated so as to provide better changes for their learning outcomes. One of the results of learning mathematics can be seen is mathematical communication ability in solving a given mathematical problem. For this reason, this quantitative descriptive research was conductide with aimed to know the differences in students' mathematical communication ability reviewed high, medium, and low achievement motivation. The data was taken using essay test and a questionnaire to 67 students as respondents at SMA Negeri 1 Purbalingga in 2021. The sampling used purposive sampling technique. Data analysis used descriptive and inferential statistics. In inferential statistics, the One Way ANOVA test with alpha 5% was used and continued using the Scheffe method. ANOVA test prerequisites for Normality test analysis using Kolmogorov Smirnov and Homogeneity test. The result are H0 is rejected with a significance value 0.002 < 0.05 or Fh=7,159 > F 0,05;2;64= 3,23 . It means that there are differences in mathematical communication reviewed achievement motivation. Then does post ANOVA test with the result are students who have high achievement motivation better mathematical communication than students with medium and low. Meanwhile, students who have medium and low achievement motivation have the same good mathematical communication ability.

HOW TO COMBINE VAM AND DIJKSTRA’S ALGORITHM Ahmad, Mizan; Aspriyani, Riski; Susilowati, Eka
Journal of Fundamental Mathematics and Applications (JFMA) Vol 8, No 1 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v8i1.24044

Solving transportation problems sometimes does not only require using one method or algorithm. Sometimes it is necessary to use several methods or algorithms at once. In this research, combining the Vogel’s Approximation Method (VAM) and Dijkstra algorithm can be carried out if three assumptions are met. These three assumptions are based on the characteristics of each VAM and Dijkstra’s algorithm, as well as the compatibility between the two.

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