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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Magister Pendidikan IPA Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL Jurnal Matematika UNAND Jurnal Varian Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) Mandalika Mathematics and Educations Journal Jurnal Karya Pengabdian Teknik Mesin Jurnal Pepadu JSN : Jurnal Sains Natural Semeton Mathematics Journal IJASDS: Indonesian Journal of Applied Statistics and Data Science
Harsyiah, Lisa
Program Studi Matematika, FMIPA, Universitas Mataram
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Analisis Regresi Komponen Utama untuk Mengatasi Multikolinearitas pada Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia Azka Fariz Hidayatullah; Dede Saputra; Filzah Inarah; Isma Evita; Muhammad Fadillah; Lisa Harsyiah
JSN : Jurnal Sains Natural Vol 2 No 1 (2024): Februari
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jsn.v2i1.497

Abstract

West Nusa Tenggara and East Nusa Tenggara provinces are among the provinces with low human development indexes. There are seven factors used in this study that are considered to affect the human development index in the two provinces, namely gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling. The method used by researchers in overcoming multicollinearity in this study is principal component regression. Therefore, this study aims to apply principal component regression in overcoming the problem of multicollinearity on the the effect of gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling on the human development index. Based on the results of the analysis that has been carried out, the principle component regression model is obtained as follows Y = 42.548 + 0.00000991X_1 + 0.0371X_2 + 0.343X_3 + 0.000005949X_4 + 0.2532 + 0.000012947X_6 + 0.1348X_7. With the coefficient of determination (R^2) 0.834.
Optimalisasi Parameter Double Exponential Smoothing menggunakan Metode Golden Section pada Peramalan Harga Saham Penutupan PT. Telkom Indonesia (Persero) Halawatun Tajalli; Lisa Harsyiah; Zulhan Widya Baskara
Semeton Mathematics Journal Vol 1 No 1 (2024): April
Publisher : Program Studi Matematika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/semeton.v1i1.205

Abstract

The process of predicting an event in the future is called forecasting. A forecasting model that functions to predict time series data with a trend pattern is Double Exponential Smoothing (DES). This study aims to compare one-parameter Brown DES with two-parameter Holt DES using the golden section method. The data used is monthly data on closing share prices of PT. Telkom Indonesia (Persero) for the period January 2011 - December 2021. Golden Section is an optimization method for finding parameter values that minimize the MAPE (Mean Absolute Percentage Error) function. The results of calculating the optimum parameter values for DES Brown α=0.420766 with a MAPE value of 4.871787804% and for DES Holt α=0.506578 and β=0.458980 with a MAPE value of 4.7233301647%. According to the MAPE value, the models used are very accurate for forecasting. DES Holt was selected as the best model for forecasting based on the smallest MAPE value.
Pengendalian Kualitas Produksi Air Minum Dalam Kemasan Menggunakan Peta Kendali Nur Halifatunnisa; Zulhan Widya Baskara; Lisa Harsyiah
Semeton Mathematics Journal Vol 1 No 2 (2024): Oktober
Publisher : Program Studi Matematika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/semeton.v1i2.239

Abstract

The most popular drinking water product produced by PT.X is the 220 ml glass packaging product. This product experienced the most production defects at 0.086% of total production. Based on observations that have been made, there are several problems that cause defective products, such as damaged packaging and poor water quality. So PT.X in maintaining production quality must improve the process of maintaining quality control. The aim of this research is in line with the problems faced by PT.X, namely controlling the quality of bottled drinking water using a decision on belief control chart. Control charts are used to monitor whether product defect data is statistically controlled or not. One of the control charts used to monitor whether product defect data is statistically controlled or not is the Decision on Belief control chart, because the Decision on Belief control chart is more sensitive to data shifts so that faster in detecting data that goes outside control limits or is out of control. Based on the graph of the results of the decision on belief control chart, of the 25 data there are 24 data that are out of the upper control limit and the lower control limit, meaning that the decision on belief control chart is sensitive to data shifts in detecting out of control data. Based on the results of the average run length calculation, it is concluded that the decision on belief control chart is weak in detecting out of control data because the shift value obtained is getting bigger.
Pemodelan Angka Kematian Ibu (AKI) Di Indonesia Menggunakan Mixed Geographically Weighted Regression (MGWR) Tamsilul Lawwamah; Lisa Harsyiah; Qurratul Aini
Semeton Mathematics Journal Vol 1 No 2 (2024): Oktober
Publisher : Program Studi Matematika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/semeton.v1i2.241

Abstract

Maternal Mortality Rate (MMR) is on of the targets for a  achieving the Sustainable Development Goals (SDGs). The aim of this research is to find the right model for estimating MMR and to look at the factors that influence MMR in Indonesia. Estimation were carried out using the Mixed Geographically Weigthed Regression (MGWR) model. The MGWR model is a combination of GWR and linear regression with variables that  having influence locally and some globally. The results obtained are that the MGWR model is superior the GWR model, because  the smallest AIC value for MGWR is 463,0564. Factors that have significant influence using the adaptive Gaussian kernel weighting are postpartum mothers (x4) , postpartum mothers receiving vitamin A (x5), giving Fe3 tablets to pregnant women (x6) ,  and handling obstetric complications (x7).
Pemodelan Tingkat Pengangguran Terbuka di Indonesia Menggunakan Analisis Regresi Data Panel Ena Setiawana; Nurul Fitriyani; Lisa Harsyiah
Eigen Mathematics Journal Vol 7 No 1 (2024): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i1.184

Abstract

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.
The Decision on Selecting the Best Laptop Using Analytical Hierarchy Process and Simple Additive Weighting Method at the Faculty of MIPA University of Mataram Fadhilah, Rifdah; Harsyiah, Lisa; Robbaniyyah, Nuzla Af’idatur
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.231

Abstract

Laptops have the potential to increase educational productivity in Indonesia. For example, students at the Faculty of Mathematics and Natural Sciences (MIPA) at the University of Mataram now feel involved. However, the decision to choose the right laptop according to the needs of students is difficult. The research population used was active students from the class of 2020-2023, Faculty of Mathematics and Natural Sciences (MIPA), University of Mataram. This research aims to determine the best laptop selection based on alternative laptop brands, namely Asus Vivobook, Acer 3, HP 14S, Dell Vostro 14, and Lenovo IP1. Further criteria include price, processor, Random Access Memory (RAM), Read Only Memory (ROM), and screen size. The methods used are the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The research results show that the first priority position is filled by the Asus Vivobook with a weight of 0,26 for the AHP method and the Lenovo IP1 with a weight of 0,898 for the SAW method. The results of priority comparisons using euclidean distance, it was found that the most optimal method for deciding on the best laptop was the AHP method. The AHP method has a value closest to 0 (zero), namely with an average value of 0,127, while the SAW method has an average value of 0,798.
Pengenalan Data Science Untuk Mempersiapkan Era Digital Pada Siswa Di SMAN 1 Gunung Sari Dina Eka Putri; Baskara, Zulhan; Lisa Harsyiah; Agus Kurnia; Nur Asmita Purnamasari; Mustika Hadijati; Lilik Hidayati; Helmina Andriani; Jihadil Qudsi; Hafizah Ilma; Adis Tia Juli Agil Asri; Yuliana Lestari; Jihan Melani; Rifdah Fadhilah; M. Syahrul; M. Naoval Husni
Jurnal Pengabdian Magister Pendidikan IPA Vol 7 No 4 (2024): Oktober-Desember 2024
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v7i4.9022

Abstract

The Fourth Industrial Revolution and Society 5.0 have created a demand for technology-based skills, including Data Science. This community service program aimed to introduce Data Science concepts to students at SMAN 1 Gunung Sari, preparing them for the digital era. Through interactive training sessions covering Data Science basics, data analysis simulations, and career discussions, both students and teachers gained essential foundational knowledge. The results showed an increase in students' knowledge and motivation towards STEM fields, as well as new skills for teachers in integrating data-driven learning. This program also strengthened the school's profile as an institution proactive in preparing students for future technological challenges.
PERBANDINGAN ANALISIS DISKRIMINAN DAN NAIVE BAYES DALAM PENGKLASIFIKASIAN STATUS PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN DI NTB HARSYIAH, LISA; HADIJATI, MUSTIKA; FITRIYANI, NURUL
Jurnal Matematika UNAND Vol 13, No 4 (2024)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.13.4.296-308.2024

Abstract

Permasalahan dalam penyaluran bantuan sosial PKH adalah ketidak tepatan penyaluran bantuan PKH. Upaya yang dapat dilakukan untuk mengatasi per masalahan tersebut adalah dengan memastikan kriteria penerimaan bantuan PKH su dah benar dan sesuai dengan kriteria KPM. Berdasarkan kriteria KPM, perlu dilakukan klasifikasi status rumah tangga penerima bantuan PKH dan yang tidak. Hal ini di lakukan dengan tujuan untuk mengetahui apakah bantuan sosial PKH yang disalurkan tepat sasaran atau tidak. Proses klasifikasi dapat dilakukan dengan menggunakan anal isis diskriminan dan metode Na¨ıve Bayes. Hasil penelitian menunjukkan bahwa ketika melakukan klasifikasi menggunakan analisis diskriminan terhadap status penerima ban tuan PKH di NTB diperoleh tingkat kesalahan klasifikasi sebesar 24,5%. Sedangkan hasil klasifikasi menggunakan metode Na¨ıve Bayes memperoleh tingkat kesalahan sebe sar 27,6%. Hasil pengklasifikasian status penerima bantuan PKH dengan menggunakan kedua metode ini tergolong akurat dan analisis diskriminan memiliki kinerja yang lebih baik dibandingkan metode Na¨ ıve Bayes untuk kasus pengklasifikasian status penerima bantuan PKH di NTB
PERBANDINGAN ANALISIS DISKRIMINAN DAN NAIVE BAYES DALAM PENGKLASIFIKASIAN STATUS PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN DI NTB HARSYIAH, LISA; HADIJATI, MUSTIKA; FITRIYANI, NURUL
Jurnal Matematika UNAND Vol. 13 No. 4 (2024)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.13.4.296-308.2024

Abstract

Permasalahan dalam penyaluran bantuan sosial PKH adalah ketidak tepatan penyaluran bantuan PKH. Upaya yang dapat dilakukan untuk mengatasi per masalahan tersebut adalah dengan memastikan kriteria penerimaan bantuan PKH su dah benar dan sesuai dengan kriteria KPM. Berdasarkan kriteria KPM, perlu dilakukan klasifikasi status rumah tangga penerima bantuan PKH dan yang tidak. Hal ini di lakukan dengan tujuan untuk mengetahui apakah bantuan sosial PKH yang disalurkan tepat sasaran atau tidak. Proses klasifikasi dapat dilakukan dengan menggunakan anal isis diskriminan dan metode Na¨ıve Bayes. Hasil penelitian menunjukkan bahwa ketika melakukan klasifikasi menggunakan analisis diskriminan terhadap status penerima ban tuan PKH di NTB diperoleh tingkat kesalahan klasifikasi sebesar 24,5%. Sedangkan hasil klasifikasi menggunakan metode Na¨ıve Bayes memperoleh tingkat kesalahan sebe sar 27,6%. Hasil pengklasifikasian status penerima bantuan PKH dengan menggunakan kedua metode ini tergolong akurat dan analisis diskriminan memiliki kinerja yang lebih baik dibandingkan metode Na¨ ıve Bayes untuk kasus pengklasifikasian status penerima bantuan PKH di NTB
Peranan Statistika di Era Transformasi Digital untuk Agen Perubahan di SMAN 1 Gunungsari Lombok Barat Purnamasari, Nur Asmita; Mustika Hadijati; Lilik Hidayati; Desy Komalasari; Zulhan Widya Baskara; Lisa Harsyiah; Jihadil Qudsi; Helmina Andriani; Dina Eka Putri; Fara Fid
Jurnal Pengabdian Magister Pendidikan IPA Vol 8 No 1 (2025): Januari-Maret 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v8i1.10423

Abstract

The digital transformation era and technological advancements demand rapid adaptability from human resources, including the increasingly important utilization of data science across various industries. Statistics, as a core component of data science, plays a crucial role in transforming data into valuable information for decision-making. Considering the significance of statistical analysis, this skill has become one of the most sought-after in today's industrial world, especially for the younger generation, such as high school students, who will become agents of change in the future. Community service activities at SMA Negeri 1 Gunungsari, Lombok Barat, aim to enhance students understanding of the role of statistics in the digital transformation era. These activities include raising awareness about the importance of statistics in career choices and the application of statistical tools in digital contexts. Furthermore, the material delivered also covers how statistics can be used as a tool to address future industrial challenges. The evaluation of this activity shows an increase in students understanding, as evidenced by the post-test results, which show significant improvement compared to the pre-test. This demonstrates that raising awareness about statistics is effective in equipping students with relevant skills in the digital era. Therefore, similar activities are expected to be implemented in other schools to strengthen students readiness to utilize statistics as agents of change in the digital transformation era.
Co-Authors Adis Tia Juli Agil Asri Adis Tia Juli Agil Asri Agus Kurnia Asri, Adis Tia Juli Agil Attina Ulansari Azka Fariz Hidayatullah Azrianti Ardania Baskara, Zulhan Dara Puspita Anggraeni Dede Saputra Dede Saputra Desy Komalasari Dewa Nyoman Adi Paramartha Dina Eka Putri Dina Eka Putri Eka Putri, Dina Emmy Dyah Sulistiowati Emmy Dyah Sulistyowati Emmy Dyah Sulistyowati, Emmy Dyah Ena Setiawana Fadhilah, Rifdah Fara Fid Filzah Inarah Hafizah Ilma Halawatun Tajalli Halifatunnisa, Nur Helmina Andriani Isma Evita Jihan Melani Jurnal Pepadu Jurniati, Jurniati Lailatul Pahmi Lawwamah, Tamsilul Lilik Hidayati, Lilik Lingking, Fransiska Prisilia M. Naoval Husni M. Syahrul Meliyana, Hesti Muhammad Fadillah Muhammad Rijal Alfian Mustika Hadijati Nirwanto Nirwanto, Nirwanto Nur Asmita Purnamasari Nur Halifatunnisa Nurul Fitriyani Nurul Fitriyani PURNAMASARI, NUR ASMITA Qabul Dinanta Utama Qudsi, Jihadil Qurratul Aini Qurratul Aini Rifdah Fadhilah Rizki Fitri Ananda Robbaniyyah, Nuzla Af’idatur Salwa Salwa Sulpaiyah Sulpaiyah Syaftirridho Putri Syamsul Bahri Syamsul Bahri Tajalli, Halawatun Tamsilul Lawwamah Vidya Atika Ramdani Widya Baskara, Zulhan Yuliana Lestari Zulhan Widya Baskara