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All Journal Saintia Matematika Journal of Mathematics Education and Application (JMEA) Sinkron : Jurnal dan Penelitian Teknik Informatika CARTESIUS : Jurnal Pendidikan Matematika MES: Journal of Mathematics Education and Science Journal of Research in Mathematics Trends and Technology Caring : Indonesian Journal of Nursing Science FARABI: Jurnal Matematika dan Pendidikan Matematika JURNAL MITRA PRIMA Journal of Mathematics Technology and Education Cartesius Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Jurnal Agro Estate Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam
Open Darnius
Program Studi Sarjana Matematika, FMIPA, Universitas Sumatera Utara, Medan-Indonesia 20155
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Zero-Inflated Poisson Regression Testing In Handling Overdispersion On Poisson Regression Mutia Sari; Open Darnius
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13591

Abstract

The classical linear regression analysis is an analysis aimed at knowing the relationship between the response variables and the explanatory variables assuming the normal distribution data, but in the applied data is often not the case. Generalized Linear Model (GLM) was developed for data in the form of categorical and discrete distribution. In this study the data was raised which has a poisson distribution by as much as n, with average  λ and the odds appearing zero p. Poisson regression is GLM for Poisson-distributed data assuming that Var(X ) = E(X ), but asusumption is rare in applied data. For rare occurrences of a specified interval X variables are often zero-valued, thus causing overdispersion (Var(X ) E(X )). Lambert (1992) introduced a method for overcoming overdispersion in poisson regression i.e. the Zero-Inflated Poisson regression (ZIP). In this research conducted a ZIP regression test in overcoming overdispersion to see the opportunity limit p appears zero- valued as the value that causes overdispersion. Testing is done with RStudio ver. 1.1.463.0 software. Based on the simulated data obtained that Regression ZIP stopped overcoming overdis persion at the condition n = 500, λ = 0.7 with the odds p = 0.2 with a dispersion ratio of  τ = 1.010.
Analysis Of Mutual Fund Performance In Indonesia Using Robust Regression Doloksaribu, Arsella F; br Tarigan, Ernita Dewi; Syahmarani, Aghni; Darnius, Open; Hasibuan, Citra Dewi
MES: Journal of Mathematics Education and Science Vol 10, No 1 (2024): Edisi Oktober (Progress)
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mes.v10i1.9595

Abstract

The Least Squares Method (MKT) is the best linear regression estimator if the classical assumption tests are met. However, if there are outliers in the data, this approach can provide inaccurate prediction results. Therefore, the purpose of this research is to overcome the inappropriate model due to outliers in variables that affect mutual fund performance in Indonesia. The variables in this study are risk level, inflation, fund size, turnover ratio, and cash flow. Robust regression is an approach designed to provide more accurate estimates without discarding observational data that indicates outliers. One of the estimation approaches is Least Trimmed Squares (LTS). This estimation minimizes the sum of squared residuals from h observations that are not considered outliers. The results showed that there were 10 outliers in the data, and the risk level variable had no effect on mutual fund performance, while the inflation, fund size, turnover ratio, and cash flow variables had a significant effect. So by comparing the  value and residual standard error of the two methods, it is found that the  value in the LTS method is greater than the MKT method, namely 0.589 0.273, and the residual standard error in the LTS method is smaller than the MKT method, namely 9.73 48.59. Therefore, it can be concluded that the Least Trimmed Squares method provides better estimation results and is more effective for handling outliers than the MKT method. 
ANALISIS KEPUASAN MAHASISWA TERHADAP PROGRAM STUDI INDEPENDEN BERSERTIFIKAT DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL Hartaty, Rembulan; Nasution, Putri Khairiah; Darnius, Open; Br Tarigan, Enita Dewi
MES: Journal of Mathematics Education and Science Vol 9, No 2 (2024): Edisi April
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mes.v9i2.9234

Abstract

Studi independen bersertifikat merupakan program Kampus Merdeka yang memberikan kesempatan bagi mahasiswa untuk menguasai keterampilan spesifik. Untuk mengetahui keberhasilan dari program ini penting untuk mengetahui apakah program ini memenuhi harapan mahasiswa atau tidak. Penelitian ini bertujuan untuk menganalisis kepuasan mahasiswa terhadap program studi independen bersertifikat dan mengidentifikasi variabel apa saja yang berpengaruh secara signifikan dengan menggunakan metode regresi logistik ordinal. Data yang digunakan adalah data primer dengan menyebarkan kuisioner (goggle form). Hasil penelitian menunjukkan bahwa variabel yang berpengaruh secara signifikan terhadap kepuasan mahasiswa adalah pengajar (x_3 ) dan proyek akhir (x_7). Sedangkan variabel yang tidak berpengaruh secara signifikan antara lain materi pembelajaran (x_1), sistem pembelajaran (x_2), fasilitas dan akses yang diberikan oleh mitra (x_4), dukungan dari universitas (x_5), motivasi dan minat mahasiswa (x_6), interaksi sosial (x_8), dan sistem penilaian oleh mitra (x_9). Kepuasan mahasiswa secara keseluruhan terhadap program studi independen bersertifikat dipengaruhi oleh variabel independen sebesar 54.8%. Dari hasil odds ratio, setiap peningkatan satu unit dalam skala variabel pengajar (x_3) dan proyek akhir (x_7) mengakibatkan perubahan tingkat kepuasan mahasiswa masing-masing sebesar 1,43 dan 1,34 kali. Kata Kunci: Kepuasan Mahasiswa, Regresi Logistik Ordinal, Studi Independen Bersertifikat.
REGULARISASI REGRESI LINIER BERGANDA PADA DATA BERDIMENSI TINGGI UNTUK MENGATASI EFEK MULTIKOLINEARITAS Nasution, Muhammat Rayyan; Sutarman, S; Darnius, Open; Rosmaini, Elly
MES: Journal of Mathematics Education and Science Vol 10, No 1 (2024): Edisi Oktober (Progress)
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mes.v10i1.9469

Abstract

Penelitian ini membahas model regresi linier berganda yang diberikan regularisasi dalam kasus data berdimensi tinggi (???? ≫ ????), bertujuan untuk mengatasi efek multikolinearitas yang terdiri dari efek singularitas dan kualitas model yang buruk. Dalam penelitian ini mengembangkan model regresi linier berganda dengan menambahkan parameter penalti pada fungsi tujuan. Adapun data yang digunakan adalah data primer yang dibangkitkan dengan bahasa pemrograman python dengan tiga skenario sesuai dari penelitian sebelumnya. Metode yang digunakan yaitu Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (LASSO) dan Ridge dalam mengestimasi parameter model regresi. Mean Squared Error (MSE) digunakan sebagai metrik evaluasi kinerja model yang dibangun. Dari hasil simulasi yang dilakukan, diperoleh bahwa metode LASSO memberikan kualitas model terbaik dengan memberikan nilai MSE terendah dibandingkan model lainnya.
KLASIFIKASI PROVINSI DI INDONESIA BERDASARKAN LUAS PENGUSAHAAN DAN PRODUKTIVITAS TANAMAN KELAPA SAWIT MENGGUNAKAN ANALISA KLASTER Siahaan, Megawati; Darnius, Open
Agro Estate Vol 7 No 2 (2023): Desember 2023
Publisher : Institut Teknologi Sawit Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47199/jae.v7i2.164

Abstract

Luas areal perkebunan kelapa sawit di Indonesia semakin meningkat terus dengan pengusahaan oleh perkebunan besar negara, perkebunan besar swasta dan perkebunan rakyat. Produktivitas tanaman kelapa sawit bervariasi di perusahaan-perusahaan tersebut demikian juga dengan distribusinya di berbagai provinsi. Kelapa sawit ditanam dan tumbuh di berbagai provinsi di Indonesia, perlu dikelompokkan bagaimana pengelolaan kelapa sawit di masing-masing provinsi untuk mengetahui variasi pengelolaan kelapa sawit antar provinsi dan diperoleh upaya memperbaiki produktivitas berdasarkan pengelompokan tersebut. Data yang akan dianalisis merupakan data sekunder yang dikumpulkan dengan studi pustaka. Data dianalisa dengan analisis klaster menggunakan program IBM SPSS 22, dengan variabel terikat yang digunakan adalah provinsi di Indonesia sedangkan variabel bebasnya adalah luas areal perkebunan kelapa sawit yang pengusahaannya oleh perkebunan besar negara, perkebunan besar swasta dan perkebunan rakyat serta produktivitas kelapa sawit di masing-masing perkebunan tersebut sehingga ada total 6 variabel. Hasil dari analisis menunjukkan bahwa diperoleh 5 klaster provinsi di Indonesia yang mengelola kelapa sawit yaitu provinsi yang terbaik adalah Riau, Provinsi yang baik adalah Jambi, Provinsi yang cukup baik adalah Kalimatantan Barat, provinsi yang kurang baik adalah Sumatera Utara, Sumatera Selatan, Kalimantan Selatan, Provinsi yang paling tidak baik adalah Aceh, Sumatera Barat, Bengkulu, Lampung, Jawa Barat, Banten, Sulawesi Selatan, Sulawesi Tenggara.
OPTIMIZATION MODEL IN CLUSTERING THE HAZARD ZONE AFTER AN EARTHQUAKE DISASTER Bangun, Monica Natalia; Darnius, Open; Sutarman
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11598

Abstract

There are a large number of approaches to clustering problems, including optimization-based methods involving mathematical programming models to develop efficient and meaningful clustering schemes. Clustering is one of the data labeling techniques. K-means clustering is a partition clustering algorithm that starts by selecting k representative points as the initial centroid. Each point is then assigned to the nearest centroid based on the selected specific proximity measure. This writing is focused on the grouping of post-earthquake hazard zones based on grouping with regard to certain characteristics which aim to describe the process of partitioning the N-dimensional population into K-sets based on the sample. This research consists of three steps, namely standardization, data clustering using K-means and data interpolation using the K-means clustering algorithm and zoning of 7 variables, namely magnitude, depth, victim died, the victim didn’t die, public facilities were heavily damage, public facilities were slightly damage, and affected areas.
Modelling of Subject Scheduling Systems Using Hybrid Artificial Bee Colony Algorithm Lingga, Sri Wahyuni; Sutarman; Darnius, Open
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.12560

Abstract

A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the Engineering of Artificial Bee Colony (ABC) and genetic algoritms, especially on the crossover process to better address the schedule problems. The research procedure used is to design and implement a course scheduling system using the Hybrid ABC algorithm. The results of the research demonstrate that the Hybrid ABC algorithm is effective in generating optimal course schedule schedules, in line with time limits, room needs, and lecturer requirements and can automate course schedule processes, saving time and resources, while ensuring optimal schedules.
Inventory Model for Order Quantity Optimization with Partial Backlogging on Greater Demand at The Beginning Aritonang, Reanty Teresa; Darnius, Open; Sutarman, Sutarman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12600

Abstract

This article discusses the model of inventory with greater demand at the beginning which allows shortages. During the shortage period, it is assumed that there is a backlogged demand, and the remainder is considered lost sales. This research is completed by using the deterministic inventory model method, namely the EOQ model. The result of using the EOQ method is to determine the inventory lot size and length, with the goal of minimizing the total cost of inventory and generating maximum profits related to the inventory model. An numerical example is given to show the use of this model.
Simplifying Complexity: Linearization Method for Partial Least Squares Regression Simanullang, Herlin; Sutarman, Sutarman; Darnius, Open
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12754

Abstract

This research investigates Romera’s local linearization approach as a variance prediction method in partial least squares (PLS) regression. By addressing limitations in the original PLS regression formula, the local linearization approach aims to improve accuracy and stability in variance predictions. Extensive simulations are conducted to assess the method's performance, demonstrating its superiority over traditional algebraic methods and showcasing its computational advantages, particularly with a large number of predictors. Additionally, the study introduces a novel computational technique utilizing bootstrap parameters, enhancing computational stability and robustness. Overall, the research provides valuable insights into the local linearization approach's effectiveness, guiding researchers and practitioners in selecting more reliable and efficient regression modeling techniques.
Path Analysis Model in Determining the Crime Rate During the Covid 19 Pandemic in North Sumatera Wardani, Yuni; Darnius, Open
Journal of Mathematics Technology and Education Vol. 1 No. 2 (2022): Journal of Mathematics Technology and Education
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.11 KB) | DOI: 10.32734/jomte.v1i2.7750

Abstract

Path analysis is a research method that is mainly used to examine the strength of direct and indirect relationships among various variables. This is in line with one of the objectives of research in social science, namely to determine the existence of a causal relationship. Path analysis can be applied to a social issue, currently the world is worried about the spread of COVID-19. The existence of policies in controlling the spread of COVID-19 disrupts various aspects of life. This study was conducted to determine the factors that cause crime in North Sumatra and to determine the effect of the COVID-19 on the rise of crime rates using the path analysis method. In this study, it was concluded that although there are several exogenous variables that have decreased influence on endogenous variables in 2020, but in this year, which is the pandemic period, the coefficient of determination has the best level.
Co-Authors Aghni Syahmarani Aritonang, Reanty Teresa Asima Manurung Bangun, Monica Natalia Br Tarigan, Enita Dewi br Tarigan, Ernita Dewi Diah Lestari Nasution Doloksaribu, Arsella F Eddy, Thaswin Elly Rosmaini Elvin Juliani Gulo Evi Indriani Br Karo Fahrul Rozi Harahap Hardiyanti, Siska Hardiyanti, Widya Hartaty, Rembulan Hasibuan, Citra Dewi Hasoloan M Nababan Henry Rani Sitepu Ide Prasanti Hutagalung Israil Sitepu Kristina Pestaria Sinaga Lingga, Sri Wahyuni Lisna Astria Mayanti, Utari Sri Meili Yanti Muthia Ferliani Balqis Mutia Sari Nasution, Muhammat Rayyan Nasution, Putri Khairiah Nikmah, Herlin Nofrida Elly Zendrato Nur Afidarti Parapat Gultom Pasaribu, Albert Pasukat Sembiring Rifalin Delustia Purba Rifan halomoan tua sinaga S, Benar Saragih, Yoga Fromega Siahaan, Megawati Simanullang, Herlin SRI WULANDARI Sutarman Sutarman Sutarman Sutarman Sutarman Sutarman, S Tarigan, Enita Dewi Tulus Tulus Ujian Sinulingga Wardani, Yuni Yanti, Maulida Yessica Thania Silaban