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All Journal BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Matematika Sains dan Teknologi Jambura Journal of Mathematics Jurnal Pengabdian kepada Masyarakat Nusantara Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Forum Analisis Statistik Jurnal Pengabdian pada Masyarakat
Wirda Andani
Universitas Tanjungpura
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Transportation Other

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Perbandingan Metode k-Nearest Neighbor, Regresi Logistik Biner, dan Pohon Klasifikasi pada Analisis Kelayakan Pemberian Kredit Shantika Martha; Wirda Andani; Setyo Wira Rizki
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 10 Issue 2 December 2022
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/euler.v10i2.16751

Abstract

Kredit Tanpa Anggunan (KTA) are bank loans given to debtors without asking for guarantees. Some debtors who have made KTA but still need additional loan funds can top up. However, offering this facility to the public cannot be separated from the risk that the debtor and/or other parties fail to fulfill their obligations to the bank. In an effort to assess the feasibility of prospective debtors, banks need decision-making tools so that they can easily and quickly estimate which debtors are able to pay off credit on time (good credit). The tool that is often used is classification. In this study, we will compare 3 classification methods, namely k-nearest neighbor, binary logistic regression, and classification tree, to obtain the best method for analyzing the feasibility of giving KTA top-up. Based on the accuracy value in each method, the classification tree produces the highest accuracy value compared to the other two methods. Thus, for this study, the classification tree is the best method, with an accuracy value of 87.68%. The variables used in the classification tree are DBR, length of service of a debtor, credit limit, type of debtor's occupation, the total income of the debtor, the area where the debtor lives, and the credit period of the debtor is 1 month.
Pelatihan Software Minitab Pada Evaluasi Hasil Belajar Siswa Nurfitri Imro'ah; Dadan Kusnandar; Naomi Nessyana Debataraja; Shantika Martha; Wirda Andani; Evy Sulistianingsih; Hendra Perdana; Neva Satyahadewi; Ray Tamtama; Setyo Wir Rizki
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2 (2022): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (543.339 KB)

Abstract

Pengolahan data dapat dilakukan dengan perhitungan manual ataupun menggunakan alat bantu aplikasi software pengolah data. Salah satu software untuk mengolah data statistik yang dapat digunakan adalah software Minitab. Pengenalan software Minitab kepada kalangan guru khususnya guru SMP Negeri 5 Pontianak merupakan tujuan dari kegiatan Pengabdian Kepada Masyarakat (PKM) yang dilaksanakan oleh Program Studi Statistika FMIPA Universitas Tanjungpura. Kegiatan dilaksanakan dengan dua tahap, yaitu tahap pelatihan dan tahap pendampingan. Tahap pertama bertujuan untuk memperkenalkan software Minitab sebagai alat bantu pengolahan data dan diharapkan agar para guru yang menjadi khalayak dapat memperluas pengetahuan dan meningkatkan motivasi untuk melakukan penelitian yang berkaitan dengan data. Tahap kedua bertujuan untuk membantu para guru agar lebih mampu menganalisis data hasil penelitian yang telah dilakukan dan menambah motivasi untuk membuat publikasi hasil penelitiannya. Selanjutnya dilakukan monitoring terhadap pelaksanaan pelatihan pengolahan data menggunakan software Minitab. Selain itu juga dilakukan survey tanggapan kepada guru-guru terkait tanggapan tentang pelatihan yang dilakukan. Berdasarkan hasil analisis menggunakan uji paired sample t test didapat bahwa rata-rata nilai posttest lebih tinggi secara signifikan dibandingkan dengan rata-rata nilai pretest. Hal ini berarti bahwa pelatihan yang diberikan pada kegiatan PKM ini memberikan pengaruh pada kemampuan olah data para guru SMP Negeri 5 Pontianak.
ANALISIS REGRESI VARIABEL MEDIASI DENGAN METODE KAUSAL STEP Muhammad Asyorori; Wirda Andani
Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 12, No 1 (2023): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v12i1.62844

Abstract

Analisis regresi merupakan sebuah metode yang mana di analisis suatu hubungan guna menentukan nilai dugaan hingga membentuk sebuah persamaan dari variabel independen ke dependen. Analisis regresi me miliki ketepatan dalam estimasi, namun tidak hanya berhubungan variabel independen terhadap variabel dependen melainkan ada variabel lain masuk kedalam persamaan hingga disebut variabel mediasi. Variabel mediasi merupakan variabel munculnya diantara kedua variabel dependen dan independen dan memediasi hubungan kausal di antara kedua variabel tersebut. Tujuan dari penelitian ini menentukan model persamaan regresi variabel mediasi menggunakan metode kausal step dengan menentukan apakah belanja modal memiliki hubungan antara dana bagi hasil terhadap PDRB per kapita dan menentukan model akhir menggunakan metode kausal step, sehingga variabel M dapat dinyatakan sebagai variabel mediasi sempurna atau mediasi parsial. Persamaan regresi menggunakan metode kausal step adalah: , , . Berdasarkan penelitian ini diperoleh bahwa variabel belanja modal dinyatakan sebagai variabel mediasi parsial menjelaskan bahwa dana bagi hasil masih tetap berpengaruh signifikan terhadap PDRB per kapita ketika belanja modal dimasukkan ke persamaan regresi. Kata Kunci : Variabel Mediasi, Analisis Regresi, Kausal step.
Pendampingan Masyarakat untuk Mendukung Program Posyandu dalam Usaha Penanganan Kasus Stunting di Desa Arang Limbung Neva Satyahadewi; Amriani Amir; Desriani Lestari; Wirda Andani; Ari Hepi Yanti; Herina Marlisa; Esta Br Tarigan
Lumbung Inovasi: Jurnal Pengabdian kepada Masyarakat Vol. 8 No. 1 (2023): Maret
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/linov.v8i1.1085

Abstract

Stunting adalah terganggunya pertumbuhan anak yang menyebabkan tinggi badan anak lebih pendek daripada usia sebayanya. Stunting dapat menyebabkan tingginya resiko penyakit degeneratif pada anak, dapat mempengaruhi perkembangan psikomotorik dan kemampuan kognitif anak, sehingga menghasilkan sumber daya manusia yang tidak unggul. Rendahnya jumlah kunjungan warga masyarakat  ke posyandu adalah salah satu faktor penyebab terjadinya stunting. Untuk dilakukan kegiatan pengabdian kepada masyarakat (PKM) dalam program Bina  desa oleh tim Fakultas MIPA Universitas Tanjungpura di Desa Arang Limbung. Kegiatan ini  bertujuan untuk mengedukasi dan mendampingi masyarakat Arang Limbung untuk aktif dan berperan serta dalam usaha penanganan stunting melalui optimalisasi kunjungan ke posyandu. Kegiatan ini dilakukan pada bulan September-Desember 2022, dilaksanakan di wilayah kerja Posyandu Arang Jaya, dengan melibatkan keluarga pengunjung tetap posyandu tersebut, yang terdiri dari ibu hamil, ibu menyusui dan anak usia bawah lima tahun (balita) serta golongan usia lanjut.  Dari hasil kegiatan menunjukkan terjadinya peningkatan kesadaran dan kepedulian warga masyarakat untuk menangani stunting melalui kunjungan yang rutin dan mengikuti program yang dilaksanakan di posyandu Arang Jaya meliputi penimbangan berat badan ibu hamil dan balita, imunisasi dasar lengkap pada balita, pemberian makanan tambahan (PTM) bagi ibu hamil dan balita, pengukuran tensi dan pemberian tambah darah bagi wanita hamil, serta pemeriksaan kesehatan secara umum. Community Assistance to Support the Posyandu Program in Efforts to Handle Stunting Cases in Arang Limbung Village Stunting is a child's growth disturbance which causes the child's height to be shorter than his or her age.. Stunting can cause a high risk of degenerative diseases in children, can affect the psychomotor development and cognitive abilities of children, resulting in the production of human resources that are not superior. The low number of community visits to posyandu is one of the factors causing stunting. To carry out community service (PKM) activities in the village development program by the Tanjungpura University Faculty of Mathematics and Natural Sciences team in Arang Limbung Village. This activity aims to educate and assist the Arang Limbung community to be active and participate in stunting management efforts by optimizing visits to posyandu. This activity was carried out in September-December 2022, carried out in the working area of ??the Arang Jaya Posyandu, involving families of regular visitors to the posyandu, consisting of pregnant women, nursing mothers and children under five years old (toddlers) and the elderly. The results of the activity show an increase in awareness and concern for community members to deal with stunting through regular visits and following programs implemented at the Arang Jaya Posyandu including weighing pregnant women and toddlers, complete basic immunization for toddlers, providing supplementary food (PTM) for mothers pregnant and toddlers, measuring blood pressure and giving blood supplements for pregnant women, as well as general health checks.
ANALISIS VALUE AT RISK PORTOFOLIO SAHAM LQ45 DENGAN METODE SIMULASI MONTE CARLO CONTROL VARIATES Westi Widiyatari; Evy Sulistianingsih; Wirda Andani
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 17, No 1 (2023)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v17i1.9536

Abstract

Value at Risk (VaR) with the Monte Carlo (MC) simulation is an estimate of the maximum loss over a given period of time and with a specific degree of confidence. MC VaR uses the Control Variates (CV) technique which is one of the reduction techniques in the MC method to improve the efficiency of VaR estimation. This study also aims to analyze the risk of the LQ45 indexed stock portofolio with Monte Carlo Control Variates (MCCV) VaR. In addition, this study compares MCCV VaR with standar MC VaR. The closing prices of the shares of PT Bank Negara Indonesia Tbk (BBNI) and PT Bank Central Asia Tbk (BBCA) were the source of the data for this study. The 95% confidence level is used for this study to estimate one-day MCCV VaR. The results obtained show that MCCV is able to reduce the variance of the estimate more quickly than the standar MC VaR. Thus, MCCV VaR is more efficient than the standard MC VaR.
Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM Rahadi Ramlan; Neva Satyahadewi; Wirda Andani
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20860

Abstract

Twitter is one of the social media with the most active users, which is 24 million active users. Information published on twitter contains comments from users on an object. Sentiment analysis is used to determine whether the data includes negative comments or positive comments because the comments taken on twitter are textual data. The method used in this sentiment analysis is Support Vector Machine (SVM) about public comments on fuel price increases on twitter. The comment data used was 258 data on September 4, 2022 because on that date it was exactly the day after the fuel price increase. First, preprocessing is done to remove unnecessary words or information. Then the data is divided into training data by 80% and testing data by 20%. The accuracy rate is 82.69%, sensitivity is 100%, and specificity is 79.07%. Then from the results of testing 52 data obtained the results of 43 negative comments and 9 positive comments so that it can be concluded that more people disagree with the increase in fuel prices.
PENERAPAN MODEL GEOMETRIC BROWNIAN MOTION DAN PERHITUNGAN NILAI VALUE AT RISK PADA SAHAM BANK CENTRAL ASIA TBK Fadhilah Rizky Aulia; Evy Sulistianingsih; Wirda Andani
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 17, No 2 (2023)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v17i2.9537

Abstract

Stock price fluctuations are difficult to predict, resulting in uncertain profits. Therefore, a mathematical model is needed to predict future stock prices, namely the Geometric Brownian Motion (GBM) model based on a stochastic process. Stocks are also accompanied by risks that have potential for loss. The risk can be measured using Value at Risk (VaR) which can estimate the maximum loss that may happen from an investment at a certain level of confidence and period of time. The purpose of this research is to implement the GBM model in predicting stock prices and estimating the maximum loss of stock investment using VaR. This research analyzes the daily closing stock price of PT Bank Central Asia (BBCA) for the period November 1, 2021, to December 31, 2022. The stock price predictions with the GBM model are used to estimate the VaR value. Based on the analysis results, GBM is highly accurate model with an average MAPE value of 5.77% and the smallest MAPE value of 1.45%. The VaR values obtained at the 80%, 90%, 95% and 99% confidence level are 1,17%, 1,74%, 2,19% and 2,86% of the total fund investment for the next one-day period, respectively.
Analisis Pengaruh Indeks Pembangunan Manusia (IPM), Ketersediaan Infrastruktur Listrik, dan Sanitasi terhadap Ketimpangan Pendapatan Antar Daerah di Kalimantan Barat: Analysis of the Effect of Human Development Index (HDI), Electricity and Sanitation Infrastructure Availability on Inter-Regional Income Inequality in West Kalimantan Luis Maria Carla; Wirda Andani; Anis Fakhrunnisa
Jurnal Forum Analisis Statistik Vol. 3 No. 2 (2023): Jurnal Forum Analisis Statistik (FORMASI)
Publisher : Badan Pusat Statistik Provinsi Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57059/formasi.v3i2.57

Abstract

Ketimpangan distribusi pendapatan meliputi keberadaan tingkat kesejahteraan masyarakat Indonesia yang masih berada di bawah garis kemiskinan dan ketidakmerataan pendapatan antar rumah tangga menjadi isu berkelanjutan dalam pembangunan ekonomi di Indonesia. Di Kalimantan Barat sendiri, pembangunan sarana dan prasarana infrastruktur menjadi perhatian, tantangan, sekaligus modal dalam menerobos kemajuan dan pemerataan kesejahteraan bagi masyarakat. Dalam penelitian ini, akan diteliti mengenai variabel Gini Ratio yang mewakili tingkat ketimpangan pendapatan dan variabel Indeks Pembangunan Manusia (IPM), persentase sumber penerangan listrik PLN, dan persentase rumah tangga yang memiliki akses sanitasi layak sebagai variabel dependen yang diduga memiliki pengaruh terhadap ketimpangan pendapatan tahun 2018 – 2022 di Provinsi Kalimantan Barat. Analisis yang digunakan yaitu regresi data panel menggunakan estimasi Random Effect Model (REM). Dari hasil analisis dan pembahasan ini ditunjukkan bahwa Indeks Pembangunan Manusia (IPM) dan persentase rumah tangga yang memiliki akses sanitasi layak memiliki pengaruh yang signifikan terhadap Gini Ratio sebagai variabel pengukur tingkat ketimpangan pendapatan. Sedangkan persentase sumber penerangan listrik PLN secara signifikan tidak memiliki pengaruh terhadap tingkat ketimpangan pendapatan di Kalimantan Barat.
Implementation of Random Oversampling Technique in the K-Nearest Neighbor Method for Creditworthiness Analysis Ayu Dhita Putri Wulandari; Shantika Martha; Wirda Andani
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.4909.2024

Abstract

Banks are financial institutions, one of whose main activities is providing credit to their customers. The existence of credit granting activities requires the bank to know the feasibility of prospective debtors in receiving credit. Because in practice, credit granting activities still often have bad credit problems. The problem of bad credit can be overcome by analyzing the feasibility of granting credit to prospective debtors. The data used in this study consists of 10 independent variables and 1 dependent variable is collectibility (kol). The collectibility (col) data consists of 500 data for the current debtor class and 26 data for the non-current debtor class, this indicates an imbalance class. So in this study, the application of the random oversampling (ROS) technique is used to overcome the imbalance class with the K-Nearest Neighbor (KNN) method in classifying current and non-current debtor data. ROS was chosen because it can generally provide better results and does not eliminate information from existing data. The analysis results obtained show that the use of the KNN method with the application of ROS is better than the KNN model without ROS, with an accuracy of 84.91% at data testing. The KNN model with ROS can improve the model's ability to classify noncurrent debtor data or the specificity value of the model increases by 25%. In the KNN model without ROS the model cannot classify non-current debtor data correctly at all, this can endanger the bank in making decisions.
EFEKTIVITAS PELATIHAN POWER BI DALAM MENINGKATKAN LITERASI DATA ADMIN SATU DATA KALIMANTAN BARAT Neva Satyahadewi; Evy Sulistianingsih; Shantika Martha; Nurfitri Imro'ah; Hendra Perdana; Wirda Andani; Ray Tamtama; Yuyun Eka Pratiwi; Muhammad Fikri; Pitriani; Annisa Auliarahmi; Nazwa Nursyifa; Yohanna Gabriel Richsita; Louis Putra Jaya; Jessica Audrey Valeria
Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat Vol 3 No 1 (2026): Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat
Publisher : LPPM Universitas Panca Bhakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54035/dianmas.v3i1.626

Abstract

This Community Service Program (PKM) aimed to enhance data literacy and information visualization skills among Satu Data administrators of local government agencies (OPD) through Microsoft Power BI training at the West Kalimantan Provincial Communication and Information Agency (Diskominfo). The program was implemented through preparation, face-to-face training, and evaluation stages using pre-test and post-test instruments. The training covered fundamental concepts of data analysis, data visualization techniques, and hands-on dashboard development using regional sectoral data. The results of the paired sample t-test analysis indicated a statistically significant improvement between participants’ pre-test and post-test scores, demonstrating the effectiveness of the training. Furthermore, analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that training material quality had a positive and significant effect on participants’ learning outcomes, while other supporting factors such as training duration, facilitator performance, and technical aspects did not show significant effects. These findings highlight that well-structured and relevant training materials play a critical role in improving participants’ competencies. Overall, the program contributed to strengthening analytical skills and supporting the implementation of the Satu Data Indonesia policy toward transparent and evidence-based data governance
Co-Authors Amriani Amir Anis Fakhrunnisa Annisa Auliarahmi Ari Hepi Yanti Ayu Dhita Putri Wulandari Desriani Lestari Esta Br Tarigan Evy Sulistianingsih Fadhilah Rizky Aulia Hendra Perdana Herina Marlisa Jessica Audrey Valeria Louis Putra Jaya Luis Maria Carla Muhammad Asyorori Muhammad Fikri Naomi Nessyana Debataraja Nazwa Nursyifa Neva Satyahadewi Nurfitri Imro’ah Pitriani Rahadi Ramlan Setyo Wir Rizki Setyo Wira Rizki Shantika Martha Tamtama, Ray Westi Widiyatari Yohanna Gabriel Richsita Yuyun Eka Pratiwi