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All Journal Lentera Pustaka Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Berkala Bioteknologi Jambura Journal of Mathematics InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Mahasiswa Ilmu Komputer Jurnal Statistika dan Komputasi (STATKOM) JPLED Sciencestatistics: Journal of Statistics, Probability, and Its Application
Khoirin Nisa
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Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Penerapan Model Generalized Space Time Autoregressive (GSTAR) pada Data Inflasi Beberapa Kota Ulfa Putri Rahmani; Khoirin Nisa; Nurmaita Hamsyiah
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 3 No. 1 (2025): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v3i1.7526

Abstract

Model yang umum digunakan untuk data space time adalah model Vector autoregressive (VAR), Space Time Autoregressive (STAR), dan Generalized Space Time Autoregressive (GSTAR). Untuk lokasi yang memiliki karakteristik yang berbeda (heterogen), model GSTAR lebih baik digunakan dibandingkan model STAR. Tujuan dari penelitian ini adalah menerapkan model GSTAR pada data time series dari tiga lokasi berbeda. Data yang digunakan pada penelitian ini adalah data inflasi Palembang, Bandar Lampung, dan DKI Jakarta bulan Januari 2012 hingga Juni 2019. Bobot Lokasi yang digunakan adalah bobot lokasi invers jarak dan bobot lokasi normalisasi korelasi silang. Pada penelitian ini pendugaan parameter dilakukan dengan metode Generalized Least Square (GLS). Dari hasil analisis diperoleh model yang terbaik adalah model GSTAR(11) dengan bobot lokasi invers jarak karena memiliki rata-rata RMSE terkecil yaitu 0.467767. The models commonly used for space time data are the Vector autoregressive (VAR), Space Time Autoregressive (STAR), and Generalized Space Time Autoregressive (GSTAR) models. For locations that have different (heterogeneous) characteristics, the GSTAR model is better to use than the STAR model. The aim of this research is to apply the GSTAR model to time series data from three different locations. The data used in this research is inflation data from Palembang, Bandar Lampung, and DKI Jakarta from January 2012 to June 2019. The location weights used are distance inverse location weights and cross-correlation normalized location weights. In this research, parameter estimation was carried out using the Generalized Least Square (GLS) method. From the analysis results, it was found that the best model was the GSTAR(11) model with inverse distance location weights because it had the smallest average RMSE, namely 0.467767.
Penerapan Model Geographically Weighted Poisson Regression untuk Demam Berdarah Dengue Di Kabupaten Bojonegoro Nisa, Khoirin
Jurnal Statistika dan Komputasi Vol. 1 No. 1 (2022): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v1i1.444

Abstract

Latar   Belakang:    Kasus Demam Berdarah Dengue (DBD) di Kabupaten Bojonegoro meningkat dari tahun 2017 sampai tahun 2019. Hal ini menjadi sulit karena wilayah geografis yang sangat luas di setiap Kecamatan. Untuk menganalisis masalah ini, perlu diberikan pemodelan regresi spasial yang memperhitungkan perbedaan wilayah. Tujuan: Menganalisis pengaruh variabel-variabel prediktor terhadap banyaknya kasus DBD per Kecamatan di Kabupaten Bojonegoro dengan model Geographically Weighted  Poisson Regression (GWPR). Metode: Menerapkan metode kuantitatif berupa pemodelan GWPR dengan perbandingan kernel yaitu kernel fixed Gaussian, fixed bi-square, adaptive bi-square, dan adaptive Gaussian. Sumber data yang digunakan adalah data sekunder diperoleh dari laporan Badan Pusat Statistik (BPS) dan Dinas Kesehatan Bojonegoro pada tahun 2017-2019 per Kecamatan di Kabupaten Bojonegoro. Hasil: Diperoleh model GWPR terbaik untuk kernel fixed bi-square dengan nilai deviance sebesar 610,5541 dan AIC sebesar 647,6348. Dari 28 Kecamatan di Kabupaten Bojonegoro, kepadatan penduduk memiliki pengaruh signifikan positif pada 1 Kecamatan dan negatif 10 Kecamatan, fasilitas kesehatan mempunyai pengaruh signifikan positif pada 19 Kecamatan dan negatif 1 Kecamatan, dan tenaga kerja kesehatan memiliki pengaruh signifikan positif pada 11 Kecamatan dan negatif 3 Kecamatan. Kesimpulan: Pemodelan GWPR memberikan masukan pengetahuan bahwa kepadatan penduduk, fasilitas kesehatan, dan tenaga kerja kesehatan secara spasial signifikan mempengaruhi kasus DBD di Kabupaten Bojonegoro.
Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator Saputri, Gustina; Herawati, Netti; Ruby, Tiryono; Nisa, Khoirin
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.24828

Abstract

Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.
Ordinal logistic regression model on the level of job relevance of graduates Sunanto, Rahma Faelasofi; Nisa, Khoirin
Desimal: Jurnal Matematika Vol. 6 No. 3 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i3.19716

Abstract

The ordinal logistic regression model is one of the statistical models used to classify ordinal data. The purpose of this study is to identify the relevance of FKIP Muhammadiyah Pringsewu University Lampung graduates. The population in this study were FKIP Muhammadiyah Pringsewu University Lampung graduates in three academic years from 2018-2021. The total of the sample in this study was 133 graduates. There was missing data that was classified as Missing Random (MAR). The ordinal logistic regression model used an odds proportion model. This study will analyze the relationship between the response variable is the relevance of the graduate's work according to the field of work that has three classifications high, medium, and low, against the nine predictor variables predicted affecting the predictor variable. The result of the data description available stated that 80.3% of graduates had a high level of job relevance, 11.4% of graduates had a moderate level of job, and 8.3% of graduates had a low level of job relevance. Then the result of the ordinal logistic regression model using the proportional odds model showed that the variable predictor IPK graduates with categories 3.01-3.50; 3.406; and 3.51-4.00, predictor variable of looking for or getting a job either before or after, and variables predictor types of permanent jobs give a significant influence on the level of job relevance of graduates.
Modeling with generalized linear model on covid-19: Cases in Indonesia Saidi, Subian; Herawati, Netti; Nisa, Khoirin
International Journal of Electronics and Communications Systems Vol. 1 No. 1 (2021): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v1i1.9299

Abstract

The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distribution. Where the three distributions will be compared to get the best model. The variables used in this study were the number of confirmed Covid-19 cases per day and the number of deaths due to Covid-19 per day. This study also aims to see how much influence the confirmation of Covid-19 has on the number of deaths due to Covid-19 per day. By using 3 types of exponential family distribution, the best result is the Gaussian distribution GLM. Selection of the best model using Akaike Information Criterion (AIC).
ROBUST CLUSTERING OF COVID-19 PANDEMIC WORLDWIDE Wibowo, Rizki Agung; Nisa, Khoirin; Venelia, Hilda; Warsono, Warsono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (459.524 KB) | DOI: 10.30598/barekengvol16iss2pp687-694

Abstract

COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis. Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.
Isolasi dan Aktivitas Antikapang Bakteri Asam Laktat dari Tape Ketan Kemasan Plastik terhadap Fusarium sp. Nisa, Khoirin; Jannah, Siti Nur; Rukmi, Isworo
Berkala Bioteknologi Vol. 6, No. 2, November 2023
Publisher : Berkala Bioteknologi

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

Abstract

Fusarium sp. merupakan patogen tular tanah yang memiliki kemampuan bertahan dalam tanah selama bertahun-tahun, pada kondisi lingkungan yang tidak menguntungkan. Bakteri asam laktat (BAL) diketahui menghasilkan senyawa antikapang yang dapat digunakan sebagai salah satu alternatif pengendalian Fusarium sp. yang ramah lingkungan. BAL dapat ditemukan dalam produk fermentasi, antara lain pada tape ketan kemasan plastik. Tujuan penelitian ini adalah untuk mendapatkan isolat murni bakteri asam laktat (BAL) dari tape ketan kemasan plastik dan mengetahui aktivitas antikapang isolat BAL yang yang diperoleh dalam menghambat pertumbuhan kapang Fusarium sp. Penelitian ini menggunakan kultur BAL, supernatan BAL bebas sel (CFS) dan supernatan bebas sel BAL yang dinetralkan (CFSN). Uji aktifitas antikapang dilakukan dengan metode sumuran pada medium MRSA. Hasil penelitian menunjukkan bahwa dari tape ketan kemasan plastik diperoleh enam isolat BAL (BTP1, BTP2, BTP3, BTP4, BTP5, BTP6). Lima isolat BAL (BTP1, BTP2, BTP3, BTP4, BTP5) mampu menghambat pertumbuhan kapang Fusarium sp. dengan aktivitas antikapang fungistatik. Supernatan bebas sel BAL yang dinetralkan (CFSN) BTP1 menunjukkan aktivitas antikapang yang paling besar dan berbeda nyata dengan isolat BAL lainnya.
Analysis of Food Security Factors in Indonesia using SEM-GSCA with the Alternating Least Squares Method Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20378

Abstract

An economic recession, characterized by prolonged economic decline, increased unemployment, and decreased spending, is projected to occur globally in 2023, potentially impacting production capacity within the food sector. Experts have identified various contributing factors such as shifts in global trade dynamics and geopolitical tensions, highlighting the need to understand the broader global economic context leading to this recession. To achieve this goal, in this research SEM is used to analyze the relationship between variables that influence food security. Furthermore, GSCA is used to handle complex structural models and non-normal data distribution. Special considerations include the use of ALS methods to estimate parameters effectively and consistently. The findings of this research are the important role of availability, access and utilization in shaping food security in Indonesia, with a contribution of 98% of the overall influence shown by the model. These insights help governments design targeted interventions to improve food security, especially amidst challenges posed by a potential global economic downturn. Implementing strategies to increase availability, increase access and optimize utilization is very important in maintaining food security amidst economic uncertainty.
Converting Corn Cobs into Briquettes in Braja Harjosari Village, Braja Salebah Subdistrict, East Lampung Regency Sutrisno, Agus; Zakaria, La; Aziz, Dorrah; Nisa, Khoirin
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 6 No. 2 (2025)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v6i2.164

Abstract

Corn cobs are agricultural waste that can be processed into an alternative firewood. Carbonization (pyrolysis) followed by briquetting is one method to process biomass into solid charcoal. According to a survey conducted, the large amount of corn cob waste is due to a lack of knowledge in processing waste which causes health and environmental problems. Converting corn cob waste into briquettes transforms it into a valuable commodity. In fact, transforming corn cob waste is essentially applying the zero waste concept to agricultural production systems. Based on potential and agreements with farmer groups, community members, and local government, this service activity was carried out. The productivity of the briquette charcoal business made from corn waste is increased through training and assistance.
Enhancing Tuberculosis Diagnosis: Effective Naive Bayes Classification using SMOTE and Tomek Links for Imbalanced Data Faulina, Naflah; Nisa, Khoirin; Warsono, Warsono
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 2 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i2.41463

Abstract

Naive Bayes classification, grounded in Bayes' theorem, is a well-established probabilistic and statistical method. However, it often faces challenges when dealing with datasets that have skewed class distributions. A common issue with unbalanced data is that the classifier tends to predict the majority class more accurately, leading to high accuracy for the majority class but low accuracy for the minority class. Resampling techniques such as oversampling, undersampling, or a combination of both can be employed to address this. This research introduces a novel approach to balancing training data using a hybrid method that combines SMOTE (Synthetic Minority Oversampling Technique) and Tomek Links by applying this method to tuberculosis (TB) diagnosis data from Mayjend HM Ryacudu Kotabumi Hospital. We evaluate the Naive Bayes classifier's performance on the original and newly balanced data.  We used 826 patient data for training and 207 for testing out of 1,033. Of the 826 records in the training dataset, 306 patients had a TB diagnosis, whereas 520 patients did not. To achieve a better balance between the majority and minority classes, we oversampled 214 data in the minority class to match the number in the majority class. If necessary, we also reduce 214 data from the majority class. The results demonstrate that this hybrid approach significantly enhances the performance of the Naive Bayes model in terms of data balancing and overall accuracy. Specifically, the hybrid method achieves an average specificity of 96%, sensitivity of 88%, false positive fraction (FPF) of 4%, and false negative fraction (FNF) of 12%. These findings highlight the effectiveness of combining SMOTE and Tomek Links, providing a robust solution for improving classification performance in unbalanced datasets.Keywords: Naive Bayes classification; SMOTE; Tomek Links; SMOTE+Tomek Links; tuberculosis. AbstrakKlasifikasi Naive Bayes, yang didasarkan pada Teorema Bayes, adalah metode probabilistik dan statistik yang sudah mapan. Namun, metode ini sering menghadapi tantangan ketika berhadapan dengan kumpulan data yang memiliki distribusi kelas yang miring (tidak seimbang). Masalah umum pada data yang tidak seimbang adalah bahwa pengklasifikasi cenderung memprediksi kelas mayoritas dengan lebih akurat, yang mengarah pada akurasi tinggi untuk kelas mayoritas namun menghasilkan akurasi rendah untuk kelas minoritas. Untuk mengatasi masalah ini, teknik resampling seperti oversampling, undersampling, atau kombinasi keduanya dapat digunakan. Penelitian ini memperkenalkan pendekatan baru untuk menyeimbangkan data pelatihan menggunakan metode hibrida yang menggabungkan SMOTE (Synthetic Minority Oversampling Technique) dan Tomek Links. Dengan menerapkan metode ini pada data diagnosis tuberculosis (TB) dari Rumah Sakit Mayjend HM Ryacudu Kotabumi. Kami mengevaluasi kinerja pengklasifikasi Naive Bayes pada data yang tidak seimbang asli dan data yang sudah seimbang. Kami menggunakan 826 data pasien untuk pelatihan dan 207 untuk pengujian dari total 1.033. Dari 826 catatan dalam dataset pelatihan, 306 pasien didiagnosis dengan TB, sedangkan 520 pasien tidak. Untuk mencapai keseimbangan yang lebih baik antara kelas mayoritas dan minoritas, kami melakukan oversampling sebanyak 214 data pada kelas minoritas agar jumlahnya seimbang dengan kelas mayoritas. Selain itu, kami juga mengurangi 214 data dari kelas mayoritas. Hasilnya menunjukkan bahwa pendekatan hibrida ini secara signifikan meningkatkan kinerja model Naive Bayes dalam hal keseimbangan data dan akurasi keseluruhan. Secara spesifik, metode hibrida ini mencapai spesifisitas rata-rata sebesar 96%, sensitivitas sebesar 88%, fraksi positif palsu (FPF) sebesar 4%, dan fraksi negatif palsu (FNF) sebesar 12%. Temuan ini menyoroti efektivitas penggabungan SMOTE dan Tomek Links, serta memberikan solusi yang tangguh untuk meningkatkan kinerja klasifikasi di tengah kumpulan data yang tidak seimbang.Kata Kunci: klasifikasi Naive Bayes; SMOTE; Tomek Links; SMOTE+Tomek Links; tuberkulosis. 2020MSC: 68T05, 62R07.
Co-Authors Azis, Dorrah Aziz, Dorrah Deta Erviana Dorrah Aziz Faulina, Naflah Irfan, Miftahul Isa Ansori Junia Rahma Nur Imani MG Isworo Rukmi Muhammad Luthfi Mustofa Usman Mustofa Usman Nurhafifah, Fifi Nurmaita Hamsyiah Nusyirwan Pradana, Aditya Putra Rizki Agung Wibowo Ruby, Tiryono Saidi, Subian Samosir, Amril Saputri, Gustina Siti Nur Jannah Sunanto, Rahma Faelasofi Sutrisno, Agus Ulfa Putri Rahmani Veithzal Rivai Zainal Venelia, Hilda Wardhani Utami Dewi Warsono Warsono Widiarti Zakaria, La