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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
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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IMPELEMENTASI K-NEAREST NEIGHBORS, DECISION TREE DAN SUPPORT VECTOR MECHINE PADA DATA DIABETES Irfan, Miftahul; Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
Jurnal Mahasiswa Ilmu Komputer Vol. 4 No. 2 (2023): Jurnal Mahasiswa Ilmu Komputer October 2023
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v4i2.4007

Abstract

Diabetes merupakan salah satu penyakit yang menjadi penyebab kematian terbesar didunia. Kasus kematiannya pun tercatat lebih dari 4 juta pada tahun 2019. Diabetes juga dapat menyebabkan timbulnya penyakit lainnya. Bahaya diabetes ini menjadi perhatian khusus WHO. Seiring dengan perkembangan teknologi ini, banyak sekali kolaborasi antara bidang kesehatan, statistic dan computer untuk menanggulangi berbagai macam penyakit. Algortima machine learning menjadi popular dalam proses klasifikasi data dan sudah banyak diterapkan pada data kesehatan. Dengan begitu pada artikel ini akan dilakukan perbandingan algoritma machine learning KNN, Decision Tree, dan SVM untuk melihat algortima mana yang paling cocok untuk klasifikasi data diabetes. Hasil menunjukkan bahwa KNN dan SVM memiliki akurasi yang cukup besar yaitu 81,13%. Sehingga kedua algortima tersebut dapat menjadi rekomendasi proses klasifikasi data diabetes sehingga dapat membantu dokter dalam menanggulangi penyakit diabetes. Hasil ini juga menunjukkan bahwa 8 variabel yang digunakan berpengaruh terhadap resiko diabetes
PELATIHAN STATISTIKA DESKRIPTIF UNTUK DATA ADMINISTRATIF DI KECAMATAN TELUK BETUNG BARAT KOTA BANDAR LAMPUNG Herawati, Netti; Nisa, khoirin; saidi, subian; Sutrisno, Agus; Azis, Dorrah; Zakaria, La
BUGUH: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 4 No. 1 (2024): Maret 2024
Publisher : Badan Pelaksana Kuliah Kerja Nyata Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/buguh.v4n1.2299

Abstract

Data administratif merupakan kumpulan data yang memiliki sifat nyata tentang sesuatu yang dianggap penting dan disimpan secara sistematis dalam sebuah sistem untuk mendapatkan suatu informasi tentang suatu hal yang berhubungan dalam ruang lingkup tertentu. Catatan administrasi dijaga untuk mendisiplinkan arah barang dan informasi dan dapat digunakan untuk tujuan pengambilan keputusan tertentu, sehingga adanya satuan identitas yang memiliki kesesuaian dengan catatan tertentu sangat penting. Untuk itu, perlu dilakukan analisis terhadap data yang sudah diperoleh agar agar masyarakat dapat melihat gambaran jelas terkait data yang diperoleh dan juga memudahkan perangkat daerah untuk mengambil keputusan terkait kebijakan yang akan diambil. Oleh karena itu akan dilakukan pelatihan dan sosialisasi terkait pelatihan analisis data deskriptif bagi perangkat daerah di kecamatan teluk betung barat kota bandar lampung. Sehingga diharapkan kedepannya gambaran terkait data dapat dipublikasikan dalam bentuk yang mudah untuk dipahami yaitu dengan statistika deskriptif.
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.
Robust Clustering of Open Access Journal Based on Scopus Journal Metrics Database Wibowo, Rizki Agung; Nisa, Khoirin; Samosir, Amril
Lentera Pustaka: Jurnal Kajian Ilmu Perpustakaan, Informasi dan Kearsipan Vol 10, No 2 (2024): December
Publisher : Library and Information Science Study Program, Faculty of Humanities, Univ. Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/lenpust.v10i2.68282

Abstract

Background: Open-access is free online access to articles, journal, conferences proceedings, book series and trade journal which provides unrestricted and permit the users to read, download, print, copy and link to the articles. Many articles that discuss the journal metrics using basic statistical methods to discribe the journal.Objective: This research groups journals based on numerical quality measures, identifying quality characteristics for each group. The findings provide a reference for researchers to select suitable journals and for journal owners to improve journal quality.Methods: There is another method to describe the open-access journal by grouping it into groups with the homogeneous characteristics based on five types of numerical quality measure that are analyzed simultaneously, namely cluster analysis. By using cluster analysis, the article’s owner can determine which journals he can choose to publish it in according to the desired journal quality. Based in the result, 5146 open-access journals can be divided into four clusters by using CLARA algorithm. Cluster 1, 2 and 3 have high characteristics in all numerical quality measure and cluster 4 have low characteristics in all numerical quality measure. So that researchers can choose journals in clusters 1, 2, and 3 as a place to publish their research by adjusting the journal's scope.Results: This study demonstrates that the CLARA algorithm successfully grouped 5146 open-access journals indexed by SCOPUS into four clusters based on quality characteristics. Cluster 1 consists of 39 journals with high values across all quality variables, Cluster 2 includes 50 journals with similarly high values, Cluster 3 contains 430 journals with comparable characteristics, and Cluster 4, comprising 4627 journals, exhibits low values in all quality variables. Furthermore, the majority of journals (89.914%) have numerical quality measures below the average.Conclusion: This study concludes that journals in Clusters 1, 2, and 3 can be recommended as suitable options for researchers to publish their work, considering the relevance of the journal's scope. Additionally, these findings can serve as a reference for journal owners to improve the quality of their journals to meet higher standards.
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.
Co-Authors Agus Sutrisno Azis, Dorrah Aziz, Dorrah Faulina, Naflah Irfan, Miftahul Isa Ansori La Zakaria MG Isworo Rukmi Misgiyati Muhammad Luthfi Mustofa Usman Netti Herawat Rizki Agung Wibowo Ruby, Tiryono Saidi, Subian Samosir, Amril Saputri, Gustina Siti Nur Jannah Sunanto, Rahma Faelasofi Sutrisno, Agus Venelia, Hilda Wardhani Utami Dewi Warsono Warsono Zakaria, La