Article Per Year (5 Year)
p-Index From 2021 - 2026
1.168
P-Index
This Author published in this journals
All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Sains Dasar Indonesian Journal of Applied Statistics Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Jambura Journal of Informatics Jurnal Pengabdian kepada Masyarakat
Isnaini, Bayutama
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Analisis Sentimen Komentar YouTube terhadap Pemindahan Ibu Kota Negara Menggunakan Metode Naïve Bayes Huwaida, Shafira Faira; Kusumawati, Rosita; Isnaini, Bayutama
Jambura Journal of Informatics VOL 6, N0 1: APRIL 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v6i1.24718

Abstract

Pemindahan Ibu Kota Indonesia berupa pembangunan IKN Nusantara di Kabupaten Penajam Paser Utara, Kalimantan Timur dimulai pada pertengahan Maret 2022 dengan target pemindahan bertahap mulai tahun 2024 hingga 2045. Hal tersebut mengundang berbagai reaksi dari masyarakat hingga meramaikan media sosial termasuk YouTube. Penelitian ini menggunakan  algoritma  naïve bayes dengan dan tanpa teknik SMOTE untuk mengetahui sentimen pengguna YouTube tentang pemindahan ibu kota negara. Data yang digunakan berupa komentar dari video yang ada di YouTube. Terpilih tiga video teratas berdasarkan kriteria relevansi dengan keyword dan jumlah komentar. Tahapan pre-processing data meliputi drop duplicates, case folding, tokenizing, cleaning, stemming, convert slangword, removal stopword, dan drop missing value. Pelabelan data teks terbagi menjadi 3 kelas sentimen yaitu Positif, Netral, dan Negatif menggunakan bantuan kamus vader lexicon. Pembagian data train dan test dilakukan mengggunakan tiga perbandingan, yaitu 70%:30%, 80%:20%, dan 90%:10%. Hasil penelitian menunjukkan bahwa performa terbaik diperoleh dari penerapan SMOTE naive bayes pada perbandingan data train 90% dengan data test 10%. Model SMOTE naïve bayes mampu memberikan nilai balanced accuracy 76,01%, AUC Score 0,8711, dan G-mean 0,8089.
Workshop on Visual Data Analysis with R Program Wutsqa, Dhoriva Urwatul; Kismiantini, Kismiantini; Kusumawati, Rosita; Subekti, Retno; Setiawan, Ezra Putranda; Isnaini, Bayutama; Brilliant, Indira Ihnu
Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Vol. 8 No. 2 (2024): Vol 8, No 2 (2024)
Publisher : Yogyakarta State University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpmmp.v8i2.71583

Abstract

Statistics data analysis generally focuses more on mathematical procedures than visual. Visual analysis is very useful for research and this is still very limited to study at Universitas Mercu Buana Yogyakarta, so the UNY Statistics lecturer's service activity is holding visual data analysis workshop with the R program, where this program is open source and is complete for visual analysis. The material for this activity is about procedures and uses for visual data analysis, introduction to the R program, data management with the R program, visual data analysis for group descriptions and comparisons, and visual data analysis for relationships between variables. Evaluation of participants' ability to understand the material is measured through 14 questions with four Likert Scale responses. Based on 40 questionnaires, 27,86% answered "Strongly Agree", 71,96% "Agree", and 0,18% "Disagree" regarding understanding and applying visual data analysis techniques with the R program. Therefore, it can be concluded that the majority of participants could understand the workshop material and follow the training well.
IDENTIFIKASI FAKTOR YANG MEMENGARUHI JUMLAH KEMATIAN IBU DI PROVINSI JAWA TIMUR MENGGUNAKAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) Kurnilasari, Furry Anggi; Subekti, Retno; Isnaini, Bayutama
Jurnal Sains Dasar Vol 13, No 1 (2024): April 2024
Publisher : Faculty of Mathematics and Natural Science, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jsd.v13i1.72380

Abstract

Jumlah kematian ibu merupakan indikator kesehatan yang sangat penting dalam mengevaluasi efektivitas sistem pelayanan kesehatan maternal di suatu wilayah. Provinsi Jawa Timur merupakan salah satu provinsi yang memiliki tingkat kematian ibu tinggi. Penyebab jumlah kematian ibu pada suatu kabupaten/kota yang berdekatan dapat saling berkaitan dan menunjukkan adanya indikasi keberagaman karakteristik serta efek spasial pada wilayah-wilayah tersebut. Penelitian ini bertujuan untuk mengetahui pemodelan Geograpgically Weighted Negative Binomial Regression (GWNBR) pada jumlah kematian ibu dan faktor-faktor yang berpengaruh signifikan terhadap jumlah kematian ibu di kabupaten/kota Provinsi Jawa Timur.Data yang digunakan dalam penelitian ini adalah data sekunder dari website resmi Badan Pusat Statistik (BPS) Provinsi Jawa Timur dan publikasi Profil Kesehatan Provinsi Jawa Timur tahun 2022 yang terdiri dari 29 kabupaten dan 9 kota. Variabel respon yang digunakan adalah jumlah kematian ibu dan beberapa variabel prediktor seperti ibu nifas mendapat vitamin A, ibu hamil K1, ibu hamil K4, KB aktif, dan air minum layak. Metode analisis spasial yang digunakan yaitu dengan pendekatan GWNBR.Hasil penelitian berdasarkan model GWNBR menggunakan pembobot terpilih yaitu adaptive bisquare kernel menghasilkan 9 pengelompokkan kabupaten/kota berdasarkan variabel yang signifikan memengaruhi jumlah kematian ibu. Beberapa variabel yang berpengaruh secara lokal adalah ibu hamil K1, ibu hamil K4, KB aktif, dan air minum layak, sedangkan variabel yang berpengaruh secara global adalah ibu nifas mendapat vitamin A.
PENINGKATAN LITERASI STATISTIKA : MEWUJUDKAN SANTRI CERDAS SEBAGAI UPAYA OPTIMALISASI ZAKAT DAN PEMBERDAYAAN POTENSI UMMAT Slamet, Isnandar; Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Sugiyanto, Sugiyanto; Susanto, Irwan; Isnaini, Bayutama; Afanyn Khoirunissa, Husna; Adi Wicaksono, Nanda; Indra Raditya , Dionisius
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

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

Abstract

Pemberdayaan Potensi Ummat”. Tujuan utama kegiatan adalah membekali peserta dengan pengetahuan dasar statistika sebagai alat berpikir rasional dan analitis, serta memperkuat kesadaran akan kewajiban dan keutamaan (fadhilah) zakat dalam kehidupan sosial-keagamaan. Kegiatan diikuti oleh 114 peserta, terdiri dari 102 santri dan 12 ustadz. Materi yang disampaikan meliputi statistika dasar, konsep kewajiban zakat menurut syariat Islam, serta fadhilah zakat dalam rangka pemberdayaan umat. Tim pengabdian berasal dari Grup Riset Statistika dan Sains Data Bidang Industri dan Ekonomi, Universitas Sebelas Maret (UNS). Metode pelaksanaan meliputi pre-test, penyampaian materi secara interaktif, praktik pengolahan data sederhana, diskusi aplikatif, dan post-test. Hasil evaluasi menunjukkan peningkatan signifikan pada pemahaman peserta terhadap materi yang disampaikan. Kegiatan ini diharapkan menjadi langkah awal dalam membentuk generasi santri yang cerdas secara statistik, sadar zakat, dan siap berkontribusi dalam penguatan ekonomi umat berbasis pesantren.
Exploring Spatial Nonstationarity in the Number of Motor Vehicles in East Java Using Robust Geographically Weighted Regression with an MM-Estimator Isnaini, Bayutama; Isnandar Slamet; Sulandari, Winita; Khoirunissa, Husna Afanyn
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.90866

Abstract

The number of motor vehicles in each region is quite diverse, not least in East Java Province. The variety of number of motor vehicles can be affected by local factors that are important to study so that vehicle growth can be adequately anticipated. On the other hand, economic growth incentives are influenced by people's purchasing power. In addition, motor vehicles are one of the essential things in the sustainability of economic activities. This study aims to evaluate a robust, geographically weighted regression model with an MM-estimator (RGWR MM-estimator), which is considered suitable for analyzing the number of motor vehicles in East Java. The results showed that the RGWR MM-estimator model generates an estimate of the number of motor vehicles based on the HDI explanatory variables, road length, sex ratio, poverty gap index, and the number of colleges that is accurate compared to other models formed. In addition, there are significant differences in the influence of the five explanatory variables in each region. Districts/cities located near the capital of East Java Province tend to have many explanatory variables that have a significant effect compared to regencies/cities far from the provincial capital.
Analisis Faktor-Faktor Penyebab Inflasi di Indonesia Menggunakan Regresi Ridge, LASSO, dan Elastic-Net Khoirunissa, Husna Afanyn; Wijaya, Andreas Rony; Isnaini, Bayutama; Ferawati, Kiki
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.96921

Abstract

The economic condition of a country can be measured using one of the indicators, the inflation rate. Therefore, the inflation needs to be maintained so that its rate can be controlled. To support this, it is necessary to pay attention to several factors that influence the inflation rate. These factors include the amount of exports, imports, narrow money (M1), broad money (M2), the rupiah exchange rate against the USD, interest rates, rice prices in wholesale trade, farmer exchange rates (NTP), world crude oil prices, bank investment credit, GDP, and foreign exchange reserves. In this study, we analyze the significant factors influencing the inflation rate in Indonesia using the best model of the Ridge regression, LASSO regression, and Elastic-Net methods. In this modeling, the γ and λ values from the three methods are optimized first. The data used in this study consist of inflation data in Indonesia and its factors for 2020-2024, sourced from the BPS. Among the three high-dimensional data methods, the LASSO regression is the best method with the smallest MSE for modeling inflation data in Indonesia. The LASSO regression model produces 8 predictor variables that significantly influence inflation data, i.e., imports, M1, interest rates, and world crude oil prices with positive coefficient signs, as well as rice price variables in wholesale trade, NTP, GDP, and foreign exchange reserves with negative coefficient signs.Keywords: inflation; ridge regression; lasso regression; elastic-net.
Analisis Pertumbuhan Ekonomi Provinsi Jawa Timur Beserta Faktor-Faktornya Menggunakan Model Robust Geographically Weighted Regression-Pendugaan M Mushaharah, Halimah Nur; Kusumawati, Rosita; Isnaini, Bayutama
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.90381

Abstract

Economic growth is a successfull benchmark of nation-building and plays a role in a nation’ welfare. Economic growth in districts/cities measured by gross regional domestic product at constant prices (GRDP constant prices). East Java is the second province after DKI Jakarta as the biggest contributor to the Indonesian economy in 2022 with  economic growth rate values calculated by GRDP constant prices. However, East Java’s economic growth has gaps. As 23 of 38 districts/cities in East Java have economic growth values lower than national economic growth. Glaring gaps could be identified as an outlier and it could happen by many factors. This study aims to model economic growth and its factors with a robust analysis model against an outlier called robust geographically weighted regression-M estimation (RGWR-M). This study used secondary data from BPS Provinsi Jawa Timur and KEMENKEU’s SIKD data portal namely HDI, public health center, population density, economic activity, capital expenditure, and GRDP constant prices in East Java Province in 2022. The result showed that RGWR-M is the best model when an outlier is detected compared to OLS and GWR in analyzing factors that are suspected to affect economic growth in East Java Province in 2022 with  MAD’s values. RGWR-M model produces six mapping groups of significantly influential variables and one group of uninfluential variables. The groups formed to have different parameter estimation results in each district/cities.Keywords: GRDP constant prices; outlier; RGWR-M.
Co-Authors Adi Wicaksono, Nanda Andreas Rony Wijaya Brilliant, Indira Ihnu Dhoriva Urwatul Wutsqa Ezra Putranda Setiawan Husna Afanyn Khoirunissa Huwaida, Shafira Faira Indra Raditya , Dionisius Irwan Susanto Isnandar Slamet Isnandar Slamet, Isnandar Kiki Ferawati Kismiantini Kismiantini Kurnilasari, Furry Anggi Mushaharah, Halimah Nur Retno Subekti Rosita Kusumawati Sri Subanti Sugiyanto - Winita Sulandari Zukhronah, Etik