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All Journal Jurnal Ilmu Dasar Jurnal Gaussian KADIKMA BERKALA SAINSTEK Tadris: Jurnal keguruan dan Ilmu Tarbiyah Prosiding Seminar Matematika dan Pendidikan Matematik Jurnas Nasional Teknologi dan Sistem Informasi UNEJ e-Proceeding International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia Majalah Ilmiah Matematika dan Statistika (MIMS) JURNAL BIOSHELL Pancaran Pendidikan Jurnal Statistika dan Komputasi (STATKOM)
I Made Tirta
Program Studi Magister Matematika, FMIPA, Universitas Jember
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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POLA-POLA JALUR PADA PATH ANALISYS UNTUK ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP NILAI UN SMA DI KABUPATEN LUMAJANG Isdarmawan, Agus; Tirta, I Made; Dewi, Yuliani Setia
Kadikma Vol 4 No 1 (2013): April 2013
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v4i1.1118

Abstract

Abstract. Path analysis is a technique to analyze the effect of free and bound variables in which every variable correlates or associates with cause and effect directly or indirectly. This study was conducted to determine some factors which influenced National Examination at Senior High School in Lumajang. The Data were analyzed using path analysis. The results of the study were explained as follows: 1. The correlation of variables in path analysis followed the pattern of direct, indirect and mixed. 2. Path analysis could be applied to the analysis of the relationship between exogenous variables (Practical Training (X1), Assignment (X2), and Daily Test (X3)) with endogenous variables (Mid-Term Test (Y1), Final-Term Test (Y2), and National Examination (Z)). Daily Test (X3) contributed directly to Mid-Term Test (Y1). On the other hand, Practical Training (X1) and Daily Test (X3) did not contribute significantly to the Final-Term Test (Y2). 3. Assignment (X2) has direct and indirect influence on National Examination (Z) through Final-Term Test (Y2). 4. Daily Test (X3) did not have a direct influence to Final-Term Test (Y2) but it had a direct impact either through National (Z or through Mid-Term Test (Y1) and Final-Term Test (Y2) which contributed 19.6% of the total site. The direct contribution of Mid-Term Test (Y1) to National Examination (Z) was the highest direct contribution in this study with 40% of the total site. While, the contribution of Practical Training (X1), Assignment (X2), Daily Test (X3), Mid-Term Test (Y1), and Final-Term Test (Y2) simultaneously influenced National Examination (Z) with 93.5% . Abaut 6.5% was influenced by the other factors which could not be described in this study. Key Words : National Examination, Path Analysis, Variable Exogenous, endogenous variables
Application of Black Scholes Method in Determining Agricultural Insurance Premium Based On Climate Index Using Historical Burn Analysis Method Sholiha, Aminatus; Fatekurohman, Mohamat; Tirta, I Made
BERKALA SAINSTEK Vol 9 No 3 (2021)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v9i3.22920

Abstract

Climate index insurance is an insurance that provides reimbursement for losses due to decreased harvest rates or crop failures caused by weather. The use of Historical Burn Analysis (HBA) method in determining climate index based on rainfall resulted in a concept of the agricultural insurance payment in Pasuruan Regency. The application of The Black Scholes method in determining agricultural insurance premiums is obtained when rainfall more than 17 mm the premium is Rp 221,234. If the rainfall are 13 mm ≥ RR < 17 mm, the nominal premium paid by farmers to the insurance party is Rp 147,489. Respondents in the study were farmers who owned rice fields. Instrument quality testing (questionnaire) using validity test and reliability test using the help of SPSS statistical software. It can be concluded that the questionnaire is valid and reliable. Based on the results of the questionnaire, farmers considered that the nominal agricultural insurance premiums are in accordance with farmers' income.
Comparison of Online and Offline Learning During The COVID-19 Pandemic using Naïve Bayes Method and C4.5 Aulia, Andini Cahya; Fatekurohman, Mohamat; Tirta, I Made
BERKALA SAINSTEK Vol 11 No 3 (2023)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v11i3.31737

Abstract

Learning is a process of interaction between educators and students who meet the elements of learning carried out in an educational environment, so that learning can develop student’s abilities, interests and talents optimally. In today's era learning is done online and inversely with offline. The purpose of this study is to analyze the comparison of percentages and classification results as well as the results of learning evaluations using the Naïve Bayes method and C4.5. This test is carried out with 4 variables and a comparison of the two methods. The results showed that the accuracy of Naïve Bayes was 74.07% and C4.5. of 77.77% so that the comparison results show that the level of accuracy of the C4.5 method is better than Naïve Bayes. The resulting importance variables are time and effectiveness as well as the results of the classification of learning decisions, namely the offline category as many as 16 data on the Naïve Bayes method and 19 data on the Decision Tree algorithm C4.5 method from 27 input testing data.
ANALISIS REGRESI KELAS LATEN UNTUK DATA KATEGORIK DENGAN SATU KOVARIAT Haeruddin, Haeruddin; Tirta, I Made; Dewi, Yuliani Setia
BERKALA SAINSTEK Vol 1 No 1 (2013)
Publisher : Universitas Jember

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

Abstract

Analisis regresi kelas laten merupakan analisis multivariat untuk data kategorik. Estimasi parameter pada analisis regresi kelas laten menggunakan algoritma EM (ekspektasi-maksimisasi) yang dilanjutkan dengan metode Newton-Raphson. Dalam penelitian ini, analisis regresi kelas laten digunakan untuk mengklasifikasikan responden berdasarkan persepsinya terhadap peluang (opportunity) dan ancaman (treath) bagi distributor produk Unilever, PT. Panahmas Dwitama Distrindo Regional Jember. Lamanya responden berlangganan terhadap distributor ini dijadikan sebagai kovariat. Hasil analisis menunjukkan bahwa berdasarkan persepsinya terhadap opportunity, responden dikelompokkan menjadi tiga kelompok, sedangkan terhadap treath dikelompokkan menjadi dua kelompok.
Analisis Ketahanan Hidup Pasien COVID-19 Menggunakan Pendekatan Multivariate Adaptive Regression Spline (MARS) Khoirunnisa, Wilda; Fatekurohman, Mohamat; Tirta, I Made
Jurnal Statistika dan Komputasi Vol. 3 No. 1 (2024): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v3i1.2700

Abstract

Latar   Belakang: Tahun 2019 dunia digemparkan dengan terjadinya penyebaran penyakit baru yaitu Coronavirus Disease 19 (COVID-19) yang merupakan penyakit menular disebabkan oleh jenis corona virus bernama Severe Acute Repiratory Syndrome Coronavirus 2 (SARS-CoV-2). Virus ini menyebabkan gangguan pada sistem pernapasan, infeksi paru-paru, pneumonia akut, bahkan kematian, sehingga dilakukan analisis ketahanan hidup pasien COVID-19. Tujuan: Mendapatkan model dan mengetahui faktor paling mempengaruhi ketahanan hidup pasien COVID-19 di RSD dr. Soebandi Jember berdasarkan variabel prediktor yang digunakan. Metode: Penelitian ini menggunakan metode pendekatan MARS untuk menganalisis data. Data yang digunakan yaitu data rekam medis pasien COVID-19 tahun 2020 – 2021 di RSD dr. Soebandi Jember. Hasil: Model MARS terbaik berdasarkan kombinasi Basis Function (BF), Maximum Interaction (MI), dan Minimum Observation (MO) yang bernilai masing-masing 24, 3, dan 0 dengan nilai Generalized Cross Validation (GCV) terkecil yaitu 0,135. Berdasarkan model MARS yang diperoleh, 7 dari 12 variabel prediktor yang digunakan berpengaruh pada ketahanan hidup pasien COVID-19 yaitu usia, jenis kelamin, status gagal napas, status hipertensi, status pneumonia, status koagulopati, dan status penyakit lainnya. Kesimpulan: Variabel yang paling mempengaruhi ketahanan hidup pasien COVID-19 di RSD dr. Soebandi menggunakan pendekatan MARS berdasarkan variabel prediktor yang digunakan adalah status gagal napas.  
Enhancing Students' Combinatorial Thinking for Graceful Coloring Problem: A STEM-Based, Research-Informed Approach in ATM Placement Adawiyah, Robiatul; Kristiana, Arika Indah; Dafik, Dafik; Asy’ari, Muhammad Lutfi; Tirta, I Made; Ridlo, Zainur Rasyid; Kurniawati, Elsa Yuli
Tadris: Jurnal Keguruan dan Ilmu Tarbiyah Vol 8 No 1 (2023): Tadris: Jurnal Keguruan dan Ilmu Tarbiyah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/tadris.v8i1.15176

Abstract

Combinatorial generalization thinking, a component of higher-order thinking skills, encompasses perception (pattern identification), expressions (pattern illustration), symbolic expressions (pattern formulation), and manipulation (combinatorial results application). Implementing a research-based learning (RBL) model with a Science, Technology, Engineering, and Mathematics (STEM) approach can effectively transform students' learning processes, promoting experiential learning through the integration of STEM elements. This study employs a mixed-method research design, combining quantitative and qualitative methodologies, to evaluate the impact of this RBL-STEM model on students' ability to solve graceful coloring problems, hence developing their combinatorial thinking skills. Two distinct classes, one experimental and one control, were analyzed for statistical homogeneity, normality, and independent t-test comparisons. Results indicated a significant post-test t-score difference between the two groups. Consequently, we conclude that the RBL model with a STEM approach significantly enhances students' combinatorial generalization thinking skills in solving graceful coloring problems. As this research provides empirical evidence of the effectiveness of a STEM-based RBL model, educators, and curriculum developers are encouraged to incorporate this approach into their instructional strategies for enhancing combinatorial thinking skills. Future research should consider various contexts and diverse student populations to further validate and generalize these findings.
GAMLSS application for modeling the level of open unemployment in East Java Maulidani, Noor Dyah; Tirta, I Made; Fatekurohman, Mohamat
Majalah Ilmiah Matematika dan Statistika Vol. 25 No. 1 (2025): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v25i1.51548

Abstract

This research analyzes the application of Generalized Additive Model for Location, Scale, and Shape (GAMLSS) using penalized spline smoothing and Rigby-Stasinopoulos (RS) algorithm for modeling open unemployment rate in East Java Province in 2022. Predictor variables in this research are labor force participation rate, average years of schooling, average wages, economic growth, and registered job vacancies. GAMLSS allows the estimation of several distribution parameters (location, scale, and shape) thereby providing a broader and more flexible approximation model. The number of parameters that can be estimated depends on the type of distribution that is suitable for the data. This research uses a penalized spline as a smoothing predictor variable for the nonparametric part. The RS algorithm is an iterative procedure developed for GAMLSS models and used to estimate model parameters efficiently. Several distributions were evaluated and Normal distribution was obtained as the most suitable with two parameters (𝜇,𝜎). The Normal distribution is chosen based on model evaluation standards Generalized Akaike Information Criterion (GAIC). The effectiveness of this model was further verified through significance test and stepwise procedure. The estimation results of the location parameter (𝜇) are modeled by economic growth, average years of schooling, and registered job vacancies with the identity link function, while the scale parameter (𝜎) is modeled by economic growth and average wage with the log link function.
Implementasi Random Forest Menggunakan SMOTE untuk Analisis Sentimen Ulasan Aplikasi Sister for Students UNEJ Anjani, Anisa Fitri; Anggraeni, Dian; Tirta, I Made
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 2 (2023): Agustus 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i2.2023.163-172

Abstract

Pendidikan di era digital sangat memanfaatkan teknologi dan informasi sebagai prasarana  pembelajaran melalui aplikasi milik perguruan tinggi tertenu. Sister for Students (SFS) merupakan aplikasi yang dikembangkan oleh UPT-TIK Universitas Jember yang memiliki peran sangat penting untuk menunjang kegiatan pembelajaran di Universitas Jember, sehingga perlu dilakukan analisis kualitas layanan aplikasi tersebut berdasarkan komentar oleh pengguna menggunakan analisis sentimen. Analisis sentimen merupakan klasifikasi teks yang dilakukan dengan tujuan memperoleh informasi dari pengguna mengenai kualitas layanan SFS. Masalah yang sering terjadi pada proses klasifikasi yaitu adanya data imbalance, salah satunya pada klasifikasi teks. SMOTE dilakukan untuk menangani data imbalance dengan cara membangkitkan data sintetis pada kelas minoritas, hal ini diharapkan agar kinerja klasifikasi lebih baik. Penelitian ini menggunakan metode klasifikasi Random Forest dan SMOTE dengan perbandingan proporsi splitting data  dan  untuk analisis sentimen pada ulasan aplikasi SFS. Data yang digunakan sebanyak 913 data dimana kelas positif sejumlah 363 dan negatif sejumlah 550. Hasil model terbaik yaitu model Random Forest menggunakan SMOTE dengan proporsi 90:10 dengan akurasi testing 98,9%, recall 100%, precision 96,7%, f1-score 98,3% dan nilai AUC sebesar 99,2%. Informasi yang diperoleh dari analisis sentimen SFS UNEJ diperoleh kata yang mengarah positif  yaitu “bagus”, “mantap”, “keren”, “bantu”, “lumayan”, “lebihbaik”, “mudah”, “unej” dan “suka”. Kata yang mengarah pada sentimen negatif yaitu “eror”, “tidakbisa”, “presensi”, “jelek”, “update”, “ribet”, “sulit”, “forceclose” dan “qrcode”.
Co-Authors A As’ad Agus Isdarmawan Agustina Pradjaningsih Anak Agung Gede Sugianthara Andina Ishmah Almira, Andina Ishmah Anjani, Anisa Fitri Arika Indah Kristiana Asy’ari, Muhammad Lutfi As’ad, A Aulia, Andini Cahya Budi Lestari Budi Lestari D. Dafik Dian Anggraeni Dian Anggraeni Erfan Syahuri Habibi, Azwar Haeruddin Haeruddin Haeruddin Haeruddin Holipah, Holipah Indriasih Yanuwijaya Istiqomah, Fikriana Nur Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Khoirunnisa, Wilda Kurniawati, Elsa Yuli Lutfi Ardining Tyas M Fatekurrahman M. Isnaini Marino Marino, Marino Maulidani, Noor Dyah Miftahul Ulum Mohamad Fatekurohman Mutiarasari, Yusna Nawal Ika Susanti, Nawal Ika Ridlo, Zainur Rasyid Rizka Dwi Hidayati Robiatul Adawiyah Romarizka, Cyndy Sholiha, Aminatus T C P Utama Tika Clarinta Putri Utama Yeni Rismawati Yuliani Setia Dewi