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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 Indonesian Journal of Applied Statistics International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia Majalah Ilmiah Matematika dan Statistika (MIMS) JURNAL BIOSHELL Jurnal Ilmiah Matematika Pancaran Pendidikan JPMA Jurnal Statistika dan Komputasi (STATKOM) Jurnal Hasil Pengabdian Kepada Masyarakat Universitas Jember
I Made Tirta
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Jember
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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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”.
Peningkatan Literasi Statistik dan Pemanfaatan Data Kriminalitas melalui Model GWR di Jawa Tengah dan D.I. Yogyakarta Paramitha, Luckyta Citra Ayu; Dewi, Yuliani Setia; Tirta, I Made; Hadi, Alfian Futuhul
Jurnal Hasil Pengabdian kepada Masyarakat Universitas Jember Vol 4 No 1 (2025): Jurnal Hasil Pengabdian Kepada Masyarakat Universitas Jember
Publisher : LP2M Universitas Jember

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

Abstract

Crime is a complex problem influenced by various structural and cultural factors, such as economic, social, and demographic conditions. Based on the 2019 Crime Statistics data published by BPS, Central Java and D.I. Yogyakarta Provinces are among the 15 provinces with the highest crime rates in Indonesia in 2018. This community service activity aims to convey the results of spatial crime analysis to local governments and the community through an applied statistical approach, especially using the Geographically Weighted Regression (GWR) method. The analysis was carried out to identify factors that locally influence crime rates, as well as to provide information that can be used as a basis for formulating data-based policies. The GWR model applied shows spatial variation in the influence of variables on crime rates. This model is better than the ordinary linear regression (OLS) model. The results show that the population density variable (X2) has a significant effect on crime rates in all districts/cities. Based on the similarity of variables that significantly affect crime, six groups of districts/cities were formed. This activity is expected to encourage wider use of open government data and increase the capacity of local policy makers in using statistical approaches for more responsive and targeted development planning.  
Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor Wilujeng, Dian Tri; Fatekurohman, Mohamat; Tirta, I Made
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.58426

Abstract

Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms.  Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning.  One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN).  The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data.  The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit.  The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN. Keywords: Machine learning, KNN, NWKNN
Co-Authors A As’ad Agus Isdarmawan Agustina Pradjaningsih Alfian Futuhul Hadi Anak Agung Gede Sugianthara Andina Ishmah Almira, Andina Ishmah Ani Rimadani Anisa Fitri Anjani 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 Dian Anggraeni Elsa Yuli Kurniawati Erfan Syahuri Habibi, Azwar Haeruddin Haeruddin Haeruddin Haeruddin Holipah, Holipah Ikhsanul Halikin, Ikhsanul Indriasih Yanuwijaya Istiqomah, Fikriana Nur Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Khoirunnisa, Wilda Kurniawati, Elsa Yuli Kusbudiono Kusbudiono, Kusbudiono Lutfi Ardining Tyas M Fatekurrahman M. Isnaini Marino Marino, Marino Maulidani, Noor Dyah Miftahul Ulum Mohamad Fatekurohman Muhammad Lutfi Asy’ari Mutiarasari, Yusna Nawal Ika Susanti, Nawal Ika Paramitha, Luckyta Citra Ayu Ridlo, Zainur Rasyid Rizka Dwi Hidayati Robiatul Adawiyah Robiatul Adawiyah Romarizka, Cyndy Sholiha, Aminatus T C P Utama Tika Clarinta Putri Utama Wilujeng, Dian Tri Yeni Rismawati Yuliani Setia Dewi Zainur Rasyid Ridlo