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All Journal Jurnal Ilmu Dasar Jurnal Statistika Universitas Muhammadiyah Semarang Unisda Journal of Mathematics and Computer Science (UJMC) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika JAST : Jurnal Aplikasi Sains dan Teknologi EIGEN MATHEMATICS JOURNAL Mandalika Mathematics and Educations Journal Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Southeast Asian Journal of Islamic Education Journal of Community Research and Engagement
Gangga Anuraga
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Classification of Underdeveloped Areas in Indonesia Using the SVM and k-NN Algorithms Al Azies, Harun; Anuraga, Gangga
Jurnal ILMU DASAR Vol 22 No 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v22i1.16928

Abstract

The determination or classification of underdeveloped areas essentially consists of classifying several observations taking into account existing indicators. The classification method used is K-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). This study aims to analyze the accuracy of the classification between SVM and k-NN algorithms in the classification of underdeveloped areas in Indonesia. The data source used in this study is secondary data obtained from the Central Bureau of Statistics (BPS). The data used are 514 districs and municipalities of Indonesia. After analysis, the conclusion is that there are 122 districs and municipalities that are left behind out of a total of 514 districs and municipalities in Indonesia. The most underdeveloped areas are on the island of Papua, followed by the areas of the islands of Bali and Nusa Tenggara, and Sulawesi. Based on the results of the classification of underdeveloped areas using the method SVM with the kernel RBF has the best results with the parameters C = 1 and γ = 0.05 while the results of the classification of underdeveloped areas using the method k-NN obtains the best results with k = 15 Based on the results of classification of underdeveloped areas using the SVM and the k-NN method, including the level of classification is very good. The two methods compared have the same precision value of 92.2% and can be used to determine the classification of underdeveloped areas. Keywords: classification, machine learning, supervised learning, underdeveloped areas.
Pengukuran Kualitas Pendidikan Kabupaten Sidoarjo pada Jenjang SMP dengan Structural Equation Modeling Fitriani, Fenny; Pramesti, Wara; Anuraga, Gangga
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 10 No 1 (2024): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Mathematics and Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v10i1.6624

Abstract

The better the quality of education in a country, it can be considered that the quality of human resources in the country is qualified and can be a good development support for the country. However, there is a gap in education in Indonesia. This gap also occurs at a more regional scope such as districts/cities. One of the districts/cities experiencing education gaps is Sidoarjo district. This gap is thought to be influenced by differences in the factors that shape the quality of education in each school. Therefore, it is necessary to study how much influence each factor has on the quality of education. This article explores the quality of education in Sidoarjo district using structural equation modeling (SEM) at the junior high school level. The use of SEM is based on its ability to analyze two or more variables that cannot be measured directly. From the results of the analysis, it was found that infrastructure and socioeconomic factors have a significant effect on education quality. Infrastructure factors have a greater effect on the quality of education when compared to socioeconomic factors
GenAI Acceptance Modeling in Islamic Higher Education: An Integration of TAM and EVT Using PLS-SEM Fernanda, Jerhi Wahyu; Donasari, Renita; Anuraga, Gangga; Rahman, Fathur
Southeast Asian Journal of Islamic Education Vol 7 No 2 (2024): Southeast Asian Journal of Islamic Education, December 2024
Publisher : Faculty of Education and Teacher Training of UINSI Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/sajie.v7i2.9815

Abstract

Generative artificial intelligence (GenAI) technology is currently receiving special attention and has numerous benefits. In the education field, this technology can help obtain information quickly to complete a thesis. This research aims to conduct GenAI Modeling based on the Technology Accepted Model (TAM) and Expected Value Theory (EVT) framework using the Partial Least Square Structural Equation Model (PLS-SEM). The research used primary data obtained from surveys. The population was all Tarbiyah faculty students who took a thesis in the Even Semester of the 2023/2024 academic year with a total of 1266. The sample in this research was 191 students who were completing their thesis and had used Gen AI technology to help complete their thesis. The sampling technique used cluster random sampling with a procedure of dividing students into 8 clusters based on the study program. The research instrument used a questionnaire consisting of 5 latent variables: Perceived Usefulness, Perceived Ease of Use, Intrinsic Motivation, Perceived Value, and Behavioral Intention to Use. The results of the analysis using the PLS-SEM method showed that Intrinsic Motivation has a significant relationship with Perceived Ease of Use, and Intrinsic Perceived Usefulness and Perceived Value have a significant relationship with Behavioral Intention to Use. These results show that students choose GenAI Technology to help complete their thesis based on its benefits, such as making it easier to prepare backgrounds, research instruments, and data analysis steps, as well as providing insight into knowledge related to the topic being researched. The research results imply the need for policies regarding the use of GenAI technology for theses so that students are wiser in using GenAI technology.
Pengelompokan Stunting Di Provinsi Nusa Tenggara Timur Menggunakan Finite Mixture Partial Least Square (FIMIX-PLS) Anuraga, Gangga; Madeira, Izequela De Jesus
Mandalika Mathematics and Educations Journal Vol 7 No 2 (2025): Edisi Juni
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i2.9020

Abstract

Stunting is a chronic nutritional problem that affects the growth and development of children in developing countries, including Indonesia. The prevalence of stunting in Indonesia in 2023 reached 21.5%, with East Nusa Tenggara (NTT) Province recording a significantly high rate of 37.9%. This study aims to analyze the factors influencing stunting among children under five in NTT Province in 2023 using the Finite Mixture Partial Least Square (FIMIX-PLS) approach. The factors analyzed include healthcare services, socioeconomic conditions, environment, and immunization. The data analysis technique involved modeling using Partial Least Squares-based Structural Equation Modeling (PLS-SEM), beginning with construct validity and reliability testing, followed by data segmentation using FIMIX-PLS to identify heterogeneity and classify districts/cities based on the pattern of relationships among latent variables. The results of the analysis indicate the presence of data heterogeneity across regions, with several indicators showing significant variation between areas. These findings are expected to provide deeper insights into the contributing factors of stunting and assist in formulating more effective policies to reduce stunting rates in NTT.
Pemodelan Kejadian Penyakit Tuberkulosis di Provinsi Jawa Barat Tahun 2023 Menggunakan Metode Geographically Weighted Negative Binomial Regression (GWNBR) Ratu Bunga Prawesti Arie Salim; Anuraga, Gangga
Mandalika Mathematics and Educations Journal Vol 7 No 2 (2025): Edisi Juni
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i2.9318

Abstract

Tuberculosis (TB) is the leading cause of death due to infection by the bacteria Mycobacterium tuberculosis, which can attack the lungs and other organs. Reducing TB rates is one of the main targets in the Sustainable Development Goals (SDGs). In 2023, Indonesia will be ranked second in the world for TB cases after India, with West Java Province as one of the main contributors experiencing a significant increase, namely 160,966 cases in the productive age group (≥15 years) and 50,993 cases in the children's group (0–14 years). This study aims to analyze the factors that influence the number of TB cases in West Java Province in the productive age group using the Geographically Weighted Negative Binomial Regression (GWNBR) method, which considers spatial aspects between regions and is able to handle overdispersion problems in count data. The six independent variables tested include population density, percentage of public places that meet health requirements, number of hospitals, percentage of the population who smoke, air quality index, and number of HIV sufferers. The modeling results using the GWNBR method with Fixed Kernel Gaussian weighting produced ten regional groups, each with different risk factor characteristics for the number of TB cases.
Integrative Bioinformatics and Statistical Approaches for Identifying Prognostic Biomarkers and Therapeutic Targets in Breast Cancer Zulhan Widya Baskara; Anuraga, Gangga; Anurogo, Dito; Fitriani, Fenny; Rochmanto, Hani Brilianti; Baskara, Zulhan Widya
Eigen Mathematics Journal Vol 8 No 1 (2025): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i1.277

Abstract

Breast cancer is a leading cause of cancer-related mortality worldwide, necessitating the identification of reliable biomarkers for prognosis and targeted therapy. This study employed an integrative bioinformatics and statistical approach to analyze differentially expressed genes (DEGs) in breast cancer using datasets GSE70947 and GSE22820 from the gene expression omnibus (GEO). A protein-protein interaction (PPI) network was constructed to identify hub genes, followed by functional enrichment analysis to determine their biological significance. Survival analysis using the KMplot database revealed that CDC45, KIF2C, CCNB1, KIF4A, CENPE, CHEK1, KIF15, AURKB, NCAPG, and HJURP were significantly associated with poor prognosis. These genes were primarily enriched in cell cycle regulation, mitotic spindle organization, and DNA damage response, highlighting their role in tumor progression. Among them, CCNB1, CHEK1, and AURKB were strongly linked to cell cycle progression and checkpoint regulation, while KIF2C and CENPE played essential roles in mitotic division. High expression levels of these genes correlated with reduced overall survival, suggesting their potential as prognostic biomarkers and therapeutic targets in breast cancer.These discoveries help us better understand how breast cancer develops and point to potential targets for tailored treatments.
Peningkatan Literasi Bioinformatika bagi Siswa Sekolah Menengah melalui Pelatihan Implementasi Sains Data Anuraga, Gangga; Fitriani, Fenny; Adawiyah, Rabiatul; Utami, Diva Aprilia Trisha; Faramaysty, Laura Sekar
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 9, No 1 (2025): EDISI JUNI 2025
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v9i1.7081

Abstract

Bioinformatics is an interdisciplinary field that integrates biology, statistics, and computer science to analyze large-scale biological data. In the context of secondary education, students' understanding of this concept is still minimal. This study aims to evaluate the effectiveness of a training on the Implementation of Data Science in Bioinformatics, organized by the Statistics Study Program at Universitas PGRI Adi Buana Surabaya as part of a community service activity. The training methodology used a hybrid approach combining offline and online sessions. Twelfth-grade science students from five partner high schools participated. The training materials covered the basics of statistics, an introduction to bioinformatics, and biological data analysis case studies. The training showed increased participants' conceptual understanding and interest in data science. Furthermore, active interaction between students and speakers demonstrated the success of the participatory approach in learning activities. This activity also created collaborative relationships between partner universities and schools, extending the educational impact to secondary education environments. This training demonstrates the importance of integrating bioinformatics in secondary education to prepare young people to face the challenges of data-driven science.ABSTRAK Bioinformatika merupakan bidang interdisipliner yang mengintegrasikan biologi, statistika, dan ilmu komputer untuk menganalisis data biologis dalam skala besar. Dalam konteks pendidikan menengah, pemahaman siswa terhadap konsep ini masih sangat terbatas. Penelitian ini bertujuan untuk mengevaluasi efektivitas pelatihan bertema Implementasi Sains Data pada Bidang Bioinformatika yang diselenggarakan oleh Program Studi Statistika Universitas PGRI Adi Buana Surabaya sebagai bagian dari kegiatan pengabdian kepada masyarakat. Metodologi pelatihan menggunakan pendekatan hybrid yang menggabungkan sesi luring dan daring. Siswa kelas XII jurusan IPA dari lima SMA mitra dilibatkan sebagai peserta. Materi pelatihan mencakup dasar-dasar statistika, pengenalan bioinformatika, serta studi kasus analisis data biologis. Hasil pelatihan menunjukkan adanya peningkatan pemahaman konseptual dan minat peserta terhadap bidang sains data. Selain itu, terjadi interaksi aktif antara siswa dan narasumber yang mencerminkan keberhasilan pendekatan partisipatif dalam kegiatan pembelajaran. Kegiatan ini juga menciptakan hubungan kolaboratif antara universitas dan sekolah mitra, memperluas dampak edukatif ke lingkungan pendidikan menengah. Pelatihan ini membuktikan pentingnya integrasi bioinformatika dalam pendidikan menengah untuk mempersiapkan generasi muda menghadapi tantangan ilmu pengetahuan berbasis data.
Sentiment Analysis of NU Online Applications Using Artificial Neural Network Lusia, Dwi Ayu; Anuraga, Gangga; Rahman, Fathur
Southeast Asian Journal of Islamic Education Vol 6 No 2 (2024): Southeast Asian Journal of Islamic Education, June 2024
Publisher : Faculty of Education and Teacher Training of UINSI Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/sajie.v6i2.8822

Abstract

The NU Online app on the Playstore serves the needs of Muslims, especially those in Islamic boarding schools, by providing information and services. Its success is gauged not just by the number of downloads or popularity but by the quality of user interactions and how well it meets user needs. Sentiment analysis of user reviews provides deeper insights into these aspects. This research focused on finding words influencing sentiment from NU online and producing the best performance of artificial neural networks. This study collected user reviews from the NU Online app between February 9, 2021, and May 31, 2024, totalling 12613 reviews. After preprocessing, 8546 reviews remained. Using the Indonesian Sentiment Lexicon (INSET), 66% of the reviews showed positive sentiment, 21% were neutral, and 13% were negative. The words "aplikasi" (application) and "nya" (its) appeared in the top three across all sentiment classes, while "fitur" (feature) was common in both positive and negative sentiments. For neutral sentiments, "nan" was frequently mentioned. The data were split into training and testing sets in an 80:20 ratio, preserving the proportions of each sentiment class. Sentiment analysis was performed using a neural network, with input neurons ranging from the top 10 words from each sentiment class to all words. Accuracy improved as more words were used, peaking at 0.95 for the top 1690 words, compared to 0.71 for the top 10 words. The findings highlight the importance of using a comprehensive set of words to train the ANN. Including more words significantly enhances the model's performance, indicating that a richer vocabulary captures sentiment nuances better.
FOSTERING CRITICAL THINKING IN BIVARIATE DATA ANALYSIS INSTRUCTION FOR SENIOR HIGH SCHOOL TEACHERS IN NGANJUK REGENCY Adawiyah, Rabiatul; Anuraga, Gangga; Sadewa, Arief Triatmaja Permana
Journal of Community Research and Engagement Vol. 2 No. 1 (2025): MAY
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/jcore.v2i1.1227

Abstract

Statistics education is a vital component of the learning process, particularly in fostering logical, analytical, and quantitative thinking skills. Within this context, critical thinking plays a crucial role. Critical thinking is defined as the ability to objectively analyze information, evaluate arguments, identify assumptions, and draw logical conclusions. The development of critical thinking skills in statistics education aligns closely with the demands of the 21st century. This seminar aims to positively impact educators and students, specifically high school teachers in Nganjuk Regency, through the subtopic "Critical Thinking in Bivariate Data Analysis Learning." This theme was selected due to its high relevance to contemporary needs and its potential to provide extensive insights for teachers regarding the importance of critical thinking in statistics education, especially bivariate data analysis. The seminar's objectives extend beyond providing technical knowledge, aiming also to cultivate a critical mindset among teachers. The seminar activities include preparatory stages, theoretical and practical approaches, case studies, and interactive sessions such as question-and-answer and feedback discussions designed to achieve the seminar’s primary goals. Throughout these phases, the seminar enhances teachers' understanding of the importance of raising awareness and developing students’ critical thinking skills in statistics education. Consequently, teachers can more effectively foster students' critical thinking abilities through statistics learning. Keywords: Critical Thinking; Bivariate; Learning; Community Service; High School
PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PREVALENSI BALITA STUNTING DI PROVINSI ACEH TAHUN 2022 Anggi Emeliani; Gangga Anuraga
Seminar Nasional Hasil Riset dan Pengabdian Vol. 7 (2025): Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Ke 7 Tahun 2025
Publisher : LPPM Universitas PGRI Adi Buana

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

Abstract

Indonesia merupakan negara berkembang yang memiliki berbagai macam masalah kesehatan salah satunya yaitu stunting. Angka prevalensi balita stunting negara Indonesia menempati urutan ke-27 dari 154 negara yang memiliki data stunting dan menjadi urutan ke-5 diantara negara negara di Asia berdasarkan UNICEF dan WHO tahun 2022. Prevalensi stunting negara Indonesia tahun 2022 sebesar 21,6% dan Provinsi Aceh menjadi angka stunting tertinggi ke-5 yaitu sebesar 31,2% berdasarkan data dari Studi Status Gizi Indonesia. Stunting merupakan masalah gizi kronis yang terjadi karena kekurangan asupan gizi dalam jangka waktu lama sehingga mengakibatkan terganggunya pertumbuhan pada balita. Tujuan penelitian ini untuk mengetahui pemodelan Geographically Weighted Regression dan faktor yang berpengaruh signifikan terhadap kasus prevalensi balita stunting pada kabupaten/kota di Provinsi Aceh tahun 2022. Penelitian ini menggunakan metode Geographically Weighted Regression (GWR) dengan fungsi pembobot Fixed Kernel Gaussian. Hasil penelitian menunjukkan variabel independen yang berpengaruh signifikan terhadap kasus prevalensi balita stunting di Provinsi Aceh menggunakan α = 10% yaitu persentase bayi diberi vitamin A (?1), persentase baduta yang pernah diberi asi (?3), persentase perempuan pernah kawin usia 15-49 tahun yang sedang menggunakan alat KB (?4), jumlah tenaga gizi (?5) dan jumlah posyandu (?6). Model GWR dapat memberikan hasil terbaik dengan nilai R2 sebesar 79,30% dibandingkan dengan model OLS sebesar 51,38%.
Co-Authors Achmad Chikham Nouriel Rosyadi Alfisyahrina Hapsery Anggi Emeliani Arianto, Bagus Wicaksono Bambang Widjanarko Otok Dito Anurogo, Dito Dwi Cahya Julia Kartikasari Edy Sulistiyawan, Edy Erika Zahra Fitriananta Faramaysty, Laura Sekar Fathur Rahman Fitriani, Fenny Hani Brilianti Rochmanto Harun Al Azies Intan Amelia Haryanto Izequela De Jesus Madeira Jerhi Wahyu Fernanda, Jerhi Wahyu Lusia, Dwi Ayu Madeira, Izequela De Jesus Permadina K. Arieska Pingky Febriyanti pramesti, wara Pretesya Septiliani Syukur Putri Amelia Divaio Rabiatul Adawiyah Ratu Bunga Prawesti Arie Salim Renita Donasari, Renita Risa Mudji Istiawati Sadewa, Arief Triatmaja Permana Saputri Dyah Pratiwi Utami, Diva Aprilia Trisha Zulhan Widya Baskara