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All Journal Jurnal Ilmu Dasar BULETIN PENELITIAN SISTEM KESEHATAN Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) JUITA : Jurnal Informatika Unisda Journal of Mathematics and Computer Science (UJMC) J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika JAST : Jurnal Aplikasi Sains dan Teknologi Mandalika Mathematics and Educations Journal Budimas : Jurnal Pengabdian Masyarakat Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) MATHunesa: Jurnal Ilmiah Matematika Southeast Asian Journal of Islamic Education UNP Journal of Statistics and Data Science Kanigara Indonesian Journal of Statistics and Its Applications Journal of Community Research and Engagement
Gangga Anuraga
Universitas PGRI Adi Buana Surabaya
Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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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.
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%.
PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017 Hardani Prisma Rizky; Wara Pramesti; Gangga Anuraga
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.563

Abstract

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).
PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI DIABETES MELITUS TIPE 2 DI INDONESIA TAHUN 2023 MENGGUNAKAN METODE REGRESI NONPARAMETRIK SPLINE TRUNCATED Katarina Rosa Leonida; Gangga Anuraga; Muhammad Athoillah; Artanti Indrasetianingsih
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p128-139

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruh Diabetes melitus tipe 2 (DM2) di Indonesia tahun 2023 dengan menggunakan metode regresi nonparametrik spline truncated. Metode ini dipilih karena fleksibilitasnya dalam menganalisis pola yang tidak linier dalam data melalui penggunaan fungsi spline dan tiitk knot. Pemilihan titik knot optimal dapat dilakukan dengan menggunakan metode Generalized Cross Validation (GCV). Data sekunder dalam penelitian ini diperoleh dari Badan Pusat Statistik (BPS) dan Survey Kesehatan Indonesia yang mencakup 38 provinsi di Indonesia. Lima variabel independen yang dianalisis meliputi proporsi konsumsi makanan manis, proporsi aktivitas fisik, proporsi konsumsi rokok tembakau, prevalensi penderita obesitas dan proporsi konsumsi makanan olahan. Hasil penelitian menunjukkan bahwa model regresi nonparametrik spline truncated terbaik adalah menggunakan tiga titik knot. Terdapat empat variabel dalam penelitian yang berpengaruh signifikan yaitu variabel proporsi konsumsi makanan manis, proporsi konsumsi rokok tembakau, prevalensi penderita obesitas, dan proporsi konsumsi makanan olahan. Kata Kunci: Diabetes Melitus Tipe 2, Regresi Nonparametrik Spline Truncated, Titik Knot, Indonesia
Geographically Weighted Machine Learning Model for Untangling Spatial Heterogeneity of Dengue Incidence in West Java Auralia Putri Astutiningsih; Gangga Anuraga; Hani Brilianti Rochmanto; Muhammad Athoillah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30181

Abstract

Dengue hemorrhagic fever continues to pose a significant public health challenge, particularly in West Java Province, Indonesia, which consistently reports the highest incidence rates in the country. This study examined the factors influencing dengue fever incidence using Random Forest Regression (RFR) and Geographically Weighted Random Forest (GWRF) methodologies. Utilizing secondary data from 2022 to 2024 across 27 districts/cities, the data from 2022 to 2023 served as training data, while the 2024 data were used for testing. The findings revealed that the optimal RFR model, with ntree = 1000 and mtry = 1, achieved an RMSE of 1796.409, a MAPE of 0.482, and an  of 0.685. Conversely, the GWRF model, which employed an adaptive kernel and an optimal bandwidth of 45 nearest neighbors, exhibited superior performance, with an RMSE of 1,756.713, MAPE of 0.466, and  of 0.700. This enhancement in the model performance suggests that spatial weighting improves the model's capacity to capture spatial heterogeneity. In addition, variations in local feature importance indicate spatial non-stationarity across regions. These results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.
Peningkatan Literasi Bioinformatika bagi Siswa Sekolah Menengah melalui Pelatihan Implementasi Sains Data Gangga Anuraga; Fenny Fitriani; Rabiatul Adawiyah; Diva Aprilia Trisha Utami; Laura Sekar Faramaysty
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.
Monitoring The Quality of Getcontact Reviews Using Integration Sentiment Analysis and Statistical Process Control Maria Kristina Lusia Geong; Hani Brilianti Rochmanto; Fenny Fitriani; Gangga Anuraga
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss3/504

Abstract

The protection of digital communications becomes important as cybercrime rises. Getcontact, a digital communication app that offers services ranging from caller identification to spam detection, has garnered a range of opinions on Google Play Store, which serve as a basis for evaluating service quality. This study aims to monitor Getcontact’s service quality through integration of sentiment analysis and control charts based on reviews. The research data consists of reviews obtained through web scraping during the period from January 1, 2023, to September 30, 2025. The data was classified using SVM and then analyzed using p-control charts and Laney p-control charts. The results show that the negative category dominates the reviews data. SVM achieved excellent performance with accuracy 98%, precision 98%, specificity 98%, sensitivity 97%, and F1-score 97%. Control charts indicate that the process is not yet fully under control, with the Laney p chart being more representative. Pareto chart shows that complaints are dominated by issues in number identification accuracy. It is concluded that Getcontact’s service quality still needs improvement. Integration of sentiment analysis and control charts is effective for continuous quality monitoring, with the Laney p chart being more suitable.
Co-Authors Achmad Chikham Nouriel Rosyadi Agus Sulistiono Alfisyahrina Hapsery Anggi Emeliani Antonito Hornay Cabral Anwardi Anwardi Arianto, Bagus Wicaksono Arief Triatmaja Permana Sadewa Auralia Putri Astutiningsih Bambang Widjanarko Otok Betty Roosihermiatie Diva Aprilia Trisha Utami Dwi Cahya Julia Kartikasari Edy Sulistiyawan, Edy Erika Zahra Fitriananta Fathur Rahman Fitriani, Fenny Hani Brilianti Rochmanto Hardani Prisma Rizky Harun Al Azies Indrasetianingsih, Artanti Intan Amelia Haryanto Izequela De Jesus Madeira Jerhi Wahyu Fernanda, Jerhi Wahyu Katarina Rosa Leonida Laura Sekar Faramaysty Lusia, Dwi Ayu M. A. Mauludin Madeira, Izequela De Jesus Maria Kristina Lusia Geong Mariana Yonasti Udus Muhammad Athoillah, Muhammad Permadina K. Arieska Pingky Febriyanti Pomalingo, Suwito pramesti, wara Pretesya Septiliani Syukur Putri Amelia Divaio Rabiatul Adawiyah Ratu Bunga Prawesti Arie Salim Renita Donasari, Renita Rezzy Eko Caraka Risa Mudji Istiawati Sakhinah Abu Bakar Saputri Dyah Pratiwi Silfia Febriani Jamlean Tety Rachmawati Vidila Rosalina Wara Pramesti Wara Pramesti Wara Pramesti