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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan INTEGRITAS : Jurnal Pengabdian Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Mitra Teras: Jurnal Terapan Pengabdian Masyarakat
Ladayya, Faroh
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Penerapan Regresi Conway Maxwell Poisson untuk Mengatasi Overdispersi pada Jumlah Kematian Bayi di Provinsi Jawa Barat Santi, Vera Maya; Kamil, Adine Ihsan; Ladayya, Faroh
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.31356

Abstract

Infant Mortality Rate (IMR) is a key public health indicator reflecting the social, economic, environmental, and healthcare service quality conditions of a population. In 2023, West Java recorded the highest number of infant deaths in Indonesia. These data are count-type in nature and are commonly analyzed using Poisson regression. However, due to the frequent occurrence of overdispersion, the Poisson method becomes less appropriate. As an alternative, the Conway-Maxwell Poisson (CMP) regression is employed, offering greater flexibility in handling violations of the equidispersion assumption. This study aims to apply CMP regression to address overdispersion in the number of infant deaths in West Java Province using the Maximum Likelihood (ML) estimation method. The data used in this study comprise the total number of infant deaths in 2023 across 27 districts and cities in West Java Province. The ML parameter estimation analysis shows that the dispersion parameter values obtained from the CMP and Poisson models are 10.92 and 126.49, respectively. In terms of model evaluation criteria, the CMP model yields an AIC of 402.455 and BIC of 415.41, whereas the Poisson model shows an AIC of 4183.46 and BIC of 4195.12. These results indicate that the CMP model outperforms the Poisson model in handling infant mortality data. Furthermore, four variables are found to be statistically significant in explaining the number of infant deaths in West Java Province, namely the percentage of antenatal care coverage (K4), the number of health facilities by district/city, the percentage of households with clean and healthy living behavior (PHBS), and the percentage of neonatal asphyxia complications, with a significance level of alpha = 5%.
MULTILEVEL REGRESSION WITH MAXIMUM LIKELIHOOD AND RESTRICTED MAXIMUM LIKELIHOOD METHOD IN ANALYZING INDONESIAN READING LITERACY SCORES Santi, Vera Maya; Kamilia, Rifa; Ladayya, Faroh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.412 KB) | DOI: 10.30598/barekengvol16iss4pp1423-1432

Abstract

The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure. The problem with this data structure is that individuals in the same group tend to have the same characteristics, so the observations at lower levels are not independent. Education research often produces a hierarchical structure, one of which is PISA data, where students as level-1 nested within schools as level-2. In the PISA 2018 survey, reading literacy is the main focus. The data are sourced from the Organisation for Economic Co-operation and Development (OECD). The survey results show that the reading literacy scores of Indonesian students have decreased, thus placing Indonesia at 74th out of 79 countries. However, it is still very rare to research the reading literacy of Indonesian students' using a multilevel regression model. This study aims to apply a multilevel regression model to determine the factors influencing Indonesian reading literacy scores in PISA 2018 survey data. The results of this study indicate that the factors that influence response variable are gender, grade level, mother's education, facilities at home, age at school entry, student discipline behavior at school, and failing grade, while at the school level are the type of school and school location. The magnitude variance of student reading literacy scores can be explained by the explanatory variables the student level is 11,42% and the school level is 60,66%, while the rest is explained by another factor outside the study.
Pelatihan Analisis One-Way Anova dalam Rangka peningkatan Kualitas Penelitian Guru di Wilayah Kabupaten Kepulauan Seribu Ladayya, Faroh; Handayani, Dian; Meganingtyas, Devi Eka Wardani; Kameela, Ishmah Azzah; Kamil, Adine Ihsan; Madani, Zikri Muhammad
Mitra Teras: Jurnal Terapan Pengabdian Masyarakat Vol. 2 No. 2 (2023): Mitra Teras: Jurnal Terapan Pengabdian Masyarakat, Volume 2 Nomor 2, Desember 2
Publisher : MJI Publisher by PT Mitra Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58797/teras.0202.03

Abstract

Abstract Kepulauan Seribu has high enrollment rates, namely 99.51% for elementary school, 98.91% for junior high school, and 75.66% for senior high school. The high of enrollment rates needs to be supported by great quality of teachers as educators. Conducting research and writing papers is one way to improve the quality of teachers. Research in education can improve the pedagogic competence and professionalism of teachers and enhance knowledge. In conducting research, statistical analysis is important but many teachers still have problems when it comes to statistical analysis. The One Way ANOVA method is a statistical method used to compare the mean of more than two data groups. This method has been applied in many studies, including research on education. Consider the importance of doing research and writing papers for teachers and how this method can be applied, One Way ANOVA training was carried out in Kepulauan Seribu. Based on the questionnaire before and after the training, the results represented that the training was able to improve the participants' abilities in analysis using the One Way ANOVA. Participants felt they had gained new knowledge, understood the material well, were motivated for further learning, and received new ideas for developing research in education. Abstrak Angka Partisipasi Sekolah di Kepulauan seribu termasuk tinggi yaitu 99,51% untuk jenjang SD, 98,91% untuk jenjang SMP, dan 75,66% untuk jenjang SMA. Tingginya APS perlu diimbangi dengan baiknya kualitas guru sebagai pengajar. Pembuatan karya tulis ilmiah adalah salah satu cara untuk meningkatkan kualitas guru sebagai pendidik. Penelitian dapat meningkatkan kompetensi pedagogik dan profesionalitas guru, serta memperluas cakrawala ilmu pengetahuan. Analisis statistik memegang peranan penting dalam penyusunan penelitian namun masih banyak guru yang terkendala. Metode One Way ANOVA adalah salah satu metode statistika yang digunakan untuk membandingkan lebih dari dua kelompok data. One Way Anova mampu menguji kemampuan dari signifikansi hasil penelitian. Metode ini telah diterapkan pada banyak penelitian termasuk penelitian tentang pendidikan. Melihat pentingnya penulisan karya tulis ilmiah bagi guru serta bagaimana metode One Way ANOVA dapat diterapkan maka dilaksanakan pelatihan One Way ANOVA di Wilayah Kepulauan Seribu. Berdasarkan kuesioner sebelum dan sesudah pelatihan didapatkan hasil bahwa pelatihan One Way Anova ini mampu meningkatkan kemampuan peserta dalam analisis menggunakan metode One Way Anova. Hal ini ditunjukan dengan hasil kuesioner setelah pelatihan yang nilainya lebih tinggi dari pada sebelum pelatihan. Peserta merasa mendapatkan pengetahuan baru, memahami materi dengan baik, termotivasi untuk pembelajaran lanjutan, dan mendapat ide baru untuk pengembangan karya ilmiah.
Pelatihan Konsep Dasar Statistika dan Penyajian Data untuk Meningkatkan Literasi Statistika Siswa SMP di Kabupaten Sukabumi Ladayya, Faroh; Handayani , Dian; Rahayu, Widyanti; Meganingtyas, Devi Eka Wardani; Kemalasari, Erin Naudy; Rahmadani , Zahra Ayu
Mitra Teras: Jurnal Terapan Pengabdian Masyarakat Vol. 3 No. 2 (2024): Mitra Teras: Jurnal Terapan Pengabdian Masyarakat, Volume 3 Nomor 2, Desember 2
Publisher : MJI Publisher by PT Mitra Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58797/teras.0302.06

Abstract

Statistika dan peluang merupakan salah satu materi yang dipelajari dalam pelajaran matematika pada jenjang Sekolah Menengah Pertama. Pentingnya pengetahuan tentang statistika menjadikannya sebagai salah satu materi pada kurikulum. Siswa diharapkan mampu mengolah, menginterpretasi, dan menyajikan data hasil pengamatan. Berdasarkan nilai assesmen nasional siswa nasional didapatkan hasil bahwa persentase siswa yang memenuhi kompetensi minimum pada literasi numerik hanya 32,29% , walaupun memenuhi target nasional namun angka tersebut masih sangat kecil. Diperlukan inovasi pada penyampaian materi matematika khususnya statistika pada jenjang SMP. Penggunaan alat peraga statistika dapat mempermudah siswa dalam memahami statistika terutama dalam penyajian data. Solusi dari permasalahan tersebut adalah menggunakan alat peraga “StatTools” guna meningkatkan kemampuan literasi statistika siswa SMP di Kabupaten Sukabumi. Pelatihan ini diselenggarakan dengan metode ceramah, demonstrasi, dan praktek. Guna mengukur keefektifan dari pelatihan, diberikan kuesioner sebelum dan sesudah pelatihan. Berdasarkan analisis yang dilakukan didapatkan hasil kuesioner setelah pelatihan yang nilainya lebih tinggi dari pada sebelum pelatihan. Peserta merasa mendapatkan pengetahuan baru, memahami materi dengan baik, dan termotivasi untuk pembelajaran lanjutan.
Pengembangan Kompetensi Guru Sekolah Dasar Melalui Telaah Miskonsepsi Materi Operasi Bilangan Pecahan Meganingtyas, Devi Eka Wardani; Ladayya, Faroh; Muktar, Bhayu Phermana Sachty; Azzahra, Rizka Anjani; Dj, Sitta Aliya
INTEGRITAS : Jurnal Pengabdian Vol 7 No 2 (2023): AGUSTUS - DESEMBER
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat - Universitas Abdurachman Saleh Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36841/integritas.v7i2.3688

Abstract

Salah satu permasalahan konsep dasar matematika yang ada di sekolah dasar adalah kesulitan dalam memahami konsep (miskonsepsi) materi operasi bilangan, khususnya bilangan pecahan. Oleh karena itu, diperlukan kegiatan kepada masyarakat dalam bentuk pelatihan kepada guru Sekolah Dasar (SD) untuk meningkatkan kompetensi guru sehingga dapat meminimalisir miskonsepsi yang dialami siswa. Pelaksanaan kegiatan dilakukan di wilayah Kota Bekasi dengan bekerja sama dengan mitra KKG Jatiasih Keota Bekasi. Berdasarkan hasil pengamatan guru, masih terdapat banyak miskonsepsi yang ditemui di sekolahnya terkait konsep materi bilangan pecahan dan operasinya.
LINEAR MIXED MODEL-LASSO WITH MLE AND REML ESTIMATION ON POVERTY DATA IN JAVA ISLAND Santi, Vera Maya; Indriana, Devi; Ladayya, Faroh; Tonah
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09202

Abstract

Poverty in Indonesia, especially in Java, remains a major challenge despite the island being the economic and political centre of the country. The government has made many efforts but has not been effective in overcoming poverty. The hierarchical structure of poverty data may cause higher-level clusters to be random effect. One approach that can be used to represent the relationship between the poverty rate in each regency/city in Java and the factors that influence it with the province as a random effect is a linear mixed model (LMM). The number of factors that can affect poverty results in multicollinearity. The application of LASSO is used in this study to overcome multicollinearity, select, and generate variables that are significant to poverty in Java. The data used in this study consists of 85 regencies and 34 cities in Java Island involving 20 independent variables. The results show that the factors that influence the poverty rate are average years of schooling, non-food expenditure, number of households with housing assets owned, percentage of households with a dirt floor, and percentage of households with PLN lighting. The LMM-LASSO is a linear model augmented with a LASSO penalty function to address multicollinearity and incorporates random effects into the model. This approach is suitable for modeling the poverty rate, as indicated by its smaller AIC and BIC values compared to the conventional linear mixed model. In addition, based on the ICC value, the province as a random effect contributes significantly to the variability of the data at the district/city observation level in Java Island.
PERFORMANCE EVALUATION OF WORD EMBEDDING TECHNIQUES IN TWITTER SENTIMENT ANALYSIS USING LSTM Ladayya, Faroh; Rahayu, Widyanti; Rohimah, Siti Rohmah; Saputra, Ferdiansyah Rizki; Maulana, Thoriq Akbar; Madinah, Najwa Nur
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09206

Abstract

Opinions expressed on social media can be used as feedback on a product, both goods and services. The sentiment analysis was utilized for analyzing opinions given by the public via social media. The sentiment contained in an opinion can be positive, negative, or neutral. This study aims to compare the performance of three word embedding techniques—Word2Vec, GloVe, and FastText—when combined with a Long Short-Term Memory (LSTM) model for sentiment classification of Indonesian Twitter data. LSTM was selected due to its ability to model sequential text data and capture long-term contextual dependencies that are often present in natural language. To enable sentiment classification using LSTM, textual data from social media were transformed into numerical vectors. Thus, the word embedding technique is used to convert text into a vector. The vector that had been obtained will be used as input for LSTM. All embeddings were evaluated under the same preprocessing pipeline and LSTM architecture to ensure a fair comparison. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC/AUC metrics. The results indicate that the LSTM model effectively captures sentiment patterns in Indonesian tweets, with Word2Vec achieving the best overall performance, followed by GloVe and FastText. These findings suggest that domain-adapted word embeddings remain highly effective for sentiment analysis in Indonesian social media contexts.
Co-Authors Azzahra, Rizka Anjani Dian Handayani Dj, Sitta Aliya Handayani , Dian Indriana, Devi Kameela, Ishmah Azzah Kamil, Adine Ihsan Kamilia, Rifa Kemalasari, Erin Naudy Madani, Zikri Muhammad Madinah, Najwa Nur Maulana, Thoriq Akbar Meganingtyas, Devi Eka Wardani Muktar, Bhayu Phermana Sachty Rahmadani , Zahra Ayu Rohimah, Siti Rohmah Saputra, Ferdiansyah Rizki Tonah Vera Maya Santi Widyanti Rahayu