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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Community Development Journal: Jurnal Pengabdian Masyarakat Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Journal of Advanced Technology and Multidiscipline (JATM)
Ratih Ardiati Ningrum
Universitas Airlangga
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Other

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Short birth intervals classification for Indonesia’s women Ratih Ardiati Ningrum; Indah Fahmiyah; Aretha Levi; Muhammad Axel Syahputra
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3432

Abstract

Birth interval is closely related to maternal and infant health. According to world health organization (WHO), the birth interval between two births is at least 33 months. This study is the first to discuss the short birth interval (SBI) in Indonesia and used data from the Indonesian Demographic and Health Surveys 2017 with a total of 34,200 respondents. Birth interval means the length of time between the birth of the first child and the second child. Categorized as SBI if the distance between births is less than 33 months. The variables used include mother's age, mother's age at first giving birth, father's age, household wealth, succeeding birth interval, breastfeeding status, child sex, residence, mother's education, health insurance, mother's working status, contraception used, child alive, total children, number of living children, and household members. Machine learning algorithms including logistic regression, Naïve Bayes, lazy locally weighted learning (LWL), and sequential minimal optimization (SMO) are applied to classify SBI. Based on the values of accuracy, precision, recall, F-score, matthews correlation coefficient (MCC), receiver operator characteristic (ROC) area, precision-recall curve (PRC) area, the Naïve Bayes is the best algorithm with scores obtained 0.891, 0.889, 0.891, 0.885, 0.687, 0.972, and 0.960 respectively. Additionally, 18.25% of mothers were classified as still giving birth within a short interval.
Flagging clickbait in Indonesian online news websites using fine-tuned transformers Muhammad Noor Fakhruzzaman; Sa'idah Zahrotul Jannah; Ratih Ardiati Ningrum; Indah Fahmiyah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2921-2930

Abstract

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding layer is then combined with a 100 node-hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6,632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross-validation, it has an accuracy score of 0.914, an F1-score of 0.914, a precision score of 0.916, and a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. The usage of multilingual BERT in the Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of clickbait detection are discussed.
ESKALASI KEMAMPUAN MENGOLAH DATA BAGI KADER DESA MENGGUNAKAN TEKNOLOGI INFORMASI Ratih Ardiati Ningrum; Chandrawati Putri Wulandari; Mohammad Ghani; Dwi Rantini; Fikri Arif Abdillah; Adam Maurizio Winata
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2023): Volume 4 Nomor 1 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i1.12130

Abstract

Peran penting pengolahan data semakin disadari karena diperlukan untuk memberikan gambaran dan informasi penting tentang suatu kumpulan data. Pengolahan data yang tepat dapat menjadi pedoman dalam pembuatan kebijakan. Perkembangan teknologi yang sangat pesat mendorong hampir semua lingkungan untuk memanfaatkan teknologi informasi secara maksimal. Menggunakan data kependudukan dapat membantu mengidentifikasi kondisi sosial-demografi desa. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan kader desa dalam menggunakan Google Data Studio dan Microsoft Excel untuk mendukung analisis data dalam pengelolaan data kependudukan. Kegiatan berlangsung pada bulan Agustus 2022 dengan melibatkan 25 orang kader desa sebagai peserta pelatihan. Peningkatan kemampuan peserta kegiatan ditunjukkan oleh peningkatan skor post-test dibandingkan dengan skor pre-test. Dengan demikian, kegiatan ini terbukti efektif dalam meningkatkan pengetahuan dan keterampilan kader desa di bidang teknologi informasi. Adanya kegiatan ini juga mendukung tujuan pembangunan berkelanjutan (SDGs) pada indikator ke-4 dan ke-10 yaitu pendidikan bermutu dan berkurangnya kesenjangan. Oleh karena itu diharapkan setelah mengikuti kegiatan ini para kader dapat mengolah atau menganalisis data kependudukan lebih cepat dan akurat dengan visualisasi yang tepat untuk membantu mereka mengambil keputusan.
Pelatihan Analisis Data Bagi Kader Desa dan Guru Sekolah Dasar di Jombang Ratih Ardiati Ningrum; Chandrawati Putri Wulandari; Mohammad Ghani; Dwi Rantini; Fikri Arif Abdillah; Adam Maurizio Winata
Darmabakti : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 1 (2023): Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Lembaga Peneliian dan Pengabdian Masyarakat (LPPM) Universitas Islam Madura (UIM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/darmabakti.2023.4.1.81-88

Abstract

Peran penting pengolahan data semakin dirasa perlu untuk memberikan gambaran dan informasi penting terhadap suatu kumpulan data. Pengolahan data yang tepat dapat dijadikan acuan untuk pengambilan suatu kebijakan. Kemajuan teknologi yang sangat cepat, mendorong hampir semua aspek kehidupan untuk dapat memanfaatkan teknologi informasi secara maksimal. Pemanfaatan pada data kependudukan dapat membantu melihat kondisi sosiodemografi desa. Begitu pula untuk data sekolah, teknologi informasi dapat dimanfaatkan untuk membantu proses belajar mengajar. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan pada kader desa dan guru sekolah dasar dalam memanfaatkan Google Data Studio dan Microsoft Excel untuk mendukung analisis data berkaitan dengan bidang pekerjaan masing-masing. Kegiatan ini juga mendukung capaian SDGs pada indikator ke-4 dan ke-10 yaitu pendidikan bermutu dan berkurangnya kesenjangan. Peningkatan keterampilan peserta ditunjukkan dengan kenaikan nilai post-test dibandingkan dengan nilai pre-test. Sehingga, kegiatan ini terbukti efektif meningkatkan pengetahuan dan keterampilan teknologi informasi para kader desa dan guru sekolah dasar.
Human Development Clustering in Indonesia: Using K-Means Method and Based on Human Development Index Categories Indah Fahmiyah; Ratih Ardiati Ningrum
Journal of Advanced Technology and Multidiscipline Vol. 2 No. 1 (2023): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v2i1.45070

Abstract

The quality of life for Indonesia's population can be measured from the human development index in each province. People who have a good quality of life indicate a prosperous life. The government has the responsibility to advance the welfare of the nation under the mandate of the constitution. The clustering of the human development index (HDI) in Indonesia is used to determine the distribution of quality of life or the distribution of social welfare. In this study, the K-Means method, which is a popular non-hierarchical clustering method, is used to classify human development in each province based on HDI indicators, namely Expected Years of Schooling, Mean Years of Schooling, Adjusted Per Capita Expenditure, and Life Expectancy at Birth. Provinces in Indonesia are clustered into 4 clusters. These results were also compared with the clustering based on HDI categories determined by Statistics Indonesia based on certain cut-off values. According to the HDI category, provinces in Indonesia fall into the medium, high, and very high categories. The results of the two groupings show that there is a trend toward appropriate characteristics for each group. Thus, K-Means can classify provinces in Indonesia according to the characteristics of the HDI indicators.
Student's Behavior Clustering based on Ubiquitous Learning Log Data using Unsupervised Machine Learning Ika Qutsiati Utami; Wu-Yuin Hwang; Ratih Ardiati Ningrum
Journal of Advanced Technology and Multidiscipline Vol. 3 No. 1 (2024): Journal of Advanced Technology and Multidiscipline
Publisher : Faculty of Advanced Technology and Multidiscipline Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jatm.v3i1.55572

Abstract

Online learning is the source of data generation related to learner's learning behaviors, which is valuable for knowledge discovery. Existing research emphasized more on an understanding of student's performance and achievement from learning log data. In this study, we presented data-driven learning behavior clustering in authentic learning context to understand students' behavior while participating in the learning process. The objective of the study is to distinguish students according to their learning behavior characteristics and identify clusters of students at risk of unsuccessful learning achievement. Learning log data were collected from ubiquitous learning applications before conducting Exploratory Data Analysis (EDA) and cluster analysis. We used partitional clustering using K-means algorithm and hierarchical clustering based on the agglomerative method to improve clustering strategies. The result of this study revealed three different clusters of students supported by data visualization techniques. Cluster 1 comprised more students with active learning behavior based on the total logs, total problems posed, and the total attempts in fraction operation and simplification. Students in clusters 2 and 3 had a higher attempt at problem-solving instead of problem-posing. Both clusters also focused on fraction's conceptual understanding. Knowledge discovery of this study used real data generated from ubiquitous learning application namely U-Fraction. We combined two different types of clustering method for delivering more accurate portrait of a student's hidden learning behaviors. The outcome of this study can be a basis for educational stakeholders to provide preventive learning strategies tailored to a different cluster of students.
Co-Authors Adam Maurizio Winata Aretha Levi Chandrawati Putri Wulandari Chandrawati Putri Wulandari Dwi Rantini Fikri Arif Abdillah Ika Qutsiati Utami Indah Fahmiyah Jannah, Sa'idah Zahrotul Mohammad Ghani Muhammad Axel Syahputra Muhammad Noor Fakhruzzaman Wu-Yuin Hwang