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All Journal Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Islamic Psychology and Behavioral Sciences
Chairani
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Religion Humanities Computer Science & IT Education Public Health Social Sciences

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Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms Juwariyem; Sriyanto; Sri Lestari; Chairani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13448

Abstract

Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data includes risk factors related to stunting, such as maternal nutritional status, child nutritional intake, access to health services, sanitation, and other socioeconomic factors. This secondary data analysis can provide an overview of the prevalence of stunting and the contributing factors. To overcome this, the right solution is needed, one solution that can be used is data mining techniques, where data mining can be used to carry out analysis and predictions for the future, and provide useful information for business or health needs. Based on this analysis, this research will use the Bagging method and Random Forest Algorithm to obtain the accuracy level of stunting predictions in toddlers. Bagging or Bootstrap Aggregation is an ensemble method that can improve classification by randomly combining classifications on the training dataset which can reduce variation and avoid overfitting. Random Forest is a powerful algorithm in machine learning that combines decisions from many independent decision trees to improve prediction performance and model stability. By combining the Bagging method and the Random Forest algorithm, it is hoped that it will be able to provide better stunting prediction results in toddlers. This research uses a dataset with a total of 10,001 data records, 7 attributes and 1 attribute class. Based on the test results using the Bagging method and the Random Forest algorithm in this research, the results obtained were class precision yes 91.72%, class recall yes 98.84%, class precision no 93.55%, class recall no 65.28%, and accuracy of 91.98%.
SOCIAL INTERACTIONS WITH TUNAGRAHITA CHILDREN AT SLB YPAC PALEMBANG Natasya Rifda Hanifah; Winda Agustia Anggarini; Alya Rizky Nur Kamila Wagiman; Hanna Azzahra Nabella; Yustika Pratiwi; Yudi Latama; Syelina Rizki Tria Umami; Ghaliyatul Ningtyas; Muhammad Feriyansyah; Regina Athia Mayalianti; Cherlin Vinanditha; Nindy Alfatikhatus Salamah; Raudhatul Fauziah; Artika Adi Prasetiani; Chairani
Journal of Islamic Psychology and Behavioral Sciences Vol. 1 No. 2 (2023): Journal of Islamic Psychology and Behavioral Sciences
Publisher : CV. Doki Course and Training

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61994/jipbs.v1i2.5

Abstract

This research was conducted to find out the social interactions of mentally retarded children while they were at the Palembang Special School for the Development of Disabled Children (SLB YPAC). The research method used is a qualitative research method with data collection techniques through interviews and observation. The subjects in this study were four grade C junior high school students at SLB YPAC Palembang, namely MS, M, A and K. Based on the results of the study it can be concluded that the way of social interaction for mentally retarded children is the same as the way of social contact and communication in accordance with the conditions of social interaction.
Visualisasi Data Mahasiswa Baru Tahun 2022 Di Institut Agama Islam Negeri Metro Menggunakan Google Looker Studio Ramadhan, Apri; Putra, Dittha Winyana; Chairani
Jurnal Ilmiah Komputasi Vol. 22 No. 4 (2023): Jurnal Ilmiah Komputasi : Vol. 22 No 4, Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.22.4.3492

Abstract

Pada tahun 2022, Institut Agama Islam Negeri Metro menerima mahasiswa baru jenjang Strata 1 (S1) sebanyak 1122 orang. Data ini didapatkan berdasarkan data yang ada dalam Sistem Akademik (SISMIK) milik Institut Agama Islam Negeri Metro. Data yang disajikan dalam sistem belum tervisualisasi dengan baik sehingga informasi yang didapat tidak maksimal. Contoh data sebaran asal sekolah mahasiswa baru belum tervisualisasikan dengan baik pada sistem tersebut sehingga bagi pengguna data akan kesulitan dalam mencari informasi terkait ini. Berasal dari contoh yang disebutkan, maka visualisasi data sangat diperlukan untuk mempresentasikan data dalam format grafis atau dalam bentuk gambar agar lebih mudah dipahami. Pada penelitian ini dan berdasarkan penelitian terdahulu, maka peneliti akan menerapkan visualisasi data mahasiswa baru tahun 2022 menggunakan Google Data Studio/Google Looker Studio dengan fokus terhadap sebaran asal sekolah mahasiswa baru tahun 2022. Dashboard digital Google Data Studio/Google Looker Studio memungkinkan tampilan data dalam berbagai bentuk seperti tabel, grafik, dan peta yang membuatnya lebih menarik dan berguna bagi pengguna. Hasil dari penelitian didapat bahwa jumlah mahasiswa baru di IAIN Metro pada tahun 2022 yang mencapai 1122 memiliki sebaran asal sekolah mulai dari SMAN, MAS, MAN, SMAS, SMKN, SMKS, PONTREN, dan Paket C. Pada nama sekolah, posisi pertama berasal dari MAN 1 Lampung Timur dengan jumlah sebanyak 44 orang. SMAN 2 Sekampung dan SMAN 5 Metro berjumlah sama yaitu 17 orang. Mahasiswa yang berasal dari SMAN 3 Metro sebanyak 15 orang.
Co-Authors Alya Rizky Nur Kamila Wagiman Apri Ramadhan, Apri Artika Adi Prasetiani Cherlin Vinanditha Dittha Winyana Putra Ghaliyatul Ningtyas Hanna Azzahra Nabella Juwariyem Muhammad Feriyansyah Natasya Rifda Hanifah Nindy Alfatikhatus Salamah Raudhatul Fauziah Regina Athia Mayalianti SRI LESTARI Sriyanto Syelina Rizki Tria Umami Winda Agustia Anggarini Yudi Latama Yustika Pratiwi