Ditemukan 3 dokumen yang sesuai dengan query :: Simpan CSV
Sutedja; Pemb. Buchari Lapau
A-233
Jakarta : FKM UI, 1976
S1 - Skripsi Pusat Informasi Kesehatan Masyarakat
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Dian Nur Hadianti; Promotor: Kemal Nazaruddin Siregar; Kopromotor: Tris Eryando, Sudibyo Alimoeso; Penguji: Anhari Achadi, Milla Herdayati, Sutanto Priyo Hastono, Indrajani Sutedja, Pungkas Bahjuri Ali
Abstrak:
Family Planning (FP) participation impacts fertility rates and population growth. The use of modern contraception among Generation Z has declined over the past two decades and is the lowest prevalence compared to other age groups. The solution offered is a predictive model for FP participation. This study aims to find a predictive model for FP participation at the community level. The methodology used is a sequential mixed method explanatory design with 5 research stages, stage 1 systemic literature review, stage 2 development of a predictive model using quantitative studies starting with statistics then machine learning, stage 3 analysis of system needs with qualitative studies on FP officers, WUSand related leadership elements, stage 4 development of a prototipe, stage 5 prototipe trials to be used as a digital intervention tool equipped with acceptance tests and efficacy tests with a Quasi-experimental design with a control group in Bandung City. The results of the study indicate that predictors of FP participation in Gen Z, with the most dominant variable related to the use of modern contraceptive methods, are the number of living children, with a p-value of 0.001 and an aOR of 26.63, while the results of feature importance in machine learning indicate that the most crucial feature is fertility status. The algorithm that can be obtained with the best performance is the Neural Network with an AUC value of 0.859, an Accuracy value of 0.839 or 83.9%, an F1-score of 0.838, a Precision 84.3%, a Recall of 0.839, and an MCC of 0.682. The results of the acceptance test of family planning officers and participants stated that the family planning participation prediction model was in the good and very good categories. The use and utilization of this prototype succeeded in improving the performance of family planning officers with performance indicators, satisfaction of WUSGen Z, knowledge, and attitudes of family planning officers. The use of this prototype succeeded in improving the attitude of WUSGen Z but was not significant in improving the knowledge of WUSGen Z. Thus, this study concludes that the family planning participation prediction model built in the Dplan KB app prototype can improve the performance of officers at the community level, and has great potential to be an innovative solution in family planning services
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Kepesertaan Keluarga Berencana (KB) berdampak pada tingkat fertilitas dan pertumbuhan penduduk. Kondisi pemakaian kontrasepsi modern pada Generasi Z selama dua dekade mengalami penurunan dan merupakan prevalensi terendah dibandingkan dengan kelompok usia lainnya. Solusi yang ditawarkan adalah model prediksi kepesertaan KB. Penelitian ini bertujuan menemukan model prediksi kepesertaan KB di tingkat komunitas. Metodologi yang digunakan adalah mixed methods sequential Explanatory design dengan 5 tahapan penelitian, tahap 1 systemic literatur review, tahap 2 pengembangan model prediksi menggunakan setudi kuantitatif diawali dengan analisis statistik kemudian machine learning, tahap 3 analisis kebutuhan sistem dengan studi kualitatif pada petugas KB, WUSdan unsur pimpinan terkait, tahap 4 pengembangan prototipe, tahap 5 uji coba prototipe untuk digunakan sebagai alat intervensi digital dilengkapi dengan uji acceptance dan uji efikasi dengan Quasi eksperimen design with control group di Kota Bandung. Hasil penelitian menunjukkan prediktor kepesertaan KB pada Gen Z dengan variabel yang paling dominan berhubungan dengan penggunaan metode kontrasepsi modern adalah jumlah anak hidup dengan p-value 0,001 dan aOR 26,63, sedangkan hasil feature importance pada machine learning, fitur yang paling krusial adalah status fertilitas. Algoritme dengan kinerja terbaik adalah Neural Network dengan nilai AUC sebesar 0,859, nilai Accuracy 0,839 atau 83,9%, F1-score sebesar 0,838 Precision 84,3% Recall 0,839, dan MCC 0,682. Hasil uji acceptance petugas KB dan peserta menyatakan model prediksi kepesertaan KB dalam kategori baik dan sangat baik. Penggunaan dan pemanfaatan prototipe ini berhasil meningkatkan kinerja petugas KB dengan indikator kinerja, pengetahuan, dan sikap petugas KB, dan berdampak pada kepuasan, pengetahuan dan sikap WUS Gen Z. Pemanfaatan prototipe ini berhasil meningkatkan sikap WUS Gen Z namun tidak signifikan dalam meningkatkan pengetahuan WUSGen Z. Dengan demikina, penelitian ini menyimpulkan bahwa Model prediksi kepesertaan KB yang dibangun dalam prototipe apps Dplan KB dapat meningkatkan kinerja petugas di tingkat komunitas, dan berpotensi besar menjadi solusi inovatif dalam pelayanan KB.
Family Planning (FP) participation impacts fertility rates and population growth. The use of modern contraception among Generation Z has declined over the past two decades and is the lowest prevalence compared to other age groups. The solution offered is a predictive model for FP participation. This study aims to find a predictive model for FP participation at the community level. The methodology used is a sequential mixed method explanatory design with 5 research stages, stage 1 systemic literature review, stage 2 development of a predictive model using quantitative studies starting with statistics then machine learning, stage 3 analysis of system needs with qualitative studies on FP officers, WUSand related leadership elements, stage 4 development of a prototipe, stage 5 prototipe trials to be used as a digital intervention tool equipped with acceptance tests and efficacy tests with a Quasi-experimental design with a control group in Bandung City. The results of the study indicate that predictors of FP participation in Gen Z, with the most dominant variable related to the use of modern contraceptive methods, are the number of living children, with a p-value of 0.001 and an aOR of 26.63, while the results of feature importance in machine learning indicate that the most crucial feature is fertility status. The algorithm that can be obtained with the best performance is the Neural Network with an AUC value of 0.859, an Accuracy value of 0.839 or 83.9%, an F1-score of 0.838, a Precision 84.3%, a Recall of 0.839, and an MCC of 0.682. The results of the acceptance test of family planning officers and participants stated that the family planning participation prediction model was in the good and very good categories. The use and utilization of this prototype succeeded in improving the performance of family planning officers with performance indicators, satisfaction of WUSGen Z, knowledge, and attitudes of family planning officers. The use of this prototype succeeded in improving the attitude of WUSGen Z but was not significant in improving the knowledge of WUSGen Z. Thus, this study concludes that the family planning participation prediction model built in the Dplan KB app prototype can improve the performance of officers at the community level, and has great potential to be an innovative solution in family planning services
D-650
Depok : FKM-UI, 2026
S3 - Disertasi Pusat Informasi Kesehatan Masyarakat
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