Ditemukan 4 dokumen yang sesuai dengan query :: Simpan CSV
Endang Indriasih; Promotor: Indang Trihandini; Kopromotor: Meiwita Paulina Budiharsana, Soewarta Kosen; Penguji: Purnawan Junadi, Besral, Budi Riyanto Wreksoatmodjo, Trihono, Pungkas Bahjuri Ali
D-390
Depok : FKM-UI, 2018
S3 - Disertasi Pusat Informasi Kesehatan Masyarakat
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Hario Megatsari; Promotor: Rita Damayanti; Kopromotor: Dian Kusuma; Penguji: Anhari Achadi, Dien Anshari, Hasbullah Thabrany, Diah Indriani, Pungkas Bahjuri Ali, Rohani Budi Prihatin
Abstrak:
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Latar Belakang: Berdasarkan beberapa survei yang ada, terdapat peningkatan prevalensi merokok remaja. MPOWER pada tingkat Kab/Kota berpotensi menjadi solusi atas permasalahan ini. Tujuan penelitian ini adalah untuk mengetahui asosiasi antara skor MPOWER di tingkat Kab/Kota dan prevalensi perilaku merokok remaja.
Metode: Penelitian ini merupakan penelitian kuantitatif dengan menggunakan data sekunder dan data primer untuk mendapatkan informasi yang dibutuhkan. Tahapan penelitian adalah adaptasi skor MPOWER, penilaian ahli, proses skoring MPOWER Kab/Kota, analisis skor MPOWER dengan prevalensi merokok remaja.
Hasil: Adaptasi komponen MPOWER berhasil dilakukan dengan memanfaatkan data regulasi dan data program dari beberapa Kementrian terkait. Hasil penilaian ahli menyatakan bahwa terdapat 1 komponen yang tidak bisa diadaptasi, yaitu komponen R, dikarenakan keterbatasan kewenangan Pemerintah Kab/Kota. Hasil skor MPOWE menunjukkan bahwa gerakan upaya pengendalian tembakau tidak hanya terpusat di Kab/Kota di Pulau Jawa saja. Hasil asosiasi skor MPOWE dengan perilaku merokok remaja menujukkan bahwa terdapat hubungan signifikan, terutama komponen P.
Kesimpulan: MPOWE di tingkat Kab/Kota dapat menjadi solusi bagi pemerintah daerah dalam menekan angka prevalensi merokok remaja.
Background: Based on several surveys, there is an increasing prevalence of youth smoking behavior. MPOWER at the district level has the potential to be a solution to this problem. The aim of this study was to determine the association between MPOWER scores at the district level and the prevalence of youth smoking behaviour.
Methods: This study is a quantitative study using secondary data and primary data to obtain the required information. The stages of the study were the adaptation of MPOWER score, expert assessment, MPOWER District/City scoring process, MPOWER score analysis with youth smoking prevalence.
Results: Adaptation of MPOWER components was successfully carried out by utilising regulatory data and programme data from several relevant Ministries. The results of the expert assessment stated that there was one (1) component that could not be adapted, namely the R component, due to the limited authority of the District / City Government. The results of the MPOWE score show that the tobacco control movement is not only centred in districts/cities in Java. The association of MPOWE scores with adolescent smoking behaviour showed that there was a significant relationship, especially component P.
Conclusion: MPOWE at the district level can be a solution for local governments to reduce the prevalence of youth smoking behavior.
D-588
Depok : FKM-UI, 2025
S3 - Disertasi Pusat Informasi Kesehatan Masyarakat
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Kurnia Sari; Promotor: Budi Hidayat; Kopromotor: Amal Chalik Sjaaf, Mardiati Nadjib; Penguji: Prastuti Soewondo, Khoirunurrofik, Eko Setyo Pambudi, Pungkas Bahjuri Ali
Abstrak:
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Efisiensi merupakan salah satu ukuran utama dari kinerja sistem kesehatan, yang mencerminkan kemampuan sistem dalam mencapai output sistem kesehatan seoptimal mungkin dengan sumber daya yang ada. Metode dan asumsi yang digunakan dalam mengukur efisiensi teknis dapat menghasilkan skor yang berbeda, tergantung metode dan asumsi yang digunakan. Penelitian ini merupakan penelitian observasional analitik dengan pendekatan kuantitatif, menggunakan data sekunder dan informasi dari berbagai institusi sumber data. Analisis kuantitatif dilakukan dengan menggunakan dua metode pengukuran efisiensi utama, yaitu Data Envelopment Analysis (DEA) data panel serta bootstrapping, yang dilanjutkan dengan regresi data panel, dan Stochastic Frontier Analysis (SFA) dengan beberapa model. Pengukuran efisiensi teknis dilakukan dengan metode DEA orientasi output dan asumsi variable return to scale (VRS) dua tahap dengan bootstrapping, serta SFA dengan model spesifikasi Cobb-Douglas dan TransLog. Orientasi dan asumsi skala lainnya digunakan sebagai pembanding. Penelitian menggunakan variabel yang telah melalui proses seleksi literatur dan statistik dengan 1 output (umur harapan hidup) dan 4 input, yaitu belanja publik (belanja kesehatan pemerintah kabupaten kota dan belanja Jaminan Kesehatan Nasional/JKN untuk non pemerintah kabupaten kota), tenaga dokter, dan cakupan layanan lifaskes, dengan menggunakan beberapa variabel kontekstual. Hasil penelitian menujukkan perbedaan skor efisiensi yang dihasilkan dari beberapa metode pengukuran, baik dengan DEA dan SFA. Analisis dengan metode DEA menghasilkan rerata efisiensi teknis 0,76-0,99 untuk asumsi VRS orientasi Output. Sementara dengan SFA yang menggunakan tiga model pengukuram (Time invariant, fixed effect dan random effect), diperoleh skor efisiensi yaitu 0,37 sampai 0,76 pada spesifikasi Translog, dan pada spesifikasi Cobb-Douglas sebesar 0,37 sampai 0,85. Analisis tahap dua menunjukkan bahwa semakin tinggi tingkat pengangguran dan kemiskinan, semakin rendah skor efisiensi (orientasi output VRS boostrtrapped). Skor efisiensi juga rendah di wilayah geografis Pulau Maluku, Papua dan Nusa Tenggara (dibandingkan dengan Sumatera), di wilayah tertinggal, dan wilayah perkotaan, serta pada wilayah dengan bencana alama yang berdampak kepada manusia. Sementara, semakin besar rasio dana JKN yang diterima fasilitas kesehatan milik pemerintah kabupaten kota terhadap non pemerintah, semakin tinggi skor efisiensi teknis.
Efficiency is one of the main measures of health system performance, reflecting the system's ability to achieve optimal health system output with available resources. The methods and assumptions used in measuring technical efficiency can produce different scores, depending on the methods and assumptions used. This study is an analytical observational study with a quantitative approach, using secondary data and information from various data source institutions. Quantitative analysis was conducted using two main efficiency measurement methods, namely Data Envelopment Analysis (DEA) panel data and bootstrapping, followed by panel data regression, and Stochastic Frontier Analysis with several models. Technical efficiency measurement was conducted using the output-oriented DEA method and the two-stage variable return to scale assumption with bootstrapping, as well as SFA with Cobb-Douglas and TransLog specification models. Other orientation and scale assumptions were used as comparisons. The study utilised variables selected through literature and statistical analysis, comprising 1 output (life expectancy) and 4 inputs: public expenditure (government health expenditure at the district/city level and JKN expenditure for non-government entities at the district/city level), doctor workforce, and healthcare service coverage, along with several contextual variables. The results show differences in efficiency scores produced by various measurement methods, including DEA and SFA. Analysis using the DEA method yielded an average technical efficiency of 0.76–0.99 for the VRS output orientation assumption. Meanwhile, using SFA with three measurement models (time-invariant, fixed-effect, and random-effect), efficiency scores ranged from 0.37 to 0.76 under the Translog specification and from 0.37 to 0.85 under the Cobb-Douglas specification. The second-stage analysis shows that the higher the unemployment and poverty rates, the lower the efficiency scores (output-oriented VRS bootstrapped). Efficiency scores are also low in the geographical regions of Maluku, Papua, and Nusa Tenggara (compared to Sumatra), in underdeveloped regions, urban areas, and regions affected by natural disasters with human impacts. Meanwhile, the higher the ratio of JKN funds received by government-owned health facilities in cities and districts compared to non-government facilities, the higher the technical efficiency score.
D-581
Depok : FKM-UI, 2025
S3 - Disertasi 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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