Tujuan peneltian ini adalah diketahuinya proporsi pemberian ASI eksklusif dan tingkat kecerdasan serta hubungan antara durasi pemberian ASI dengan kecerdasan anak pada siswa-siswi SDSN Pekayon Jaya VI Kota Bekasi. Penelitian ini menggunakan disain crosssectional model regresi logistik ganda dengan responden siswa/i kelas 1-3 yang berumur 7-9 tahun dan ibunya sebanyak 175 responden. Namun jumlah sampel yang terkumpul hanya 166 (94,8%) responden. Penelitian ini dilaksanakan pada bulan Mei 2013. Pada siswa-siswi dilakukan tes kecerdasan dengan menggunakan tes Raven sedangkan ibunya mengisi kuesioner.

Hasil penelitian didapatkan tingkat kecerdasan tinggi 57,2%, rata-rata 36,7% dan rendah 6%. Variabel yang berhubungan dengan kecerdasan adalah durasi pemberian ASI (p=0,043, OR=0,487, 95%CI=0,254-0,932) dan Pendidikan ibu (p=0,047, OR=3,730, 95%CI=1,119-12,432). Pendidikan ibu adalah faktor yang pengaruhnya lebih besar terhadap kecerdasan, bahwa ibu yang berpendidikan tinggi berpeluang memiliki anak dengan kecerdasan tinggi yaitu : 3,556 kali lebih besar dibandingkan ibu berpendidikan rendah setelah dikontrol variabel durasi ASI.

Saran untuk Dinas Pendidikan Kota Bekasi agar menyelenggarakan berbagai aktivitas seperti seminar/pelatihan/konseling bagi orang tua murid tentang pentingnya peran orang tua terhadap tumbuh kembang anak.

The purpose this of research was to the determine the proportion of exclusive breastfeeding and the level of intellegence also the relationship between duration of breastfeeding with the level of students intellegence in SDSN Pekayon Jaya VI Bekasi city. This research used a cross-sectional design employed multiple logistic regression analisys technique. Students as the respondents age 7 ? 9 year-old who were selected using systematic random sampling technique and his mothers was about 175 respondents. However collected just the number of sampels was 166 (94,8%) responden. This study was conducted in May 2013 from second week to third week. The students intellegence was tested using the Raven test while her mothers was requested to fill out a questionnaire about their breastfeeding history, background caracteristic and parenting style.

The results showed the level of childrens intellegence was high (57.2%), average was (36.7%) and low was (6%). Those variables which related to the intellegence level were duration of breastfeeding (p = 0.043, OR = 0.487, 95% CI = 0.254-0.932) and the level of mothers education (p = 0.047, OR = 3.730, 95% CI = 1.119 to 12.432). The mothers education level is one of the factors which has higher effect. Againts the childrens intellegence. Those mothers who have high level education will have probability 3,556 to have their children with high level intellegence (after controlling the duration breastfeeding).

The following sugestion is made to the Department of Education Bekasi city to organize activities relevant to the inproving of parents in growth and development of their children through seminars / training / counseling.

ABSTRAK Nama : Fitri Annisa Ahlul Jannah Program Studi : Kesehatan Masyarakat Judul : Analisis Efektivitas Asesmen Risiko dan Sistem Rekomendasi Kanker Payudara Berbasis Artificial Intelligence Pembimbing : R. Sutiawan, S.Kom., MSi Angka kejadian kanker payudara meningkat setiap tahun, namun angka cakupan deteksi dini sebagai program penanggulangannya masih sangat rendah. Salah satu penyebab hal tersebut adalah tingginya beban kerja tenaga kesehatan di puskesmas. Tujuan penelitian ini adalah untuk mengembangkan chatbot asesmen risiko dan sistem rekomendasi, serta menilai efektivitasnya untuk membantu puskesmas dalam meningkatkan cakupan deteksi dini secara efektif dan efisien melalui pendekatan selektif. Desain penelitian menggunakan desain kualitatif studi kasus di Puskesmas Jayagiri Lembang untuk mengetahui efektivitas chatbot dalam menilai risiko kanker payudara dengan menerapkan pendekatan model waterfall dalam membangun model aplikasi chatbot. Chatbot—yang dikembangkan menggunakan teknologi natural language understanding dan conditional statements sehingga membuatnya lebih dinamis dalam berinteraksi dan mengurangi error—teruji sangat efektif untuk melakukan asesmen awal risiko kanker payudara. Namun, sistem ini perlu dilakukan upaya revalidasi dan pengembangan lebih lanjut sebelum dapat digunakan oleh masyarakat secara masif. Kata kunci: Kecerdasan buatan, asesmen risiko, kanker payudara, deteksi dini, informatika kesehatan

ABSTRACT Name : Fitri Annisa Ahlul Jannah Study Program : Bachelor of Public Health Title : Analysis of the Effectiveness of the Artificial Intelligence-Based Breast Cancer Risk Assessment and Recommendation System Counsellor : R. Sutiawan, S.Kom., MSi The incidence of breast cancer increases every year, but the coverage rate for early detection as a prevention program is still low. One of the reasons is the high workload of health workers at the public health center. The purpose of this study was to develop a risk assessment and recommendation system and assess its effectiveness to assist public health center in increasing the coverage of early detection effectively and efficiently through a selective approach. A qualitative case study design at the Jayagiri Lembang Public Health Center to determine the effectiveness of chatbots in assessing breast cancer risk by applying the waterfall model approach in building a chatbot application model was used for the research design. Natural language understanding and conditional statements were used by the developed chatbot hence made it more dynamic in interacting and preventing errors. It was also proven very effective in doing early risk breast cancer assessment. However, this system needs to be revalidated and further developed before it can be used by the community on a massive scale. Keywords: artificial intelligence, risk assessment, breast cancer, early detection, health informatics

The tuberculosis treatment success rate in Indonesia in 2023 did not reach the 90% target. Treatment success impacts the reduction of infection spread and drug resistance cases, making early prediction of treatment success crucial. This study aims to develop a machine-learning model to predict treatment success. Data from Indonesia's Tuberculosis Information System (SITB) cohort was used. The study included productive-age patients (15-64 years) diagnosed with drug-sensitive tuberculosis who received treatment from January 1, 2020, to December 31, 2023. Data was randomly split into training (80%) and testing (20%) sets for model validation, with cross-validation performed. The algorithms used include decision tree, random forest, multilayer perception, extreme gradient boosting, and logistic regression. A consensus was reached for decision-making variables required in performing machine learning-based modeling of SITB data to predict treatment success using modeling of SITB data to predict treatment success using the Delphi method. The results of the study show that the random forest machine learning algorithm had the best performance and highest accuracy in predicting treatment success. This machine learning–based prediction tool can provide early predictions with SHAP (SHapley Additive ExPlanations) interpretation, helping healthcare workers make informed decisions more easily.