ABSTRAK

Spherical tank yang berisi Butana merupakan subjek berpotensi hazard (kebocoran, kebakaran dan ledakan) yang dapat memberikan konsekuensi terhadap fasilitas dan manusia sebagai obyek penerima yang berada pada radius pajanan. Penelitian ini menggunakan pemodelan dengan input data primer dan sekunder yang diaplikasikan dalam perangkat lunak ALOHA, Area Locations of Hazardous Atmosphere. Hasil penelitian terhadap skenario kejadian kebocoran, kebakaran dan ledakan di fasilitas pengolahan minyak dan Gas PT Z mendapatkan nilai konsukeuensi zona bahaya sampai radius satu kilometer. Zona aman setelah radius satu kilometer.

ABSTRACT

Spherical tanks containing Butane is subject of potentially hazard (leak, fire and explosion) which can bring facility and human consequences as the recipient objects which are in the radius of exposure. This research uses input modeling with primary and secondary data which applied in the Area Locations of Hazardous Atmosphere (ALOHA) software. The study of the leak, fire and explosion incidence scenario at oil and gas processing facilities of PT Z scores shows threat zone to a radius of one kilometer. Safety zone distance after radius kilometer.

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

Latar Belakang : Penyakit tidak menular masih menjadi penyebab mendasar masalah kesehatan manusia yang ada di Negara Kesatuan Republik Indonesia. Penyakit Tidak Menular (PTM) atau yang disebut juga Non Communicable Disease merupakan penyakit kronis yang berlangsung lama dan bentuk dari hasil kombinasi faktor genetik, fisiologis, lingkungan dan perilaku. Dalam hal mengkonsumsi minuman kemasan harus dilakukan secara bijak dan tidak berlebihan. Hal ini dikarenakan minuman kemasan tinggi akan kaya gula yang dapat mengakibatkan penyakit tidak menular seperti halnya Diabetes Mellitus. Masalah yang terjadi pada era globalisasi seperti sekarang ini adalah proporsi kadar gula darah puasa yang terganggu, sehingga akan menggambarkan perilaku secara sadar dan keinginan untuk dapat mengontrol penyakit gula darah tinggi. Selain itu ingin mengetahui gambaran konsumsi, pengetahuan dan sikap terhadap minuman berpemanis dalam kemasan pada PT. X
Tujuan : Ingin mengetahui gambaran konsumsi minuman berpemanis dalam kemasan yang mencakup pengetahuan, sikap dan motivasi pada karyawan di PT X.
Metode Penelitian : Disain penelitian menggunakan metode penelitian kualitatif dengan rancangan penelitian studi kasus. metode pengumpulan data dengan cara wawancara, dokumentasi, serta observasi. Informan berjumlah 5 yang diantaranya adalah 4 informan utama yang berkerja di PT. X dan 1 informan kunci seorang ahli gizi dari salah satu puskesmas di wilayah bekasi. Serta triangulasi yang digunakan adalah triangulasi sumber.
Hasil : Para informan tahu betul frekuensi dan momen ketika mereka cenderung minum manis – misalnya setiap hari kerja sekali sehari, lebih sering di kantor, atau minimal saat-saat tertentu (seperti selepas makan siang). Sikap informan terhadap dampak minuman manis cenderung meremehkan potensi efek negatif jangka pendek, karena sebagian besar tidak merasakan gangguan kesehatan yang berarti sejauh ini. Secara keseluruhan, motivasi konsumsi minuman manis pada karyawan PT. X ini bersifat kompleks dan saling berkaitan. Kombinasi dorongan internal (mengantuk, butuh energi, stress relief, selera) dan faktor eksternal (ajak teman, ketersediaan, kebiasaan kantor) menciptakan lingkungan motivasional yang kuat bagi perilaku tersebut untuk berlangsung terus.

Backgorund : Non-communicable diseases are still the underlying cause of human health problems in the Republic of Indonesia. Non-communicable diseases (NCDs) or also called Non-Communicable Diseases are chronic diseases that last a long time and are a form of a combination of genetic, physiological, environmental and behavioral factors. In terms of consuming packaged drinks, it must be done wisely and not excessively. This is because packaged drinks are high in sugar which can cause non-communicable diseases such as Diabetes Mellitus. The problem that occurs in the current era of globalization is the proportion of disturbed fasting blood sugar levels, so that it will describe conscious behavior and the desire to be able to control high blood sugar. In addition, researchers also want to know the picture of consumption, knowledge and attitudes towards sweetened drinks in packages at PT. X. Objective : Want to know the picture of consumption of packaged sweetened drinks which includes knowledge, attitudes and motivation of employees at PT X. Research Methods : The research design uses a qualitative research method with a case study research design. Data collection methods are by interview, documentation, and observation. There are 5 informants, including 4 main informants who work at PT. X and 1 key informant, a nutritionist from a health center in the Bekasi area. And the triangulation used is source triangulation. Result : The informants know very well the frequency and moments when they tend to drink sweet drinks – for example, once a day every working day, more often in the office, or at least at certain times (such as after lunch). The informants' attitudes towards the impact of sweet drinks tend to underestimate the potential for short-term negative effects, because most of them have not felt significant health problems so far. Overall, the motivation for consuming sweet drinks among PT. X employees is complex and interrelated. The combination of internal drives (sleepy, need for energy, stress relief, taste) and external factors (inviting friends, availability, office habits) creates a strong motivational environment for the behavior to continue

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.