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"Penuaan biologis mencerminkan kondisi kesehatan fisik yang sebenarnya karena menilai fungsi organ dan sistem tubuh yang sebenarnya pada setiap individu, berbeda dengan usia kronologis. Penelitian ini mengeksplorasi prediksi usia biologis menggunakan metode Support Vector Regression (SVR) dan Klemera-and-Doubal Method (KDM), yang berfokus pada pengaruh biomarker dan faktor eksternal pada proses penuaan. Pembangunan model memanfaatkan data pemeriksaan medis dari Kementerian Kesehatan Indonesia pada tahun 2011 dimana keterbaharuan dari penelitian ini adalah melibatkan semua fitur yang berperngaruh terhadap usia biologis, termasuk faktor eksternal, tidak hanya biomarker saja. Kemudian, dilakukan pemanfaatan seluruh dataset tanpa membedakan subjek sehat dan tidak sehat. Pada dataset dilakukan data preprocessing agar dataset siap digunakan dengan melakukan filtering usia di atas 30 tahun, pemisahan dataset pria dan wanita, menghapus fitur yang tidak relevan, mengubah tipe data yang tidak sesuai, mengidentifikasi dan melakukan penanganan missing value serta outliers, dan melakukan encoding untuk data beripe kategorikal. Kemudian, dilakukan feature selection dengan menggunakan Spearman’s rank Coefficient Corelation dan pembangunan model SVR dan KDM. Hasil penelitian menunjukkan bahwa terpilih 5 fitur untuk pria dan 6 fitur untuk wanita yang digunakan untuk membangun model SVR dan KDM. KDM menunjukkan performa evaluasi yang cukup baik dalam interpretasi variasi data dengan skor performa RMSE 1,39; R2 0,97; dan Adjusted R2 0,97 untuk pria dan RMSE 1,00; R2 0,99; dan Adjusted R2 0,99 untuk wanita. Metode ini lebih unggul daripada SVR yang cenderung menunjukkan performa yang kurang memuaskan dimana memiliki skor performa RMSE 6,36; R2 0,44; dan Adjusted R2 0,36 untuk pria dan RMSE 5,90; R2 0,57; dan Adjusted R2 0,53 untuk wanita. Berdasarkan hasil analisis dari berbagai teknik analisis yang dilakukan (analisis evaluasi performa, analisis hubungan usia kronologis dengan usia biologis, dan analisis evaluasi dengan melihat pola hasil estimasi) terlihat bahwa metode KDM lebih unggul dalam memprediksi usia biologis dibandingkan dengan SVR, terutama dalam hal konsistensi dan akurasi. Selain itu, analisis hubungan setiap fitur dengan usia biologis untuk tiap model menggambarkan pengaruh fitur-fitur tersebut terhadap fungsi organ tubuh seseorang.

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The biological aging reflects the actual physical health condition as it assesses the real function of organs and body systems in each individual, different from chronological age. This research explores the prediction of biological age using the Support Vector Regression (SVR) method and the Klemera-and-Doubal Method (KDM), focusing on the influence of biomarkers and external factors on the aging process. The model development utilized medical examination data from the Indonesian Ministry of Health in 2011, where the novelty of this research is involving all features that affect biological age, including external factors, not just biomarkers. Then, the entire dataset was utilized without distinguishing between healthy and unhealthy subjects. In the dataset, data preprocessing was performed to make the dataset ready to use by filtering ages above 30 years, separating datasets for men and women, removing irrelevant features, changing inappropriate data types, identifying and handling missing values and outliers, and encoding for categorical data. Subsequently, feature selection was conducted using Spearman's Rank Coefficient Correlation, and then the SVR and KDM models were built. The research results showed that 5 features for men and 6 features for women were selected to build the SVR and KDM models. KDM showed fairly good evaluation performance in interpreting data variations with performance scores of RMSE 1.39, R^2 0.97, and Adjusted R^2 0.97 for men and RMSE 1.00, R^2 0.99, and Adjusted R^2 0.99 for women. This method outperformed SVR, which tended to show less satisfactory performance with performance scores of RMSE 6.36, R^2 0.44, and Adjusted R^2 0.36 for men and RMSE 5.90, R^2 0.57, and Adjusted R^2 0.53 for women. Based on the analysis results from various techniques performed (performance evaluation analysis, analysis of the relationship between chronological age and biological age, and evaluation analysis by looking at the pattern of estimation results), it appears that the KDM method is superior in predicting biological age compared to SVR, especially in terms of consistency and accuracy. In addition, the analysis of the relationship of each feature with biological age for each model illustrates the influence of these features on the organ function of an individual."

"Terdapat dua macam perubahan faali yang terjadi dalam tu buh selama gerak badan. Pertama, perubahan faali yang terjadi akibat kerja fisik (exercise) dan kedua, perubahan faali yang terjadi secara bertahap dalam tubuh akibat latihan fisik (training) yang teratur. Beberapa peneliti melaporkan selama kerja fisik terjadi peningkatan kadar elektrolit serum, sedangkan latihan fisik dapat menyebabkan turunnya kadar elektrolit serum.Penelitian ini bertujuan membuktikan adanya pengaruh ker ja fisik dengan beban maksimal dan latihan fisik yang teratur selama 6 minggu terhadap kadar elektrolit serum, sehingga diharapkan dapat memberikan sumbangan data yang bermanfaat dalam menentukan apakah perlu penambahan air atau elektrolit sesudah kerja fisik dan latihan fisik.Telah dilakukan penelitian terhadap 10 atlit balap sepeda dari Pelatda DKI Jaya mengenai kadar elektrolit serum (natrium, kalium, klorida, kalsium dan magnesium) yang dilakukan sebelum dan sesudah menjalani latihan fisik selama 6 minggu. Pemeriksaan kadar elektrolit serum baik sebelum maupun sesudah latihan fisik dilakukan masing-masing 4 kali, yaitu sebelum kerja fisik (menit ke 0), waktu melakukan kerja . fisik menggunakan ergosikel Monark dengan beban maksimal {150 watt) menit ke 5, saat kerja fisik maksimal dan waktu melakukan pemulihan aktif pada menit ke 20 sesudah kerja fisik maksimal.Hasil penelitian menunjukkan bahwa selama kerja fisik dengan beban maksimal kadar natrium, kalium, korida, kalsium total dan magnesium total serum meningkat secara bermakna, se dangkan sesudah pemulihan aktif kadarnya menurun dan tidak berbeda lagi dengan kadar sebelum kerja fisik. Sesudah latihan fisik selama 6 minggu terjadi penurunan kadar semua elektrolit serum yang diperiksa, baik sebelum maupun sesudah latihan fisik menunjukkan pada perubahan kadar elektrolit serum yang hampir sama.Melihat hasil pemeriksaan kadar elektrolit serum sesudah latihan fisik selama 6 minggu dan kurangnya ?intake? natrium, kalium, kalsium dan magnesium, penulis mengusulkan selama latihan fisik perlu penambahan air dan elektrolit terutama kalsium dan magnesium. Sedangkan sesudah kerja fisik dengan beban maksimal, tidak perlu penambahan air dan elektrolit.untuk mengetahui apakah turunnya kadar elektrolit serum sesudah latihan fisik selama 6 minggu mengganggu peningkatan prestasi yang diharapkan, serta membuktikan kebenaran hipotesis turunnya kadar elektrolit serum karena kehilangan lewat keringat atau karena masuknya elektrolit ke dalam eritrosit dan sel otot yang sedang berkontraksi, penulis mengusulkan dilakukan penelitian lanjutan antara PKO Senayan dan Bagian Patologi Klinik FKUI-RSCM mengenal hal-hal tersebut. Di usulkan pula untuk melanjutkan penelitian serupa pada berbagai cabang olahraga yang lain untuk mengetahui apakah ada pola khusus."

"In modern medicine, a radiation scans is an very important examination tool for making a diagnosis and subsequent treatment plan. Among the range of medical examinations, Computed tomography (CT) is being performed in an increasing number of cases and a CT scan uses the most radiation of any diagnostic exam. On the other hand, radiation protection during scanning is not typical for bodily regions other than those designated for examination. Therefore, the aim of this study was to develop a lead-free fused radiation shielding fiber (RSF) and to evaluate its effectiveness with a view to reducing radiation exposure to only the effective dose or less in a CT scan by means of a multilayer structural coating. A GE High Speed Advantage Spiral CT was used to conduct measurements using a FH-40G (Eberline, USA) proportional digital counter survey meter. In a brain CT scan, abdominal CT scan, and knee CT scan, two-way ANOVA was used to analyze the changes in radiation dosage and to examine the correlation based on body parts and thickness of the RSF. In addition, when significant results were obtained, a Duncan post hoc test was used to examine the difference depending on each condition. In the brain CT scan, the highest exposure to secondary radiation was measured in the chest, which was closest in distance. The use of a 3- mm shielding fiber resulted in a shielding effect of approximately 65% shielding effect compared to the initial exposure dose. In the abdominal CT scan, no exposure dose was detected in the head area, which had been shielded with the 3-mm shielding fiber. In a knee CT scan, 1-mm shielding fiber was sufficient to demonstrate a shielding effect. The RSF developed in this study may help reduce low-dose exposure to secondary X-rays, such as scattered rays."

"Penuaan merupakan kumpulan perubahan biologis pada tubuh manusia yang terjadi secara bertahap dan dapat meningkatkan risiko terjadinya penyakit bahkan kematian. Hingga saat ini, usia kronologis menjadi indikator penuaan yang paling umum digunakan dalam dunia kesehatan. Akan tetapi, munculnya konsep usia biologis diyakini mampu memberikan pengukuran yang lebih akurat terkait penuaan pada manusia dibandingkan dengan usia kronologis. Usia biologis dipengaruhi oleh berbagai faktor yang disebut biomarker. Penelitian ini berfokus pada prediksi usia biologis berdasarkan usia kronologis dan fitur (biomarker) lainnya dengan memanfaatkan metode machine learning Extreme Gradient Boosting (XGBoost) dan Support Vector Regression (SVR). Dataset yang digunakan berupa data pemeriksaan medis oleh Kementerian Kesehatan RI. Pada dataset tersebut dilakukan data preprocessing, seleksi fitur menggunakan Spearman’s Rank Correlation Coefficient, dan pembangunan model. Model dievaluasi menggunakan metrik evaluasi pada model regresi yaitu Root Mean Square Error (RMSE), Coefficient of Determination , dan Adjusted . Ketiga metrik ini masing-masing menghitung selisih nilai prediksi dengan nilai aktual dan menunjukkan seberapa baik variabel dependen dapat dijelaskan oleh variabel independen pada model. Dengan metode XGBoost diperoleh nilai RMSE 8,0560, 0,2894, dan Adjusted 0,2006 untuk data pria, serta RMSE 6,3851, 0,4252, dan Adjusted 0,3938 untuk data wanita. Dengan metode SVR, diperoleh RMSE 8,0697, 0,2870, dan Adjusted 0,1979 untuk data pria, serta RMSE 6,7147, 0,3643, dan Adjusted sebesar 0,3296. Metode XGBoost lebih unggul dalam memprediksi usia biologis baik pada model pria maupun wanita dibandingkan metode SVR. Usia kronologis dan biomarker (fitur) lainnya terkait kesehatan juga ditemukan berpengaruh positif terhadap usia biologis seorang individu.

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Aging is a collection of biological changes in the human body that occur gradually and can increase the risk of disease and even death. Until now, chronological age is the most commonly used indicator of aging in the medical sector. However, the emergence of the concept of biological age is believed to be able to provide a more accurate measurement of aging in humans compared to chronological age. Biological age is influenced by various factors called biomarkers. This research focuses on predicting biological age based on chronological age and other features (biomarkers) by utilizing the Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) machine learning methods. The dataset used is medical examination data by the Indonesian Ministry of Health. Data preprocessing was performed on this dataset, followed by feature selection using the Spearman Rank Correlation Coefficient, and subsequent model development. The model is evaluated using evaluation metrics in the regression model, namely Root Mean Square Error (RMSE), Coefficient of Determination , and Adjusted . These three metrics each calculate the difference between the predicted and actual values and indicate how well the dependent variable can be explained by the independent variables in the model. Using the XGBoost method, RMSE values were obtained of 8,0560, 0,2894, and Adjusted 0,2006 for male data, as well as RMSE 6,3851, 0,4252, dan Adjusted 0,3938 for female's data. Using the SVR method, RMSE 8,0697, 0,2870, and Adjusted 0,1979 were obtained for male data, as well as RMSE 6.7147, 0.3643, and Adjusted of 0,3296 for female's data. The XGBoost method demonstrates better performance in predicting biological age for both male and female models compared to the SVR method. Chronological age and other health-related biomarkers (features) were also found to have a positive impact on an individual's biological age."

"Pemeriksaan kesehatan pranikah merupakan serangkaian tes yang harus dilakukan calon pengantin sebelum menikah untuk mencegah terjadinya permasalahan kesehatan pada calon pengantin dan keturunannya kelak. Tidak semua orang mempunyai riwayat kesehatan yang baik walaupun dalam keadaan sehat. Skripsi ini membahas kebijakan negara-negara yang melaksanakan pemeriksaan kesehatan pranikah sebelum calon pengantin melangsungkan pernikahan. Ketentuan pemeriksaan kesehatan pranikah tiap negara juga berbeda-beda baik dalam kewajibannya juga rangkaian tes yang dilakukan. Penelitian ini adalah review literatur dengan desain deskriptif. Hasil penelitian menunjukan bahwa Indonesia masih jauh dari negara lainnya dalam pelaksanaan PHE, dibutuhkan evaluasi kebijakan sehingga dipatuhi dan berjalan lebih baik, meningkatkan promosi kesehatan untuk meningkatkan kesadaran dan perilaku kesehatan dalam masayarakat.

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Premarital screening is defined as testing couples who are going to be married in order to prevent common genetic blood disease and infectious disease that may affect their next generation. Some people might look healthy but they might be a carrier for hereditary disease. This tresearch focused on policies concerning on premarital screening in Indonesia and other countries. The research uses Literature Review (LR) with descriptive approach. The result is PHE in Indonesia is still far from other countries implementation of PHE. Evaluation of PHE policy is needed to support PHE, ensure intervention health promotion to raise awareness and attitude of PHE."

"Panjang badan lahir merupakan ukuran valid dalam memperlihatkan keterhambatan pertumbuhan dan perkembangan dalam kandungan di awal trimester kehamilan. Penelitian ini bertujuan untuk mengetahui model prediksi dan faktor dominan yang memengaruhi panjang badan lahir bayi. Desain penelitian yang digunakan adalah cross-sectional dengan menggunakan data sekunder dari rekam medis, laporan bagian kebidanan, dan laporan pertolongan persalinan ibu. Model prediksi panjang badan lahir yang diperoleh adalah z=-9,548 + 1,176 tinggi badan ibu + 0,942 berat badan pra hamil + 0,525 pertambahan berat badan selama kehamilan + 0,822 paritas + 1,25 usia gestasi + 0,315 status pekerjaan + 0,619 total kunjungan antenatal care + 0,952 jenis kelamin bayi. Dari model tersebut, usia gestasi ibu merupakan faktor dominan yang memengaruhi panjang badan lahir bayi, disusul oleh tinggi badan ibu dan jenis kelamin bayi setelahnya. Adapun faktor lain yang memengaruhi panjang badan lahir adalah berat badan pra hamil (BBpH), paritas, total kunjungan antenatal care (ANC), pertambahan berat badan (PBB) selama kehamilan, dan status pekerjaan. Dengan demikian, disarankan agar sektor terkait lebih meningkatkan fokus pada penurunan kejadian prematur melalui perbaikan BBpH dengan melaksanakan penyuluhan gizi seimbang pada remaja dan ibu pra-hamil di karang taruna dan pembinaan kesejahteraan keluarga (PKK), serta perbaikan PBB selama kehamilan melalui kunjungan ANC.

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The focus of this study is internal training design at PT Aetra Air Jakarta. This research

is qualitative, the data were collected by literature study and interview. The result of this

study showing that internal training design at PT Aetra Air Jakarta include : a) training

calendar, b) training site, c) trainer, d) training method, e) training module, f) pre test

and post test, and g) training observer. There were include training need analysis and

training evaluation at PT Aetra Air Jakarta. The researcher suggest that should be

conduct sharing session for all unit managers in order to realize them to evaluating the

employees that finished their training program"

"[ABSTRAKPemeriksaan kesehatan secara umum merupakan bagian yang umum dari perawatan kesehatan di beberapa negara. Jumlah permintaan layanan kesehatan di Taiwan mengalami peningkatan selama sepuluh tahun terakhir. Kenaikan permintaan tersebut didorong oleh beberapa faktor, termasuk populasi yang semakin menua, dan peningkatan jumlah kasus penyakit kronis. Fluktuasi jumlah kedatangan peserta tes kesehatan yang tidak menentu, membuat rumah sakit sulit untuk memberikan pelayanan yang memuaskan. Rumah sakit perlu membuat strategi perencanaan, seperti manajemen kesehatan untuk menangani masalah tersebut dengan cara memprediksi kedatangan peserta uji kesehatan. Aplikasi data mining dalam perawatan kesehatan adalah pembuktian bahwa data mining dapat memberikan informasi yang sangat berguna untuk semua pihak yang terlibat dalam industri kesehatan, seperti meningkatkan kualitas pelayanan rumah sakit. Penelitian ini menggunakan pengelompokan dan aturan asosiasi untuk mengetahui pola dari data pemeriksaan penyakit cerebrovascular, dengan tujuan memprediksi kedatangan kembali peserta tes kesetahan. Algoritma Apriori pembobotan dapat mengetahui hubungan antar item menggunakan nilai support, confidence, dan bobot masing-masing item sebagai tingkat prioritas dari aturan asosiasi, karakteristik aturan asosiasi dapat diketahui, yang mana hasil tersebut dapat membantu rumah sakit dalam meningkatkan kualitas pelayanan. Pada dasarnya, data memiliki partisi yang berbeda satu sama lain, atas dasar tersebut maka dalam penelitian ini dilakukan pengelompokan sebelum dilakukan penggalian informasi menggunakan aturan asosiasi, dimana proses tersebut merupakan salah satu proses yang penting. Setiap kelompok diharapkan mengandung asosiasi tanpa kontaminasi dari bagian kelompok lain yang memiliki pola hubungan yang berbeda. Penelitian ini menggunakan metode pengelompokan hirarki yang dikenal dengan Ward?s Agglomerative yang relatif sederhana untuk dipahami. Diimplementasikan, dan tidak perlu menentukan banyaknya jumlah kelompok pada awal proses.

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ABSTRACTGeneral health examinations are common elements of health care in some country. Taiwan demand for healthcare services has increased over the past decade. The increase has been driven by several factors, including an ageing population, and the increasing prevalence of chronic disease. The fluctuation number of examinees with unpredictable coming behavior makes hospital difficult to provide the satisfying service. Hospital needs to make strategic planning such as healthcare management to solve this problem by predicting examinee coming. Data mining applications in healthcare is the realization that data mining can generate information that very useful to all parties involved in the healthcare industry, such as improving the treatment quality of hospitals. This research used clustering and association rule task to know the pattern of cerebrovascular medical examination databases to predict examinees? re-coming. The Weighted-Apriori algorithm finds out the relationships among item sets using support, confidence, and weight of each feature as the priority rank of the association rule, the characteristic of the rule can be generated, which help the hospital to improve the service quality. The data is performed on partitions that are essentially distinct from each other is the reason why clustering performs before association rule mining is one of essential process. Each cluster would be expected to contain associations without interference or contamination from other sub groupings that have different patterns of relationships. This research used hierarchical clustering method called Ward?s agglomerative which relatively simple to understand, implement, and does not need to specify number of clusters in advance.;General health examinations are common elements of health care in some country. Taiwan demand for healthcare services has increased over the past decade. The increase has been driven by several factors, including an ageing population, and the increasing prevalence of chronic disease. The fluctuation number of examinees with unpredictable coming behavior makes hospital difficult to provide the satisfying service. Hospital needs to make strategic planning such as healthcare management to solve this problem by predicting examinee coming. Data mining applications in healthcare is the realization that data mining can generate information that very useful to all parties involved in the healthcare industry, such as improving the treatment quality of hospitals. This research used clustering and association rule task to know the pattern of cerebrovascular medical examination databases to predict examinees? re-coming. The Weighted-Apriori algorithm finds out the relationships among item sets using support, confidence, and weight of each feature as the priority rank of the association rule, the characteristic of the rule can be generated, which help the hospital to improve the service quality. The data is performed on partitions that are essentially distinct from each other is the reason why clustering performs before association rule mining is one of essential process. Each cluster would be expected to contain associations without interference or contamination from other sub groupings that have different patterns of relationships. This research used hierarchical clustering method called Ward?s agglomerative which relatively simple to understand, implement, and does not need to specify number of clusters in advance.;General health examinations are common elements of health care in some country. Taiwan demand for healthcare services has increased over the past decade. The increase has been driven by several factors, including an ageing population, and the increasing prevalence of chronic disease. The fluctuation number of examinees with unpredictable coming behavior makes hospital difficult to provide the satisfying service. Hospital needs to make strategic planning such as healthcare management to solve this problem by predicting examinee coming. Data mining applications in healthcare is the realization that data mining can generate information that very useful to all parties involved in the healthcare industry, such as improving the treatment quality of hospitals. This research used clustering and association rule task to know the pattern of cerebrovascular medical examination databases to predict examinees? re-coming. The Weighted-Apriori algorithm finds out the relationships among item sets using support, confidence, and weight of each feature as the priority rank of the association rule, the characteristic of the rule can be generated, which help the hospital to improve the service quality. The data is performed on partitions that are essentially distinct from each other is the reason why clustering performs before association rule mining is one of essential process. Each cluster would be expected to contain associations without interference or contamination from other sub groupings that have different patterns of relationships. This research used hierarchical clustering method called Ward?s agglomerative which relatively simple to understand, implement, and does not need to specify number of clusters in advance., General health examinations are common elements of health care in some country. Taiwan demand for healthcare services has increased over the past decade. The increase has been driven by several factors, including an ageing population, and the increasing prevalence of chronic disease. The fluctuation number of examinees with unpredictable coming behavior makes hospital difficult to provide the satisfying service. Hospital needs to make strategic planning such as healthcare management to solve this problem by predicting examinee coming. Data mining applications in healthcare is the realization that data mining can generate information that very useful to all parties involved in the healthcare industry, such as improving the treatment quality of hospitals. This research used clustering and association rule task to know the pattern of cerebrovascular medical examination databases to predict examinees’ re-coming. The Weighted-Apriori algorithm finds out the relationships among item sets using support, confidence, and weight of each feature as the priority rank of the association rule, the characteristic of the rule can be generated, which help the hospital to improve the service quality. The data is performed on partitions that are essentially distinct from each other is the reason why clustering performs before association rule mining is one of essential process. Each cluster would be expected to contain associations without interference or contamination from other sub groupings that have different patterns of relationships. This research used hierarchical clustering method called Ward’s agglomerative which relatively simple to understand, implement, and does not need to specify number of clusters in advance.]"

"KeepSight adalah kampanye dengan tujuan utama mendorong orang-orang dengan diabetes untuk melakukan pemeriksaan mata rutin. Dengan mengidentifikasi masalah sejak dini saat masih bisa diobati, inisiatif ini berpotensi mencegah kebutaan yang terkait dengan diabetes. Kampanye ini menekankan pentingnya pemeriksaan mata secara rutin dan bertujuan meningkatkan kesadaran di antara pasien diabetes tentang risiko terhadap penglihatan mereka, memastikan mereka mengambil langkah-langkah proaktif untuk menjaga kesehatan mata mereka. KeepSight juga bekerja sama dengan penyedia layanan kesehatan, memanfaatkan keahlian dan sumber daya mereka untuk menjangkau audiens yang lebih luas dan memaksimalkan dampak pesannya. Melalui materi edukasi, penjangkauan masyarakat, dan kemitraan dengan profesional medis, KeepSight berusaha menjadikan pemeriksaan mata rutin sebagai praktik standar bagi individu dengan diabetes. Meskipun Australia memiliki beberapa ahli optometri dan oftalmologi terbaik di dunia, banyak orang Australia dengan diabetes kehilangan penglihatan mereka karena mereka tidak menyadari bagaimana kondisi mereka mempengaruhi mata mereka pada tahap awal. Kurangnya kesadaran dan intervensi tepat waktu ini menyebabkan kebutaan. KeepSight mengatasi masalah ini dengan membuat lebih mudah bagi individu untuk mengatur pemeriksaan mata diabetes rutin, dengan demikian mempromosikan deteksi dini dan pengobatan kondisi mata terkait diabetes. Kampanye ini menyederhanakan proses penjadwalan pemeriksaan mata, memberikan pengingat dan dukungan untuk memastikan pasien tidak melewatkan janji mereka. Dengan menghilangkan hambatan untuk mengakses perawatan mata, KeepSight bertujuan mengurangi secara signifikan kejadian kehilangan penglihatan terkait diabetes di Australia, sehingga meningkatkan kualitas hidup mereka yang terkena dampak kondisi ini.

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KeepSight is a campaign with the primary goal of encouraging people with diabetes to sign up for routine eye exams. In identifying issues early when they are still treatable, this initiative presents a unique opportunity to prevent diabetes-related blindness. The campaign emphasizes the importance of regular eye check-ups and aims to increase awareness among diabetic patients about the risks to their vision, ensuring they take proactive steps to maintain their eye health. KeepSight also collaborates with healthcare providers, leveraging their expertise and resources to reach a wider audience and maximize the impact of its message. Through educational materials, community outreach, and partnerships with medical professionals, KeepSight strives to make routine eye exams a standard practice for individuals with diabetes. Although Australia is home to some of the best optometrists and ophthalmologists in the world, many Australians with diabetes lose their sight because they do not recognize how their condition impacts their eyes at an early enough stage. This lack of awareness and timely intervention leads to blindness. KeepSight addresses this issue by making it more straightforward for individuals to arrange routine diabetes eye exams, thereby promoting early detection and treatment of eye conditions related to diabetes. The campaign simplifies the process of scheduling eye exams, providing reminders and support to ensure patients do not miss their appointments. By removing barriers to accessing eye care, KeepSight aims to significantly reduce the incidence of diabetes-related vision loss in Australia, ultimately improving the quality of life for those affected by this condition."