Classification of Anaemia Status Using The K-Nearest Neighbor Algorithm

Early detection and accurate diagnosis of anemia are crucial for public health management, with conventional methods like complete blood count often being costly and unavailable in remote areas. The use of machine learning techniques, specifically the k-nearest neighbor (KNN) algorithm, shows promise in classifying medical conditions including anemia with competitive accuracy compared to traditional methods. The implementation of KNN not only offers accuracy but also time and cost efficiency, providing reliable results quickly for medical professionals in the field. The algorithm's application involves determining the appropriate k-value for optimal accuracy, calculating distances using Euclidean distance, and voting for class prediction based on nearest neighbors. The analysis showcases the model's efficiency in predicting anemia status with an accuracy of 94.72% and promising precision and recall rates.arly detection and accurate diagnosis of anemia are crucial for public health management, with conventional methods like complete blood count often being costly and unavailable in remote areas. The use of machine learning techniques, specifically the k-nearest neighbor (KNN) algorithm, shows promise in classifying medical conditions including anemia with competitive accuracy compared to traditional methods. The implementation of KNN not only offers accuracy but also time and cost efficiency, providing reliable results quickly for medical professionals in the field. The algorithm's application involves determining the appropriate k-value for optimal accuracy, calculating distances using Euclidean distance, and voting for class prediction based on nearest neighbors. The analysis showcases the model's efficiency in predicting anemia status with an accuracy of 94.72% and promising precision and recall rates

Child Care Pattern Model: A Nursing Science and Islamic Education Perspective

Parenting plays an important role in shaping the character and health of children physically, mentally, and spiritually. This study aims to formulate a holistic parenting model by integrating nursing science and Islamic education approaches to address modern parenting challenges. This study used a qualitative method with a case study design, involving four participants, consisting of pediatric nurses and Islamic religious educators. Interviews showed that both nursing science and Islam emphasize the importance of meeting children's needs holistically through love (asih), physical care (asuh), and intellectual-social stimulation (asah). Meanwhile, Islam adds spiritual values and role modeling through prophetic parenting. The democratic parenting model is the most appropriate because it encourages two-way communication, empathy, and value-based discipline. Nurses play an important role in assisting parents through contextualized and collaborative education. The findings conclude that the integration of nursing and Islamic values in parenting can create a generation that is physically healthy, mentally strong, and noble. This research is expected to be a practical reference in local value-based parenting that is relevant to the context of Indonesian society.   Keywords: parenting model, pediatric nursing, holistic parenting, Islamic education, Prophetic parentin

Application of Taqrir Method in Strengthening Santriwati's Al-Qur'an Memory at Hidayatullah Islamic Boarding School Ampenan Sari Garden, Mataram City

This article aims to outline the application of the takrir method in enhancing the memorization of the Qur'an and the factors that facilitate or hinder this implementation among female students at the Al-Iman Foundation, Hidayatullah Islamic Boarding School, located in Kebun Sari Ampenan, Mataram City. The research employs a qualitative approach through observation, interviews, and documentation. The findings reveal that the takrir method is applied through four specific techniques, which include group takrir, individual takrir, takrir during prayers, and takrir in the presence of a teacher. These techniques are integrated with tahfidz and tasmi activities: Factors that support and impede the use of the takrir method are identified as follows: Supporting factors include teachers who are strict and consistent despite their limited numbers, parental motivation, a favorable environment for memorization, diligent peers, although some may show laziness and adequate cognitive abilities. Conversely, the hindrances consist of student conditions, such as laziness, fatigue, diminishing enthusiasm, emotional states, issues with memory retention, limited educational resources, particularly regarding teachers/ustazah, and constraints on time.   Keywords: Takrir Method, Memorizing the Qur’an, Islamic Boarding School  

Study on Flood Control of the Sumber Pinang River in Sidogiri, Pasuruan Regency through River Normalization, Levees, and Parapet Walls

The Sumber Pinang River in Pasuruan Regency frequently experiences flooding due to its insufficient capacity. This study aims to analyze flood control measures using hydrological and hydraulic approaches with the HEC-RAS 6.4.1 software. The data used include maximum rainfall, river cross-section profiles, and land use. The planned flood discharge was calculated using the Nakayasu Synthetic Unit Hydrograph (SUH) method, resulting in a peak discharge of 442.823 m³/s. The analysis results indicate that 81% of the river cross-sections are prone to overflow. Proposed control measures include river normalization and the construction of levees and parapet walls. River normalization is planned for the entire river body, while levees and parapet walls are proposed at critical points to enhance the river's capacity. This study provides recommendations for more effective flood control planning in the future.The Sumber Pinang River in Pasuruan Regency frequently experiences flooding due to its insufficient capacity. This study aims to analyze flood control measures using hydrological and hydraulic approaches with the HEC-RAS 6.4.1 software. The data used include maximum rainfall, river cross-section profiles, and land use. The planned flood discharge was calculated using the Nakayasu Synthetic Unit Hydrograph (SUH) method, resulting in a peak discharge of 442.823 m³/s. The analysis results indicate that 81% of the river cross-sections are prone to overflow. Proposed control measures include river normalization and the construction of levees and parapet walls. River normalization is planned for the entire river body, while levees and parapet walls are proposed at critical points to enhance the river's capacity. This study provides recommendations for more effective flood control planning in the future

Gen Z's Interest in Online Travel Agencies in Indonesia: A Modified UTAUT2

The rapid evolution of digital technology has transformed the travel and tourism industry, making Online Travel Agencies (OTAs) a preferred medium for trip planning and booking. OTAs offer seamless access to flights, accommodations, and other travel services, catering to Indonesia's mobile-first population and digitally savvy Gen Z users. This study examines factors influencing behavioral intention (BI) and use behavior (UB) toward OTAs, extending the UTAUT2 model with trust, website quality, and AI integration. Data from 300 respondents were analyzed using Structural Equation Modeling (SEM) via SPSS and AMOS. Results show that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, trust, website quality, and AI integration positively affect BI. Habit negatively influenced BI, while BI, facilitating conditions, and habit positively impacted UB. These findings highlight the need for OTA providers to align user expectations with actual experiences, enhancing satisfaction and long-term adoption among Gen Z users.The rapid evolution of digital technology has transformed the travel and tourism industry, making Online Travel Agencies (OTAs) a preferred medium for trip planning and booking. OTAs offer seamless access to flights, accommodations, and other travel services, catering to Indonesia's mobile-first population and digitally savvy Gen Z users. This study examines factors influencing behavioral intention (BI) and use behavior (UB) toward OTAs, extending the UTAUT2 model with trust, website quality, and AI integration. Data from 300 respondents were analyzed using Structural Equation Modeling (SEM) via SPSS and AMOS. Results show that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, trust, website quality, and AI integration positively affect BI. Habit negatively influenced BI, while BI, facilitating conditions, and habit positively impacted UB. These findings highlight the need for OTA providers to align user expectations with actual experiences, enhancing satisfaction and long-term adoption among Gen Z users

Game Based Learning Using Construct for Basic English Vocabulary at Elementary School Students

English basic vocabulary learning plays a very important role for elementary school students, as it serves as the main foundation in mastering language skills, including reading, writing, listening, and speaking.  Thus, this research is focused on development.  Basic English Vocabulary Education Based on Construct, which is an interactive learning method utilizing digital technology of educational games to increase the effectiveness for learning.  Construct, as a platform for developing educational games for students can understand basic English vocabulary in a more enjoyable and non-monotonous way.  This study applies the Multimedia Development Life Cycle (MDLC) method with a constructivist approach, which allows students not only to receive information passively but also to actively build their understanding through exploration, experimentation, and direct interaction with various elements in educational games.  Based on the feasibility assessment results, the satisfaction level of respondents obtained from the questionnaire reached 93% out of a total of 10 respondents. The construct based educational game is an effective tool for improving students' grasp of basic English vocabulary through educational game-based learning.Pembelajaran kosakata dasar bahasa Inggris memainkan peran yang sangat penting bagi siswa sekolah dasar, karena berfungsi sebagai fondasi utama dalam menguasai keterampilan bahasa, termasuk membaca, menulis, mendengarkan, dan berbicara. Dengan demikian, penelitian ini difokuskan pada pengembangan. Pendidikan Kosakata Dasar Bahasa Inggris Berdasarkan Construct, yang merupakan metode pembelajaran interaktif yang memanfaatkan teknologi digital dari permainan edukatif untuk meningkatkan efektivitas pembelajaran. Construct, sebagai platform untuk mengembangkan permainan edukatif, digunakan Untuk menciptakan pengalaman belajar yang lebih menarik, interaktif, dan efektif, siswa dapat memahami kosakata dasar bahasa Inggris dengan cara yang lebih menyenangkan dan tidak monoton. Studi ini menerapkan metode (MDLC) dengan pendekatan konstruktivis, yang memungkinkan siswa tidak hanya menerima informasi secara pasif tetapi juga secara aktif membangun pemahaman mereka melalui eksplorasi, eksperimen, dan interaksi langsung dengan berbagai elemen dalam permainan edukatif. Berdasarkan hasil penilaian kelayakan, tingkat kepuasan responden yang diperoleh dari kuesioner mencapai 93% dari total 10 responden. Temuan studi ini sangat menyarankan bahwa Construct adalah alat yang efektif untuk meningkatkan pemahaman siswa tentang kosakata dasar bahasa Inggris melalui pembelajaran berbasis permainan edukatif

The Effect of H₂SO₄ Catalyst and Electric Voltage on Hydrogen Gas Production via the Electrolysis of Distilled Water

Hydrogen is an environmentally friendly alternative energy source with great potential in the renewable energy sector. One efficient method for hydrogen production is water electrolysis, which can be influenced by catalyst concentration and applied electric voltage. This study aims to analyze the effect of varying H₂SO₄ concentrations and electric voltage on hydrogen production via water electrolysis. The concentrations used were 0.05 M, 0.075 M, 0.1 M, 0.25 M, and 0.5 M, while the applied voltages were 16 V, 18 V, 20 V, 22 V, and 24 V. Constant parameters included 500 mL of distilled water and a 2-minute hydrogen collection time. Gas analysis using Gas Chromatography-Thermal Conductivity Detector (GC-TCD) showed hydrogen detection at a retention time of 2.88 minutes. The highest hydrogen content, 11.143% mol, was achieved at 0.075 M H₂SO₄ and 24 V, with a maximum gas volume of 0.000659 m³. Based on RSNI ISO 14687:2019, the minimum fuel-grade hydrogen requirement is 50% mol. Therefore, further optimization is necessary to improve efficiency. Future studies are recommended to explore alternative catalysts, extend electrolysis time, or modify electrodes, as well as include tests without catalysts to evaluate reaction sustainability and compare hydrogen yields with and without catalytic influence.Hydrogen is an environmentally friendly alternative energy source with great potential in the renewable energy sector. One efficient method for hydrogen production is water electrolysis, which can be influenced by catalyst concentration and applied electric voltage. This study aims to analyze the effect of varying H₂SO₄ concentrations and electric voltage on hydrogen production via water electrolysis. The concentrations used were 0.05 M, 0.075 M, 0.1 M, 0.25 M, and 0.5 M, while the applied voltages were 16 V, 18 V, 20 V, 22 V, and 24 V. Constant parameters included 500 mL of distilled water and a 2-minute hydrogen collection time. Gas analysis using Gas Chromatography-Thermal Conductivity Detector (GC-TCD) showed hydrogen detection at a retention time of 2.88 minutes. The highest hydrogen content, 11.143% mol, was achieved at 0.075 M H₂SO₄ and 24 V, with a maximum gas volume of 0.000659 m³. Based on RSNI ISO 14687:2019, the minimum fuel-grade hydrogen requirement is 50% mol. Therefore, further optimization is necessary to improve efficiency. Future studies are recommended to explore alternative catalysts, extend electrolysis time, or modify electrodes, as well as include tests without catalysts to evaluate reaction sustainability and compare hydrogen yields with and without catalytic influence

Penggunaan Algoritma Greedy dan Deep Reinforcement Learning untuk Optimasi Jadwal Operasi dalam Adaptive Scheduling

Operating room scheduling faces persistent challenges in healthcare facilities worldwide, with inefficiencies leading to resource wastage, extended patient waiting times, and staff burnout. This study addresses these challenges through three methodologies: greedy algorithm, deep reinforcement learning (DRL), and a novel hybrid model. Analysis of 35,000 surgical procedures revealed significant inefficiencies in current practices, including OR overutilization (463.87%), substantial waiting times (170.07 minutes), and frequent delays (58.39% of procedures). The hybrid model demonstrated superior performance, achieving a 34.2% reduction in OR utilization, 55.9% reduction in waiting times, and 87.5% improvement in on-time procedures compared to baseline. These improvements translated into significant clinical benefits, including reduced staff overtime (57.1%) and enhanced emergency case accommodation (17.6%). The hybrid model's resilience to operational disruptions and balanced performance across multiple dimensions provides compelling evidence for implementing adaptive scheduling methodologies in clinical practice, offering a comprehensive solution that balances efficiency, adaptability, and patient-centered care.Penelitian ini membahas optimalisasi penjadwalan ruang operasi melalui tiga pendekatan utama: algoritma greedy, deep reinforcement learning (DRL), dan model hibrida yang inovatif. Analisis terhadap 35.000 prosedur bedah mengungkapkan berbagai ketidakefisienan dalam praktik penjadwalan saat ini, termasuk pemanfaatan ruang operasi yang berlebihan (463,87%), waktu tunggu yang tinggi (170,07 menit), serta keterlambatan prosedur yang sering terjadi (58,39%). Model hibrida menunjukkan kinerja yang unggul dengan penurunan pemanfaatan ruang operasi sebesar 34,2%, pengurangan waktu tunggu sebesar 55,9%, dan peningkatan ketepatan waktu pelaksanaan prosedur sebesar 87,5% dibandingkan kondisi awal. Peningkatan ini berdampak signifikan secara klinis, termasuk pengurangan lembur tenaga medis sebesar 57,1% dan peningkatan kapasitas penanganan kasus gawat darurat sebesar 17,6%. Ketahanan model hibrida terhadap gangguan operasional serta kinerjanya yang seimbang dalam berbagai aspek memberikan dasar yang kuat untuk penerapan metode penjadwalan adaptif dalam praktik klinis, dengan menawarkan solusi menyeluruh yang menyeimbangkan efisiensi, adaptabilitas, dan pelayanan berpusat pada pasien

Synthesis of Aragonite Polymorphs from Five Types of Sea Shells by Carbonation Method

Shell waste accumulates along the coastal areas of Surabaya and Sidoarjo, with various types of shells including blood shells, green shells, feather shells, kampak shells, and batik shells. Currently, the waste from these shells is not being utilized optimally. Marine shell waste contains more than 95% calcium carbonate (CaCO3), making it suitable for the production of aragonite crystals, which can serve as raw materials for applications such as bone regeneration, paper manufacturing, and as fillers in rubber and plastics. The objective of this research is to synthesize aragonite from five types of marine shells and to investigate the effect of carbonation temperature on the percentage of aragonite formation. The method employed for synthesizing aragonite crystals is carbonation. Initially, the CaCO3 derived from marine shells is calcined at 900°C to produce calcium oxide (CaO). This calcium oxide is then dissolved in hydrochloric acid (HCl) to form calcium chloride (CaCl2). Subsequently, sodium hydroxide (NaOH) is added to CaCl2, and carbon dioxide (CO2) gas is bubbled through the solution to precipitate CaCO3 along with by-products of NaCl and water. The variables examined include comparisons among five types of shells (blood shells, green shells, feather shells, kampak shells, and batik shells) at calcination temperatures of 30°C, 60°C, and 90°C. The best results were obtained from the synthesis using green shells, yielding an aragonite crystal polymorph percentage of 76.4% at a carbonation temperature of 90°C. Conversely, the smallest yield of aragonite crystal polymorph was observed with batik shells at 26.0%.Shell waste accumulates along the coastal areas of Surabaya and Sidoarjo, with various types of shells including blood shells, green shells, feather shells, kampak shells, and batik shells. Currently, the waste from these shells is not being utilized optimally. Marine shell waste contains more than 95% calcium carbonate (CaCO3), making it suitable for the production of aragonite crystals, which can serve as raw materials for applications such as bone regeneration, paper manufacturing, and as fillers in rubber and plastics. The objective of this research is to synthesize aragonite from five types of marine shells and to investigate the effect of carbonation temperature on the percentage of aragonite formation. The method employed for synthesizing aragonite crystals is carbonation. Initially, the CaCO3 derived from marine shells is calcined at 900°C to produce calcium oxide (CaO). This calcium oxide is then dissolved in hydrochloric acid (HCl) to form calcium chloride (CaCl2). Subsequently, sodium hydroxide (NaOH) is added to CaCl2, and carbon dioxide (CO2) gas is bubbled through the solution to precipitate CaCO3 along with by-products of NaCl and water. The variables examined include comparisons among five types of shells (blood shells, green shells, feather shells, kampak shells, and batik shells) at calcination temperatures of 30°C, 60°C, and 90°C. The best results were obtained from the synthesis using green shells, yielding an aragonite crystal polymorph percentage of 76.4% at a carbonation temperature of 90°C. Conversely, the smallest yield of aragonite crystal polymorph was observed with batik shells at 26.0%

Compressing Large Language Models (LLMs) using Knowledge Distillation for Optimizing Inference Time and Model Size

Large Language Models (LLMs) contain a vast number of parameters and are significantly large in size. For instance, the DeepSeek-V3 model consists of approximately 671 billion parameters and has a file size of up to 720GB. The sheer number of parameters in LLMs reflects their high complexity, which can serve as both an advantage and a drawback, particularly when deployed in environments with limited computational resources. This study focuses on compressing a custom-built lightweight model using knowledge distillation techniques applied to LLMs. The results indicate that the model’s parameters can be reduced by up to 94.18%, its file size by up to 71.00%, and its inference time by up to 1.13%. Notably, despite these reductions, the model remains capable of performing specialized tasks with satisfactory accuracy. This finding underscores the potential of knowledge distillation as an effective method for reducing model size while maintaining operational efficiency, particularly in scenarios where computational constraints lead to mismatched capabilities. Efficiency in knowledge distillation is achieved through a combination of model size reduction and the alignment of computational capacity with task-specific requirements