The development of e-government has encouraged every Regional Apparatus Organization (OPD) within the Padang City Government to submit various digital applications to improve the quality of public services. However, the large number of applications often creates challenges in determining priorities, primarily due to limited resources and budgets. This research aims to design a Hybrid Decision Support System (DSS) that combines the WASPAS (Weighted Aggregated Sum Product Assessment) method and the development of the K-Means Clustering method to provide a more objective and measurable priority classification. The WASPAS method is used to provide a ranking of alternatives based on predetermined criteria, such as urgency of need, service impact, funding availability, and alignment with the regional strategic plan. Next, the K-Means algorithm is applied to group the calculation results into several priority classes, ranging from the most urgent to the least urgent. As an innovation, this research also utilizes image processing techniques to visualize the K-Means classification results, allowing for a more intuitive and easily understood presentation of priority grouping patterns for decision-makers. In this research, data were collected from 52 OPDs within the Padang City Government as a case study. The test results show that the hybrid DSS approach combining WASPAS and K-Means successfully produces priority scale classification with an accuracy level of 94.75%, which demonstrates consistency and accelerates the application evaluation process at OPDs. Integration with image processing for visualization of clustering results also successfully helps clarify data interpretation and facilitates analysis. Thus, this system is expected to support more effective, transparent decision-making in accordance with the principles of electronic-based governance in Padang City.

The medical and technology fields are rapidly advancing, leading many people to use computers to help diagnose, prevent, and treat human diseases. One major issue in the medical world is the imbalance between the number of patients and doctors. Additionally, most people lack medical training, so when experiencing symptoms of a disease, it is often difficult to immediately know the correct steps to take. Eye diseases vary in severity, ranging from mild to severe. One common eye disorder affecting many people is refractive error, which generally falls into two categories: hyperopia (farsightedness) and myopia (nearsightedness). Early detection of symptoms related to refractive errors requires accurate and prompt diagnosis. Therefore, with the rapid development of technology, it is essential to develop systems capable of early detection of eye diseases, especially refractive errors, by using technology that mimics human expert capabilities, such as expert systems. This expert system integrates expert knowledge within two main environments: the development environment and the consultation environment, helping the community diagnose diseases more easily and efficiently. For example, the use of the Certainty Factor method in expert systems enables the calculation of diagnostic certainty levels based on the combination of symptoms reported by patients and expert knowledge, achieving a confidence level of up to 96.7%. This demonstrates that expert system technology can be a valuable tool in addressing the imbalance between patients and doctors while improving access to faster and more accurate diagnoses. To build such systems, Microsoft Visual Studio .NET provides a complete set of tools for developing ASP.NET web applications, XML Web Services, desktop applications, and mobile applications. Within Visual Studio, .NET programming languages such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp) are used in a unified integrated development environment (IDE), enabling developers to efficiently share tools and resources to create reliable and user-friendly expert system applications. The system was developed and tested using symptom data from 40 patients collected at the Tanjung Sarang Elang Community Health Center. The testing showed a diagnostic accuracy of up to 96.7% in detecting symptoms of both hyperopia and myopia.

Penelitian ini bertujuan untuk mengeksplorasi dan mengembangkan model klasifikasi berbasis K-Nearest Neighbors (KNN) dengan berbagai metode perhitungan jarak, yaitu Euclidean, Manhattan, Minkowski, dan Hamming Distance. Untuk meningkatkan akurasi model, hasil dari setiap metode jarak dikombinasikan menggunakan teknik weighted average. Dataset yang digunakan adalah dataset Iris dan Breast Cancer yang berasal dari UCI Machine Learning Repository. Preprocessing dilakukan dengan normalisasi menggunakan StandardScaler untuk memastikan skala fitur yang seragam. Model diuji menggunakan teknik validasi silang dan dievaluasi dengan metrik akurasi serta confusion matrix untuk mengetahui performa klasifikasi. Berdasarkan hasil eksperimen, penggunaan berbagai metode jarak dengan pendekatan weighted average menghasilkan peningkatan akurasi dibandingkan dengan metode KNN konvensional yang hanya menggunakan satu jenis perhitungan jarak. Hasil penelitian ini menunjukkan bahwa pendekatan kombinasi metode jarak dalam KNN dapat meningkatkan performa model dalam tugas klasifikasi. Studi ini diharapkan dapat memberikan kontribusi dalam pengembangan algoritma KNN yang lebih adaptif terhadap karakteristik data yang beragam

Generally, new prospective students often experience confusion in choosing a major in Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu that truly matches their academic abilities and interests. Often, the decision to choose a major is influenced by the environment, such as following close friends or advice from parents, without considering the students' true potential and talents. However, choosing an inappropriate major can negatively impact their academic future and career. Therefore, it is crucial for prospective students to recognize and understand their academic abilities as well as their special interests and talents. The expert system for major determination developed using the Certainty Factor method is expected to provide an effective solution by combining various indicators of interests, talents, and academic abilities of prospective students. This method utilizes established rules to calculate the certainty level (CF value) for each possible major based on the data and characteristics possessed by the student. By combining CF values from various facts, the system can provide recommendations for the major that best fits the student's greatest potential. This model does not rely solely on academic scores but also considers special interests such as social, creative, and artistic talents possessed by students, making the major selection decision more precise and directed. Thus, this Decision Support System can assist the Faculty of Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu in selecting truly potential new students and providing appropriate recommendations, thereby minimizing the risk of wrong major choices and increasing the likelihood of academic and career success for prospective students.

Penelitian ini berfokus pada pengembangan Sistem Informasi Manajemen Puskesmas (SIMPUS) berbasis Web Mobile yang dilatarbelakangi oleh kebutuhan Puskesmas Rawat Inap Jungkat untuk meningkatkan pelayanan informasi dan manajemen data. Permasalahan yang sedang dihadapi Puskesmas Rawat Inap Jungkat adalah pengelolaan data pasien yang berkunjung di puskesmas masih bersifat manual dan dilakukan secara berulang-ulang, yang mengakibatkan efisiensi dan efektivitas pelayanan menjadi terhambat dan redudansi data. Pengembangan mobile web SIMPUS menerapkan Next.js untuk front-end dan Laravel untuk back-end, dengan GraphQL sebagai protokol pengambilan data. Sistem ini dirancang untuk mempermudah proses pendaftaran pasien, manajemen data poliklinik dari pasien terdaftar, serta pengelolaan layanan yang efisien. Bentuk Penelitian ini menggunakan metode Design Science Research (DSR) dengan teknik pengumpulan data berupa observasi, wawancara, dan studi dokumentasi untuk merancang mobile web sistem informasi dan manajemen puskesmas yang berfokus pada pendaftaran pasien hingga data pasien yang mendaftar langsung masuk pada poliklinik yang dituju. Adapun pemodelan sistem dengan Unified Modeling Language (UML) dan pengujian White-Box untuk memastikan fungsionalitas sistem. Hasil penelitian diharapkan dapat memberikan kontribusi positif dalam meningkatkan kualitas pelayanan kesehatan di Puskesmas Rawat Inap Jungkat, meningkatkan efisiensi dan efektivitas operasional, serta memudahkan akses informasi bagi pasien dan petugas puskesmas.

The development of digital technology has changed the way information is interacted with and managed, including in the tourism sector. Nature tourism, as an important part of Indonesia's tourism industry, has also undergone a significant transformation in terms of technology use. This research investigates the application of information technology through websites in improving the accessibility, efficiency, management, and promotion of Pagubugan Melung Tourism in Banyumas. By adopting a website-based ticketing application, Wisata Pagubugan Melung is able to provide more complete and interactive information for visitors and simplify the process of ticket sales and visitor management. Evaluation of user acceptance of the ticketing application was conducted using the Heuristic Evaluation method and Partial Least Square Equation Model (PLS-SEM) statistical analysis. The variables that become indicators in evaluating user acceptance of ticketing applications using PLS-SEM statistical analysis are Visibility of System Status, Match Between System and the Real World, User Control and Freedom, Consistency and Standards, Error Prevention, Recognition Rather Than Recall, Flexibility and Efficient of Use, Aesthetic and Minimalist Design, Help Users Recognize, Diagnose, and Recover from Errors, Help and Documentation and Usability. The results showed that most features were well received by users. Overall, the Wisata Pagubugan Melung ticketing app is popular, showcasing the blend of technology and tourism. This research provides a better understanding of user acceptance of technological innovations in the context of tourism and provides guidance for further development in improving technology-based tourism services.

he challenge faced by craft stores in the era of digitization is the difficulty in choosing the right suppliers, resulting in unstable product availability and uncompetitive prices. Inefficient supplier selection can also disrupt the supply chain and overall business performance. The objective of developing a supplier data system software using Decision Trees is to enhance supplier selection optimization, ensure the best product quality, and improve decision-making quality. The solution design method used is Extreme Programming (XP), focusing on forming small teams. Testing is conducted using white-box methods that meet functional requirements for several software features. The result of this research is the development of a supplier data website software for Bambu Makmur Jaya store using the Laravel framework. It aims to assist Bambu Makmur Jaya store owners in easily viewing supplier and product recommendations, ensuring warranty deadlines are not missed through real-time notifications, and providing positive impacts in applying technology to business, as well as efficiency in supplier data processing and inventory management.

Pertumbuhan penduduk merupakan fenomena yang memiliki dampak signifikan terhadap pembangunan suatu daerah. Suatu tindakan diperlukan untuk mengantisipasi pertumbuhan penduduk yang semakin cepat ini, salah satunya adalah dengan melakukan prediksi jumlah penduduk. Penelitian ini bertujuan untuk mengaplikasikan metode Regresi Linear Berganda dalam memprediksi laju pertumbuhan penduduk di Kota Pekanbaru. Variabel jumlah kelahiran, jumlah laki-laki, dan jumlah perempuan digunakan sebagai prediktor dalam model regresi. Optimasi dilakukan dengan metode Gradient Descent untuk meningkatkan akurasi prediksi. Data historis dari tahun 2003 hingga 2022 digunakan untuk melatih dan menguji model. Hasil evaluasi menunjukkan bahwa model regresi linear berganda yang dioptimalkan mampu memberikan prediksi yang akurat, dengan MAPE sebesar 1.09% dan RMSE sebesar 0.20618. Pengembangan lebih lanjut dapat dilakukan dengan mempertimbangkan faktor-faktor tambahan dan penggunaan metode optimasi yang lebih canggih untuk meningkatkan akurasi prediksi. Penelitian ini diharapkan dapat memberikan kontribusi dalam pemahaman terhadap faktor-faktor yang memengaruhi pertumbuhan penduduk kota dan memberikan dasar bagi perencanaan pembangunan kota yang lebih efektif dan berkelanjutan.

Tujuan dari penelitian yang diusulkan adalah untuk membandingkan dan menguji keakuratan algoritma klasifikasi data mining. Bandingkan algoritma yang bergantung pada parameter berbeda dari kumpulan data tertentu. Sejumlah algoritma pembelajaran dan klasifikasi digunakan untuk menganalisis, mempelajari dan mengklasifikasikan data yang tersedia. Namun permasalahannya adalah menemukan algoritma terbaik dan hasil yang diinginkan yang selalu memiliki akurasi tertinggi dalam memprediksi nilai atau kejadian masa depan dari kumpulan data... Dimana model klasifikasi yang digunakan adalah algoritma C4.5 dan Naïve Bayes. Tahapan penelitian dimulai dengan Penentuan Judul Penelitian, Identifikasi Masalah : Untuk menganalisa serta menguji dari algoritma klasifikasi terhadap data tertentu. Analisa Kebutuhan : Kebutuhan sistem berupa data yang dijadikan sampel sebagai data latih dan data uji dimana dataset yang digunakan diambil dari data problem mesin ATM. Pengujian yang dilakukan dengan bantuan tools Rapidminer yang digunakan untuk membuktikan algoritma klasifikasi yang berpotensi efektif dan efisien. Pengujian yang telah dilakukan membuktikan bahwa algoritma C4.5 mempunyai performa terbaik dengan nilai akurasi sebesar 78,01% dan nilai AUC menghasilkan nilai paling mendekati angka 1 yaitu 0.86 sehingga algoritma C4.5 terbukti merupakan algoritma klasifikasi yang berpotensi efektif dan efisien