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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Intelek Dan Cendikiawan Nusantara
Ahmad Rifki Dharmawan
Universitas Asahan
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Analisis Algoritma Backpropagation Dengan SVM Dalam Menentukan Prediksi Nilai Ujian Nasional Siswa SMP Ahmad Rifki Dharmawan
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 3 No. 01 (2026): Februari - Maret 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Dalam menghadapi Ujian Nasional tingkat Sekolah Menengah Pertama, berbagai sekolah dan dinas pendidikan setempat sering mengadakan try out dadakan serta program bimbingan belajar. Langkah ini bertujuan untuk memastikan siswa dapat lulus ujian tersebut dan mencapai standar kelulusan yang diperlukan. Selain pendekatan tersebut, alternatif lain adalah mengembangkan sistem yang mampu memperkirakan nilai Ujian Nasional siswa SMP. Penelitian ini bertujuan untuk membandingkan akurasi prediksi nilai Ujian Nasional siswa SMP menggunakan algoritma backpropagation dan Support Vector Machine (SVM). Dataset yang digunakan mencakup nilai Ujian Nasional pada mata pelajaran Bahasa Indonesia, Bahasa Inggris, Matematika, dan Ilmu Pengetahuan Alam dari siswa SMP. Kami merancang arsitektur algoritma backpropagation dengan dua model: yang pertama memiliki 5 node pada lapisan tersembunyi, dan yang kedua dengan 7 node. Kedua algoritma menerima 7 variabel sebagai input, dengan dataset berjumlah 701 baris, 561 baris untuk pelatihan dan 140 baris untuk pengujian serta menghasilkan nilai Ujian Nasional sebagai output. Hasil evaluasi menunjukkan bahwa algoritma backpropagation memberikan nilai Mean Squared Error (MSE) terendah, dengan rata-rata 103,3. Struktur yang digunakan adalah 7 node pada lapisan input, 5 node pada lapisan tersembunyi, dan 1 node pada lapisan output. Sementara itu, struktur backpropagation dengan 7 node input, 7 node tersembunyi, dan 1 node output menghasilkan MSE rata-rata 106,6. Adapun algoritma SVM mencatat MSE rata-rata sebesar 200
ANALISIS PERSEBARAN DAN PENGELOMPOKAN SARANA PRASARANA DI AEK KUASAN BERBASIS SISTEM INFORMASI GEOGRAFIS MENGGUNAKAN METODE K-MEANS Ahmad Rifki Dharmawan; Muhammad Yasin S
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6641

Abstract

Equitable distribution of public facilities and infrastructure is a crucial challenge in regional governance in Aek Kuasan District, Asahan Regency. Currently, data inventories of educational, health, and religious facilities are still managed conventionally in tabular format or as a recapitulation of physical documents. This method makes it difficult for sub-district governments and stakeholders to identify spatial distribution patterns of facilities, often resulting in disparities in accessibility between villages, such as significant differences in facility density between densely populated residential areas and plantations. This study aims to design a web-based Geographic Information System (GIS) capable of automatically visualizing and analyzing public facility clustering. The method used is the K-Means Clustering algorithm with Euclidean distance measurement to partition the region into several clusters based on proximity of location coordinates. The frontend is built using React.js and the Leaflet map library for interactive visualization, while the backend uses Node.js to handle all the algorithm's computational logic, including distance calculations and centroid determination. Data storage is performed using a PostgreSQL relational database that stores location attributes in double-precision numeric format to ensure coordinate accuracy without relying on the database's spatial extensions. System testing results indicate that the application is capable of precisely mapping the locations of infrastructure facilities in seven villages/sub-districts in Aek Kuasan District. The implementation of the K-Means algorithm successfully classified areas into dense and sparse cluster zones, visualized through differences in marker colors on the digital map. This analytical information is expected to provide an empirical basis for the Aek Kuasan District Government in making more equitable and targeted development planning decisions.
Co-Authors Muhammad Yasin S