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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi JOMLAI: Journal of Machine Learning and Artificial Intelligence
Alyah Octafia
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Computer Science & IT Engineering

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DESAIN DAN IMPLEMENTASI SITEM INFORMASI PADA PENDAFTARAN SISWA BARU BERBASIS ONLINE UNTUK MENINGKATKAN EFISIENSI PROSES SELEKSI Nani Hidayati; Alyah Octafia; Dandi Dwi Putra Batubara; Michael Kevin Artado Sihombing; Roger Susilo Napitupulu
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 01 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i01.973

Abstract

Penelitian ini berfokus pada desain dan implementasi sistem informasi pendaftaran siswa baru berbasis online yang bertujuan untuk meningkatkan efisiensi proses seleksi. Latar belakang penelitian ini adalah kebutuhan untuk mengotomatisasi dan menyederhanakan proses pendaftaran yang sebelumnya dilakukan secara manual, yang sering kali memakan waktu dan rentan terhadap kesalahan. Metodologi yang digunakan dalam penelitian ini mencakup analisis kebutuhan pengguna, perancangan sistem menggunakan pendekatan berorientasi objek, pengembangan aplikasi web dengan bahasa pemrograman PHP dan basis data MySQL, serta serangkaian pengujian untuk memastikan fungsionalitas dan keandalan sistem. Hasil penelitian menunjukkan bahwa implementasi sistem informasi berbasis online ini memberikan dampak signifikan terhadap efisiensi proses pendaftaran dan seleksi siswa baru. Sistem ini berhasil mengurangi waktu proses pendaftaran sebesar 40% dan meminimalkan kesalahan input data hingga 60% dibandingkan dengan sistem manual sebelumnya. Selain itu, sistem ini juga meningkatkan aksesibilitas bagi calon siswa dan memudahkan pihak sekolah dalam mengelola dan menganalisis data pendaftar. Kesimpulannya, penelitian ini membuktikan bahwa penerapan teknologi informasi dalam proses pendaftaran siswa baru dapat secara efektif meningkatkan efisiensi operasional dan akurasi data, serta memberikan pengalaman yang lebih baik bagi semua pihak yang terlibat dalam proses seleksi.
Diagnosis of Skin Diseases Using Artificial Neural Networks with Backpropagation Algorithm Dony Jordan Pangomoan Sirait; Angga Priandi; Yemima Pepayosa Sembiring; Alyah Octafia; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5643

Abstract

Skin health is a vital aspect as it functions as the body's primary protector from the external environment. Various skin diseases can arise due to infections, allergies, autoimmune disorders, or environmental factors, and often exhibit similar symptoms, making diagnosis difficult. Artificial intelligence technology, such as Artificial Neural Networks (ANN), offers an innovative solution for accurate diagnosis. One popular ANN method is Backpropagation, which updates network weights iteratively based on the errors produced. This research focuses on applying the Backpropagation algorithm to diagnose skin diseases based on patient symptoms. With a binary data-based system and training using Backpropagation, this system is expected to accurately map symptoms to types of skin diseases. The methodology involves problem identification , data collection (types of skin diseases and symptoms, encoded in binary), dataset and diagnosis rule formation , ANN design (input, hidden, and output layers) , and training and testing using binary data and one-hot encoding. The results indicate that the application of ANN with Backpropagation is effective in assisting the automatic diagnosis process for skin disease cases , achieving an accuracy of 90%. This demonstrates the significant potential of this method in automated medical expert systems.
Diagnosis of Gastric Disease Based on Artificial Neural Network with Hebb Rule Algorithm Victor Asido Elyakim P; Alyah Octafia; Yemima Pepayosa Sembiring; Dony Jordan Pangomoan Sirait; Angga Priandi
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6543

Abstract

Gastric disorders are among the most common health problems faced by society, often caused by irregular eating habits, unhealthy lifestyles, and high stress levels. The symptoms are diverse, ranging from abdominal pain and nausea to weight loss, making accurate and timely diagnosis essential to prevent more serious complications. This study aims to develop a diagnostic system for gastric diseases using Artificial Neural Networks (ANN) with the Hebb Rule algorithm, a learning principle that strengthens the connections between neurons when they are activated simultaneously. The research utilized binary-encoded data consisting of ten types of gastric diseases and twenty associated symptoms to establish patterns of correlation between symptoms and diagnoses. The results demonstrate that the system successfully recognized all test data with outcomes consistent with the expected targets, proving that the Hebb Rule is effective in mapping symptom-disease relationships even when applied to simple binary data. These findings highlight the practicality and efficiency of the Hebb Rule in building an intelligent diagnostic framework, while also showing its potential for further development with more complex datasets, such as symptom severity levels or laboratory test results. Ultimately, this research contributes to the advancement of smart medical systems that can support both healthcare professionals and the general public in performing early detection of gastric diseases quickly, accurately, and effectively.
Co-Authors Angga Priandi Dandi Dwi Putra Batubara Dony Jordan Pangomoan Sirait Michael Kevin Artado Sihombing Nani Hidayati Roger Susilo Napitupulu Victor Asido Elyakim P Yemima Pepayosa Sembiring