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

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A Sistem Informasi di Apotek Whitney menggunakan metode Waterfall Nani Hidayati; Angga Priandi; Yemima Pepayosa Sembiring; Dony Jordan Pangomoan Sirait; Ikhwan Fachry Utama
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.v7i01.970

Abstract

Apotek Whitney merupakan apotek yang sudah berdiri sejak tahun 2021, dan Apotek ini memiliki kendala dimana pencatatan stok dan penjualan obat-obatan masih menggunakan sistem manual dan terkadang harga yang dijual tidak sesuai dengan harga yang seharusnya, sehingga tak jarang menyebabkan terjadinya kesalahan dan kurang akurat. Penulis mengamati bahwa cara manual ini kurang efisien. Maka dengan uraian diatas penulis tertarik untuk melakukan penelitian dan melakukan pembangunan sistem berbasis web dengan menggunakan metode waterfall. Dari hasil wawancara dan observasi yang telah dilakukan, terdapat masalah dalam pelayanan apotek dimana selama ini masih dilakukan dengan cara manual. Sehingga solusi dalam permasalahan tersebut adalah dilakukannya rancang dan implementasi sistem informasi berbasis web yang bertujuan agar sistem pelayanan berjalan dengan efisien dan efektif serta memudahkan dalam pembuatan laporan bulanan. Metode waterfall adalah model pengembangan perangkat lunak yang mengikuti pendekatan sekuensial atau linier. Dalam metode ini, setiap fase pengembangan harus diselesaikan sepenuhnya sebelum fase berikutnya dimulai, turun dari satu fase ke fase berikutnya tanpa kembali ke fase sebelumnya, dalam alur kerja seperti air terjun.
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 Alyah Octafia Angga Priandi Dony Jordan Pangomoan Sirait Ikhwan Fachry Utama Nani Hidayati Victor Asido Elyakim P