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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA CommIT (Communication & Information Technology) International Journal of Computer Science and Humanitarian AI
Lili Ayu Wulandhari
Bina Nusantara University
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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DETEKSI DIABETES MELITUS UNTUK WANITA DAN PENYUSUNAN MENU SEHAT DENGAN PENDEKATAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DAN ALGORITMA GENETIKA (GA) Yuanita Sinatrya; Lili Ayu Wulandhari
JURNAL TEKNIK INFORMATIKA Vol 12, No 1 (2019): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (37.372 KB) | DOI: 10.15408/jti.v12i1.9578

Abstract

Diabetes melitus (DM) merupakan salah satu penyakit kronis (menahun) yang disebabkan berkurangnya produksi insulin dari pankreas maupun insulin yang dihasilkan tidak efektif dalam mengurangi kadar gula darah. Keadaan ini akan meningkatkan kadar gula darah sehingga merusak sistem kekebalan tubuh.  Penanganan awal pada penderita DM adalah dengan mengubah gaya hidup yaitu mengkonsumsi makanan dengan kandungan nutrisi yang diperlukan oleh tubuh dan memperbanyak aktivitas fisik. Untuk mengatur pola makan  pada penderita DM maka diperlukan diet dengan mengatur komposisi pola makanan dan mengendalikan kadar gula darah. Penelitian ini bertujuan untuk membuat suatu model penyusunan menu makanan sehat berdasarkan  jumlah kebutuhan kalori per hari, sehingga memenuhi kriteria gizi seimbang dan memenuhi variasi makanan berupa makanan pokok, lauk pauk, sayuran dan buah. Pada penelitian ini metode Adaptive Neuro Fuzzy Inference System (ANFIS) dan Algoritma Genetika (GA) digunakan untuk memberikan saran penyajian  makanan yang memenuhi jenis menu dan jumlah porsi yang ideal bagi penderita DM. Hasil penelitian menunjukkan bahwa metode yang diusulkan memperoleh nilai untuk accuracy training sebesar 89.1% dengan menggunakan metode ANFIS dan untuk pemenuhan nutrisi yang dicapai sebesar 98.9% dengan menggunakan GA yang artinya bahwa metode ANFIS dan GA dapat memberikan hasil akhir yang sangat baik yaitu dengan menghasilkan menu sehat yang memenuhi gizi yang optimal dan tercapainya keanekaragaman makanan sesuai dengan 4 pilar gizi seimbang.Kata kunci :Diabetes Melitus, ANFIS, Algoritma Genetika, menu sehat. Diabetes mellitus (DM) is one chronic disease caused by reduced production of insulin from the pancreas and insulin produced is not effective in reducing blood sugar levels. This situation will increase blood sugar levels, thus damaging the immune system. Initial treatment in diabetics is to change the lifestyle of eating foods with nutritional content needed by the body and increase physical activity. To regulate the diet in people with diabetes melitus, it takes the diet by adjusting the composition of diet patterns and control blood sugar levels. This study aims to create a healthy food menu based on the number of caloric needs per day, so that it meets the criteria of balanced nutrition and meets the variety of foods in the form of main dishes, side dishes, vegetables and fruits. In this research, Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) method are used to provide suggestions for serving foods that meet the menu type and ideal portion for DM patients. The results showed that the proposed method scored 89.1% accuracy training by using ANFIS method and for fulfillment of nutrition reached 98.9% by using GA which means that ANFIS and GA method can give excellent result that is by producing Healthy food menu that meets optimal nutrition and achievement of food diversity in accordance with 4 pillars of balanced nutrition. Keyword :Diabetes Melitus, ANFIS, Genetic Alghorithm, healthy menu. 
Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks Arie Qur'ania; Prihastuti Harsani; Triastinurmiatiningsih Triastinurmiatiningsih; Lili Ayu Wulandhari; Alexander Agung Santoso Gunawan
CommIT (Communication and Information Technology) Journal Vol. 14 No. 1 (2020): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.5952

Abstract

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.
Hardware sales forecasting using clustering and machine learning approach Rani Puspita; Lili Ayu Wulandhari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1074-1084

Abstract

This research is a case study of an information technology (IT) solution company. There is a problem that is quite crucial in the hardware sales strategy which makes it difficult for the company to predict the number of various items that will be sold and also causes the excess or shortage in hardware stocking. This research focuses on clustering to group various of items and forecast the number of items in each cluster using a machine learning approach. The methods used in clustering are k-means clustering, agglomerative hierarchical clustering (AHC), and gaussian mixture models (GMM), and the methods used in forecasting are autoregressive integrated moving average (ARIMA) and recurrent neural network-long short-term memory (RNN-LSTM). For clustering, k-means uses two attributes, namely "Quantity and Stock" as the best feature in this case study. Using these features the k-means obtain silhouette results of 0.91 and davies bouldin index (DBI) values of 0.34 consisting of 3 clusters. While for forecasting, RNN-LSTM is the best method, where it produces more cost savings than the ARIMA method. The percentage of the difference in saving costs between ARIMA and RNN-LSTM to the actual cost is 83%.
Assessing University Website Performance: A Comparative Analysis Using GTmetrix Davin Nayaka Pandya; Doddy Suryadharma; Lili Ayu Wulandhari; Islam Nur Alam
International Journal of Computer Science and Humanitarian AI Vol. 1 No. 1 (2024): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v1i1.12152

Abstract

As the reliance on websites to disseminate information increases, universities are no exception, using websites as an important platform for their information systems. However, ensuring optimal website performance is imperative, as slow or unresponsive websites can lead to decreased user satisfaction and affect the university's reputation. This study aims to analyze and compare the performance of university information system websites from Indonesia’s top five universities against the world’s leading institutions in Computer Science and Information Systems based on QS World University Rankings by Subject 2023. Utilizing GTmetrix, a comprehensive performance assessment tool, key performance metrics such as First Contentful Paint (FCP), Speed Index (SI), Largest Contentful Paint (LCP), Time to Interactive (TTI), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS) were evaluated. The findings reveal a significant performance gap between the top universities globally and in Indonesia. While global universities demonstrate good performance across various metrics, Indonesian universities exhibit areas in need of improvement, particularly in metrics like FCP, SI, LCP, TTI, and CLS. Nevertheless, Indonesian universities excel in blocking time, suggesting strategic strengths that can be leveraged for overall performance enhancement. This study underscores the importance of regular attention to website performance to enhance user experience and maintain the university's reputation within the academic community.
Co-Authors Alexander Agung Santoso Gunawan Arie Qur’ania Davin Nayaka Pandya Doddy Suryadharma Islam Nur Alam Prihastuti Harsani Rani Puspita Triastinurmiatiningsih, Triastinurmiatiningsih Yuanita Sinatrya