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Ichwanul Muslim Karo Karo
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Journal of Informatics and Data Science (J-IDS)
Published by Universitas Negeri Medan
ISSN : -     EISSN : 29640415     DOI : https://doi.org/10.24114/j-ids.xxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Journal of Informatics and Data Science (J-IDS) is a scientific journal managed by the Computer Science Study Program, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia which contains scientific writings on pure research and applied research in the field of computer science and data science as well as summarizing general developments in related theories, methods and applied sciences. Focus dan Scope J-IDS covers: Artificial Intelligence Science Computation Data Mining Data Science Big Data Natural Language Processing Computer Vision Expert System Text and Web Mining Parallel Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 2 (2023): November" : 5 Documents clear
Application of the Naïve Bayes Algorithm for Web-Based Classification of Family Hope Program Beneficiaries Nafisa, Anti Nada; Al Idrus, Said Iskandar
Journal of Informatics and Data Science Vol 2, No 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.47256

Abstract

The government realizes the importance of the problem of poverty by making various efforts, one of which is holding social assistance programs for the poor. One of the government policies is the Family Hope Program (PKH). The situation in the community indicates that those who receive PKH assistance from the government usually use the assistance to meet the health needs of their families, schools and daily needs, which are generally consumptive. The process of processing PKH beneficiary data in the Timbang Deli sub-district is still done manually, therefore this study aims to carry out data processing with the Naïve Bayes classification by creating a system to make it easier for officers in the Timbang Deli sub-district to determine PKH beneficiaries. The method used in this study is the Naive Bayes classification method. The variables used in this study were the head of the family, number of dependents, occupation, income, number of cars, number of motorcycles, status of residence, and condition of the house. The data in this study were 100 data from PKH beneficiaries and non-recipients of Timbang Deli Village, 80 as training data, and 20 as testing data. Based on the results of a study of 20 test data for recipients and non-recipients of PKH assistance in Timbang Deli Village, Medan Amplas District, the accuracy of the truth is 80% where there are 16 data that have values according to the test data, and 4 data that have values that do not match the test data.
Detection of Participants Facial Expressions in Video Conference Using Convolutional Neural Network Algorithm Karimuddin Hakim Hasibuan; Hermawan Syahputra
Journal of Informatics and Data Science Vol 2, No 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.49060

Abstract

Purpose: The purpose of this research is to develop an architecture based on the Convolutional Neural Network (CNN) algorithm to detect facial expressions during video conferences. The goal is to address the problem of understanding participants' emotions and expressions during online video conferencing sessions. The aim is to create a system that can analyze facial expressions in images and determine the corresponding emotions.Methods/Study design/approach: Data was collected by capturing facial expression images from 10 students using a webcam. Preprocessing techniques, such as cropping, converting images to grayscale, and data augmentation, were applied to ensure data variation. The CNN model was trained using the processed data and evaluated using test data (a subset of the dataset), new data (external data) and video conference recording. Result/Findings: The CNN model achieved a high training accuracy of 97.5% using an image size of 128x128 and 2000 epochs. The model architecture consists of 2 Conv2D layers, 3 BatchNormalization layers, 2 MaxPooling layers, 2 dropout layers, 1 flat layer, 1 dense layer, and 1 output layer. When tested on facial expression data, the model achieved with 97,5% accuracy on the training data and 93,33% accuracy on the test data. The model was also able to detect the facial expressions of participants in the video conference. Novelty/Originality/Value: The novelty of this research lies in developing a CNN-based system to detect facial expressions in video conferences by analyzing facial images. This approach addresses the challenge of understanding participants' emotions and expressions during online video conferencing sessions, which can contribute to better communication and interaction among participants.
Analysis of Prediction of Glove Production Quantity Using Sugeno's Fuzzy Logic (Case Study: PT Medisafe Technologies) R Simaremare, Martin Hans
Journal of Informatics and Data Science Vol 2, No 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.50839

Abstract

Purpose: These days there are often problems in the world sometimes have uncertain or vague answers. Therefore, fuzzy logic is one method for conducting such uncertain analysis. This thesis discusses the application of fuzzy logic Analysis of Prediction of Glove Production Quantities using the Sugeno method. The problem that is solved is to predict or predict the amount of production of goods  because some workers in the company predict production figures by filling or the minds of the workers themselves based on the previous year's production output dataStudy method/design/approach: The first step for this study is to determine the input and output variables that are firm sets and then convert each variable into a fuzzy set consisting of Little, Medium, and Many by fuzzification process. It then processes the fuzzy set data through base rules defined by the minimum method to retrieve the smallest membership degree value previously calculated through the membership function representation. And the last one is the Sugeno Method Defuzzification, which is to find the value of the average weight centrallyResults/Findings: Based on prediction analysis calculations using Stock and production data from December 2018 to January 2023, the predicted amount obtained in the following year is higher than the actual production amount in the previous year. In January 2022, the actual production output obtained from PT. Medisafe Technologies amounted to 181,822,894 pcs, while the prediction results from calculations using the Sugeno fuzzy logic model amounted to 327,147,796 pcs. The error accuracy value using MAPE is 1.66%, which means that the accuracy of truth is 99.4%. So forecasting the amount of production using the Sugeno fuzzy logic model is very good for the company.Novelty / Originality / Value: The novelty of this study lies in the development of a model using the fuzzy sugeno method to predict the amount of glove production. This approach discusses to forecast the number of glove production in a company per month interval based on data on the amount of production in the previous year  as an output  variable and raw material inventory data as an input variable.
Implementing Combined FEFO and FIFO Methods in Inventory System (Case Study: UD Ilham Pilly Beef Merchant) Ramadhan, Ilham; Usman, Ari; Sarudin, Sarudin
Journal of Informatics and Data Science Vol 2, No 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.51505

Abstract

Stock inventory is an important aspect of supply chain management. The success of the company's operations in maintaining stock availability and avoiding losses due to damage or expiration of goods is very dependent on the use of the right method of managing inventory. The purpose of this study is to combine the FEFO (first expired first out) and FIFO (first in first out) methods in the UD. Ilham Pilly Beff Merchant stock inventory system to avoid losses due to expired goods and increase stock rotation because the FEFO and FIFO methods are operational management in determining inventory. The results of this study are that the system that has been designed can facilitate managers in the process of collecting data on incoming and outgoing goods so that the risk of managing product stocks can be minimized and with an inventory system that has been built
EXPERT SYSTEM FOR DIAGNOSING DISEASES IN CATTLE USING DEMPSTER-SHAFER METHOD (Case Study: Aek Gareder Farm) yola, beby; s, kana saputra
Journal of Informatics and Data Science Vol 2, No 2 (2023): November
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i2.50610

Abstract

Sapi merupakan salah satu jenis hewan ternak yang populer di Indonesia. Mereka biasanya digunakan sebagai sumber protein, seperti susu dan daging, namun beberapa petani juga menggunakannya untuk produksi bulu dan kulit. Jumlah ternak meningkat secara signifikan setiap tahunnya. Untuk memperoleh ternak yang berkualitas, pengembangan ternak harus memperhatikan peraturan perundang-undangan, nutrisi yang tepat, dan memperhatikan kesehatan ternak. Salah satu tantangan dalam menjaga kualitas ternak adalah adanya penyakit yang menyerang ternak. Kurangnya pengetahuan peternak mengenai penyakit ternak menjadi kendala bagi mereka. Pada penelitian ini dikembangkan sistem pakar dengan metode Dempster-Shafer untuk mendiagnosis penyakit pada sapi di peternakan Aek Gareder. Tujuan penelitian ini adalah menerapkan metode Dempster-Shafer, membangun sistem pakar yang memudahkan diagnosis penyakit sapi bagi peternak, dan mengevaluasi validitas dan efektivitas sistem pakar. Hasil penelitian menunjukkan tingkat akurasi diagnosis sistem pakar mencapai 95%. Dengan hadirnya sistem pakar ini diharapkan dapat meningkatkan pengetahuan peternak, memudahkan diagnosis penyakit ternak, dan memberikan kontribusi positif terhadap pemeliharaan ternak secara keseluruhan.

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