cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ichwanul Muslim Karo Karo
Contact Email
cs@unimed.ac.id
Phone
+6285262688968
Journal Mail Official
jids@unimed.ac.id
Editorial Address
Gedung 77, FMIPA di Jalan Willem Iskandar, Pasar V Medan Estate, Percut Sei Tuan, Deli Serdang
Location
Kota medan,
Sumatera utara
INDONESIA
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 33 Documents
 1   2   3   4 
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
Classification of North Sumatra Batak Ulos Based on Ethnicity Using Convolutional Neural Network Algorithm Approach Kiswanto, Dedy
Journal of Informatics and Data Science Vol 3, No 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.60388

Abstract

Ulos is a piece of cloth produced through a weaving process that reflects a rich cultural heritage and has high value. The patterns contained in woven ulos often contain philosophical meanings, reflecting the traditional values, beliefs and history of the communities that produce them. However, in reality there are still many Batak young men and women and the general public who are not yet able to distinguish between types of ulos. This research aims to help identify types of uos with the hope of providing deeper insight into the diversity of ulos based on ethnicity in North Sumatra. The dataset used in this research consists of 600 datasets which are divided into 6 types of ulos. Before the classification process is carried out, the data is cleaned through data preprocessing by cropping the image data to produce the same image data size. The research results show a classification accuracy rate of 96%. This finding confirms that the Convolutional Neural Network (CNN) method can be applied to classify ulos based on ethnicity. This has important implications in increasing understanding and appreciation of the traditional arts of the Batak tribe and supporting efforts to preserve this valuable cultural heritage
Sentiment Analysis of Twitter Users Regarding Taxation Topics in Indonesia Utilizing Multinomial Naive Bayes Tarigan, Dewan Dinata; Al Idrus, Said Iskandar
Journal of Informatics and Data Science Vol 3, No 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.52465

Abstract

The country's income is heavily dependent on taxes, which contribute to improved public well-being. Public confidence in tax authorities plays a key role in increasing tax receipts. Therefore, it is important to measure this level of confidence. One of the methods used is sentimental analysis, which helps to understand public views on regulations, services, performance, and tax policies. One of the purposes of this study is to measure the sentiment of Twitter users towards taxation in Indonesia. Sentiment analysis involves data collection processes, initial data processing, separation of datasets, feature extraction, classification, and evaluation. The classification model used is Multinomial Naive Bayes with a comparison of 80% training data and 20% test data. The results show that 89.65% of tweets about taxation in Indonesia have negative sentiment. The model evaluation was carried out on two test scenarios, namely initial data and randomly under-sampleed data. Classification on initial data achieved accuracy of 89.97%, precision of 46.68%, and sensitivity of 33.61%. Whereas on undersampling data results, accuration reached 53.28%, accurateness of 52.66%, and sensibility of 52.52%. Analysis showed significant differences between the two scenarios in which undersammpling techniques resulted in a more balanced distribution of data. Despite this, the model still faces difficulties in classifying positive and neutral data due to the dominance of negative sentiment.
Development Of An Expert System For Identifying Dental Diseases Using Certainty Factor Method (Case Study : UPT Puskesmas Parmaksian) Sirait, Gian Patar P.; Refisis, Nice Rejoice
Journal of Informatics and Data Science Vol 3, No 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.51274

Abstract

Dental health is crucial for human well-being, yet awareness of its significance is often low. This study addresses the need for an expert system to identify dental diseases, employing the Certainty Factor method. This method allows the system to express the level of certainty in expert statements, facilitating personalized use. The developed expert system calculates Certainty Factor values for each symptom, resulting in an 83% accuracy rate in identifying dental diseases based on tests conducted with 47 out of 56 cases. This research contributes to the field by providing an effective tool for dental disease identification, enhancing both awareness and practical applications in oral health.
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.
Application of Random Forest for Heart Disease Classification with SMOTE Approach to Balance Data K, Fachriz
Journal of Informatics and Data Science Vol. 3 No. 2 (2024): November 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i2.66481

Abstract

In order to increase the accuracy and efficiency in heart disease detection, this work intends to develop a Random Forest algorithm based on machine learning into a heart disease prediction model. There are 255 samples in the dataset including 17 independent variables covering lifestyle and health elements. This work uses the SMote (Synthetic Minority Over-sampling Technique) technique to balance the class distribution by including synthetic data to the minority class given the data imbalance between the "Yes" (heart disease) and "No" (no heart disease) classes. With an accuracy of 94.7% and an AUC of 0.983, the Random Forest model built showed quite good results indicating that this model can effectively separate persons with and without heart disease. This work shows that the application of SMOTE considerably enhances model performance in handling data imbalance issues and helps to build machine learning-based predictive systems for heart disease classification. This work is novel in the use of the SMOTE technique to overcome data imbalance in heart disease prediction, so providing an efficient solution for data-driven medical decision making.
Melody Transcription from Monophony Audio with Fast Fourier Transform Simanjorang, Rio Givent A; Kana Saputra S; Said Iskandar Al Idrus; Zulfahmi Indra
Journal of Informatics and Data Science Vol. 3 No. 2 (2024): November 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Music has been an inseparable part of human life since ancient times. One form of music that is often studied is monophonic music, which consists of a single note played at a time. In the digital era, melody transcription has become an important aspect of music processing, allowing sound to be converted into musical notation. This study focuses on melody transcription from monophonic sound recordings using the Fast Fourier Transform (FFT) method. The research aims to analyze the accuracy of FFT in extracting frequency components from monophonic signals and converting them into musical notation. The research methodology involves collecting monophonic sound recordings from piano and guitar, preprocessing the audio to remove noise and normalize volume, applying FFT to extract frequency features, and mapping these frequencies into musical notation. The evaluation process is conducted using Dynamic Time Warping (DTW) and a confusion matrix to measure accuracy, precision, recall, and F1-score. The results show that the FFT-based transcription system achieves an accuracy rate of 99.24% for piano and 98.86% for guitar. The study also highlights the impact of noise and audio quality on transcription accuracy, as well as the limitations of FFT in detecting closely spaced frequencies. Despite these limitations, FFT proves to be an efficient method for melody transcription in simple monophonic music. Future research could explore hybrid approaches combining FFT with other pitch detection algorithms to improve transcription accuracy.
EXPERT SYSTEM FOR IDENTIFYING DENTAL DISEASES USING THE DEMPSTER-SHAFER METHOD BASED ON ANDROID khatulistiwa, jamrud; arnita, arnita
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.51022

Abstract

The ability of an expert system to identify symptoms is not as optimal as that of a specialist because there are still uncertainties that can cause errors in identification. Therefore, the Dempster-Shafer method is used to calculate expert system uncertainty. This dental disease identification system is designed as an Android application. Then, system accuracy is tested by comparing system identification results with test data from medical records of patients with dental disease. From the results of the tests that have been carried out, the match value is obtained for as many as 26 suitable cases. So as to produce a percentage of accuracy of 86.67% in 30 cases. Overall, it can be concluded that the use of the Dempster-Shafer method in this expert system is valid for identifying dental diseases.
Performance Comparison of VGG16, VGG19 and Alexnet Pre-Trained Transfer Learning Architecture Models in the Convolutional Neural Network Algorithm in Classification of Lung Disease Harahap, Fahri Aulia Alfarisi
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.51163

Abstract

This study aims to comprehend the performance of transfer learning architectures (VGG16, VGG19, and Alexnet) in a Convolutional Neural Network for classifying lung diseases. Another objective is to determine the most superior transfer learning approach in this classification scenario. The dataset consists of 5 classes: normal lungs, pneumonia, bronchopneumonia, tuberculosis, and bronchitis. The data was sourced from Sinar Husni Deli Serdang Hospital through the radiology laboratory. The dataset was divided 80:20 for training and testing, with hyperparameters including a batch size of 32, 50 epochs, and optimization using Adaptive Momentum Optimization with a learning rate of 0.001. The research findings reveal that the VGG19 transfer learning architecture achieves the best performance with an accuracy of 59.17%, precision of 62%, recall of 59.2%, and an f-1 score of 58.8%. VGG16 ranks second with an accuracy of 55.83%, precision of 58%, recall of 55.8%, and an f-1 score of 55.2%. Alexnet has an accuracy of 49.17%, precision of 53.2%, recall of 49.2%, and an f-1 score of 50.6%. In an external test with 50 data points, VGG16 attains an accuracy of 54%, VGG19 scores 42%, and Alexnet records 46%. These models perform better in classifying normal lungs and tuberculosis compared to pneumonia, bronchopneumonia, and bronchitis. Analysis of lung image data demonstrates that homogeneity of RGB pixel values within a class supports transfer learning performance in classification. Conversely, heterogeneity in RGB pixel values can diminish the evaluation of that class.
Development Of An Expert System For Identifying Dental Diseases Using Certainty Factor Method (Case Study : UPT Puskesmas Parmaksian) Sirait, Gian Patar P.; Refisis, Nice Rejoice
Journal of Informatics and Data Science Vol. 3 No. 1 (2024): JUNE 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v3i1.51274

Abstract

Dental health is crucial for human well-being, yet awareness of its significance is often low. This study addresses the need for an expert system to identify dental diseases, employing the Certainty Factor method. This method allows the system to express the level of certainty in expert statements, facilitating personalized use. The developed expert system calculates Certainty Factor values for each symptom, resulting in an 83% accuracy rate in identifying dental diseases based on tests conducted with 47 out of 56 cases. This research contributes to the field by providing an effective tool for dental disease identification, enhancing both awareness and practical applications in oral health.

Page 3 of 4 | Total Record : 33

 1   2   3   4 

Filter by Year

2022 2024


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2024): November 2024 Vol 3, No 1 (2024): JUNE 2024 Vol. 3 No. 1 (2024): JUNE 2024 Vol 2, No 1 (2023): VOL 2, NO 1 (2023): VOL 2, NO 1 (2023) Vol. 2 No. 2 (2023): November Vol 2, No 2 (2023): November Vol 1, No 2 (2022): Vol 1, No 2 (2022) Vol 1, No 1 (2022): Vol 1, No 1 (Juni 2022)