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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol. 22, No. 1, January 2024" : 7 Documents clear
MACHINE LEARNING JOURNAL ARTICLE RECOMMENDATION SYSTEM USING CONTENT BASED FILTERING Rianti, Afika; Majid, Nuur Wachid Abdul; Fauzi, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1193

Abstract

Indonesia is a country that hasn’t studied much about artificial intelligence. This has resulted in a small number of publications related to that field including areas within such as machine learning. For that reason, it caused difficulties in finding relevant journal articles. The purpose of this study is to know the performance of the Content Based Filtering method in providing machine learning journal article recommendations. The research procedure used is CRISP-DM with algorithms used are TF-IDF and Cosine Similarity. The dataset used consists of 100 machine learning journal articles. Based on the research that has been done, it’s concluded that the performance of the Content Based Filtering method in providing machine learning journal article recommendations as measured using the precision evaluation matrix showed a score of 76%, which means the result is quite good. However, the model couldn’t be used properly for some data due to the small number of datasets which affects the limited recommendations. 
CLASSIFICATION OF LUNG AND COLON CANCER TISSUES USING HYBRID CONVOLUTIONAL NEURAL NETWORKS Nisa', Chilyatun; Suciati, Nanik; Yuniarti, Anny
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1225

Abstract

Colon and lung cancers are two highly lethal kinds of cancer which can often coexist and pose a new challenge for accurate diagnosis. While research often concentrates on detecting a single cancer in a specific organ, this study proposes an innovative machine-learning approach to identify both colon and lung cancers. The objective is to create a hybrid machine learning classification model to enhance diagnostic precision. The LC25000 dataset comprises 25,000 color histopathological image samples of lung and colon cell tissues, indicating the presence or absence of cancer (adenocarcinoma). Image features are extracted using the pre-trained VGG-16 model. The cancer type is identified through three machine learning classification algorithms: Stochastic Gradient Descent (SGD), Random Forest (RF), and K-Nearest Neighbor (KNN). The model's evaluation employed a 10-fold cross-validation technique, with CNN-SGD exhibiting the highest performance based on evaluation metrics. On a scale of 0 to 100, it scored 99.8 for Area Under Curve (AUC) and 98.88 for Classification Accuracy (CA). CNN-RF, a model with performance closely following CNN-SGD, demonstrates training times 58.3 seconds faster than CNN-SGD. Meanwhile, CNN-KNN ranks last among the models evaluated in this study based on its F1, recall, AUC, and CA scores.
MOBILE-BASED ONLINE QUEUE APPLICATION DEVELOPMENT AT GRIBIG PUBLIC HEALTH CENTER IN REALTIME Hutomo, Dwiky Satria; Oktavia, Chaulina Alfianti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1197

Abstract

Puskesmas is one of the health service facilities that organizes community and individual health efforts at level one. Gribig Health Center is one of the health centers in the city of Malang, East Java, which has several health services. To get services from the Puskesmas, each patient is required to register and complete the required files for further processing by the administration. However, the imbalance between the number of patients and the availability of services is the cause of queues. This study aims to create a mobile-based queuing application at the Gribig Health Center in real-time. The ar-chitectural concept used in developing applications is client-server. The queuing method used in the system is a combina-tion of FCFS (First Come First Served) and PS (priority service) methods. In system development, the development method used in this research is the waterfall method. For system testing, the author uses the Black Box Testing method to ensure that all application functionality is appropriate. The purpose of developing this application is to make it easier for pa-tients to get queue numbers for Gribig Health Center services anywhere and anytime, make it easier for patients to make registration bookings for other days in advance, exchange queue numbers, notifications when their turn is approaching, find out the estimated time to get service, and queue information. up-to-date for each service at the Puskesmas. The results of this study are successful in developing an online queuing application at the Gribig Health Center in real-time by utiliz-ing the QR Code to verify the queue and there is also a notification feature as a patient reminder.
COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS Febriani, Kristina; Fatichah, Chastine
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1209

Abstract

Forecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5.11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1.07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 and R-Square 0.88, RNN with MAPE value of 5.19%, MAE 2.91 and R-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error values among the other models. With forecasting results that have a small error rate, it can help management in making decisions to minimize costs in coal ordering and warehouse management.
SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK Kusnanti, Eka Alifia; Vantie, Lauretha Devi Fajar; Yuhana, Umi Laili
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1199

Abstract

Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).
OVERSAMPLING HYBRID METHOD FOR HANDLING MULTI-LABEL IMBALANCED Tursina, Dara; Anggraeni, Sherly Rosa; Fatichah, Chastine; Irfan Subakti, Misbakhul Munir
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1208

Abstract

Data and information continue to increase along with the development of digital technology. Data availability is becoming increasingly numerous and complex. The existence of unbalanced data causes classification errors due to the dominance of majority-class data over the minority class. Not only limited to the binary class, but data imbalance is also often encountered in multi-label data, which become increasingly important in recent years due to its vast application scope. However, the problem of class imbalance has been a characteristic of many complex multi-label datasets, making it the focus of this research. Handling unbalanced multi-label data still has a lot of potential for development. One approach, Synthetic Oversampling of Multi-Label Data Based on Local Label Distribution (MLSOL) and Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-Label Data (UCLSO), has been developed. UCLSO's attention only focuses on the majority class, which can lead to data imbalance and excessive overfitting. Although effective in preventing majority class domination, this approach cannot overcome the lack of variation within the minority class. By contrast, MLSOL focuses on minority classes, allowing for variations in multi-label data and significantly improving classification performance. This research aims to overcome the problem of data imbalance by combining the MLSOL and UCLSO oversampling methods. Combining these two approaches is expected to exploit the strengths and reduce the weaknesses of each, resulting in significant performance improvements. The trial results show that the hybrid oversampling method produces the highest value on biological data with an F-1 score of 88%. Meanwhile, the single oversampling methods UCLSO and MLSOL on biological data produce an F-1 score of 67% and 62%, respectively.
UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Baskoro, Fajar
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1214

Abstract

In geographic analysis, land cover plays an important role in everything from environmental analysis to sustainable planning methods and physical geography studies. The Indonesian National Standard (SNI 7645:2014) classifies vegetation analysis based on density. There are four categories of vegetation density index: non-vegetation, bare, medium, and high. Technically, vegetation data can be obtained through remote sensing. Satellite and UAV data are two types of data used in remote sensing to collect information. This research will analyze land cover based on vegetation density information that can be collected through remote sensing. Based on vegetation density information from remote sensing, the information can help in land processing, Land Cover Classification is carried out based on vegetation density. Convolutional neural networks (CNN) have been trained extensively to apply their properties to land cover classification. This research will evaluate features extracted from Convolutional Neural Networks (ResNet 50, Inception-V3, DenseNet 121) which have previously been trained and continued with Decision Tree algorithms, Random Forest, Support Vector Machine and eXtreme Gradient Boosting to perform classification. From the comparison results of classification tests between machine learning methods, Support Vector Machine is superior to other machine learning methods. This is proven by the accuracy results obtained at 85% with feature extraction using ResNet-50 where the processing time is 8 minutes. Followed by the second-best model, namely ResNet-50 with XGBoost which obtained accuracy results of 82% with a processing time of 55 minutes. Meanwhile, the use of feature extraction using the DenseNet-121 method was obtained using a combination of the Support Vector Machine method and the XGBoost method with the accuracy obtained being 81%.

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