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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
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Articles 20 Documents
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Search results for , issue "Vol. 12 No. 3 (2023): NOVEMBER" : 20 Documents clear
Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN Vannya, Regina; Hermawan, Arief
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1740

Abstract

Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.
Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features Yulina, Syefrida; Rachmawati, Heni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1742

Abstract

Facial Expression Recognition (FER) is currently widely explored by researchers in the field of Computer Vision. The application of Machine Learning and Deep Learning methods is useful in developing an intelligent system that is accurate in recognizing facial expressions such as emotions. This is inseparable from the type of dataset and classification method used which certainly affects the desired results. To choose the right method, it is necessary to compare the performance of these methods. This study focuses on comparing the performance results of four classification methods namely, Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) on a multiclass dataset for seven classes of facial emotion labels based on Eigenface feature selection uses the Personal Component Analysis (PCA) algorithm. The test parameters used to perform method comparisons are accuracy, recall, precision, f1-score, as well as the Receiving Operating Characteristic (ROC) and Area Under Curve (AUC) curves. The results of the analysis state that the SVM method has the highest accuracy value, while other methods show varying performance based on recall, precision, f1-score, and ROC and AUC analysis. This research was conducted on the FER 2013 dataset which showed that the classification method tested had quite good performance according to the test parameters.
User Acceptance Analysis Dana Application E-Wallet Using UTAUT 2 and UX TAM Meliana, Putri; Mutiah, Nurul; Rahmayuda, Syahru
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1750

Abstract

DANA is a digital wallet application that has an open platform concept, meaning it can be used on different platforms but is integrated with one another. However, there were complaints that were felt by DANA application users which were conveyed in Google Playstore reviews, namely frequent errors and delays in the transaction process. This is the basis for measuring the level of acceptance of DANA application users based on the user's experience. This research model is an integration of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model and the User Experience technology Acceptance Model (UX TAM). The data analysis technique used Partial Least Square-Structural Equation Modeling (PLS-SEM) and used SmartPLS 3 tools Data collection was carried out by randomly distributing questionnaires to 100 respondents, namely the Pontianak community with an age range of 15-40 years. Data collection was carried out by distributing questionnaires to 100 respondents, namely the Pontianak community. Of the 21 hypotheses proposed, 10 hypotheses stated there was a relationship between the two variables and the other 11 hypotheses had no relationship. The hypotheses that have an influence are Effort Expectancy on Behavior Intention, Habit on Behavior Intention, Efficiency on Effort Expectancy, Efficiency on Performance Expectancy, Output Quality on Performance Expectancy, Dependability on Habit, Stimulation on Hedonic Motivation, Output Quality on Perceived Usefulness, Dependability on Perceived Ease Of Use, and Behavioral Intention to Use Behavior. The results of the research are in the form of recommendations that are expected to improve the performance of the DANA application.
Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem Riti, Yosefina Finsensia; Iskandar, Jonathan Steven; Hendra, Hendra
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1756

Abstract

The Sumba region, Indonesia, is known for its extraordinary natural beauty and unique cultural richness. There are 19 interesting tourist attractions spread throughout the area, but tourists often face difficulties in planning efficient visiting routes. From this case, it can be solved by applying graph theory in terms of searching for the shortest distance which is completed using the shortest path search algorithm. Then these 19 tourist objects are used to build a weighted graph, where the nodes represent the tourist objects and the edges of the graph describe the distance or travel time between these objects. Therefore, this research aims to compare the shortest path search algorithm with parameters to compare the shortest distance results, algorithm complexity and execution time for tourism in the Sumba area. The results of this research involve a comparison of several shortest path search algorithms, with the aim of finding the shortest distance results, algorithm complexity, and execution time for tourism in the Sumba area. Based on the test results of the five algorithms with the parameters that have been prepared, and the findings show that each algorithm has its own characteristics, the results are as follows: Dijkstra's algorithm can be used to calculate the shortest route for single-source and single-destination types. This resembles the Bellman-Ford algorithm, only the Bellman-Ford algorithm can be used simultaneously on graphs that have negative weight values. Meanwhile, the Floyd-Warshall algorithm is suitable for use on the all-pairs type. Then, the Johnson Algorithm can be used to determine the shortest path from all pairs of paths where the destination node is located in the graph. Finally, the Ant Colony algorithm to compute from a node to each pair of destination nodes.
Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data Wibawa, Putu Widyantara Artanta; Pramartha, Cokorda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1787

Abstract

Emotions are a person's response to an event. Emotions can be expressed verbally or nonverbally. Over time people can express their emotions through social media. Considering that emotion is a reflection of society's response, it is important to classify emotions in society to find out the community's response as information for consideration in decision-making. This study is aimed to identify and analyze the datasets, methods, and evaluation metrics that are being used in the classification of emotional texts in textual data from research data from 2013 to 2022. Based on the inclusion and exclusion design in selecting literature, a total of 50 kinds of literature were used in extracting and synthesizing data. Analysis of the data shows that out of 50 pieces of literature, there are 36 works of literature that use public datasets while 14 kinds of literature use private datasets. In the method of developing models for classifying, the SVM and Naive Bayes models are the most popular among the other models. In evaluating the model, the F-measure or F1-score metric is the most widely used metric compared to other metrics. There are three main contributions identified in this study, namely methods, models, and evaluation
Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study Yuranda, Rezky; Sutabri, Tata; Wahyuningsih, Delpiah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1808

Abstract

In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future
Comparison of Gabor Filter Parameter Characteristics for Dorsal Hand Vein Authentication Using Artificial Neural Networks Putra, Wahyu Irwan; Yudono, Muchtar Ali Setyo; Sujjada, Alun
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1819

Abstract

The importance of digital security in today's technological era requires various innovations in creating a reliable security system for humans. Biometrics is an authentication method and the most effective system for performing personal recognition because biometrics have unique characteristics. Dorsal hand vein become biometrics for the individual recognition process in this study using feature extraction of gabor filters and neural network backpropagation to classify recognition into five classes of human individuals, which are expected to be able to provide a higher accuracy value when compared to research on the introduction of dorsal hand vein. This classification process has several stages, namely input image, image pre-processing, segmentation, feature extraction, and image classification. The test results show that the percentage of success based on the five test scenarios has an average value of 75%. In this study, the results of the greatest test accuracy in the fourth scenario were 91%.
Determining Promotional Package Recommendations Using the Frequent Pattern Growth Algorithm at The Java Cafe Astuti, Dwi; Samsinar, Samsinar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1904

Abstract

Data analysis and processing is very important to support business development. One example is The Javanese Café which requires analysis and processing to determine promotional menu package recommendations. To carry out data analysis and processing, of course you need technology to make these activities easier. The technology that can be used to overcome this problem is data mining. Data mining has an association rule method which functions to form association patterns. Researchers also use the FP-Growth algorithm to speed up the data processing process. The sales transaction data processing resulted in 14 association patterns with the highest confidence values and 9 menu items with the lowest support values. Then the results were analyzed again and produced 4 recommendations for promotional menu packages that could be used to support product marketing strategies.
Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Abstract

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Analysis of Behavioral Use of Academic Information Systems with the Implementation of UTAUT 2 Integration at the Muhammadi-Palembang Institute of Health Science and Technology Donan, Hendri; Negara, Edi Surya Negara Surya; Sutabri, Tata; Firdaus, Firdaus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1978

Abstract

The utilization of Information Technology (IT) in higher education setting aims to enhance the quality of education, and this initiative is realized through the implementation of Information Technology at the Institute of Health Sciences and Technology Muhammadiyah Palembang (IKesT MP) in the form of an Academic Information System (SIMAKAD). SIMAKAD is a vital role as a tool to manage internal data and serves as an information hub for students. This research is conducted to evaluate the acceptance level of the UTAUT2 model and the impact of both the main and target variables within the UTAUT2 model. This research utilizes a quantitative method with 150 respondents, analyzed using SMART PLS 3.0 software." software. The research findings indicate that the acceptance level of the UTAUT2 model reaches 74%, signifying a high adoption rate. Variables like Perceived Value (p-Value: 0.019) and Habit (p-Value: 0.009) significantly influence Behavioral Intention, with a p-Value < 0.05, indicating that their hypotheses are accepted. On the other hand, variables such as Performance Expectancy (p-Value: 0.660), Effort Expectancy (p-Value: 0.417), Social Influence (p-Value: 0.652), and Facilitating Conditions (p-Value: 0.292) There is no substantial influence on Behavioral Intention as a result of using Information Technology (IT), indicating that their hypotheses have not been endorsed.. Additionally, the variable Hedonic Motivation (p-Value: 0.978) also does not can significantly impact one's inclination toward a  behavior Intention. However, variables Facilitating Conditions (p-Value: 0.000) and Behavioral Intention (p-Value: 0.000) have a positive impact on Use Behavior, indicating that their hypotheses are accepted. Conversely, the variable Habit (p-Value: 0.915) Does not exert a significant impact on Uss Behavior, resulting in the rejection of its hypothesis.

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