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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.
Arjuna Subject : -
Articles 678 Documents
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Analyzing Consumer Shopping Interest via Social Media Ads with K-Means and C4.5 Algorithm Banjarnahor, Jepri; Hutagalung, Jessy Putrionom; Sitorus, Ferdinand Jery Wilkinson
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2228

Abstract

It is increasingly important to understand how advertisements affect consumers' propensity to shop as social media becomes the primary medium for advertising. This study uses the C4.5 algorithm for classification and K-Means Clustering for data segmentation to examine the level of consumer shopping interest driven by Facebook and Instagram ads. This strategy utilizes information collected from user interactions with ads on these two social media platforms to determine consumer interest trends more precisely. The research findings show that, compared to conventional methods, this combination of techniques can increase the accuracy of predicting consumer purchase intention by as much as 85%. These results not only validate the usefulness of clustering and classification methods in digital advertising data analysis, but also offer insights that companies can apply to optimize their marketing strategies. By understanding more specific consumer segments, companies can target their ads more precisely, thereby increasing conversions and the effectiveness of advertising campaigns. This research makes a significant contribution to the field of data analysis and digital marketing and opens up opportunities for further research in the integration of more sophisticated analysis methods
ISO Technology Analysis with Extended TAM : A Case Study in PT Ebako Nusantara Hutagaol, Nindhia; Pasaribu, Faizal Asrul; Simangunsong, Jumadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2231

Abstract

Implementing information systems in manufacturing aims to improve operational efficiency and service quality. This study evaluates the acceptance of Internal Service Order (ISO) applications with the Extended Technology Acceptance Model (ETAM), that adds variables such as information quality, system quality and user habits. PLS-SEM analysis of 31 respondents found that user habits significantly influenced perceived ease of use (77.8%), which in turn influenced perceived usefulness (86.4%) and user attitude (62%). However, information quality did not significantly influence user habits, suggesting a need to improve information detail. These findings will help develop a TAM model for Indonesian furniture companies. The study recommends the improvement of information accuracy, the development of real-time notification features, and user training to increase adoption and operational efficiency This study provides guidance for organizations to optimize the application of technology in manufacturing operations.
Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System Yuranda, Rezky; Negara, Edi Surya
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2234

Abstract

A web shell is a script executed on a web server, often used by hackers to gain control over an infected server. Detecting web shells is challenging due to their complex behavior patterns. This research focuses on using a deep learning approach to detect web shells on the ISB Atma Luhur web server, aiming to develop a model capable of precise detection. By training the model with labeled PHP files, malicious web shells are distinguished from benign files. The study is crucial for enhancing the server's security, preventing hacker attacks, and safeguarding sensitive data. Through preprocessing techniques such as opcode extraction and feature selection, useful pattern recognition for web shell detection is achieved. Training deep learning models like CNN and RNN with LSTM on processed data leads to accuracy evaluation using classification metrics. The CNN model demonstrates superior performance in detection, emphasizing the effectiveness of deep learning for web shell detection. The research contributes to enhancing security in web-based applications, protecting against cyber threats like web shells.
DANA App Sentiment Analysis: Comparison of XGBoost, SVM, and Extra Trees Setiawan, Muhamad Jodi; Nastiti, Vinna Rahmayanti Setyaning
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2239

Abstract

This research aims to analyze sentiment on DANA application reviews to find out user perceptions by comparing Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Extra Trees Classifier classification methods. DANA application review data is obtained from the Kaggle site which consists of 50,000 Indonesian-language reviews labeled with positive and negative sentiments. The research stages include data preprocessing to clean and prepare the review text, applying word weighting using Word2Vec to give weight to words based on their context, balancing sentiment classes using SMOTE to address the imbalance of positive and negative review classes. It should be noted that the initial proportion of data before applying SMOTE may affect the results. The data is then divided into training and testing sets, then the models are trained and evaluated using Confusion Matrix and K-Fold Cross-Validation. The results of the three classification methods are measured by the accuracy matrix and F1-Score to assess model performance, the SVM and XGBoost methods obtained an accuracy of 93% and the ETC method achieved an F1-Score value of 96% at K=6, the three models proved to be very accurate in predicting the sentiment of DANA application reviews both positive and negative. The practical implications of this research can identify areas for application improvement, develop popular features, personalize services based on user preferences, and manage application reputation.
Enhancing Outdoor Equipment Marketing through Augmented Reality: A Case Study of Sekaben Camp Sugihartono, Tri; Putra, Rendy Rian Chrisna; Dwi Sandro, Irsad
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2243

Abstract

Augmented Reality (AR) has the potential to transform product marketing by creating immersive and interactive experiences. This study presents the development of an AR-based application to enhance the marketing of outdoor equipment at Sekaben Camp, a camper rental company in Pangkalpinang, Bangka Belitung. The application allows users to visualize and interact with three-dimensional (3D) models of rental gear on their Android smartphones, making the selection process more engaging and informative. Using a prototyping approach—an iterative process of building and refining a preliminary model—the research includes gathering requirements, developing a prototype, coding the system, testing, and final deployment. Key features such as AR scanning, equipment ordering, and a price listing interface were designed to enhance product visualization and user engagement. User testing revealed that 85% of participants found the application intuitive and reported a more realistic understanding of the gear's size and functionality, resulting in a 30% increase in customer satisfaction during the rental process.
Analysis Of User Experience Of ChatGPT And Gemini Users Using The User Experience Quistionnaire (UEQ) For Education Nasrul, Ilham; Angraini, Angraini; Hamzah, Muhammad Luthfi; Saputra, Eki
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2250

Abstract

AI is becoming more and more crucial in the digital age to support kids in overcoming obstacles to learning and succeeding academically. The use of chatbots is one example of AI progress. Two well-known chatbots are Gemini and ChatGPT. Because they are useful and support a variety of learning tasks, including answering questions, producing articles, expanding knowledge, and other academic activities, both applications are highly well-liked and preferred by students. By using a case study on the Facebook community with the number of samples needed in this study as many as 377 respondents based on the Krejcie and Morgan formula, The purpose of this study was to determine whether user experiences with different applications differed. User experience measurement was carried out using the User Experience Questionnaire (UEQ) approach on the variables of Efficiency, Novelty, Attractiveness, Stimulation, Perspicuity, and Dependability. The results of the study show that all user experience variables for the ChatGPT and Gemini applications received poor ratings, and there were no significant differences in any of these variables. However, based on UEQ measurements, it was found that both applications received better scores on the stimulation and novelty variables, while the attractiveness, clarity, efficiency, and accuracy variables received poor results. To improve user experience in the ChatGPT and Gemini applications, the quality of all variables needs to be enhanced.
Analysis to Predict the Number of New Students At UNU Pasuruan using Arima Method Fitrony, Fachri Ayudi; Supraba, Laksmita Dewi; Rantung , Tessa; Agastya , I Made Artha; Kusrini , Kusrini
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2251

Abstract

New student admission is an important aspect in higher education management, including Nahdlatul Ulama University (UNU) Pasuruan. Relevant prediction of total new students is needed to support resource planning such as teaching staff, facilities, and budget. This study aims to evaluate the historical pattern of new student admissions at UNU Pasuruan and predict the number of new students in the coming years using the ARIMA (Auto Regressive Integrated Moving Average) method. The data used is historical data on new student admissions in the last five years, which is analyzed to identify trends, seasonality, and fluctuation patterns. The analysis is performed using statistical software such as Python to improve the accuracy and efficiency of the process. This study approach includes several main steps, namely collecting historical data on the number of new students, testing stationarity using the Augmented Dickey-Fuller (ADF) test, identifying model parameters through ACF and PACF graphs, and estimating ARIMA model parameters. The resulting model is evaluated using prediction error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The study findings describe that the ARIMA model (6,0,1) produces an RMSE value of 21.88 and a MAPE of 0.2%. In addition to having the smallest error score, the ARIMA model (6,0,1) also has the smallest AIC score of the various models that can be used for predictions, which is 447.44 and the largest log likelihood value, which is -214.72. The largest prediction of the number of new students is in July, which is 92.72 and the smallest in February, which is 24.43. This prediction is expected to help university management in optimizing resource planning, increasing management efficiency, and anticipating fluctuations in the number of new students in the future. This study offers new findings in the form of the use of predictive models based on historical data to support strategic decision- making, such as resource allocation and promotion planning. With these results, universities can anticipate changes in the number of enrollments more effectively, which were previously only done based on subjective estimates. The model built can also be applied to similar datasets in the future with appropriate parameter adjustments.
Hyperparameter Tuning of EfficientNet Method for Optimization of Malaria Detection System Based on Red Blood Cell Image Pamungkas, Yuri; Eljatin, Dwinka Syafira
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2257

Abstract

Nowadays, malaria has become an infectious disease with a high mortality rate. One way to detect malaria is through microscopic examination of blood preparations, which is done by experts and often takes a long time. With the development of deep learning technology, the observation of blood cell images infected with malaria can be more easily done. Therefore, this study proposes a red blood cell image-based malaria detection system using the EfficientNet method with hyperparameter tuning. There are three parameters which are learning rate, activation function, and optimiser. The learning rate used is 0.01 and 0.001, while the activation functions used are ReLU and Tanh. In addition, the optimisers used include Adam, SGD, and RMSProp. In the implementation, the cell image dataset from the NIH repository was pre-processed such as resizing, rotating, filtering, and data augmentation. Then the data is trained and tested on several EfficientNet models (B0, B1, B3, B5, and B7) and their performance values are compared. Based on the test results, EfficientNet-B5 and B7 models showed the best performance compared to other EfficientNet models. The most optimal system test results are when the EfficientNet B5 model is used with a learning rate of 0.001, ReLU activation function, and Adam optimiser, with values of 97.69% (accuracy), 98.36% (precision), and 97.03% (recall). This research has proven that proper model selection and hyperparameter tuning can maximise the performance of cell image-based malaria detection system. The development of this EfficientNet-based diagnostic method is more sensitive and specific in malaria detection using RBCs.
Implementation of Doodle Jump Game Based on Accelerometer Sensor and Kalman Filter Danuwijaya, Edghar; Putra, Yohanes Armenian; Nuha, Hilal Hudan; Oktaviani, Ikke Dian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2259

Abstract

The doodle jump game is a video game with a jumping game model assisted by accelerometer sensor technology. Placing the accelerometer sensor in the doodle jump game is a very appropriate solution to determine the accuracy of the values on the sensor. The accelerometer sensor can be measured in real time, however applying a small force to the sensor can result in interference with measurement accuracy. Therefore, creating the measurement results you need using filters can help reduce noise. The method used to use this filter is the Kalman Filter algorithm. The use of the Kalman Filter method can provide a stable level of accuracy in the movements of the main characters in the game and the accelerometer sensor so that it can become a precise algorithm. Apart from that, the use of the Kalman Filter as a tool or method for measuring numbers to provide a solution to improve the design of the previous developer.
Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) Fauzi, Ahmad; Chandra, Annisya E; Imammah, Sofyah; Zapata, Malvin; Marzuki, Marza I; Prayogi, Soni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2261

Abstract

Potato production in Indonesia has grown very rapidly, making Indonesia the largest potato producer in Southeast Asia. However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. The project "Potato Plant Leaf Disease Detector (MR-PoLod)" aims to design an android application that can classify leaves on potato plants into 3 classifications, namely healthy, early, and late blight disease. This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. The total data used for training data, data validation and data testing is 3165 images. With each division of the data process on the training data of 70%, validation of 15% & testing of 15% to test the effectiveness of the model that has been created. The performance of MR-PoLod for each class, obtained a precision value, recall, and f1-score of 0.99. Likewise, the accuracy value achieved by the model is 0.99 or 99%. Thus, the expected application can facilitate farmers in classifying diseases on potato plant leaves.

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