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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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EEG Signal Classification using K-Nearest Neighbor Method to Measure Impulsivity Level Ginting, Arico Sempana; Simanjuntak, Ruth Marsaulina; Lumbantoruan, Nurima; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2154

Abstract

Impulsivity is the tendency to act without considering consequences or without careful planning. It involves a quick response to a stimulus without sufficient consideration of the consequences. Impulsivity needs to be measured and detected because it has a significant impact on various aspects of a person's life. The factors that influence the level of impulsivity include social environment, stress level, mental health, and genetic factors. Impulsivity can be divided into multiple components, such as reduced sensitivity to unfavorable behavioral outcomes, a disregard for long-term implications, and quick and spontaneous responses to stimuli. Electroencephalogram (EEG) studies can identify specific brain wave patterns such as, Alpha, Betha, Theta, and Gamma waves everything based on an individual brain's level of impulsivity. Signals from the brain are processed to extract specific features that reflect the user's intentions. EEG records brain activity without surgery, and this information is used for the diagnosis, monitoring, and treatment of neurological diseases, as well as scientific research on the brain and mind. K-Nearest Neighbor (KNN) is a classification algorithm that functions by utilizing several K nearest data values (its neighbors) as a reference to determine the class of new data. The K-Nearest Neighbors (KNN) algorithm is used for classification, clustering, and pattern recognition in EEG data where clustering is in 4 classifications (Impulsive, Not Impulsive, Potentially Impulsive, and Very Potentially Impulsive). This classification model shows high accuracy (Training Data: 94.7%, Testing: 91.3%, and Validation Data: 91.8%). This research shows that the KNN algorithm is effective for assessing the degree of impulsivity.
Comparison of Monthly Rainfall Prediction using Long Short Term Memory and Multi Layer Perceptron Methods in South Tangerang City Gaol, GA Monang Lumban; Syafrullah, Mohammad; Supardi, Supardi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2149

Abstract

Rainfall is one of the meteorological and climatological parameters whose information must be disseminated to the public and related stakeholders. Rainfall information has an important role in the sectors of people's lives. In agriculture, the amount of rainfall has an important role in determining the planting season, so that this can prevent potential crop failure. On Disaster, South Tangerang City during the 2016-2021 period experienced floods, landslides, and droughts. Therefore, the importance of rainfall prediction information can improve meteorological and climatological information services in various sectors. Nevertheless, it is still difficult for the community and stakeholders to get monthly rainfall predictions with high accuracy in the long term. In this research, monthly rainfall prediction is designed using MLP (Multi Layer Perceptron) and LSTM (Long Short Term Memory). The data used is the monthly rainfall data of Climate Hazards Group InfraRed Precipitations (CHIRPS) for 42 years (period 1981-2022) with coordinate boundaries according to the research location, namely South Tangerang City, which is located between 106.625 º - 106.825 º East and 6.4 ° - 6.2 ° LS as many as 16 grids with a resolution of 0.05 ° each grid. Monthly rainfall prediction using MLP produces an RMSE value of 90.19, and a MAPE of 40.55, while the LSTM method produces an RMSE value of 88.12 and a MAPE of 40.49. Monthly rainfall prediction results using the LSTM method are better than the MLP method; this can be seen from the RMSE value of the LSTM method is smaller than MLP.
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.
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.
Analysis the Application of the Weighted Product Method in Decision Support Systems for Assistance Programmes for MSMEs Berno Doduk, Thomas Aquino; Supriyanto, Heri; Al Hafidz, Mohammad; Prasetya, Muhammad Septama; Karyawan, Moch Anang
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1777

Abstract

Productive Micro Business Assistance (BPUM) is a government policy. This assistance has been carried out since the Covid-19 Pandemic in Indonesia. The Mojokerto city government conducts a selection of MSMEs which is expected to avoid errors in determining MSME assistance. Therefore, a decision support system is needed that is developed using the Weighted Product method to make it easier and faster to determine MSMEs that are eligible to receive assistance. The stages of system development start from problem analysis, data collection, analysis of method application, and system development. Based on the calculation of the resulting S vector, the largest value is 0.10568 and the smallest value is 0.05886 from 9382 MSME data. The last calculation is the V vector value which produces recommendations in the form of data ranking that can be used by the Mojokerto City Diskopukmperindag to determine which MSMEs are entitled to receive assistance. The results of the selected alternatives are in accordance with the ranking with the largest value of 0.10568 and the smallest value of 0.05886. Providing recommendations by the decision support system to policy makers can be based on the largest relative preference value owned by MSMEs.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm Nurhaliza, Siti; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1944

Abstract

The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
Enterprise Architecture Planning Pada Industri Otomotif Pitcar Service Menggunakan Odoo Dewi, Nur Aela; Putri, Nessia Alfadila; Pamungkas, Lanjar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1982

Abstract

Pemanfaatan teknologi informasi memiliki peran penting dalam proses pembuatan, perubahan, penyimpanan, komunikasi, dan penyebaran informasi. Terutama dalam konteks bisnis perusahaan, terutama di bidang manajemen sistem informasi, teknologi informasi membawa manfaat yang signifikan dalam mengelola, mengorganisasi, merencanakan, dan mencapai tujuan sistem informasi. Pitcar Service merupakan sebuah entitas di sektor otomotif yang berbasis di Purwokerto, Jawa Tengah, menghadapi tantangan dalam optimalisasi kegiatan manajemen sistem informasi. Kurangnya integrasi sistem informasi mengakibatkan kendala dalam perencanaan, pemantauan, koordinasi, dan visibilitas. Untuk mengatasi hal ini, pendekatan Enterprise Architecture Planning (EAP) digunakan untuk merancang sistem informasi terintegrasi yang berbasis web dengan memanfaatkan perangkat lunak Odoo untuk manajemen proyek di Pitcar Service. Hasil dari penelitian ini dapat digunakan untuk merancang arsitektur data, arsitektur aplikasi dan teknologi, serta merencanakan implementasi sistem manajemen informasi terintegrasi selama 3 tahun ke depan. Implementasi EAP di perusahaan Pitcar Service diharapkan dapat memfasilitasi pengelolaan dan pengembangan arsitektur yang sesuai dengan kebutuhan bisnis, memberikan arahan yang jelas untuk pengembangan sistem dan teknologi, serta mengoptimalkan potensi perusahaan melalui pemanfaatan sumber daya yang efisien.
Students' Intentions to Use E-Learning during the Covid-19 Pandemic: An Extended Technological Accaptance Model (TAM) Approach Purwandari, diah -
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2014

Abstract

Online learning is a technology-based system, hence a process is required to ensure that students can embarace the technology, as the success or failure of a technology is determined by how well the user accepts it. Therefore, understanding the factors that drive the use of online learning is essential. This study aims to contribute to the literature on online learning in higher education during the COVID-19 epidemic by investigating the relationship between self-awareness and student acceptance of online learning. Several hypotheses were constructed using the TAM Model to investigate the relationship between the TAM construct and self-awareness as an antecedent. This study employed structural equation modeling (SEM-PLS) to investigate how 390 students in East Jakarta used online learning. The findings of this study revealed that self-awareness had a significant effect on perceived usefulness, perceived ease of use, and attitude, but it had no direct impact on the intention to continue using e-learning. Students' attitudes were considerably influenced by perceived usefulness and perceived ease of use. Perceived usefulness was the most influential factor on student attitudes, and attitude was a strong predictor of intention to continue utilizing online learning. The proposed model accurately predicted attitudes and intentions to continue to use e-learning.
Water Level Classification for Detect Flood Disaster Status Using KNN and SVM Akbar, Jiwa; Setyo Yudono, Muchtar Ali
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.v13i3.2166

Abstract

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.
Game and Application Purchasing Patterns on Steam using K-Means Algorithm Aulia, Salman Fauzan Fahri; Gerhana, Yana Aditia; Nurlatifah, Eva
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.v13i3.2214

Abstract

Online games are visual games that utilize the internet or LAN networks. With the growth of the gaming industry, platforms like Steam offer a wide variety of games, making it challenging for users to decide which game to play. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to address this issue by understanding user preferences. The k-means algorithm clusters game data based on similar characteristics, helping users and developers identify the most popular game types. Data sourced from Kaggle, obtained through the Steam API and Steamspy, consists of 85,103 entries. A normalization process is applied to enhance calculation accuracy. The elbow method determines the optimal number of clusters, resulting in three clusters from the k-means algorithm. The evaluation includes the silhouette coefficient, which measures the proximity between variables, and precision purity, which compares labels by assigning a value of 1 (actual) or 0 (false). The study finds an average silhouette coefficient of 0.345 and a precision purity value of 0.734, indicating that the k-means algorithm performs optimally based on the precision purity metric. The findings reveal that free-to-play games are the most popular among users, while the "Animation & Modelling" category is the most expensive based on price comparisons

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