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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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Prediction of Monthly Rainfall with Using Monte Carlo Simulation in the Medan City Area Arini, Arini; Cipta, Hendra
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.v13i3.2307

Abstract

The climate in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by rainfall, air temperature, wind speed, air humidity and light radiation intensity. Rainfall is relatively high and varies throughout the year, the average monthly rainfall is around 150-300 mm in the rainy season and 50-100 mm in the dry season. There are several characteristics of rainfall, namely convective rain, frontal rain and orographic rain. For this reason, a method is needed that can solve problems in predicting monthly rainfall properties using the Monte Carlo simulation method. From this study, the results of the prediction of rainfall properties were obtained with 36 data from 2021 to 2024 which had a MAPE test result of 12.28%. The test results came from the average calculation carried out on the Monte Carlo method prediction with 5 variables.
Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification Andreansyah, Agus; Supardi, Julian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

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Abstract

Fingerprint is a unique biometric identity commonly used as evidence in court. However, its quality can decline due to external factors such as uneven surfaces, weather conditions, or distortion. The dataset used in this study is FVC2000. Convolutional Neural Networks (CNN) were applied for fingerprint image enhancement and classification, focusing on patterns such as whorl, arch, radial loop, ulnar loop, and twinted loop. This research optimized the VGG-16 model by adding several hyperparameters. The results showed the highest accuracy of 100% on the testing data with a learning rate of 0.0001, using 50 epochs and a training-to-validation data split ratio of 80%:10% from a total of 400 fingerprint image pattern data. These findings demonstrate that the VGG-16 model successfully classified fingerprint images with optimal performance, contributing significantly to the development of CNN-based fingerprint classification systems.
BUSINESS INTELLIGENCE MODEL OF REGIONAL HOSPITALS USING HGOD DISCOVERY Hengki, Hengki; Gernowo, Rahmat; Nurhayati, Oky Dwi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

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Abstract

Based on data from the Regional General Hospital in the Bangka Belitung Islands province, the Gross Death Rate (GDR) is the general death rate for every 1000 patients discharged of 108,430 compared to the health department standard of <45. The Net Death Rate (NDR) is the death rate 48 hours after being treated for every 1000 patients discharged of 67,388 compared to the health department standard of <25. TOI (Turn Over Interval) is the average turnover period of days where a bed is unoccupied from being filled to the next time it is filled of 19,832 days compared to the health department standard of 1 to 3 days. The solution offered by the researcher develops Business Intelligence (BI) optimization with a new model called the HGO (Hierarchy, Governance, Outlook) Discovery approach as a framework model for developing business intelligence for regional general hospitals in Indonesia. This model is expected to be able to solve or reduce the dimensional problems that exist in hospitals, namely the main patient management, HR Key Resources, and the quality of inpatient health services. The HGO Discovery approach is able to find patterns in a series of events called sequences by sorting the work patterns that exist in the hospital so that the business process of regional general hospitals is faster and more interactive in decision making. The Business Intelligence approach carried out by regional hospitals with HGOD is expected to make patient health services more integrated through the hierarchy of patient services, governance and outlook in decision making. 
Prediction of Claim Fund Reserves in Insurance Companies Using the ARIMA Method Brotosaputro, Goenawan; Japriadi, Yohanes Setiawan; Windihastuty, Wiwin; Ahsani, Rivai
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

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Abstract

Insurance is a financial protection contract between a customer and an insurance company which is stated in the form of an insurance policy. Prediction of insurance claim reserve funds is necessary because the claim amount varies and the claim time can be the same. If at any time there is a claim that is so large that it exceeds the available claim reserve fund plus the claim occurs at the same time, it can cause the company to fail to pay the claim. This will certainly make the company's conduct decline, customer trust will be lost, and can cause the company to go bankrupt. The problem can be solved if the insurance company has sufficient claim fund reserves. Claim fund reserves are an important issue in insurance companies. This study aims to predict the claim fund reserves in insurance companies to anticipate varying claim amounts. Historical analysis of the value of claims with the ARIMA model approach is used to predict future claim values. We use claim value data that has been scaled in millions. 2020 to 2022 as training data and 2023 as test data. The Root Mean Square Error (RMSE) metric obtained is IDR 25,780.71; Mean Absolute Deviation (MAD) of IDR 14,421.89, and Mean Absolute Percentage Error (MAPE) of IDR 5,967.27; while the total actual claim value in 2023 is IDR 161,700.51 and the total predicted claim value is IDR 166,227.36; which means that an accuracy of 97% is obtained. The result of claim prediction value in one periodic year can give a favor to the management to make a decision, how much the claim funds should be prepared.
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
Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach Nurmalasari, Dini; Qudsi, Dini Hidayatul; Chairani, Nessa; Yuliantoro, Heri R
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.v13i3.2217

Abstract

The need to enhance library services is the focus of this study, which relies on user feedback for data-driven decision-making. Text data from library user surveys conducted at Politeknik Caltex Riau (PCR) is analyzed to categorize sentiment and identify areas for improvement. The biannual student and lecturer feedback collected from 2018 to 2023 through the institution's official survey system (survey.pcr.ac.id) is utilized, providing a comprehensive and robust picture of user needs across five years. Sentiment analysis is employed using the VADER method to classify user comments into positive or negative categories. Text preprocessing techniques, such as stemming, tokenizing, and filtering, are performed to ensure robust classification. Machine learning algorithms – Naïve Bayes, Support Vector Machine (SVM), and Random Forest – are then utilized to evaluate sentiment classification accuracy. The study offers significant findings. Both SVM and Random Forest achieve an outstanding accuracy of 99%, indicating highly reliable sentiment categorization. Notably, these algorithms also achieve 100% precision, recall, and F1-score, demonstrating their effectiveness in accurately identifying positive and negative user sentiment. While Naïve Bayes shows slightly lower accuracy at 98%, it maintains a high recall rate (100%), ensuring all negative feedback is captured. This research presents a novel approach combining user sentiment analysis with a comprehensive five-year dataset. This enables a deeper understanding of evolving user needs and priorities. The high accuracy and effectiveness of the employed algorithms highlight the potential of this methodology for libraries. Libraries can leverage user feedback for evidence-based service improvement and increased user satisfaction.
Application of Data Mining for Tuberculosis Disease Classification Using K-Nearest Neighbor Sitanggang, Delima; Simangunsong, lamria; Sundah, Geertruida Frederika; Hutahaean, Rani; Indren, Indren
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.v13i3.2218

Abstract

This study aims to find out how much the application of the K-NN method and the accuracy value obtained by the K-NN method in clarifying data of Tuberculosis patients. This research focuses on improving public health and developing science to help people prevent and overcome tuberculosis. This type of research is quantitative. The literature study used is the documentation study. The method used by the K-Nearest Neighbor Algorithm. The results of the study showed that the process of applying data mining for the classification of tuberculosis disease using the K-Nearest Neighbor method obtained a final result of 80% accuracy. Thus, it can be concluded that the K-Nearest Neighbor algorithm is good.
Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection Azizah, Wafiq; Agustin, Soffiana
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.v14i1.2225

Abstract

The preservation of traditional batik patterns, often transmitted orally and through direct practice across generations, faces significant challenges in the modern era. Globalization introduces the risk of cultural homogenization, potentially diminishing the uniqueness and diversity of these patterns. Furthermore, the manual recognition of batik motifs is labor- intensive, time-consuming, and requires specialized expertise, rendering it unsuitable for large-scale preservation initiatives. Consequently, the development of technology-based solutions capable of documenting, analyzing, and recognizing batik patterns with efficiency and precision is imperative for safeguarding this cultural heritage. This study aims to address these challenges by developing an automated system for recognizing batik patterns, focusing on Javanese batik motifs—Kawung, Megamendung, and Parang—which serve as foundational designs for the evolution of batik in other regions. The proposed methodology integrates two feature extraction techniques, Histogram of Oriented Gradients (HOG) and Texture Moments, with the Random Forest machine learning algorithm. The research process encompasses four key stages: pre-processing, feature extraction, classification, and system evaluation, where the accuracy of individual and combined feature extraction methods is analyzed. Experimental results reveal that the HOG method achieves an accuracy of 78.99%, while the Texture Moments method yields 81.88%. Notably, the combination of these two methods enhances system performance, achieving the highest accuracy of 86.23%, representing a 4.65% improvement over the single methods. These findings underscore the efficacy of integrating HOG and Texture Moments with the Random Forest algorithm for automated batik pattern recognition.
Analysis of MAXIM Application Service Quality on User Satisfaction using the E-Service Quality Method Wulandari, Putri; Dzakiyullah, Nur Rachman; Ratnasari, Asti; Heksaputra, Dadang
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.v13i3.2226

Abstract

Service quality is the comprehensive support provided by system developers to users to ensure safety, comfort, empathy and responsiveness in meeting user expectations. There are still complaints about the Maxim application in the Google Playstore review from January 16 to February 12, 2024, inaccurate pick-up or destination, inconsistent prices, slow service response so that users have difficulty getting solutions to problems. Supported by the INDEF (Institute for Development of Economics and Finance) survey on DataIndonesia.id that the popularity of Maxim's services is lower than Gojek and Grab. Thus, it is essential to employ the E-Servqual approach to perform research to determine whether the services have satisfied users. Because its dimensions are pertinent and completely satisfy the requirements of assessing the quality of electronic services, E-Service Quality is the most thorough and integrative online service quality model. Efficiency, fulfillment, system availability, privacy, responsiveness, compensation, and interaction are the seven factors used, while user satisfaction is the dependent variable. This kind of study collects data using nonprobability sampling approaches in conjunction with quantitative methods. wherein demographic components are chosen according to specific standards that are pertinent to the study's goals. Considering the findings of the analysis, the 7 proposed hypotheses consisted of 3 accepted hypotheses and 4 rejected hypotheses because the significance value < alpha (? = 0.05) and 4 hypotheses rejected because the significance value > alpha (? = 0.05. Overall, the quality of Maxim's service towards user satisfaction is not good in terms of the variables of efficiency, system availability, responsiveness, and contact. It is hoped that there will be improvements in the Maxim application such as application usage, application functions on the displayed page, application response, and contact services for communication

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