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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 20 Documents
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Search results for , issue "Vol. 13 No. 2 (2024): JULY" : 20 Documents clear
DeLone and McLean Model Analysis of Success Factors of SIDEMANG Application in Palembang City Faris, Haninda Ammar; Wedhasmara, Ari; Putra, Apriansyah; Kurnia, Rizka Dhini; Bardadi, Ali; Fitri, Shofiyah
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.v13i2.1894

Abstract

Indonesia is ranked 77th in the world in electronic-based government systems, especially the City of Plembang is ranked 89th regarding the evaluation of smart city improvement in Indonesia. One of the latest applications used in the past year is the SIDEMANG application, which is an information system that has the use and purpose of facilitating access to administrative services related to personal and agency licensing files online at the village and sub-district levels in Palembang city, but this is also not free from obstacles, especially internet signals. Therefore, an analysis is needed related to the implementation of Information Systems, to assess the success of applications that have been implemented, especially government digital services. DeLone and McLean Information System Success Model is used, to see the significant factors that cause the success of Information System implementation. The data analysis method used in this research is quantitative because the data collected is in the form of numbers and will be analyzed using the SmartPLS application statistical technique, using a sample size of 97 respondents. The results showed that the information quality factor was not significant to the intention or use of the application, the system quality factor was not significant to the intention or use of the application, the system quality factor was not significant to user satisfaction, the service quality factor was not significant to user satisfaction. Recommendations for the Palembang City Communication and Information Office are related to the evaluation and improvement of the SIDEMANG application using the DeLone and McLean Model analysis. In particular, improvements to the quality of information that can influence citizens to use the application, improvements to the quality of the system that can invite and satisfy users in using the application, and improvements to service quality factors on citizen satisfaction.
Development of Communication System between TPMS and Server using Combination of OFDM and Convolutional Code Technique Based on SDR Briantoro, Hendy; Montolalu, Billy; Farouq, Ardiansyah Al
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.v13i2.2024

Abstract

The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience. Traditionally, TPMS systems rely on dedicated hardware components for the collection and transmission of tire pressure data to the vehicle's onboard computer and the data is visible only to the driver. In this research, we have developed a wireless communication system between TPMS and a server, enabling tire pressure data to be accessible not only to the driver but also remotely traceable by others. To build a reliable communication system, we utilized a combination of Orthogonal Frequency Division Multiplexing (OFDM) and Convolutional Code technologies. This system is implemented using Software-Defined Radio (SDR) technology. This communication method employs OFDM to enhance data throughput and integrates Convolutional Code to mitigate errors in received data. Consequently, this approach achieves a maximum throughput of 119.19MBps when utilizing the OFDM system alongside 16QAM modulation. The bit error rate for received data without coding stands at 5.77%, but the application of Convolutional Code with a 1/2 code rate effectively reduces this error rate to 3.85%. This system improves the reliability of TPMS communication with the server while also ensuring a consistently high throughput. It enhances road safety and remote monitoring capabilities.  
Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods Vidyanti, Angela Citra; Riati, Itin; Ramadhanu, Agung
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.v13i2.2063

Abstract

This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.
Double Exponential Smoothing Forecasting Food Crop Yields Using Geographic Information Systems Pirmanto, Dovel
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.v13i2.2069

Abstract

Food is a source of basic needs for every living creature, so food security is an interesting issue for every country. This raises problems regarding food and land use, especially in Sungai Penuh City. Food problems arise due to a lack of information regarding appropriate land use and the productivity of the land itself. In the current industrial era 4.0, forecasting can be done using information technology tools that provide convenience and efficiency in forecasting times and can be integrated with geographic information systems. The forecasts made by the community are based on past experience without considering the factors that influence crop yields, so that they can cause losses both in terms of time and costs. Apart from that, less accurate predictions of food yields can lead to less than optimal development of food security which has an impact on meeting food needs. This research involved respondents from the Department of Agriculture and Food Security, namely agricultural and food experts. The method for collecting data in this research is observation and interviews. This research analyzes harvest data for the 2018-2023 period sourced from the Central Statistics Agency using the Double Exponential Smoothing method by considering error values with ? = 0.1 and 0.5 and ? = 0.1 and 0.5. The calculation of the smallest error value is: ME = 80.92, MAD = 5.58, MAPE = 11%, MSE = 52.69 by combining the value of ?= 0.1 and the value of ? = 0.1 to produce a prediction of the corn harvest in Kumun Debai District in 2024 of 45 tons and year 2025 as much as 40 tons.
Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors Kurniawan, Rudi; Wijaya, Harma Oktafia Lingga; Aprisusanti, Rani Purnama
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.v13i2.2071

Abstract

The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user reviews of the application have emerged. Therefore, sentiment analysis is needed to provide a deeper understanding of user perceptions and to classify user reviews of the Digital Population Identity Application. Sentiment analysis is a computational study of opinions, feelings, and emotions expressed in text, using the K-Nearest Neighbors method, which is a classification method based on the closest distance or similarity to objects in the training data. Using 5000 relevant review data from September 2022 to December 2023, after labeling them into positive, negative, and neutral sentiment classes, the results show 3581 negative sentiments, 1031 positive sentiments, and 388 neutral sentiments. Testing was conducted by applying the K-Nearest Neighbors method in the classification stage, testing this method by varying K values from 1 to 10. The best results were obtained with a training data ratio of 90% to testing data ratio of 10%. The best results were achieved at K values of 8, 9, and 10, with an accuracy of 81%, precision of 82%, recall of 95%, and an F1-Score of 88%. With a training data ratio of 70% to testing data ratio of 30%, the best results were obtained at K values of 6, 7, 8, 9, and 10, with an accuracy of 80%, precision of 81%, recall of 95%, and an F1-Score of 88%. Based on the results of this research, the K-Nearest Neighbors method can be used for sentiment classification of user reviews with good results.
Analysis and Design of Integration Model for API Management and CI/CD at Directorate General of Taxation Pramudaya, Tri; Agustina, Fenni
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.v13i2.2086

Abstract

The Directorate General of Taxes (DGT) currently  utilizes Application Programming Interface (API) to enhance efficiency in tax data exchange with external parties. DGT is facing challenges due to the rising number of published APIs and the increasing connections from external parties to the DGT system, which necessitates a speedy API issuance process. The objective of this research is to assist the Directorate General of Taxation (DGT) in developing an integrated API management system with Continuous Integration/Continuous Deployment (CI/CD). The system design process is conducted using the Standards and Architectures for E-Government Application (SAGA) framework, encompassing Enterprise Viewpoint, Technology Viewpoint, Computational Viewpoint, Information Viewpoint, and Engineering Viewpoint. A qualitative method is employed, including interviews to gain insights into the existing issues. Additionally, information regarding systems and technologies is documented for gap analysis. The results of this analysis are then utilized to design the architecture of the API management system, applications, and technologies. This research yields a model of the API management system integrated with CI/CD at DGT. The model is developed using 3Scale and Jenkins software. Following validation, the API management system at DGT operates effectively with three DGT API systems and three API users.
Comparison Of K-Means and K-Medoids Algorithm for Clustering Data UMKM in Pagar Alam City ariska, sendy; Puspita, Desi; Anggraini, Inda
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.v13i2.2090

Abstract

The aim of this research is clustering MSME data in Pagar Alam City using the K-Means and K-Medoids algorithms. This research is motivated by the lack of further management of MSME data collection, which can hinder the development and improvement of Pagar Alam City MSMEs. Meanwhile, this data is considered necessary for agencies to develop and improve Pagar Alam City MSMEs. Apart from agencies, this data is also useful for sub-districts, sub-districts and RT/RW to find out what interests, talents and potential the community has in what business fields. MSME data is processed using Rapid Miner and Python, the system development method in this research uses the Cross Industry Standard Process for Data Mining (CRISP-DM) method, where the stages include Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The test method uses the Davies-bouldin index, a DBI value that is close to 0 results in good clustering. The results of this research obtained 3 clusters. In 2020 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 1 and C2= 3 sub-district. In 2022 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 3 and C2= 1 sub-districts. The results of the 2020 sub-district DBI clustering calculation are DBI k-means = 0.134 and k-medoids = 0.523. In 2022 DBI k-means = 0.277 and k-medoids = 0.496. So it can be concluded that the K-Means algorithm in the case of grouping MSMEs in Pagar Alam City has better performance, because the DBI value is close to 0. From the results of the grouping it can help provide an overview for related parties in encouraging or providing assistance to sub-districts that are included in the low cluster.
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
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.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Comparison Between Usability and User Acceptance Testing on Educational Game Assessment Vanesha, Nellya Anggun; Rizky, Rizky; Purwanto, Agus
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.v13i2.2099

Abstract

User Acceptance Testing (UAT) and Usability Testing are two methods commonly used in evaluating software or systems. UAT is concerned with overall system acceptance, while Usability Testing is specifically aimed at assessing the user's experience in interacting with the product. These two testing methods play an important role in ensuring the quality and user satisfaction of software and systems. Including being used to evaluate the Little Panda's Forest Animals game against 106 respondents consisting of two different campuses. The purpose of this research is to see the comparison between Usability Testing and User Acceptance Testing. With the research stages of literature review, questionnaire creation, data collection, data processing, and conclusions. The results of data processing show that there are differences in results where Usability Testing gets a lower score than User Acceptance Testing. Usability Testing results received an assessment range of 65 - 84 with the Usability statement being acceptable to users, while User Acceptance Testing received a range of 81% - 100% with the score interpretation criteria being very good.
Sensitivity Analysis of Various AHP Process: A Case Study on Consumption Fish Farming Michael Siregar, Ivan; Budi Putri, Lydia Wulandari; Sugihartono, Tri
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.v13i2.2101

Abstract

The utilization of a decision support system has successfully helped many businesses in increasing their product sales. By conducting product evaluations, the sales potential of each product will be seen more accurately, thereby helping strategic decision-makers. As one of the algorithms in product selection, AHP  has been proven to solve complex problems involving multi-criteria, as many studies have successfully used it to rank products. However, in AHP implementation there are two different ways of calculating weights and consistency ratios. Due to the various AHP processes available, this paper performs testing with the most frequently used variations to determine product potential and compare the methods for multi-criteria decision-making. The criteria are harvest duration, selling price, feed production, weather conditions, and target market. The research results show that the weights of the two methods are different, but the resulting ranks are the same. The best choice type of fish to be farmed by fish farmers is catfish with the highest weight and the most difficult type of fish to farm is giant gourami. The result also show that the best way of the normalization process is squares of comparison matrices because its sensitivity does not easily change the ranking order.

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