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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 669 Documents
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Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application Burhanudin, Nazil Ilham; Laksito, Arif Dwi; Sidauruk, Acihmah; Yudianto, Muhammad Resa Arif; Rahmi, Alfie Nur
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1659

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).
Analysis of the SAW Model in the Selection of Majors at ISB Atma Luhur Fitriyani, Fitriyani; Irawan, Devi; Andrika, Yuyi; Adiwinoto, Bambang
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1679

Abstract

The department is part of the faculty, and is the focus of the field of study chosen by students. The choice of majors is very important to think about beforehand because it will have an impact on the results of these graduates and the job prospects sought later. There are many things that can be considered by high school graduates in choosing a major because if you choose rashly, you will be at risk of failing along the way. For this reason, research was carried out with an emphasis on the criteria for selecting majors at ISB Atma Luhur. The method used in this study is SAW (Simple Additive Weighting) which is a weighted sum method that is weighting criteria and alternative attributes so that normalization can be carried out and calculation of preference values is carried out and the highest and lowest rankings are obtained which are used as decision recommendations. Respondent data was taken based on the sample, namely through a non-probability sampling technique, namely in this study the respondent could only provide one opportunity to be sampled from the population so that one respondent could not be sampled twice. Respondents who were sampled were 100 people who were taken from high school graduates. The criteria presented include department accreditation, number of professionally certified lecturers, study program scholarships, and facilities. Determination of criteria based on the results of interviews with respondents and the results are taken based on the criteria with the most choices. The results of the study are alternative Information Systems with a weight or value obtained from the SAW calculation results, namely 1, Informatics Engineering with a weight of 0.8, while Digital Business is 0.6. From the results of these calculations, it can be concluded that the department that gets the highest score, namely the Information Systems major, can also be used as a recommendation as a major to be chosen by students.
Interactive Multimedia Research Trends in Higher Education: A Review of Assisted Literature NVivo 12 Pro Hasanuddin, Hasanuddin; Asgar, Hari; Jayadi, Agus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1648

Abstract

The rapid use of technology in lecture activities, especially the use of interactive multimedia since the pandemic conditions and until now, has attracted researchers to comprehensively analyze patterns of interactive multimedia utilization. This study aims to provide information on the pattern of interactive multimedia utilization and its development to date. The research method adopted a qualitative design, through a review of various studies from 2020 to 2023, and a literature search using the free Publish or Perish program. Further reduction and screening processes were carried out using the PRISMA method. The final results of the articles that have been selected are then mapped and codified with NVivo Pro 12 program, including the development and use of interactive multimedia in universities. In the development of interactive multimedia in the university, literature is grouped into forms of interactive multimedia and tools in designing interactive multimedia. The use of interactive multimedia in institutions is grouped into two, including the types of interactive multimedia used and how the impact of the use of interactive multimedia on lecture activities. Results obtained 1) Multimedia forms developed in  universities include E-modules, Software, Websites, Android applications, Autoplay, Classpoint, E-learning, and tutorial models; 2) equipment in designing interactive multimedia including Kwisoft Flopbook Maker, live streaming, Macromedia Flash, Microsoft Way, Moodle, Adobe Flash Professional CS6.5, App Inventor 2, Autocad, Power Point, and Courselab 2.4; 3) types of interactive multimedia used in learning activities in universities include Android applications, Macromedia Flash, Lectora Inspire software, Adobe Flash, Adobe Premiere, and E-learning; 4) The effect of interactive multimedia on lecture activities can increase interest or motivation, also have an impact on student achievement.
Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order Hamdani, Ibnu Mansyur; Anam, Syaiful; Shofianah, Nur; Bustamin, Syamsumar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1661

Abstract

In the field of microbiology, the counting of bacterial colonies is fundamental and mandatory. This is done to estimate the number of bacterial cells in every 1 milliliter or gram of sample. The counting takes a long time and is tedious, so it requires an accurate and fast counting method. The image quality used is very low and contains noise. Therefore, a preprocessing method is needed to reduce the noise. The Perona-Malik filter method is known to be able to remove noise well. However, it is difficult to determine the appropriate gradient threshold parameter ( ) for each different image. To find the appropriate value of , the original Sobel Mask method and Sobel Mask Fractional-Order are used to estimate the value of . The experimental results show the results of noise reduction using PMD with a value of  from the original Sobel Mask and Sobel Mask Fractional-Order. The results of the accuracy of determining the value of k with the Sobel Mask Fractional-Order (α=1.0) show higher results based on the F-Measure values for samples 1, 2, and 3 respectively 97%, 98%, and 90%.
Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation Agustino, Dedy Panji; Budaya, I Gede Bintang Arya; Harsemadi, I Gede; Dharmendra, I Komang; Pande, I Made Suandana Astika
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1682

Abstract

The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, customer data analysis becomes crucial. One common approach used to group customers is segmentation based on RFM analysis (Recency, Frequency, and Monetary). This study aims to compare the performance of two clustering algorithms, namely DBSCAN and Affinity Propagation (AP), in providing customer profile segment recommendations using RFM analysis. DBSCAN algorithm is employed due to its ability to identify arbitrarily shaped clusters and handle data noise. On the other hand, Affinity Propagation (AP) algorithm is chosen for its capability to discover cluster centers without requiring a pre-defined number of clusters. The transaction dataset used in this research is obtained from one of the business incubator tenants at STIKOM Bali. The dataset undergoes preprocessing steps before being segmented using both DBSCAN and AP algorithms. Performance evaluation of the algorithms is conducted using the Silhouette Scores and Davies-Bouldin Index (DBI) matrices. The research findings indicate that the AP algorithm outperforms DBSCAN in this customer segmentation case. The AP algorithm yields Silhouette Scores of 0.699 and DBI of 0.429, along with recommendations for 4 customer segments. Furthermore, further analysis is performed on the AP results using a statistical approach based on the mean values of each segment for the RFM variables. The four customer segments generated by the AP algorithm, based on the mean values of the RFM variables, can be associated with the concept of customer relationship management.
Sentiment Analysis of Social Media Platform Reviews Using the Naïve Bayes Classifier Algorithm Saepudin, Sudin; Widiastuti, Selviani; Irawan, Carti
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1650

Abstract

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.
Sentiment Analysis of Digital Television Migration on Twitter Using Naïve Bayes Multinomial Comparison, Support Vector Machines, and Logistic Regression Algorithms Dahlian, Ryo Benhard; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1668

Abstract

The Ministry of Communication and Information Technology (KEMENKOMINFO) has announced to the publics in Indonesia regarding the termination of analog television broadcasts or called analog switch-off, which requires the public to migrate from analog television to digital television. Regarding the process of stopping analog broadcasts this raises pros and cons by the people in Indonesia. Many people give their respective opinions through social media, especially on Twitter. A collection of pros and cons data from the public can be collected and used as research of sentiment analysis. This research will focus on comparing three classification algorithms, which is called Multinomial Naïve Bayes, Support Vector Machines, and Logistic Regression using the same dataset and the same method called Lexicon Based. The results showed that the highest accuracy is Support Vector Machines with the accuracy is 94.00%, Logistic Regression with the accuracy is 90.00%, and Multinomial Naïve Bayes with the accuracy is 88.00%.
Implementation of Grounded Theory to Analyze the Effect of Social Media Functionality on MSME Market Segmentation in Indonesia Annisa, Lolanda Hamim; Saridewi, Larasati Puspita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1683

Abstract

Empowering SMEs in the midst of globalization and high competition has forced MSMEs to face global challenges, such as increasing innovation, developing human resources and technology, and expanding the marketing area. Social networks provide a medium for processing new innovations for SMEs such as relationships with customers, work partners and also suppliers. Business process management can assist business actors in carrying out their business activities in the face of today's challenges and global competition. This study reveals that every business process can be supported by the implementation of IT by a company. This study uses a grounded theory approach. Grounded theory studies tend to follow a structured approach. This research uses case studies of hydroponic SMEs in Indonesia, which produce the types of social media social media functions that are often used by Indonesians. The main strength of this research is the functions of social media needed by SME actors. From the results of the study it was found that SMEs need social media functionality in 4 functions, namely: the interaction function, the marketing function, the reputation function, and the information function. SMEs in Indonesia need applications that contain these 4 functions to be able to market, sell, and interact with other people.
Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization) Rosyida, Anistya; Sasongko, Theopilus Bayu
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.v12i3.1716

Abstract

Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%. 
Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm Sondakh, Debby Erce; Maringka, Raissa C; Ayorbaba, Ferlien P; Mangi, Joanne S. C. B. T.; Pungus, Stenly Richard
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.v12i3.1721

Abstract

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.

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