cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fransiskus Panca Juniawan
Contact Email
Fransiskus Panca Juniawan
Phone
-
Journal Mail Official
fransiskus.pj@atmaluhur.ac.id
Editorial Address
-
Location
Kota pangkal pinang,
Kepulauan bangka belitung
INDONESIA
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 15 Documents
 1   2 
Search results for , issue "Vol. 12 No. 2 (2023): JULI" : 15 Documents clear
Exploration of Consumer Buying Interests at Tiktok Stores Live Streaming Based on the Stimulus Organism Response (SOR) Framework Dinanti, Windy Dara; Bharata, Wira
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.v12i2.1658

Abstract

Technological developments are currently increasing rapidly so that entrepreneurs advance their business ventures with various innovations. No wonder there are many emerging marketplaces that provide several conveniences in shopping. The TikTok application is one of the most popular marketplaces. This study aims to determine the effect of Store Atmosphere, Online Customer Reviews and Online Customer Ratings on Purchase Intention with Trust as a mediating variable at the TikTok Shop. This study uses quantitative data and this type of research is associative research. The population used in this research is live streaming viewers @oktaviana_tas_grosir. The sample collection technique uses a non-probability sampling technique. The sample in this study was determined by 100 respondents. The data analysis technique in this study used the Smart PLS version 3.0 tool. The results of this study indicate that Store Atmosphere, Online Customer Reviews and Online Customer Ratings have a positive effect on Trust. Online Customer Reviews and Online Customer Ratings have a positive effect on Purchase Intention, while the Store Atmosphere has a negative effect on Purchase Intention. Trust has a positive effect on Purchase Intention. Trust is able to mediate Store Atmosphere, Online Customer Reviews and Online Customer Ratings on Purchase Intention positively.
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).
Decision Support System for Selection of Pesticides in Chili Plants Using the MAUT Method Sukamto, Sukamto; Nugroho, Riki Ario; Nugrah, Randi Andri
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.v12i2.1669

Abstract

Diseases in chili plants often cause farmers to experience crop failure. Diseases that are often encountered are leaf spot disease, wilt disease, and anthracnose disease caused by fungi. Therefore, farmers are trying to prevent and reduce the disease by using pesticides. The use of pesticides in agriculture plays a role in preventing and reducing diseases in chili plants. Pesticides for chili plants are quite widely spread on the market and have the advantages of each product offered to farmers, so farmers must be more careful and understanding in choosing pesticides to be used in preventing chili plant diseases. For that we need a decision support system (SPK). The purpose of this research is to apply the MAUT method in SPK which can help chili farmers in selecting pesticides. The data used in this study are 10 types of pesticides as an alternative. While the criteria consist of price, classification, number of diseases eradicated, method of action, shelf life, and formulation concentration. Data analysis uses the MAUT method with the steps of forming a decision matrix, normalizing the decision matrix, determining the utility matrix, calculating the final utility, and ranking. The research results obtained for the three best pesticides are Tridex 80 WP, Ziflo 76 WG, and Cabriotop 60 WG.
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.
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.

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 15 No. 01 (2026): JANUARY Vol. 14 No. 4 (2025): NOVEMBER Vol. 14 No. 3 (2025): JULY Vol. 14 No. 2 (2025): MEY Vol. 14 No. 1 (2025): JANUARY Vol. 13 No. 3 (2024): NOVEMBER Vol 13, No 2 (2024): JULY Vol. 13 No. 2 (2024): JULY Vol. 13 No. 1 (2024): MARET Vol 13, No 1 (2024): MARET Vol 12, No 3 (2023): NOVEMBER Vol. 12 No. 3 (2023): NOVEMBER Vol 12, No 2 (2023): JULI Vol. 12 No. 2 (2023): JULI Vol 12, No 1 (2023): MARET Vol 11, No 3 (2022): NOVEMBER Vol 11, No 2 (2022): JULI Vol 11, No 1 (2022): MARET Vol 10, No 3 (2021): NOVEMBER Vol 10, No 2 (2021): JULI Vol 10, No 1 (2021): MARCH Vol 9, No 3 (2020): NOVEMBER0 Vol 9, No 2 (2020): JULI0 Vol 9, No 1 (2020): MARET0 Vol 9, No 3 (2020): NOVEMBER Vol 9, No 2 (2020): JULI Vol 9, No 1 (2020): MARET Vol 8, No 2 (2019): SEPTEMBER0 Vol 8, No 2 (2019): SEPTEMBER Vol 8, No 1 (2019): MARET Vol 7, No 2 (2018): September Vol 7, No 1 (2018): Maret Vol 6, No 2 (2017): September Vol 6, No 1 (2017): Maret Vol 5, No 2 (2016): September Vol 5, No 1 (2016): Maret Vol 4, No 2 (2015): September Vol 4, No 1 (2015): Maret Vol 3, No 2 (2014): September Vol 3, No 1 (2014): Maret Vol 2, No 1 (2013): Maret 2013 Vol 2, No 2 (2013): September More Issue