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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 603 Documents
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Prediction of SDG 6.2 Achievement in Indonesia Using Double Exponential Smoothing Nabila, Nazwa; Erfina, Adhitia; Warman, Cecep
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2411

Abstract

This research aims to forecast Indonesia’s progress in achieving Sustainable Development Goal (SDG) 6.2, which targets 100% access to adequate sanitation and elimination of open defecation (OD) by 2030. The Double Exponential Smoothing (DES) method was used on provincial time series data from 2013–2024 (sanitation) and 2020–2024 (OD), with performance evaluated using Mean Absolute Percentage Error (MAPE). Results showed consistently high forecasting accuracy, with DKI Jakarta (0.99%), South Sulawesi (1.87%), and DI Yogyakarta (2.21%) among the most accurate for sanitation, and Maluku (2.79%), Papua (3.03%), and Gorontalo (4.49%) for OD. Spearman correlation analysis revealed a strong national negative correlation (r = –0.991, p < 0.001) between sanitation access and OD. However, provinces like DKI Jakarta (+0.36) and DI Yogyakarta (+0.86) showed positive anomalies, indicating behavioral gaps despite infrastructure growth. These findings clearly highlight the importance of integrating behavioral interventions and localized strategies to effectively accelerate progress toward SDG 6.2.
Decision Support System for Determining Disease and Pest Handling in Chili Plants Using WP and VIKOR Methods Jalila, Muhammad Mulkan; Fuadi, Wahyu; Razi, Ar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract— Chili plants are an important horticultural commodity that plays a major role in the agricultural and economic sectors of Indonesia. However, the high risk of pest and disease attacks is a major challenge for farmers in increasing productivity. Many farmers have difficulty in determining the right handling strategy, so technology-based solutions are needed to assist the decision-making process. This study developed a Decision Support System (DSS) for handling diseases and pests in chili plants using two methods, namely Weighted Product (WP) and VIšekriterijumsko Kompromisno Rangiranje (VIKOR). The WP method is used to calculate attribute assessments by multiplication, where each criterion is weighted according to its level of importance. The final results show that the best alternative is fusarium wilt disease (Fusarium oxysporum) with code A2, having a vector score of 0.09899. In the VIKOR method, the alternative with the lowest Qi index value is considered the best solution. Alternative A2 is again ranked at the top with a Qi value of 0. The process of developing this DSS involves identifying disease and pest symptom criteria, normalizing the decision matrix, and calculating the ideal solution for each alternative. This approach has proven effective in providing accurate recommendations and helping farmers choose the most optimal management strategy. By utilizing WP and VIKOR-based SPK, it is hoped that chili farmers can increase efficiency in identifying and overcoming plant disorders, so that agricultural productivity can increase significantly.
Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis Artanto, Fenilinas Adi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2426

Abstract

The rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a lack of studies focusing on cultural sentiment such as humor in the Italian brainroot anomaly. This study provides an overview of user sentiment analysis of the game “Hantu Tung Tung Tung Sahur 3D,” a culturally viral application anomaly italian brainroot among young people on the Google Play Store during the month of Ramadan. User reviews were collected through web scraping, and data preprocessing involved tokenization, stopword removal, lowercase, stemming, and filtering to prepare the text for analysis. Feature extraction was performed using the Bag of Words method. This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. The novelty of this study lies in its focus on sentiment analysis in a unique and culturally viral digital context, as well as a comparative evaluation of classification algorithms specifically on this dataset. The results show that the Random Forest algorithm achieves the highest Area Under the Curve (AUC) score of 0.529, outperforming Naïve Bayes (0.504), SVM (0.503), and Decision Tree (0.498). These findings provide new insights into the suitability of ensemble methods such as Random Forest for sentiment analysis in specific digital phenomena, highlighting its potential for more reliable sentiment classification in similar contexts.
Expert System for Diagnosing Areca Plant Diseases Using the Certainty Factor Method Indrayani, Lilis; Zulkarnain, Zulkarnain; Kamesrar, Magrid Margaretha
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.v12i2.1493

Abstract

Areca nut is a plant that is cultivated by the people of Papua as a means of livelihood. Areca nut has many benefits, starting from the fruit, leaves, roots, and the economic value of betel nut is also quite good. The problem often encountered by areca cultivators is the damage caused by areca plant diseases, causing losses both in terms of yield and control. In this study, the expert system for diagnosing areca plant diseases uses the Certainty Factor method, the essence of this method is to measure whether it is certain or uncertain in diagnosing disease based on the type of disease and disease symptoms found in areca plants, there are 8 types of areca plant diseases, namely spotting and yellowing, red leaf rust, stem base rot, fruit rot, shoot rot, root rot, flower and leaf fall and stunted plants. With the problems that occur, researchers will create an expert system that can help areca nut cultivators in diagnosing diseases in areca palm plants, it is hoped that this system can help areca cultivators by applying the Certainty Factor method which adopts expert knowledge into a computerized system so that the system can solve problems like an expert.
Online Travel Agent Marketing Strategy Through Social Interaction During the Pandemic COVID-19 Ramadani, Mita Putri; Ambarwati, Rita; Hariasih, Misti
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.v12i2.1553

Abstract

The Covid-19 pandemic has had such a devastating impact on Online Travel Agents such as Traveloka that it has made it to the lowest phase it has ever experienced. There are government regulations that limit people's social interactions so that various strategies are carried out, from using promos or utilizing application features. The purpose of this research is to find out whether the difference in social interaction between Traveloka users before and during the Covid pandemic was significant enough to evaluate Traveloka's marketing strategy. Decreased application usage, ticket rescheduling requests to refund requests which are currently Traveloka's problems. This research is qualitative research that uses the Social Network Analysis (SNA) method using the Twitter application assisted by the Jupyter Anaconda application, Google collab, and Ghepi. From the visualization of this study, the results obtained from 3 research focuses had significant differences from social interactions using Traveloka during the pandemic. From promos that experienced a decrease in interaction during the pandemic, Traveloka Xperience experienced an increase in feature usage, and the ticket feature experienced a decline in social interaction. So that Traveloka is expected to improve the company's strategy to survive during the pandemic.
Analysis of Factors Influencing Interest and Behavior in Using ShopeePay Features Using the Unified Theory of Acceptance and Use of Technology (UTAUT2) Model Desvira, Nanda Suci; Aransyah, Muhammad Fikry
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.v12i2.1594

Abstract

This research is motivated by technological developments that continue to provide innovations aimed at facilitating human work, such as innovations in payment methods that can be done digitally and non-cash, called digital wallets. One digital wallet with the most users in Indonesia is ShopeePay. However, the existence of ShopeePay has yet to be entirely accepted by digital wallet users in Indonesia, as shown through a consumer survey in 2022 which resulted in conclusions regarding ShopeePay experiencing a decline in users. This study intends to determine the factors that influence behavioral intention and use behavior of the ShopeePay feature as measured by the variables in the UTAUT 2 model. This quantitative study uses a survey method with 100 respondents using the ShopeePay digital wallet. Samples were taken using a purposive sampling technique. The data analysis technique used is the SEM-PLS method, and the data is processed using SMART PLS 4.0 software. The results of this study indicate that the factors that significantly influence the behavioral intention of the ShopeePay feature are social influence, price value, and habit. Then, the use behavior of the ShopeePay feature is significantly influenced by facilitating conditions and habits. Meanwhile, the performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation variables do not have a significant effect on the behavioral intention of the ShopeePay feature.
Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features Indasari, Sri Suci; Tjahyanto, Aris
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.v12i2.1621

Abstract

The use of technology in line with the increasing number of internet users has caused a shift in the product sales ecosystem to the realm of electronic commerce (electronic commerce). A total of 73.23 customers made purchase transactions using e-commerce and the most purchased products were products classified as Fast Moving Consumer Goods (FMCGs). The increasingly varied FMCGs data coupled with the increasing number of marketplaces is felt to need to be broken down into specific groups. The process is carried out by analyzing e-commerce product information, especially product names, and descriptions. In this study, we propose an automatic categorization of multiple marketplaces using data from multiple marketplaces. Data text is converted into structured data with a series of preprocessing, and comprehensive experiments are carried out to see the extraction performance of variables including TF-IDF, BOW, and N-Gram.  All three methods are used to validate text data sets with K-Means grouping results used with the help of PCA to reduce data dimensions.  The results show that the performance of the TF-IDF algorithm with a dimension reduction value of 70 and the use of Python can provide optimal results for the percentage of grouping data.
Analysis of Information Technology Services Using the ITIL V.3 Framework Mahardika, Wildan Kristian; Emanuel, Andi Wahju Rahardjo
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.v12i2.1624

Abstract

Information technology services have been widely used in various agencies. This is done because information technology services help many business processes in an agency. Government agencies, companies, and education are examples of agencies that utilize information technology services to support their business processes. One educational institution that uses IT services to support its business processes is the UKDW library. The UKDW Library utilizes IT services for borrowing, returning, searching for books and journals, checking for plagiarism, and more. To further improve the quality of IT services, evaluating and developing them is necessary to better respond to business needs. In this study, researchers measured the maturity level of an IT service in the UKDW library. Researchers use the ITIL V3 framework in measuring maturity levels. This research focuses on the service operation domain and its five sub-domains (problem management, event management, incident management, access management, and request fulfillment). The final results of the study show that IT services in the UKDW library are at the managed level, which means that information technology services in the UKDW library have been planned and carried out routinely. There are standards for documentation, and process performance measurement has been implemented.
The Impact of 5G Network Technology Transformation to Replace 4G Using the Sem Amos Method Afriyanto, Dody; Destya, Senie
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.v12i2.1634

Abstract

The development of the telecommunication world has occurred very rapidly and has been brought from the first generation, namely 1G to 4G, which has developed as a future network medium in several countries, successfully implemented in transactions. This research discusses the potential of 5G technology as a substitute for the previous technology, namely 4G. How can this technology be applied in Indonesia; which areas are suitable for implementing 5G technology? Data analysis for this study has been adapted to the research model and the variables studied. The causality model is used in this study, and the AMOS program's SEM (Structural Equation Modeling) analysis method is used to assess the research assumptions. The existence of the 5G network has brought Indonesia to the threshold of development that opens the door to a world full of opportunities, 5G technology is expected to have a positive impact on the development of digital skills and entrepreneurship, which will have a better impact.
Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm Abrar, Ihya' Nashirudin; Abdullah, Asrul; Sucipto, Sucipto
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.v12i2.1643

Abstract

Diagnosing liver disease in the field of healthcare is not an easy task. However, by utilizing medical records as datasets and applying data mining methods such as K-Nearest Neighbor (K-NN), we can analyze and extract knowledge automatically. The K-NN method has proven to be more effective compared to other methods as it clusters new information by selecting the nearest neighbors based on the value of k. In this study, we employed the Elbow method to determine the optimal value of k by measuring the error rate. The test results revealed that the optimal value of k was found to be 4, with the lowest error rate. In the third test, we achieved a training accuracy of 80.5% and a testing accuracy of 78.9%. After fine-tuning the training data, we were able to improve the accuracy to 82.2% for training and 77.1% for testing. However, in the fourth test, we encountered overfitting issues due to data imbalance caused by inappropriate resampling, resulting in a model that was overly complex and prone to excessive noise.

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