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Universitas Teknologi Digital Indonesia (d.h STMIK AKAKOM) Jl. Raya Janti Jl. Majapahit No.143, Jaranan, Banguntapan, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55918
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Journal of Intelligent Software Systems
Published by Universitas Teknologi Digital Indonesia
ISSN : -     EISSN : 29627702     DOI : https://doi.org/10.26798/jiss
Core Subject : Science,
Computer Science & IT
Journal of Intelligent Software Systems (JISS) is open access, peer-reviewed international journal that will consider any original scientific article that expands the field of Intelligent Software Systems. The journal publishes articles in all Intelligent Software Systems specialities of interest to Intelligent Software Systems, physicians, and researchers.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 38 Documents
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Performance Assessment of Branch Office Assistant (KCP) Leaders Using the Simple Additive Weighting Method Wibowo, Dwi; Astuti, Femi Dwi; Astuti, Yuli; Widayani, Wiwi; Fauzi, Arma
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1502

Abstract

Abstract— Performance Appraisal is a process that allows organizations to know, evaluate, measure and assess the performance of their members appropriately and accurately. This activity is closely related and influences the effectiveness of the implementation of human resource activities in the company, such as promotion, compensation, training, career management development and others. This is because the performance appraisal function can provide important information to the company to improve decisions and provide feedback to employees about their actual performance.The implementation of the achievement and performance appraisal of KCP leaders at KSPPS Tunas Artha Mandiri Nganjuk Branch has so far still used manual and has not used a decision support sistem so that the data generated is not accurate and takes a long time. As a result, if used in decision making, it is not appropriate and causes problems such as non-transparent management, decreased quality and performance of KCP leaders. The author applies and implements the Additive Weighting method (SAW) to measure the achievement and performance assessment of the Sub-Branch Office leadership at KSPPS Tunas Artha Mandiri Nganjuk Branch. With the aim of this decision support sistem can provide information and recommendations as well as accurate and efficient performance appraisal data.Keywords-Decision Support Sistem, Simple Additive Weighting, Employee Appraisal
Metadata Forensic Analysis as Support for Digital Investigation Process by Utilizing Metadata-Extractor Arizona, Nanda Diaz; Nugroho, Muhammad Agung; Syujak, Ahmad Rois; Saputra, Rizqi Kurniawan; Sulistyowati, Istri
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1503

Abstract

Abstract — The rapid development of technology in the current era, in addition to providing positive impacts, certainly also has negative impacts. In Indonesia, based on data from the Cyber Crime Directorate (Dittipidsiber) website, the crime rate related to the ITE Law (Information and Electronic Transactions) is increasing day by day. This encourages digital forensic investigators to be able to develop a concept or method that can be adjusted to digital cases, for example cases of digital data manipulation such as photos or documents. Metadata is an information structure that describes, explains, places in a place or makes it easier to find something, use or manage and sources of information. Metadata can also be interpreted as data about data or information about information. One method or approach that can be done in cases of digital files (photos, videos or documents) can be done using forensic metadata analysis. This is because metadata stores information related to a file. By developing a library from java (metadata-extractor) based on open source and developed in the Netbeans 8.0 application, it will make it easier for an investigator or forensic investigator to conduct a forensic metadata approach, which is expected from the results can be used as valid evidence in the digital forensic investigation process. Keywords – Metadata, Digital Forensics, Cyber Crime, Metadata Analysis, Digital Evidence
BUILDING DATA WAREHOUSE FOR EMPLOYEE TRAINING MINISTRY OF LAW AND HUMAN RIGHTS Nugroho, Ari Fauzi Mukti; Kartadie, Rikie; Handayani, Latifah Nurrohmah; Isnaeni, Nenen; kholik, Moh. Abdul
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1500

Abstract

In order to improve and expand each employee's competency and knowledge, education and training are crucial for the Ministry of Law and Human Rights. They may also be utilized for employee mapping. In the past, it was necessary to gather data on things like the number of employees who attended a particular training, which training had reached its participant quota, and the number of graduates in each training. This required extensive processing and repeated cross-checking of data sources to make sure the data was accurate and legitimate before it could be compiled into a table for analysis. Information technology may be used to immediately process employee competence data and education and training results into information. Therefore, it is expected that the Nine Step method, which is part of the Kimball & Ross (2010), methodology will simplify and accelerate the process of processing training data into information presented for analysis and reporting purposes at the leadership level in each work unit. Keywords: data warehouse, OLAP, ETL, pentaho, kemenkumham, training 
Prediction of Cyber Attack Losses by Attack Type and Country with Visual Approach and Quantitative Statistics Kabahing, Sepfanner; Kartadie, Rikie; Aditomo, Sigit; da silva, Ivònia Fàtima Ruas; Xavier, Francisco; Nur aini, Nur aini
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2002

Abstract

Cyberattacks continue to be a major threat to the digital infrastructure of countries around the world, significantly impacting economic stability, data security and public trust. This research aims to analyze financial losses due to cyberattacks by country, attack type, and affected industry sector, utilizing a visual exploratory approach through interactive dashboards and descriptive statistical analysis. The data used includes 3,000 cyber incidents from 10 countries, covering various attack types such as DDoS, Phishing, Malware, and Man-in-the-Middle. Visualization was developed using Power BI with DAX (Data Analysis Expressions) SUMX aggregation formula to calculate Total_Loss in order to dynamically estimate the cost of loss based on user interaction. The analysis showed that DDoS and Phishing attacks were the most frequent attack types, while the Information Technology, Banking and Government sectors recorded the highest accumulative losses. Geographically, the UK, Germany and Brazil were the countries with the largest total losses, with the highest average loss per incident found in Man-in-the-Middle and Phishing attacks. The findings underscore the urgency for the government and private sector to develop more responsive and data-driven mitigation strategies. This research confirms that the integration of dynamic visualization systems with quantitative analysis not only improves understanding of attack patterns, but also supports the decision-making process in efforts to strengthen national cybersecurity in a sustainable manner
TEMPERATURE SENSOR DATA QUALITY ASSESSMENT IN MANUFACTURING ENVIRONMENT USING HAMPEL FILTER AND QSD P.D.P., Bambang; Andriyani, Widyastuti; Dahlan, Akhmad
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2003

Abstract

In the Industry 4.0 era, integrated temperature sensors in system production become source main data for taking decisions. However, the quality of the data produced often influenced by noise, missing values, and disturbing anomalies accuracy of analytical processes. Research This proposes a monitoring pipeline designed data quality For environment manufacturing based on the Internet of Things (IoT), with focus on usage Hampel Filter and Quality Score Delta (QSD) methods. Hampel Filter is used for detecting and handling outliers in temperature data in a way adaptive, while QSD is used for measure dynamics change data quality from time to time. Architecture system built with using Apache Kafka for data ingestion, InfluxDB For time-series storage, and Grafana for real-time visualization. Case study performed on temperature sensor data from the conveyor motor, and the results show that method. This capable detect degradation data quality in general proactive. Findings show potential big in increase reliability industrial monitoring system as well as support maintenance predictive data- based. Research This give contribution significant in developing modular and adaptive approach for management data quality in the manufacturing sector.
PERFORMANCE ANALYSIS OF LOGISTIC REGRESSION ALGORITHM IN OPINION SEGMENTATION OF INDOSAT NETWORK SERVICE REVIEWS Dwi, Sandy Ananda; Kriestanto, Danny; Anwar, Ajie Al Qadri; Ro'uf, Syahrur; Rochmana, Lintang Suci; Nugroho, Muhammad Agung
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2004

Abstract

In the era of the industrial revolution 4.0, where the use of network services has become a basic need and cannot be separated from daily activities, the massive number of network service users can be proven by the increasing number of people using digital platforms to search for information, express opinions or even just to communicate with each other, currently network services are available in the form of digital platforms that can be used to purchase network data packages or just to monitor the quality of network services, therefore this study aims to analyze user sentiment towards network services that have been launched by the Indosat provider based on the results of user reviews sourced from the digital platform using a machine learning approach and a logistic regression algorithm model to determine the segmentation of opinions that are widely expressed on the digital platform. The results of this study indicate that the logistic regression algorithm is able to analyze patterns of consumer characteristics with good accuracy in the algorithm model, and the results of the accuracy of the algorithm model in finding segmentation patterns in sentiment opinions reach an accuracy value of 85%, precision 81%, recall 77% and f1-score 79% to predict an opinion that has negative and positive sentiment during testing, then network speed, connection disruption and network data package prices are one of the factors that can influence an opinion regarding negative and positive sentiment.
DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE Laksono, Triyan Agung; Andriyani, Widyastuti; Putra, Fadhlih Girindra; Ruas da silva, Ivonia Fatima; widayani, Wiwi
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2005

Abstract

This research develops an approach for clustering digital activity locations based on Twitter geospatial data with the aim of supporting business intelligence spatiotemporal . By utilizing the Twitter Geospatial Data dataset containing more than 14 million tweets geo-tagged from the United States, this study implements and compares the DBSCAN and K- Means algorithms to identify spatial and temporal patterns of Twitter user activity. The research process begins with the data pre -processing stage using the Knowledge Discovery Database (KDD), followed by the implementation of the clustering algorithm , and ending with the integration of the results into the dashboard.business intelligence using Power BI . The results show that DBSCAN is able to detect irregular clusters that follow geographic patterns and population density, while K- Means produces a division of the region into three main clusters (West Coast, Central Region, and East Coast) with different temporal activity patterns. Integration of clustering results into a BI dashboard produces actionable business insights , such as identification of digital activity hotspots , optimal time for content delivery, geographic segmentation for marketing strategies, and temporal activity patterns for campaign scheduling. This research contributes to the development of an integrated spatiotemporal analysis pipeline to support data-driven decision making.
SALES PREDICTION OF VEGETABLE SEED PRODUCTS USING SIMPLE LINEAR REGRESSION Sari, Dini Fakta; Sofian, Muhammad Ali; Nurcahyo, Agung Wilis; Wiharyanto, Kelik; Pereira, Elisabet da Conceição
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2001

Abstract

The growth of the modern agricultural sector drives the need for an accurate sales prediction system, especially for vegetable seed products that are highly dependent on the season and market demand. An imbalance between stock and demand can cause losses, either in the form of overstock or undersupply. This condition requires a data-based planning strategy to ensure stock availability according to actual needs in the field. A historical data-based sales prediction approach is a relevant solution to optimize the distribution and procurement process. This study aims to apply a simple linear regression method in predicting vegetable seed sales based on historical data for one year. The prediction model is built using the time variable (month) as the independent variable and the number of seed requests as the dependent variable. This technique was chosen because of its ability to identify linear relationship patterns between time and sales trends in a simple but effective way. The data used comes from internal records of farmers and distributors, which are then classified into two main categories: leafy vegetable seeds (spinach, kale, mustard greens) and fruit vegetable seeds (tomatoes, chilies, eggplants). The results of the study showed that simple linear regression was able to provide fairly accurate predictive results. This model can be used as a basis for decision making in production planning, supply chain management, and seed inventory management, thus supporting the efficiency of farming businesses and reducing potential losses due to mismatches between demand and supply.

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