INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
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Factor Analysis of Intention to Use Open-Source ERP: A Case Study from East Java Area
Full text linkOpen-source ERP is an information system that supports the digitization of an organization\u27s business so that it can support business continuity in an uncertain environment during the new normal while still implementing health protocol. In fact, only a few small to medium-sized organizations have adopted it. This research aims to examine the significant factors that influence the intention to use or adopt an open-source ERP system in the MSME-based new normal era. In the manner of exploring technological readiness\u27s positive and negative effects on cognitive factors (H1 and H2), environmental uncertainty, and cognitive and organizational readiness on intention to use (H3, H4, H5). There are 420 respondents collected by non-probability sampling and have been analyzed using PLS-SEM based on five subsectors of the small and medium-scale organizations (i.e.: agricultural, fishery, fashion, handicrafts, and culinary). This research confirms that the conceptual model and the five hypotheses proposed previously have been fully proven. The findings of this study prove that the intention to adopt an open-source ERP system is influenced by readiness factors (positive and negative technological, cognitive, and organizational) and environmental uncertainty due to the past COVID-19 pandemic.Open-source ERP is an information system that supports the digitization of an organization\u27s business so that it can support business continuity in an uncertain environment during the new normal while still implementing health protocol. In fact, only a few small to medium-sized organizations have adopted it. This research aims to examine the significant factors that influence the intention to use or adopt an open-source ERP system in the MSME-based new normal era. In the manner of exploring technological readiness\u27s positive and negative effects on cognitive factors (H1 and H2), environmental uncertainty, and cognitive and organizational readiness on intention to use (H3, H4, H5). There are 420 respondents collected by non-probability sampling and have been analyzed using PLS-SEM based on five subsectors of the small and medium-scale organizations (i.e.: agricultural, fishery, fashion, handicrafts, and culinary). This research confirms that the conceptual model and the five hypotheses proposed previously have been fully proven. The findings of this study prove that the intention to adopt an open-source ERP system is influenced by readiness factors (positive and negative technological, cognitive, and organizational) and environmental uncertainty due to the past COVID-19 pandemic
Development of Measuring System using CSI on ITIL V3 for Improvement at Oil Palm Plantation Company
Full text linkMany information technology services firms now offer services and consider software availability to be a critical component of achieving excellence and competing in the industry. The most crucial factor for customers is the quality of service, consumer needs, the state of the organization/company, and the present market should all be considered when improving their service. In this study, an ITSM analysis will be carried out at an oil palm plantation company based in West Kalimantan Province using ITIL framework V3 by focusing on continual service improvement, which largely relies on service measurement because a process must be measurable to be regulated and improved. To achieve continuous service improvement (CSI) throughout the service life cycle, we use the 7-step process to improve is used to establish and take care of the stages involved in identifying, defining, collecting, processing, analyzing, presenting, and implementing changes. The result of this analysis is CSF and KPI analysis that produce metrics, such as the average resolution time, the percentage of events allocated more than once, and the initial response time, then create a measurement system that is in accordance with the needs and can be used as a reference for the company\u27s system assessment.Many information technology services firms now offer services and consider software availability to be a critical component of achieving excellence and competing in the industry. The most crucial factor for customers is the quality of service, consumer needs, the state of the organization/company, and the present market should all be considered when improving their service. In this study, an ITSM analysis will be carried out at an oil palm plantation company based in West Kalimantan Province using ITIL framework V3 by focusing on continual service improvement, which largely relies on service measurement because a process must be measurable to be regulated and improved. To achieve continuous service improvement (CSI) throughout the service life cycle, we use the 7-step process to improve is used to establish and take care of the stages involved in identifying, defining, collecting, processing, analyzing, presenting, and implementing changes. The result of this analysis is CSF and KPI analysis that produce metrics, such as the average resolution time, the percentage of events allocated more than once, and the initial response time, then create a measurement system that is in accordance with the needs and can be used as a reference for the company\u27s system assessment
Comparing Data Mining Classification for Online Fraud Victim Profile in Indonesia
Full text linkClassification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia\u27s online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%.Classification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia\u27s online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%
Performance Comparison of AHP and Saw Methods For Selection of Doc Broiler Chicken Suppliers
Full text linkChoosing the most suitable day-old chick (DOC) broiler chicken supplier is currently one of the most important issues that must be addressed. This is because selecting the most suitable supplier can reduce the amount spent on purchases and the risk of sick chickens being delivered by the supplier. Another problem related to supplier selection that has been happening so far is the quality of products that are not following company standards or rejected products. The number of products provided does not match what was ordered by the company. The decision support system (DSS) can evaluate and select suppliers using multi-criteria characteristics related to the solutions offered based on parameters quality, price, delivery, supplier certificates, and death claims after the chickens have been delivered. The Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW) methods are used in this study as a comparison to produce the best-recommended accuracy value to get the best decision results based on ranking. The test results state that the AHP and SAW methods go well. The test was carried out using a dataset of the last ten months of history of purchasing docs broiler chicken from suppliers. The comparison of the results of the F1-score value between the AHP and SAW methods is 94% and 87%, respectively. The results state that the AHP method is superior as a system recommendation that can produce the best alternative supplier.Choosing the most suitable day-old chick (DOC) broiler chicken supplier is currently one of the most important issues that must be addressed. This is because selecting the most suitable supplier can reduce the amount spent on purchases and the risk of sick chickens being delivered by the supplier. Another problem related to supplier selection that has been happening so far is the quality of products that are not following company standards or rejected products. The number of products provided does not match what was ordered by the company. The decision support system (DSS) can evaluate and select suppliers using multi-criteria characteristics related to the solutions offered based on parameters quality, price, delivery, supplier certificates, and death claims after the chickens have been delivered. The Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW) methods are used in this study as a comparison to produce the best-recommended accuracy value to get the best decision results based on ranking. The test results state that the AHP and SAW methods go well. The test was carried out using a dataset of the last ten months of history of purchasing docs broiler chicken from suppliers. The comparison of the results of the F1-score value between the AHP and SAW methods is 94% and 87%, respectively. The results state that the AHP method is superior as a system recommendation that can produce the best alternative supplier
Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method
Full text linkStowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo
Neural Networks-Based Forecasting Platform for EV Battery Commodity Price Prediction
Full text linkThis study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%.This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%
Impact of Fuzzy Tsukamoto in Controlling Room Temperature and Humidity
Full text linkDry season is a season where the room temperature exceeds the needs of the body so that it is unpleasant, unhealthy and can interfere with human productivity. In addition, the efficiency of use and resource requirements are also a concern for some people. To overcome this problem, an automatic room temperature control device was created using the ESP32 microcontroller with Tsukamoto\u27s fuzzy algorithm optimization as a data processing technique to produce optimal fan speeds in duty cycle units based on temperature and humidity conditions in realtime. Four tests by running a fan for 30 minutes on each showed that the average difference between the maximum and minimum temperatures in the room was 0.95°C, while the average difference between maximum and minimum humidity was 2.0%. In addition, the test graph shows that when the fan is rotated in a closed room without air circulation, the relative temperature change increases from the initial minute to the last minute of the test. Meanwhile, changes in relative humidity decrease, although fluctuations increase within 1-4 minutes. This study found that fans are not effective in lowering room temperature optimally. Therefore, it is recommended to replace with an exhaust fan in future research.Dry season is a season where the room temperature exceeds the needs of the body so that it is unpleasant, unhealthy and can interfere with human productivity. In addition, the efficiency of use and resource requirements are also a concern for some people. To overcome this problem, an automatic room temperature control device was created using the ESP32 microcontroller with Tsukamoto\u27s fuzzy algorithm optimization as a data processing technique to produce optimal fan speeds in duty cycle units based on temperature and humidity conditions in realtime. Four tests by running a fan for 30 minutes on each showed that the average difference between the maximum and minimum temperatures in the room was 0.95°C, while the average difference between maximum and minimum humidity was 2.0%. In addition, the test graph shows that when the fan is rotated in a closed room without air circulation, the relative temperature change increases from the initial minute to the last minute of the test. Meanwhile, changes in relative humidity decrease, although fluctuations increase within 1-4 minutes. This study found that fans are not effective in lowering room temperature optimally. Therefore, it is recommended to replace with an exhaust fan in future research
Analysis of E-Government Health Application Features Acceptance on Partner Applications During COVID-19
Full text linkThis study analyzes the factors that influence public acceptance of E-Government Health Application feature on partner applications. The current phenomenon in the health sector is the emergence of COVID-19 which has a very fast rate of human-to-human spread. To handle these cases, the government evaluates and looks for new innovations by cooperating with new partners and making E-Government Health Application feature accessible through partner applications to make it easier for the public. The successful use of the system is influenced by the acceptance and use of the individual who uses it. The research model used in this study is a modified UTAUT2 model with a total sample of 250 respondents. Model testing is done by statistical analysis using SmartPLS software. It was found that Facilitating Conditions and Behavioral Intention variables had a positive and significant effect on Use Behavior variable with t-statistic of 3.659 and 4.505. Habit had a positive and significant effect on Behavioral Intention and Use Behavior variables with t-statistic of 7.939 and 3.232. Meanwhile, the Experience moderating variable affects the Facilitating Condition on Behavioral Intention variable and affects the Behavioral Intention on Use Behavior variable with t-statistics of 2.069 and 1.972This study analyzes the factors that influence public acceptance of E-Government Health Application feature on partner applications. The current phenomenon in the health sector is the emergence of COVID-19 which has a very fast rate of human-to-human spread. To handle these cases, the government evaluates and looks for new innovations by cooperating with new partners and making E-Government Health Application feature accessible through partner applications to make it easier for the public. The successful use of the system is influenced by the acceptance and use of the individual who uses it. The research model used in this study is a modified UTAUT2 model with a total sample of 250 respondents. Model testing is done by statistical analysis using SmartPLS software. It was found that Facilitating Conditions and Behavioral Intention variables had a positive and significant effect on Use Behavior variable with t-statistic of 3.659 and 4.505. Habit had a positive and significant effect on Behavioral Intention and Use Behavior variables with t-statistic of 7.939 and 3.232. Meanwhile, the Experience moderating variable affects the Facilitating Condition on Behavioral Intention variable and affects the Behavioral Intention on Use Behavior variable with t-statistics of 2.069 and 1.97
Usability Analysis of MSME Business Accounting Applications Based on User Retention Using ISO 9241-11
Full text linkBukuWarung has been known as a MSME’s bookkeeping application in Indonesia. As the features developed, there were disappointed responses from Playstore reviews, social media, and research interviews who complained about the usability aspect of this application, thus triggering the desire for users to stop using the application. This condition motivates the author to assess the impact level of usability in aspects of Effectiveness, Efficiency, and Satisfaction on user retention. This study used a partial least square–structural equation model method. The total of 248 user samples was obtained using simple random sampling and voluntary response techniques. The research uses CSUQ, as well as questions for user retention. For data testing used Ms. Excel and SmartPLS version 3.3.3. According to the measurement model, 7 of 22 all variables were deleted. This study proves that Effectiveness, Efficiency, and Satisfaction have a positive and significant effect of up to 74.8% on user retention. However, the size of influence and relative influence is weak on user retention. User retention indicators affected by usability are increasing purchases as tenure grows, customer referrals, and premium prices. The implication for BukuWarung is to conduct usability testing on each feature.BukuWarung has been known as a MSME’s bookkeeping application in Indonesia. As the features developed, there were disappointed responses from Playstore reviews, social media, and research interviews who complained about the usability aspect of this application, thus triggering the desire for users to stop using the application. This condition motivates the author to assess the impact level of usability in aspects of Effectiveness, Efficiency, and Satisfaction on user retention. This study used a partial least square–structural equation model method. The total of 248 user samples was obtained using simple random sampling and voluntary response techniques. The research uses CSUQ, as well as questions for user retention. For data testing used Ms. Excel and SmartPLS version 3.3.3. According to the measurement model, 7 of 22 all variables were deleted. This study proves that Effectiveness, Efficiency, and Satisfaction have a positive and significant effect of up to 74.8% on user retention. However, the size of influence and relative influence is weak on user retention. User retention indicators affected by usability are increasing purchases as tenure grows, customer referrals, and premium prices. The implication for BukuWarung is to conduct usability testing on each feature
Comparison of Modified Nazief&Adriani and Modified Enhanced Confix Stripping algorithms for Madurese Language Stemming
Full text linkThe Madurese language has a unique morphology. The morphological uniqueness can be used to find basic words. The basic word process is called stemming. Stemming can be developed into an application for translating Madurese into Indonesian and even other languages. It can support the development of a Madurese language text plagiarism system. Stemming research on the Madurese language is still rare. Therefore, this study aims to find the basic words of the Madurese language using modifications to the Nazief & Adriani algorithm and Enhanced Confix Stripping (ECS) modifications. The study used 1000 Madurese words, consisting of 630 prefix words, 74 ending words, and 296 confix words. The results showed that the modification of the Nazief & Adriani algorithm was better, shown by the accuracy obtained of 88.8% with overstemming of 0.7% and understemming of 10.5%. As for ECS, an accuracy of 74.0% was obtained, 0.4% overstemming, and 25.6% understemming. In the same process, Nazief&Adriani\u27s modification is faster than the ECS modification. For the Nazief&Adriani modification, it takes 13.31 seconds while for the ECS modification, it takes 210.88.The Madurese language has a unique morphology. The morphological uniqueness can be used to find basic words. The basic word process is called stemming. Stemming can be developed into an application for translating Madurese into Indonesian and even other languages. It can support the development of a Madurese language text plagiarism system. Stemming research on the Madurese language is still rare. Therefore, this study aims to find the basic words of the Madurese language using modifications to the Nazief & Adriani algorithm and Enhanced Confix Stripping (ECS) modifications. The study used 1000 Madurese words, consisting of 630 prefix words, 74 ending words, and 296 confix words. The results showed that the modification of the Nazief & Adriani algorithm was better, shown by the accuracy obtained of 88.8% with overstemming of 0.7% and understemming of 10.5%. As for ECS, an accuracy of 74.0% was obtained, 0.4% overstemming, and 25.6% understemming. In the same process, Nazief&Adriani\u27s modification is faster than the ECS modification. For the Nazief&Adriani modification, it takes 13.31 seconds while for the ECS modification, it takes 210.88