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All Journal JURNAL SISTEM INFORMASI BISNIS Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Informatika Upgris Prosiding 2nd Seminar Nasional IPTEK Terapan (SENIT) 2017 Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Komunikasi Jurnal Sosial dan Teknologi INFORMATIKA Jurnal Pengabdian Mandiri Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Manajemen Informatika & Teknologi Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Community: Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Indonesia : Manajemen Informatika dan Komunikasi SWAGATI: Journal of Community Service Saturnus: Jurnal Teknologi dan Sistem Informasi International Journal of Information Technology and Business Systematic Literature Review Journal Journal of Engineering, Electrical and Informatics Global Science: Journal of Information Technology and Computer Science Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Publikasi Teknik Informatika
Toni Wijanarko Adi Putra
Universitas Sains Dan Teknologi Komputer
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : journal of technology informatics and engineering

 1 
MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Abstract

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.
MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Abstract

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.
Document Security System Using Arduino-Based Fingerprint And Rfid Module Mohamad Irkam; Toni Wijanarko Adi Putra
Journal of Technology Informatics and Engineering Vol 2 No 1 (2023): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v2i1.174

Abstract

Documents are valuable/important letters that can be used as evidence of printed or written information. The company where the author conducted research is a company operating in the logistics sector, which of course has a special document storage room, namely the Accounting room . The use of plastic folders containing documents and safes as a place to store important documents and files still carries a lot of risk of misuse and loss. The title of this research is Document Security System Using Arduino- Based Fingerprint and RFID Modules . One of the aims of this research is to minimize loss and prevent misuse of documents/files placed in plastic folders. A security system using fingerprint and RFID is very good to apply as document security because it has double security. The system created uses a fingerprint as a signal breaker or RFID tag electromagnetic waves which are attached to a folder containing important documents/files belonging to the company. This system is also supported by an Arduino kit Uno with an Atmega328 microcontroller as the brain for processing data from the fingerprint sensor and RFID Reader functions as an electromagnetic wave identification system for RFID Tags , therefore two pieces of hardware are needed, called Tags and Readers . Apart from the three main components mentioned, there are several other components used in making this document security system, namely micro SD kit, buzzer, push button, servo . Creating this system is very important to limit misuse of documents and the risk of losing documents in a company.
Utilizing phpMyAdmin for System Design in Enterprise Administration Wibowo, Mars Caroline; Wijanarko Adi Putra, Toni
Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.193

Abstract

In today's digital landscape, effective data management is essential for organizations, particularly small and medium-sized enterprises (SMEs) that often struggle with traditional manual methods, leading to inefficiencies and data inaccuracies. This research aims to investigate the implementation of phpMyAdmin, a web-based database management tool, to enhance administrative systems within SMEs. The study employs a mixed-methods approach, integrating qualitative case studies and quantitative surveys to gather comprehensive insights into user experiences and operational performance. The findings reveal that the adoption of phpMyAdmin significantly improves data management efficiency, with 75% of respondents expressing satisfaction with its user-friendly interface. However, challenges such as security vulnerabilities and the necessity for user training were also identified, indicating that while phpMyAdmin offers substantial benefits, organizations must address these issues to fully leverage their capabilities. The implications of this research suggest that SMEs should prioritize investing in user training and implementing robust security measures to mitigate risks associated with data management. By doing so, organizations can enhance their operational efficiency and decision-making processes. Future research should focus on the long-term impacts of phpMyAdmin and explore its integration with other management systems to further optimize organizational performance.
MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol. 1 No. 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Abstract

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.
Document Security System Using Arduino-Based Fingerprint And Rfid Module Mohamad Irkam; Toni Wijanarko Adi Putra
Journal of Technology Informatics and Engineering Vol. 2 No. 1 (2023): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v2i1.174

Abstract

Documents are valuable/important letters that can be used as evidence of printed or written information. The company where the author conducted research is a company operating in the logistics sector, which of course has a special document storage room, namely the Accounting room . The use of plastic folders containing documents and safes as a place to store important documents and files still carries a lot of risk of misuse and loss. The title of this research is Document Security System Using Arduino- Based Fingerprint and RFID Modules . One of the aims of this research is to minimize loss and prevent misuse of documents/files placed in plastic folders. A security system using fingerprint and RFID is very good to apply as document security because it has double security. The system created uses a fingerprint as a signal breaker or RFID tag electromagnetic waves which are attached to a folder containing important documents/files belonging to the company. This system is also supported by an Arduino kit Uno with an Atmega328 microcontroller as the brain for processing data from the fingerprint sensor and RFID Reader functions as an electromagnetic wave identification system for RFID Tags , therefore two pieces of hardware are needed, called Tags and Readers . Apart from the three main components mentioned, there are several other components used in making this document security system, namely micro SD kit, buzzer, push button, servo . Creating this system is very important to limit misuse of documents and the risk of losing documents in a company.
Utilizing phpMyAdmin for System Design in Enterprise Administration Wibowo, Mars Caroline; Wijanarko Adi Putra, Toni
Journal of Technology Informatics and Engineering Vol. 3 No. 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.193

Abstract

In today's digital landscape, effective data management is essential for organizations, particularly small and medium-sized enterprises (SMEs) that often struggle with traditional manual methods, leading to inefficiencies and data inaccuracies. This research aims to investigate the implementation of phpMyAdmin, a web-based database management tool, to enhance administrative systems within SMEs. The study employs a mixed-methods approach, integrating qualitative case studies and quantitative surveys to gather comprehensive insights into user experiences and operational performance. The findings reveal that the adoption of phpMyAdmin significantly improves data management efficiency, with 75% of respondents expressing satisfaction with its user-friendly interface. However, challenges such as security vulnerabilities and the necessity for user training were also identified, indicating that while phpMyAdmin offers substantial benefits, organizations must address these issues to fully leverage their capabilities. The implications of this research suggest that SMEs should prioritize investing in user training and implementing robust security measures to mitigate risks associated with data management. By doing so, organizations can enhance their operational efficiency and decision-making processes. Future research should focus on the long-term impacts of phpMyAdmin and explore its integration with other management systems to further optimize organizational performance.
Co-Authors Achmad Solechan Agung Surono Ahmad Ashifuddin Aqham Aldho Dwi Kurniawan Alex Ristanto Pratama Aloysius Rianto Andra Matarisman Arditya Himawan Arrivo Athaillah Maheswara Arsito Ari Kuncoro Aslina Baharum Budi Hartono budi hartono Budi Hartono Budi Hartono Cahyo Arief Budiman Chandani, Chicie Chicie Chandani Danang Danang Danang, Danang Dani Sasmoko Dani Sasmoko Dendy Kurniawan Devi Astri Nawangnugraeni Dimas Rahardian Dwi Prasetyaningsih Edy Siswanto Eko Siswanto Eko Siswanto Endrahadi Rahadian Endro Farhan Afif Ilmi Febryantahanuji Febryantahanuji Fitro Nur Hakim Fitro Nur Hakim Fujiama Diapoldo Silalahi Fujiama Diapoldo Silalahi Hakim , Fitro Nur Haryo Kusumo Haryo Kusumo Iman Saufik Suasana Isaac Vital Jarot Dian Susatyono Khoirur Rozikin Kusumo, Haryo Kusworo Adi Lawrence Adi Supriyono M. Zakki Abdillah Migunani Migunani Mohamad Irkam Muhammad Walid Syahrul Churum mukhamad masrur Nanda Natalia Nuris Dwi Setiawan R Rizal Isnanto Rizal Dewo Susanto Roni Eka Setiawan Rusito Siswanto Siswanto Siswanto Siswanto Siswanto, Edy Solikhin Solikhin Solikhin Solikhin Sulartopo Sulartopo Ulil Asyhar Unang Achlison Victor Gayuh Utomo Virgiawan Restu Pratama Wibowo, Mars Caroline Yehezkiel Yanu Putranto Yohanes Suhari Yuli Fitrianto Zaenal Mustofa