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All Journal JIK Jurnal Ilmu Komputer Jurnal Simetris Majalah Geografi Indonesia JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat AbdiMas IKRA-ITH ABDIMAS Aptisi Transactions on Technopreneurship (ATT) Jurnal Mantik Jurnal Abdimas Madani dan Lestari (JAMALI) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Journal Sensi: Strategic of Education in Information System Jurnal Algoritma, Logika dan Komputasi Devotion: Journal of Research and Community Service Jurnal Ilmu Komputer dan Informatika Journal of Social Research Journal Of World Science Ilmu Komputer untuk Masyarakat Jurnal Locus Penelitian dan Pengabdian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Indonesia Sosial Teknologi Jurnal Indonesia Sosial Sains Journal of Accounting Research, Organization and Economics (JAROE) Eduvest - Journal of Universal Studies Jurnal Ekonomi, Teknologi dan Bisnis Jurnal Adijaya Multidisiplin Media Abdimas Jurnal Pengabdian Masyarakat Bangsa Infact: Jurnal Sains dan Komputer Asian Journal of Social and Humanities INOVTEK Polbeng - Seri Informatika Journal of Golden Generation Multidisciplinary
Widodo, Agung Mulyo
Universitas Esa Unggul
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Implementation of Vector-Based Melody Extraction for Plagiarism Detection Using Szymkiewicz-Simpson Coefficient Nindyo Artha Dewantara Wardhana; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Jurnal Indonesia Sosial Sains Vol. 5 No. 04 (2024): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v5i04.1084

Abstract

Plagiarism is topical within the music industry. It is filled with circumstances such as the potential of massive losses coupled with a “false-positive” court ruling due to the blurred line of plagiarism factor. This research aims to solve the gray line of music plagiarism by exploring the potential of the Szymkiewicz-Simpson coefficient toward musical aspects of music. Melody and Rhythm are chosen as the main features to focus on in the research. MIDI files of music involved in court cases are used as data for the study, with limitations put on what cases can be used for the research. Using a threshold range of 0.1 to 0.25, detection accuracies for melodic plagiarism range from 45% to 60%, while rhythm plagiarism ranges from 60 to 65%. This shows that the algorithm of plagiarism detection has a tendency to detect non-plagiarism cases and is more effective towards rhythm plagiarism detection rather than melodic plagiarism detection against existing plagiarism cases.
Prediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi Machine Learning Aribowo, Budi; Tjahjono, Budi; Firmansyah, Gerry; Widodo, Agung Mulyo
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3089

Abstract

Accreditation ranking is one of the causes and indicators chosen by prospective students when choosing a study program in higher education. From the data collected, only 5% of study programs in the Computer Science group have a Superior accreditation rating and an A accreditation rating in LLDikti Region III Jakarta. So it is necessary to know the factors that influence the accreditation ranking. The machine learning methodology used in this approach is K-Nearest Neighbors (KNN) and from the data obtained there are 6 factors that can be strongly suspected to influence the study program accreditation value. The four machine learning models, namely KNN, Gaussian Naïve Bayes Decision Tree and Logistic Regression, it was found that the KNN machine learning model with 2 input variables had the highest AUC value, namely 84.38%. Meanwhile, from the model simulation run by KNN machine learning, 2 input variables can produce relatively accurate prediction results. And the results of cross validation with 10 folds support the selected machine learning with an accuracy level of 80%. In general, the KNN machine learning model with 2 input variables was able to predict the accreditation rating of Study Programs, especially from the Computer Science Cluster.Keywords – Accreditation, Area Under Curve (AUC), Department of School, Kfold Cross Validation, Machine Learning.
Loan Repayment Prediction Using XGBoost and Neural Network in Japan's Technical Internship Training Suhendry, Mohammad Roffi; Gerry Firmansyah; Nenden Siti Fatonah; Agung Mulyo Widodo
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14709

Abstract

Delayed repayment of financial aid among participants in Japan’s Technical Internship Training Program presents challenges for training institutions in managing funds efficiently. To address this issue, this study aims to compare the performance of two machine learning models: Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) in predicting the likelihood of delayed loan repayments. The research begins with data preprocessing, including handling missing values, normalization, and feature selection based on a correlation threshold of 0.06, where features with absolute correlation values below this threshold are excluded. Three models are tested: XGBoost Default, XGBoost optimized using GridSearchCV, and MLP. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The XGBoost Default model achieves the highest accuracy at 95% and precision of 95%, although its recall is slightly lower at 83%. Tuning XGBoost improves recall to 84%, albeit with a marginal reduction in accuracy to 94%. In contrast, the MLP model demonstrates the lowest performance, with an accuracy of 92% and recall of 74%, indicating limitations in identifying delayed repayments. XGBoost also outperforms MLP in terms of ROC-AUC, scoring 91% compared to MLP’s 86%. These findings suggest that XGBoost is the more effective model for this predictive task. The results have practical implications for training institutions, enabling better participant selection, reducing repayment delays, and supporting more effective financial aid management.
Metaheuristic-Optimized SVM for Stunting Risk Detection in Pregnancy Wibowo, Yudha; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14710

Abstract

Stunting is a chronic growth disorder that originates during pregnancy, making early risk detection crucial for effective prevention and long-term child development. This study introduces a stunting risk prediction model based on urine testing, employing a Support Vector Machine (SVM) algorithm enhanced through metaheuristic optimization. Three metaheuristic algorithms—Grey Wolf Optimizer (GWO), Simulated Annealing (SA), and Firefly Algorithm (FA)—were utilized to fine-tune the SVM hyperparameters (C and gamma). Clinical urine samples collected from pregnant women served as the dataset for model training and validation. The results indicate that the SVM model optimized using GWO achieved the highest prediction accuracy at 94.15%, outperforming both the default SVM (88.46%) and the models optimized using SA (94.12%) and FA (85.71%). Additionally, significant improvements were observed in precision, recall, and F1-score metrics, affirming the effectiveness of metaheuristic tuning in enhancing classification performance. These findings highlight the potential of integrating metaheuristic algorithms with SVM for robust medical prediction tasks, especially in the early detection of stunting risks. The proposed model offers a promising and non-invasive diagnostic approach that can be implemented in prenatal care settings, enabling timely interventions to mitigate stunting and improve maternal and child health outcomes.
Evaluation of IT Service Level Infrastructure In Organizations Using ITIL (Information Technology Infrastructure Library) Version 3 Standardization Randhy Hans, Achmad; Firmansyah, Gerry; Tjahyono, Budi; Mulyo Widodo, Agung
Asian Journal of Social and Humanities Vol. 2 No. 12 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i12.393

Abstract

The rapid development of business and advancements in information technology today are highly significant, especially in supporting the progress of ongoing businesses. Many businesses, particularly startups, make information technology the backbone supporting every main business process to achieve their business goals. Startups that operate 24/7 require sufficiently robust information technology, which must always be ready to provide the needed services to support the business. IT service assessment is an activity commonly carried out within an organization to eval_uate the level of information system services it possesses. An assessment, particularly an IT service assessment, can be conducted independently if the organization has adequate tools and is equipped with the correct standards. The eval_uation of information system services can be carried out using various standards, such as ITIL. In this research, the researcher will conduct an assessment using ITIL standards in the form of a website, which can serve as a tool. This website assessment application focuses on the domains of Service Management and Service Delivery, with the expectation that the services provided by PT Loyal.id will improve further.
Analysis of Information Technology Proficiency Levels For Academic Services Using The Cobit 2019 Framework: Case Study of SMP Negeri 102 Jakarta Meiharsiwi, Ismiyati; Firmansyah, Gerry; Mulyo Widodo, Agung; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 12 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i12.394

Abstract

This research intends to analyze the level of capability of information technology (IT) in academic services at SMP Negeri 102 Jakarta using the COBIT 2019 framework. The background of this research is the important role of information technology in increasing the efficiency and effectiveness of the learning process in educational institutions. COBIT 2019 was chosen as a framework because it is a best practice in IT governance that can help institutions achieve their strategic goals. This research focuses on the IT governance process implemented at SMP Negeri 102 Jakarta, the maturity level of existing information system governance, and recommendations for improving IT governance. The case study method is used with limitations on the domain within Align, Place and Organize (APO) 09 dan Deliver, Service and Support (DSS) 01 the COBIT 2019 framework. Research findings show that IT governance at SMP Negeri 102 Jakarta is at a certain level of capability that needs to be improved. This research provides recommendations for improving academic services through improving IT governance. It is hoped that the results of this research can become a reference in determining IT policies at SMP Negeri 102 Jakarta and contribute to the development of knowledge in the field of information technology governance.
Risk Management Analysis On The School Activity Plan And Budget Application Information System (ARKAS) Using Cobit 2019 Fernandes Gamaliel, Adhi; Firmansyah, Gerry; Mulyo Widodo, Agung; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 12 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i12.396

Abstract

In the era of globalization and the advancement of information technology, the application of information technology is an important need for educational institutions, including schools. This research focuses on the implementation of the School Activity Plan and Budget Application Information System (ARKAS) at the Palu Safety Center Christian Vocational School to increase efficiency and effectiveness in planning and managing school activities and budgets. However, the implementation of ARKAS is inseparable from various risks that can affect the effectiveness and success of the system. Therefore, risk management analysis is essential to ensure that all potential risks can be identified, analyzed, and minimized. This study uses the COBIT 2019 framework to manage risks in the application of information technology in schools. The study identifies challenges such as resistance to change, resource limitations, and information security risks. This study aims to explore how the implementation of ARKAS in the Palu Safety Army Christian Vocational School can be optimized through risk management analysis using the COBIT 2019 framework. The results of the study show that the use of COBIT 2019 can help in identifying and managing risks effectively, so that the implementation of ARKAS can run more efficiently and transparently. The resulting recommendations are expected to improve the quality of school budget management and become a reference for other schools that face similar challenges in the application of information technology.
Comparison of Djikstra, Hybrid-PSO algorithms for optimizing the distribution route of papaya seeds and honey products (Case Study: PT. Agro Apiari Mandiri) Gunawan, Sholeh; Mulyo Widodo, Agung; Firmansyah, Gerry; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 12 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i12.398

Abstract

Dynamic global competencies in the industrial sector drive fierce competition in capturing markets and increasing customer satisfaction, which requires efficiency in various aspects of business including distribution. PT. Agro Apiari Mandiri faces challenges in optimizing delivery routes to avoid delays. This study aims to compare the Dijkstra and Hybrid-PSO algorithms to determine the optimal distribution route in the Bogor, West Java, and Lebak, Banten regions, in order to reduce the distance and delivery time. The research methods include literature study, data collection, and route optimization model creation. The results show that PSO is more efficient in optimizing delivery routes than other methods, with variations in PSO parameters affecting total travel time, number of vehicles, and computing time. Implementation uses hardware and software such as MSI Laptops and Matlab. In conclusion, the use of PSO in distribution route optimization makes a significant contribution to the company's cost and distribution efficiency and can be a reference for further research in distribution route optimization.
Optimization of Electronic-Based Government System Architecture (SPBE) In The Application Architecture Domain In XYZ District Lisdiana, Lisdiana; Firmansyah, Gerry; Mulyo Widodo, Agung; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 12 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i12.405

Abstract

The Electronic-Based Government System (SPBE) is a government administration that utilizes information and communication technology to provide services to SPBE Users as stipulated in Presidential Regulation No. 95 of 2018. SPBE aims to create an integrated government business process between Central Agencies and Regional Governments, form a complete government unit and produce bureaucracy and high-performance public services. To support the implementation of SPBE, the National SPBE architecture is compiled as a detailed guide that covers technical and methodological aspects, ensuring the integration of government electronic systems. This architecture provides convenience in increasing efficiency and effectiveness, as well as being a guide for SPBE governance in Central Agencies and Regional Governments. The XYZ Regency Government has implemented the SPBE architecture as the basis for implementation in its area. This study analyzes the SPBE architecture in XYZ Regency based on the National SPBE architecture to provide optimization recommendations on the application architecture domain. Despite the existence of Presidential Regulation Number 95 of 2018 and Number 132 of 2022, the preparation of government architecture in Indonesia still faces challenges due to the lack of clear guidance. More detailed references are needed as derivatives of existing regulations to ensure consistent and optimal implementation, help agencies understand and implement the SPBE architecture, so that digital transformation towards Indonesia 4.0 is achieved by 2040.
Comparison of SVM, KNN, and Naïve Bayes Classification Methods in Predicting Student Transfers at BK Palu School Nugraha, William; Firmansyah, Gerry; Mulyo Widodo, Agung; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 3 No. 1 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v3i1.413

Abstract

Student transfers are a significant issue in schools and can affect the dynamics of education and student performance. This research aims to predict student transfers using a comparative analysis of three classification methods: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. The study utilizes historical data from BK Palu School, covering the years 2022 to 2024, which includes demographic, academic, socio-economic, and student quality information. The methodology involves data collection, data preparation, algorithm selection, implementation, and evaluation of the three methods. The performance of the classification methods is assessed using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM has the highest accuracy in predicting student transfers, followed by KNN and Naïve Bayes. This study contributes to identifying key factors influencing student transfers and offers schools a robust model to develop targeted strategies for reducing transfer rates. Ultimately, this research provides insights into optimizing student retention and improving the overall quality of education.
Co-Authors Achmad Fansuri Achmad Randhy Hans Adhi Fernandes Gamaliel Adilah Widiasti Agam Aprianto Ahmad Musnansyah Ahmad Mutedi Akbar, Habibullah Alexander Alexander, Alexander Alivia Yufitri Andriana, Dian Annazma Ghazalba Arif Pami Setiaji Arisandi Langgeng Tardiana Asmara, Qiqi Azzam Robbani, Muhammad Bayu Sulistiyanto Ipung Sutejo Binastya Anggara Sekti Budi Aribowo Budi Tjahjono Budi Tjahjono Budi Tjahyono Budi Tjahyono Budi Tjahyono Budilaksono, Sularso Cahya Darmarjati Catur Agus Sulistyo Deni Iskandar Deni Iskandar Desy Prastyani Doni Antoro Dulbahri Dulbahri Dwiaji, Lingga Dwiputra, Dedy Eko Prasetyo Endang Ruswanti Erry Yudhya Mulyani Ety Nurhayati Euis Heryati Fadlilatunnisa, Fanny Fatonah, Nenden Siti Fernandes Gamaliel, Adhi Fikri Saefullah Gerry Firmansyah Gerry Firmasyah Ghazalba, Annazma Gilang Romadhanu Tartila Gunawan, Sholeh Gusti Fachman Pramudi Hadi, Muhammad Abdullah Hani Dewi Ariessanti Hartono Hartono Haryoto, Iin Sahuri Hendaryatna Hendaryatna Hendry Gunawawan Heri Wijayanto I Gede Pasek Suta Wijaya Ichwani, Arief Ilham Banuaji Irawan, Bambang Ismiyati Meiharsiwi Iwan Setiawan Izhar Rahim Joniwan Joniwan Karisma Trinanda Putra kartini, kartini Kevin Valeri Khairurrahman, Rifqi Krisogonus Wiero Baba Kaju Kundang Karsono Juman Kundang Karsono Juman Kundang Karsono Juman Kus Hendrawan Muiz Lingga Dwiaji Lisdiana Lisdiana Lisdiana Lisdiana Lukman Cahyadi Made Aka Suardana Martin Saputra Massie, Julius Ivander Maulana, Syaban Meiharsiwi, Ismiyati Meria, Lista MF. Arrozi Adhikara Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Mutedi, Ahmad Muttaqin, Naufal Hafizh Nina Nurhasanah, Nina Nindyo Artha Dewantara Wardhana Nixon Erzed Nizirwan Anwar Nugraha, William Nurfilael, Gagas Nurfilae Panji Ramadhan Yudha Putra Hadjarati Pratama, Fajar Prayitno Purwano SK Rachman, Riyandi Patu Rahaman, Mosiur Randhy Hans, Achmad Restamauli br Nainggolan Rian Adi Pamungkas Ricky Salim Ricky Salim Rifqi Khairurrahman RILLA GANTINO Riris Septiana Sita Dewi Rizki Faro Khatiningsih Rizky Aulia Roesfiansjah Rasjidin Ryan Putra Laksana Sholeh Gunawan Simorangkir, Holder Suhendry, Mohammad Roffi Sunardi, Sunardi Syaban Maulana Syamsul Bahri Tyara Regina Nadya Putri Ulum, Muhamad Bahrul Ummanah Ummanah, Ummanah Vitri Tundjungsari Wahid Abdul Azis Wardhana, Nindyo Artha Dewantara Wibowo, Yudha Widiasti, Adilah William Nugraha Wisnujati, Andika Yanathifal Salsabila Anggraeni Yessy Oktafriani Yohanes Bagas Ari Widatama Yudha Putra Hadjarati, Panji Ramadhan Yulhendri Yulhendri