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All Journal JIK Jurnal Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Astonjadro CCIT (Creative Communication and Innovative Technology) Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal Of Science, Technology & Management (IJSTM) IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal Sosial dan Teknologi Devotion: Journal of Research and Community Service Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Impresi Indonesia Journal Of World Science International Journal of Islamic Education, Research and Multiculturalism (IJIERM) Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia Sosial Sains Journal Research of Social Science, Economics, and Management Eduvest - Journal of Universal Studies Asian Journal of Social and Humanities INOVTEK Polbeng - Seri Informatika Resocia: International Journal of Applied Research and Innovation Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Gerry Firmansyah
Universitas Esa Unggul
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Comparison of SVM, KNN, and Naïve Bayes Classification Methods in Predicting Student Transfers at BK Palu School William Nugraha; Gerry Firmansyah; Agung Mulyo Widodo; Budi Tjahjono
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.
Enterprise Architecture Business Model Planning Using EAP Framework (Case Study: PT. Gempita Cahaya Makmur) Ahmad Mutedi; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
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.v3i1.416

Abstract

The advancement of technology in a company has an impact on improving business quality. From this observation, architectural planning is a part that is used to build alignment between business strategy and information technology. Architecture within the business domain illustrates how a company conducts business activities and functions to achieve the company's goals. Therefore, the company's business model architecture depicts the current state of architecture by identifying business needs and activities. From this study, the business role of PT. GEMPITA CAHAYA MAKMUR, a company engaged in the procurement of goods and services, especially in the field of wholesale office stationery, printing, photocopier sales, and photocopier and laptop rentals, which has customers from medium-sized companies, large companies, both private and government. The use of the Enterprise Architecture Planning or EAP framework focuses on business architecture. The purpose of this research is expected to produce a blueprint proposal that will be beneficial for PT. GEMPITA CAHAYA MAKMUR to plan the business model architecture that will become the foundation for the design phase of application architecture.
Strategic Analysis of Information Technology Architecture With Pepprard and Ward Methods At PT. Bank XYZ Reza Irsyadul Anam; Gerry Firmansyah; Nenden Siti Fatomah; Budi Tjahjono
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.v3i1.437

Abstract

The rapid development of the digital era has placed Information Technology (IT) as a crucial element in the banking industry. PT. Bank XYZ aims to enhance its IT capabilities to achieve its business objectives and become one of Indonesia's top banks. In line with the regulatory requirements of POJK No. 11/POJK.03/2022, this research focuses on a strategic analysis of PT. Bank XYZ's IT architecture using the Pepprard and Ward methods, combined with Anita Cassidy’s approach. The research analyzes the current conditions of the bank’s IT architecture and external factors, employing PEST and Porter’s Five Forces methods, followed by a SWOT analysis to identify strengths, weaknesses, opportunities, and threats. From this analysis, strategic recommendations are provided to support the bank’s IT goals over a four-year period (2024-2028). The strategic plan consists of 19 key IT programs covering applications, data, and technology, aimed at enhancing customer experience, data quality, and infrastructure resilience. This roadmap will help PT. Bank XYZ improve its IT operations and align its technological advancements with its business objectives, positioning the bank for growth in the competitive banking sector. The research contributes practical insights into developing an IT strategy that complies with regulatory standards and supports long-term corporate goals.
Comparative Performance of Learning Methods In Stock Price Prediction Case Study: MNC Corporation Rifqi Khairurrahman; Gerry Firmansyah; Budi Tjahjono; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 2 No. 5 (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.v2i5.252

Abstract

Shares are a popular business investment, the development of information technology now allows everyone to buy and sell shares easily online, investment players, both retail and corporate, are trying to make predictions. The purpose of this study is to find out comparative performance of learning methods in stock price prediction. There are currently many research papers discussing stock predictions. using machine learning / deep learning / neural networks, in this research the author will compare several superior methods found in the latest paper findings, including CNN, RNN LSTM, MLP, GRU and their variants. From the 16 result relationships and patterns that occur in each variable and each variable is proven to show its respective role with its own weight, in general we will summarize the conclusions in chapter V below, but in each analysis there are secondary conclusions that we can get in detail. The variable that has the most significant effect on RMSE is variable B (repeatable data) compared to other variables because it has a difference in polarity that is so far between yes and no. The configuration of input timestep (history)=7 days and output timetep (prediction)=1 day is best for the average model in general.
Drug Stock Optimization at Hospital Depot Using Shuffle Frog Leaping Algorithm (SFLA) Annazma Ghazalba; Agung Mulyo Widodo; Budi Tjahjono; Gerry Firmansyah
Asian Journal of Social and Humanities Vol. 2 No. 11 (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.v2i11.409

Abstract

Optimal, efficient, and accurate drug stock management at hospital depots is crucial for ensuring the smooth operation of medical and operational services. Therefore, the use of machine learning is currently essential for managing drug stocks at hospital depots more optimally. This optimization process involves stages such as data collection, data pre-processing, attribute selection, data labeling, classification algorithm selection, model training, model eval_uation, and result interpretation. The data used in this research includes information on drug stocks at hospital depots with details on drug items, quantities, prices, depot origins, demand trends, and types of transactions. The aim of using these algorithms is to classify drug stock items into categories such as "sufficient," "deficient," and "excess" based on historical data patterns and relevant attributes. Model eval_uation is carried out by comparing classification results with actual data and measuring eval_uation metrics such as accuracy, precision, recall, and F1-score. It is hoped that the classification results will indicate the need for optimization in the previously implemented algorithms and provide new solutions for managing drug stocks at hospital depots. The Shuffle Frog Leaping algorithm (SFLA) implemented will help drug stock management staff identify demand patterns more optimally, efficiently, and accurately. Thus, this research has the potential to make significant contributions to optimizing drug stock management and decision-making at hospital depots, which will also positively impact the progress of hospital services.
Analysis of Knowledge Management Strategies for Handling Cyber Attacks with the Computer Security Incident Response Team (CSIRT) in the Indonesian Aviation Sector Dwiaji, Lingga; Widodo, Agung Mulyo; Firmansyah, Gerry; Tjahyono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 6 (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.v2i6.261

Abstract

Cyber attacks are one of the genuine threats that have emerged due to the evolution of a more dynamic and complex global strategic environment. In Indonesia, several cyber attacks target various government infrastructure sectors. The National Cyber and Crypto Agency (BSSN) predicts Indonesia will face approximately 370.02 million cyber attacks in 2022. The majority of cyber attacks target the government administration sector. The National Cyber and Crypto Agency (BSSN) officially formed a Computer Security Incident Response Team (CSIRT) to tackle the rampant cybercrime cases. CSIRT is an organization or team that provides services and support to prevent, handle, and respond to computer security incidents. The current CSIRT does not have a data storage process and forensic preparation. CSIRT will repeat the procedure, and so on. This is a repeating procedure; the attack will occur once, and only a technical problem will arise. Therefore, the research entitled "Analysis of Knowledge Management Strategies for Handling Cyber Attacks with the Computer Security Incident Response Team (CSIRT)" is expected to implement this Knowledge Management Strategy to manage existing knowledge so that it can make it easier for the CSIRT team to handle cyber attacks that occur.
Assessment of the level of student understanding in the distance learning process using Machine Learning Widiasti, Adilah; Widodo, Agung Mulyo; Firmansyah, Gerry; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 2 No. 6 (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.v2i6.272

Abstract

As technology develops, data mining technology is created which is used to analyse the level of understanding of students. This analysis is conducted to group students according to their ability to understand and master the subject matter. This research can provide guidance and insight for educators, as well as artificial intelligence, machine learning, association techniques, and classification techniques. Researchers and policymakers are working to optimise learning and improve the quality of student understanding. This study aims to analyse the level of student understanding in simple and structured terms. Using the Machine learning method to analyse the level of student understanding has the potential to impact the quality of education significantly. In addition, machine learning categories are qualified to be applied to the concept of data mining. The data mining techniques used are association and classification. Association techniques are used to determine the pattern of distance student learning. The following process of classification techniques is used to determine the variables to be used in this study using the Logistic Regression model where data that have been classified are grouped or clustered using the K-Means algorithm into three, namely the level of understanding is excellent, sound, and lacking, based on student activity, assignment scores, quiz scores, UTS scores, and UAS scores.
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.
Co-Authors Achmad Randhy Hans Adhi Fernandes Gamaliel Adilah Widiasti Agam Aprianto Agung Mulyono Widodo Ahmad Mutedi Akbar, Habibullah Alexander Alexander, Alexander Amelia Sholikhaq Andriana, Dian Andrianto, Eko Andrih Setiawan Andriyanti Asianto Anisa Aulia Annazma Ghazalba Anwar Solihin, Muhamad Ardiansyah, Miri Arif Pami Setiaji Arisandi Langgeng Tardiana Asmara, Qiqi Aulia, Anisa Ayu Larasati Azizah, Anik Hanifa Azzam Robbani, Muhammad Bayu Sulistiyanto Ipung Sutejo Bob Tjahjono Budi Aribowo Budi Tjahjono Budi Tjahyono Budi Tjahyono Budi Tjahyono Catur Agus Sulistyo Devi Irawan Dian Fajar Septianto Dodo, La Dudy Fathan Ali Dwi Pamungkas, Eric Dwiaji, Lingga Dwiputra, Dedy Edi Kartawijaya Eric Dwi Pamungkas Farida Farida Fathan Ali, Dudy Fatonah, Nenden Siti Fernandes Gamaliel, Adhi Fiqar Haytsam Muhammad Dzul Qornain Ghazalba, Annazma Gilang Banuaji Gilang Romadhanu Tartila Gunawan, Sholeh Gusti Fachman Pramudi Hadi, Muhammad Abdullah Hani Dewi Ariessanti Haryoto, Iin Sahuri Hendaryatna Hendaryatna Herwanto, Agus Husni Sastra Mihardja Husni Satra Mihardja Husni Satra Mihardja Ichwani, Arief Intan Setya Palupi Irawan, Devi Irsyadul Anam, Reza Ismiyati Meiharsiwi Kailani Ridwan, M Kevin Valeri Khairurrahman, Rifqi La Dodo Lingga Dwiaji Lisdiana Lisdiana Lisdiana Lisdiana M Bahrul Ulum, M Bahrul Marzuki Pilliang Master Maruahal Sidabutar Maulana, Syaban Meiharsiwi, Ismiyati Mochamad Welly Rosadi Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Muhammad Kailani Ridwan Munawar Muslih, Muhamad Mutedi, Ahmad Narul Sakron Nasihin, Anwar Nenden Siti Fatomah Nila Rusiardi Jayanti Nindyo Artha Dewantara Wardhana Noval Rizky Ramadhan Nugraha, William Nugroho Budhisantosa Putra, Sipky Jaya Rachman, Riyandi Patu Randhy Hans, Achmad Reza Irsyadul Anam Rifqi Khairurrahman Riris Septiana Sita Dewi Riya Widayanti Riyan Asep Susanto Rizky Yananda Rosnanto, Imam Rudy Setiawan Ryan Tri Pamungkas Sabri Alim Sakron, Narul Sea, Rona Aulia Wangsa Sholeh Gunawan Sigit Purworaharjo Siti Fatomah, Nenden Sri Redjeki Sugiyanti, Sri Dewi Suhatati Tjandra Suhendry, Mohammad Roffi Supardi Supardi Supriyade Supriyade Supriyade, Supriyade Syaban Maulana Trifina Sartamti Valencia Patrice Gracia Pangaribuan Wardhana, Nindyo Artha Dewantara Wibowo, Yudha Widiasti, Adilah Widodo, Agung Mulyo Widodo, Agung Mulyono Wijaya, Jacob S William Nugraha William Nugraha Pratama Laua Yessy Oktafriani Yoggy Montana Hendry Yudha Putra Hadjarati, Panji Ramadhan Yulhendri Yulhendri