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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 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 Jurnal Ilmu Komputer dan Informatika Journal of Social Research 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 Media Abdimas Jurnal Pengabdian Masyarakat Bangsa Infact: Jurnal Sains dan Komputer Asian Journal of Social and Humanities INOVTEK Polbeng - Seri Informatika
Widodo, Agung Mulyo
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.
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.
Optimizing Workforce Scheduling Using Ant Colony Optimization Algorithm: Case Study PT. Cloud Hosting Indonesia Nurfilael, Gagas Nurfilae; Widodo, Agung Mulyo; Anwar, Nizirwan; Ichwani, Arief
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3766

Abstract

Effective workforce scheduling is crucial for enhancing productivity and maintaining service quality at PT. Cloud Hosting Indonesia. Workforce scheduling is the process of organizing and allocating labor to various tasks and responsibilities within an organization. Ant Colony Optimization (ACO) is a probabilistic technique used to solve computation problems by finding the best path through a graph. Inspired by the behavior of ants, particularly how they find food, Ant Colony Optimization can optimize shift schedules, reduce conflicts, and improve employee performance. However, there are current irregularities, insufficient rest periods, and unpredictable holidays. Ant colony optimization is applied to address these problems. The result of this shows that the Ant Colony Optimization algorithm is capable of producing more optimal schedules with high efficiency, achieving a Best Cost of 100 in 1 minute and 6 seconds. This is better compared to other methods such as Particle Swarm Optimization (PSO), which achieved a Best Cost of 7600 in 4 seconds, and Genetic Algorithm (GA), which achieved a Best Fitness of 8500 in 5 seconds.
Implementation of Convolutional Neural Network for Detecting Cataract Disease Severity in Eye Images Fadlilatunnisa, Fanny; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.712

Abstract

Cataract is a condition that causes clouding of the lens of the eye, leading to blindness and poor vision. According to the WHO, around 18 million people suffer from cataract-related blindness, making it one of the leading causes of blindness globally. Prompt and accurate diagnosis is essential to prevent more serious outcomes. This research aims to develop a deep learning model that utilises Convolutional Neural Networks (CNN) in categorising cataract severity into four groups: hypermature, normal, immature and mature. This model is expected to provide a more efficient and accurate alternative to traditional methods in diagnosing cataracts. To achieve this, we implemented transfer learning using three popular CNN architectures: VGG16, VGG19, and ResNet50. Experiments were conducted using a dataset of pre-labelled eye images for training. Model performance was evaluated by calculating F1-score, recall, accuracy, and precision using a confusion matrix. The results showed that VGG19 produced 88% accuracy and F1-score of 0.87, while VGG16 had the best accuracy. On the other hand, ResNet50 showed the lowest accuracy with 63% and F1-score of 0.59. These findings highlight the importance of selecting the right CNN architecture for cataract diagnosis, while underlining the potential application of deep learning in ophthalmology.
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.
Comparative Analysis of Enterprise Architecture Frameworks Using TOGAF ADM and SPBE Architecture Based on Presidential Regulation No. 132 of 2022 Hadjarati, Panji Ramadhan Yudha Putra; Widodo, Agung Mulyo; Tjahjono, Budi
Eduvest - Journal of Universal Studies Vol. 5 No. 3 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i3.1772

Abstract

This research aims to conduct a comparative analysis between two enterprise architecture frameworks: TOGAF ADM and Electronic-Based Government System Architecture (SPBE) based on Presidential Regulation No. 132 of 2022. TOGAF ADM is a framework commonly used in various types of organizations in the private and public sectors, while the SPBE Architecture is specifically designed for the Indonesian government sector. Through a qualitative descriptive approach, this study analyzes the principles, concepts, processes, and guidelines underlying each framework. This research is expected to provide insight for policy makers and enterprise architecture practitioners in choosing and implementing the framework that best suits the context and needs of their organization. In addition, this study also provides recommendations to improve the efficiency and effectiveness of implementing enterprise architecture in the public and private sectors in Indonesia. As well as its contribution to the efficiency and quality of government services. This research reviews the challenges in implementing the Electronic-Based Government System (SPBE) in Indonesian government institutions and proposes a solution by comparing TOGAF ADM and SPBE Architecture based on Presidential Regulation No. 132 of 2022. The motivation for this study is to improve the effectiveness and efficiency of SPBE implementation by selecting the most suitable framework. The method used involves analyzing structure, flexibility, technology integration, regulatory compliance, practical implementation, performance, and case studies. The results show that the implementation of TOGAF ADM and SPBE Architecture has their respective strengths and weaknesses, but a combination of both can achieve better outcomes in enhancing government performance and efficiency.
Co-Authors Achmad Fansuri Achmad Randhy Hans Adhi Fernandes Gamaliel Adhikara, M. F. Arrozi Adilah Widiasti Ahmad Musnansyah Ahmad Mutedi Akbar , Habibullah Akbar, Habibullah Alivia Yufitri Andriana, Dian Annazma Ghazalba Ari Widatama, Yohanes Bagas Arif Pami Setiaji Bambang Irawan Bambang Irawan Bambang Irawan Bayu Sulistiyanto Ipung Sutejo Binastya Anggara Sekti Budi Aribowo Budi Tjahjono BUDI TJAHJONO Budi Tjahjono Budi Tjahyono Budi Tjahyono Budi Tjahyono Cahya Darmarjati Deni Iskandar Deni Iskandar Dewi, Riris Septiana Sita Doni Antoro Dulbahri Dulbahri Dwiaji, Lingga Eko Prasetyo Endang Ruswanti Endang Ruswanti Erry Yudhya Mulyani Erry Yudhya Mulyani Erry Yudhya Mulyani Euis Heryati Fadlilatunnisa, Fanny Fatonah, Nenden Siti Fikri Saefullah Firmansyah, Gerry Gerry Firmansyah Gerry Firmansyah Gerry Firmasyah Gusti Fachman Pramudi Habibullah Akbar Hadi, Muhammad Abdullah Hadjarati, Panji Ramadhan Yudha Putra Hani Dewi Ariessanti Hartono Hartono Haryoto, Iin Sahuri Hendaryatna Hendaryatna Heri Wijayanto I Gede Pasek Suta Wijaya Ichwani, Arief Ichwani, arief Ipung Sutejo, Bayu Sulistiyanto Ismiyati Meiharsiwi Iwan Setiawan Izhar Rahim Joniwan Joniwan Karisma Trinanda Putra Kartini Kartini Krisogonus Wiero Baba Kaju Kundang Karsono Juman Kundang Karsono Juman Kundang Karsono Juman Kus Hendrawan Muiz Lingga Dwiaji Lisdiana Lisdiana Martin Saputra, Martin Massie, Julius Ivander Maulana, Syaban Meria, Lista Muhamad Bahrul Ulum Muhamad Bahrul Ulum Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Muttaqin, Naufal Hafizh Nainggolan, Restamauli br Nina Nurhasanah Nindyo Artha Dewantara Wardhana Nixon Erzed Nixon Erzed Nizirwan Anwar Nizirwan Anwar Nurfilael, Gagas Nurfilae Nurhayati, Ety Pratama, Fajar Prayitno Purwano SK Rahaman, Mosiur Rian Adi Pamungkas Rifqi Khairurrahman RILLA GANTINO Rizki Faro Khatiningsih Rizky Aulia Roesfiansjah Rasjidin Sholeh Gunawan Simorangkir, Holder Suardana, Made Aka Sularso Budilaksono Sulistyo, Catur Agus Sunardi, Sunardi Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu Tjahjono, Budi Tyara Regina Nadya Putri Ulum, Muhamad Bahrul Ummanah Ummanah, Ummanah Vitri Tundjungsari Wahid Abdul Azis Wardhana, Nindyo Artha Dewantara Widiasti, Adilah William Nugraha Wisnujati, Andika Yanathifal Salsabila Anggraeni Yessy Oktafriani Yulhendri Yulhendri