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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
Widodo, Agung Mulyo
Universitas Esa Unggul
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Comparative Analysis of NIJ and NIST Methods for MicroSD Investigations: A Technopreneur Approach Anwar, Nizirwan; Widodo, Agung Mulyo; Sekti, Binastya Anggara; Ulum, Muhamad Bahrul; Rahaman, Mosiur; Ariessanti, Hani Dewi
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 2 (2024): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i2.407

Abstract

This research aims to compare the performance of two forensic investigation methods, the National Institute of Justice (NIJ) and the National Institute of Standards and Technology (NIST), specifically for evidence analysis of MicroSD cards. MicroSD cards are frequently used as external storage in various digital devices, making them critical in digital forensic investigations. The study evaluates the effectiveness of these methods using tools such as Access Data FTK Imager and autopsy. The NIJ method enthis comparative passes detailed stages of preparation, collection, examination, analysis, and reporting, while the NIST method includes stages of collection, examination, analysis, and reporting. Results indicate that the NIJ method provides more comprehensive and detailed results, while the NIST method offers a faster investigation process. Additionally, tables and graphs illustrating performance metrics are included to substantiate the findings. This comparative analysis provides valuable insights for technopreneurs in optimizing digital forensic methods for better data integrity and efficiency, ultimately enhancing decision-making processes in technological entrepreneurship. Furthermore, this study aligns with the United Nations' Sustainable Development Goals (SDGs), particularly Goal 9: Industry, Innovation, and Infrastructure, by promoting innovative forensic methods that support the development of resilient infrastructure and foster innovation in the digital age. This study highlights the importance of effective forensic methods in supporting technopreneurial ventures.
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.
Penerapan Algoritma Pengklasifikasi Untuk Mengukur Kepuasan Pelanggan E-Commerce (Studi Kasus : Shopee) Syamsul Bahri; Agung Mulyo Widodo
Jurnal Adijaya Multidisplin Vol 3 No 01 (2025): Jurnal Adijaya Multidisiplin (JAM)
Publisher : PT Naureen Digital Education

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengukur tingkat kepuasan pelanggan Shopee dengan menggunakan beberapa algoritma machine learning, yaitu Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, dan Naive Bayes. Tingkat kepuasan pelanggan dikategorikan ke dalam lima tingkat: Sangat Tidak Puas, Tidak Puas, Netral, Puas, dan Sangat Puas. Data survei diperoleh dari 1.000 Responden, mencakupi lima variabel utama, yaitu kualitas produk, layanan pengiriman, kualitas pelayanan, harga dan promosi, serta pengalaman berbelanja. Analisis dilakukan dengan heatmap korelasi untuk memahami hubungan antar variabel, serta feature importance menggunakan Random Forest untuk menentukan kontribusi relatif setiap faktor terhadap kepuasan pelanggan. Hasil penelitian menunjukkan bahwa harga dan promosi memiliki pengaruh tertinggi, diikuti oleh kualitas pelayanan dan pengalaman berbelanja. Penelitian ini memberikan wawasan strategis bagi Shopee untuk meningkatkan kualitas layanan berdasarkan analisis data dan memperkuat daya saing di pasar e-commerce.
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.
Co-Authors Achmad Fansuri Achmad Randhy Hans Adhi Fernandes Gamaliel Adhikara, M. F. Arrozi Adilah Widiasti Ahmad Musnansyah Ahmad Mutedi Akbar, Habibullah Alexander Alexander, Alexander Alivia Yufitri Andriana, Dian Annazma Ghazalba Ari Widatama, Yohanes Bagas Arif Pami Setiaji Azzam Robbani, Muhammad Bayu Sulistiyanto Ipung Sutejo Binastya Anggara Sekti Budi Aribowo Budi Tjahjono Budi Tjahyono Budi Tjahyono Budi Tjahyono Budilaksono, Sularso Cahya Darmarjati Deni Iskandar Deni Iskandar Dewi, Riris Septiana Sita Doni Antoro Dulbahri Dulbahri Dwiaji, Lingga Dwiputra, Dedy Eko Prasetyo Endang Ruswanti Endang Ruswanti Erry Yudhya Mulyani Erry Yudhya Mulyani Erry Yudhya Mulyani Ety Nurhayati Euis Heryati Fadlilatunnisa, Fanny Fatonah, Nenden Siti Fernandes Gamaliel, Adhi Fikri Saefullah Gerry Firmansyah Gerry Firmasyah Ghazalba, Annazma Gunawan, Sholeh Gusti Fachman Pramudi Hadi, Muhammad Abdullah Hadjarati, Panji Ramadhan Yudha Putra 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 Khairurrahman, Rifqi Krisogonus Wiero Baba Kaju Kundang Karsono Juman Kundang Karsono Juman Kundang Karsono Juman Kus Hendrawan Muiz Lingga Dwiaji Lisdiana Lisdiana Lisdiana Lisdiana Martin Saputra, Martin Massie, Julius Ivander Maulana, Syaban Meiharsiwi, Ismiyati Meria, Lista Muhamad Bahrul Ulum Muhamad Bahrul Ulum Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Mutedi, Ahmad Muttaqin, Naufal Hafizh Nainggolan, Restamauli br Nina Nurhasanah Nindyo Artha Dewantara Wardhana Nixon Erzed Nizirwan Anwar Nugraha, William Nurfilael, Gagas Nurfilae Pratama, Fajar Prayitno Purwano SK Qiqi Asmara, Abdullah Rahaman, Mosiur Randhy Hans, Achmad Rian Adi Pamungkas Rifqi Khairurrahman RILLA GANTINO Rizki Faro Khatiningsih Rizky Aulia Roesfiansjah Rasjidin Roesfiansjah Rasjidin Ryan Putra Laksana Sholeh Gunawan Simorangkir, Holder Suardana, Made Aka Suhendry, Mohammad Roffi Sulistyo, Catur Agus Sunardi, Sunardi Syamsul Bahri Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu 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 Yudha Putra Hadjarati, Panji Ramadhan Yulhendri Yulhendri