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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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Evaluation of Transfer Learning-Based Convolutional Neural Networks (InceptionV3 and MobileNetV2) for Facial Skin-Type Classification Muttaqin, Naufal Hafizh; Widodo, Agung Mulyo
Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.264

Abstract

Manual classification of facial skin types often suffers from subjectivity and inconsistency due to reliance on human expertise. Accurate identification of skin types is crucial for selecting appropriate skincare solutions. This study evaluates the performance of two transfer-learning-based Convolutional Neural Networks (CNNs), InceptionV3 and MobileNetV2, for classifying facial skin types into four categories: normal, oily, dry, and acne-prone. A total of 1,733 facial images were collected from Kaggle and Roboflow and split into training, validation, and testing sets with a 70:20:10 ratio. Preprocessing involved normalization, augmentation, and resizing based on each model’s input size. Both models were fine-tuned and evaluated using accuracy, precision, recall, and F1-score metrics. InceptionV3 achieved the highest accuracy of 90.12% and a macro F1-score of 89.47%, particularly excelling in identifying normal and acne-prone skin. MobileNetV2 reached 81.15% accuracy and performed well on dry skin types. Confusion matrices and evaluation on new, unseen data confirmed the models’ generalization capabilities, though misclassifications still occurred among visually similar classes. These findings suggest that CNNs with transfer learning provide a robust foundation for developing AI-assisted facial skin-type classification systems, offering potential integration into dermatological applications.
Enterprise Architecture Design of Indonesian Engineers Association Using The Open Group Architecture Framework (TOGAF) Qiqi Asmara, Abdullah; Firmansyah, Gerry; Tjahjono, Budi; Mulyo Widodo, Agung; Yudha Putra Hadjarati, Panji Ramadhan
Devotion : Journal of Research and Community Service Vol. 5 No. 9 (2024): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v5i9.7640

Abstract

The Indonesia Engineers Association has used Microsoft Dynamics Axapta (AX) enterprise resource planning (ERP) software as its operational support system. However, there are obstacles that are obstacles to completing business processes effectively by users, which have an impact on declining work performance and not achieving company targets. In addition, for the next 3 years, IT solutions are also needed to be able to support business development in the company. The implementation of Enterprise Architecture is expected to be the answer for the Indonesia Engineers Association in the next 3 years, so that the company can be more productive and develop as well as there is alignment between the business strategies owned by the company to optimize the use of information systems and information technology owned by the Indonesia Engineers Association. The basis for choosing using the TOGAF ADM method in designing Enterprise Architecture is that TOGAF ADM has a complete methodology, clear and structured stages, so that the design and specifications become easier and reduce the implementation risks faced by the Indonesia Engineers Association. This research is expected to provide insights for policymakers and enterprise architecture practitioners in selecting and implementing the framework that best suits the context and needs of their organizations. In addition, this study also provides recommendations to improve the efficiency and effectiveness of the implementation of enterprise architecture in the Indonesia Engineers Association.
Utilization of Query Expansion Using Data Mining Method In Analyzing Documents on The Irama Nusantara Website Aulia, Rizky; Widodo, Agung Mulyo
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 3 No. 11 (2024): JETBIS : Journal Of Economich, Technology and Business
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v3i11.156

Abstract

In Indonesia, many local websites, such as Irama Nusantara, hold valuable information related to music and culture. Although rich in data, the utilization of this information is still limited. This research aims to utilize query expansion techniques through data mining methods in analyzing data from the Irama Nusantara website. Data was collected from the Irama Nusantara website through a crawling process, resulting in 5404 entries covering audio, images and text. The analysis was conducted using Natural Language Processing (NLP) techniques starting with the preprocessing stage. Next, the K-Means algorithm was applied for clustering, and the Term Frequency-Inverse Document Frequency (TF-IDF) method was used for term weighting. Classification models were built using Support Vector Machine (SVM) and Naive Bayes for comparison. The analysis shows that the use of query expansion significantly improves the accuracy of information retrieval on the Irama Nusantara website. The method evaluation showed that SVM gave better results in terms of accuracy and precision compared to Naive Bayes. In addition, Principal Component Analysis (PCA) shows that 70-95% of the variance in the data can be explained by the resulting principal components, which signifies the efficiency of the applied method. This research not only provides a deeper insight into the patterns and trends in the analyzed data, but also contributes to the development of information technology in the field of culture in Indonesia. This research successfully developed an effective analysis model to utilize data from the Irama Nusantara website.
Utilization of Query Expansion Using Data Mining Method In Analyzing Documents on The Irama Nusantara Website Aulia, Rizky; Widodo, Agung Mulyo
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 3 No. 11 (2024): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v3i11.156

Abstract

In Indonesia, many local websites, such as Irama Nusantara, hold valuable information related to music and culture. Although rich in data, the utilization of this information is still limited. This research aims to utilize query expansion techniques through data mining methods in analyzing data from the Irama Nusantara website. Data was collected from the Irama Nusantara website through a crawling process, resulting in 5404 entries covering audio, images and text. The analysis was conducted using Natural Language Processing (NLP) techniques starting with the preprocessing stage. Next, the K-Means algorithm was applied for clustering, and the Term Frequency-Inverse Document Frequency (TF-IDF) method was used for term weighting. Classification models were built using Support Vector Machine (SVM) and Naive Bayes for comparison. The analysis shows that the use of query expansion significantly improves the accuracy of information retrieval on the Irama Nusantara website. The method evaluation showed that SVM gave better results in terms of accuracy and precision compared to Naive Bayes. In addition, Principal Component Analysis (PCA) shows that 70-95% of the variance in the data can be explained by the resulting principal components, which signifies the efficiency of the applied method. This research not only provides a deeper insight into the patterns and trends in the analyzed data, but also contributes to the development of information technology in the field of culture in Indonesia. This research successfully developed an effective analysis model to utilize data from the Irama Nusantara website.
Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations Dwiputra, Dedy; Mulyo Widodo, Agung; Akbar, Habibullah; Firmansyah, Gerry
Journal of World Science Vol. 2 No. 8 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i8.403

Abstract

This study focuses on applying data mining techniques, especially association rules mining using the Apriori and FP-GROWTH algorithms, for market basket analysis on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology uses a dataset of 100,498 transactions originating from 432,356 rows of data covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH algorithms are applied for association rules mining. The results show that FP-GROWTH has the fastest execution time of 84,655 seconds. However, the memory usage for the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21 MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH, produce the same number of rules and values of support, confidence, lift, Bi-Support, Bi-Confidence, and Bi-Lift. In conclusion, Apriori is recommended for sales datasets if memory usage and ease of implementation are important. However, if the speed of execution time and a large amount of data are considered, FP-GROWTH is a better choice because the execution time is faster for large amounts of data. However, the choice of algorithm depends on the specific analysis objectives, itemset size, data scale, and computational capabilities. Results from association rules mining provide evidence of product popularity, purchasing patterns, and opportunities for strategic marketing and inventory management. These findings can help PT. XYZ improves business efficiency, understands customer behavior, and increases profitability.
Analisis dan Design Knowledge Management System pada PT XYZ dengan Menggunakan Metode Tiwana Alexander, Alexander; Firmansyah, Gerry; Tjahjono, Budi; Mulyo Widodo, Agung; Akbar , Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4686

Abstract

Dalam dunia bisnis, keberlangsungan dan pertumbuhan suatu perusahaan sangat dipengaruhi oleh kualitas sumber daya manusia (SDM) atau karyawan yang dimiliki, karyawan yang kompeten dan memiliki wawasan pengetahuan yang luas dianggap sebagai aset penting yang dapat menjadi pembeda utama antara perusahaan dengan para pesaingnya. Maka dari itu, perusahaan biasanya berinisiatif untuk melakukan berbagai macam program pelatihan dan pengembangan yang bertujuan untuk meningkatkan kemampuan dan pengetahuan karyawannya secara berkelanjutan, hal lain yang dapat dilakukan oleh perusahaan adalah dengan mengelola pengetahuan dari karyawannya melalui sebuah tools yaitu Knowledge Management System (KMS). Sebagai perusahaan konsultan properti terkemuka dengan jaringan global yang luas, PT XYZ memiliki komitmen untuk dapat mengembangkan pengetahuan karyawannya, PT XYZ selalu berupaya untuk menciptakan lingkungan kerja yang mendukung pertumbuhan pengetahuan, terutama bagi karyawan di bidang Information and Technology (IT). Penelitian ini menggunakan metode Tiwana yang dilakukan hingga tahapan ke-6 dari 10 tahapan yang ada, dengan tujuan penelitian untuk menghasilkan blueprint sebagai landasan untuk merancang KMS yang sesuai dengan kebutuhan perusahaan. Adapun hasil dari penelitian ini adalah sebuah blueprint rancangan KMS.
Integration Of Garch Models And External Factors In Gold Price Volatility Prediction: Analysis And Comparison Of Garch-M Approach Tardiana, Arisandi Langgeng; Akbar, Habibullah; Firmansyah, Gerry; Widodo, Agung Mulyo
Eduvest - Journal of Universal Studies Vol. 4 No. 5 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i5.1195

Abstract

This study investigates the volatility of gold prices by applying the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and extending it with the GARCH-M model, incorporating the Federal Reserve's interest rate as an external variable. The GARCH(1,1) model revealed a positive average daily return for gold, with high sensitivity to recent price changes, indicated by the significant estimation of mu and a high alpha1 value. The persistence of past volatility on current volatility is reflected by a beta1 value close to one. In the GARCH-M model development, a significant negative relationship was found between the Federal Reserve's interest rates and gold returns, suggesting that an increase in the Federal Reserve's interest rates could potentially decrease gold returns. An increase in the Log Likelihood value and improvements in information criteria such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) indicate that the GARCH-M model provides a better fit than the GARCH(1,1) model that uses only gold price data. The study concludes that macroeconomic factors like the Federal Reserve's interest rates play a crucial role in influencing gold price volatility, and these findings can aid investors and portfolio managers in devising more effective risk management strategies. Additionally, the findings contribute to financial theory by highlighting the importance of multivariate models in the analysis of asset price volatility.
Product Recommendations Using Adjusted User-Based Collaborative Filtering on E-Commerce Platforms Tartila, Gilang Romadhanu; Akbar, Habibullah; Firmansyah, Gerry; Widodo, Agung Mulyo
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50224

Abstract

Product recommendations on e-commerce platforms play a crucial role in supporting customers' purchasing decisions by leveraging user data to provide relevant product suggestions. With the increasing volume of e-commerce data, recommendation methods are needed that are not only accurate but also capable of being applied to diverse datasets. This research focuses on evaluating three product recommendation methods, namely User-Based Collaborative Filtering, Item-Based Collaborative Filtering, and Content-Based Filtering, using various datasets from the Kaggle platform, including transaction data and user reviews. The main problem identified is how to ensure that these three recommendation methods remain optimal despite using different datasets. Through an experimental approach, this research aims to implement and evaluate the performance of these recommendation methods. The results of this study are expected to demonstrate that one of the recommendation methods can work generally on various datasets, thereby making a significant contribution to the selection of the appropriate product recommendation method on e-commerce platforms.
Evaluation Of It System Operational Services Using The Itil Framework In The Service Desk Domain (A Case Study Of PT Erafone Dotcom) Nainggolan, Restamauli br; Tjahjono, Budi; Widodo, Agung Mulyo; Akbar , Habibullah
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.51908

Abstract

PT Erafone dotcom is one of the mobile phone and tablet retailer companies in Indonesia from various well-known brands. PT Erafone dotcom uses the service desk as an after-sales support system service for customers or users in the smooth transaction process. The current problem with service desk services is the slow response to handling and resolving obstacles. Evaluation is needed to be able to improve operational services. The Information Technology Infrastructure Library (ITIL) V4 will be used to evaluate service desk services in IT Operational at PT Erafone Dotcom. The purpose of this study is to evaluate IT Support in operational services using the ITIL V4 framework with 2 practices in the domain of General Management Practice and 5 practices in the domain of Service Management Practice. The results of this study are that the level of service in IT Operational and the level of capability are at level 3 (Defined), which means that IT Operational support to users has run optimally referring to management practice procedures and response to incidents. To increase the value of IT Operational support from the maturity level to match expectations and can improve management. The recommendation for improvement is that even though it is at level 3, there is still a gap in the practices used so that it is necessary to improve the recording of incidents and problems that occur, so that they can be analyzed and identified to help handle and prevent the recurrence of incidents and problems.
Comparative Performance of Learning Methods In Stock Price Prediction Case Study: MNC Corporation Khairurrahman, Rifqi; Firmansyah, Gerry; Tjahjono, Budi; Mulyo Widodo, Agung
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.
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