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All Journal Jurnal Ilmiah Universitas Batanghari Jambi Syntax Literate: Jurnal Ilmiah Indonesia JURNAL TEKNOLOGI DAN OPEN SOURCE KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Jamu Indonesia JUKI : Jurnal Komputer dan Informatika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Ekonomi KIAT Journal of Dinda : Data Science, Information Technology, and Data Analytics STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer J-Icon : Jurnal Komputer dan Informatika Kohesi: Jurnal Sains dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT Indonesian Journal of Jamu NJCOM: Community Service Journal
Ary Prabowo
Universitas Esa Unggul, Jakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Identification of Java Tea Adulteration by Babadotan and Tekelan using Machine Learning Ary Prabowo; Wisnu Ananta Kusuma; Annisa; Mohamad Rafi
Jurnal Jamu Indonesia Vol. 7 No. 3 (2022): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v7i3.273

Abstract

Java Tea (Orthosiphon aristatus) is a common herbal medicinal plant that functions as a health treatment and treats various diseases. The high demand for Java Tea causes high prices and a decrease in the amount of medicinal plant raw materials, causing various quality control problems such as the content of various bioactive components and adulteration from babadotan and tekelan. So far, the detection of adulteration has been carried out by various analyzes, including chemical analysis and statistical methods to process data. The data used is of high dimension with a very high-density level, thus causing difficulties in classification. The mixed data of Orthosiphon aristatus consists of 1201 features with a total sample of 216. This study uses a Random Forest (RF) method with a total of 100 trees, and the RF method is combined with the Recursive Feature Elimination (RFE) method. In the RF and RFE that can be produced, the optimum value for the number of features is 244. The experimental evaluation results revealed that the proposed method could achieve a high accuracy of 81.82% in identifying Orthosiphon aristatus.
Enterprise Risk Managemenut (ERM) and Corporate Value in Indonesia Poppy Camenia Jamil; Sinta Yulyanti; Ary Prabowo
Jurnal Ekonomi KIAT Vol. 35 No. 1 (2024): Juni
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/kiat.2024.17807

Abstract

Enterprise Risk Management (ERM) create value added for the company by facilitate management in controling various risk which caused uncertainties condition by integrating all types of risk using integrated tools and tachniques, and then cordinate thr activites of risk management to all operating unit within an organization so that all types of risk can be minimized. The implementation of Enterprise Risk Management (ERM) of the firm. The objectives of this research is to identify the effect of Enterprise Risk Management (ERM) and firm size, ROA, and manajerial ownership as control variables on firm value that is proxied by Tobin’s Q. population of this research was manufacturing companies listed in indonesia stock exchange (IDX) in 2019-2020. The used method in this researc is multiple linier Regression-Ordinary Last Square ( OLS) and hypotheses testing using t-test to test the regresion coefficients with level of signification of 5%. Results showed that Enterprise Risk Management (ERM) hassignificant positive effect on the firm value. Size of the company has significant positive effect on the firm value. ROA has significant positive effect on the firm value. While the managerial owner shipe has significant negative effect on the firm value.
IMPLEMENTASI DEEP LEARNING TERHADAP PRESENSI MAHASISWA MENGGUNAKAN METODE MTCNN DAN FACENET : (STUDI KASUS: KAMPUS ESA UNGGUL BEKASI) Latumapayahu, Febrian Firmansyah; Herwanto, Agus; Akbar, Habibullah; Prabowo, Ary
Kohesi: Jurnal Sains dan Teknologi Vol. 7 No. 3 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v7i3.11681

Abstract

The development of digital technology has opened opportunities for educational institutions to improve the efficiency and accuracy of administrative systems, including student attendance recording. The current attendance system, which relies on RFID cards, often encounters issues such as damaged, lost, or unreadable cards, leading to long queues and the need for manual administration. This study aims to address these problems by developing an automatic attendance system based on facial recognition using deep learning technology. The proposed system integrates the Multi-task Cascaded Convolutional Neural Networks (MTCNN) algorithm for face detection and FaceNet for face recognition. Data collection is conducted by acquiring student facial images as the dataset for model training. The data is processed through normalization, face detection, and feature extraction using FaceNet embeddings. The system is integrated with a MySQL database to record student attendance in real time. Testing results show that the system performs well in detecting and recognizing student faces with satisfactory accuracy levels, despite variations in lighting conditions. By reducing dependency on physical cards, this system can streamline the attendance process and provide ease of use for users. This study demonstrates that the application of deep learning technology has the potential to improve the efficiency of attendance management in higher education institutions.
UTAUT Analysis of E-learning Users: A Case Study at ABC University Go, Ratna Yulika; Prabowo, Ary; Hidayah, Qori Halimatul
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 1 (2025): March 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i1.21007

Abstract

The concept of e-learning has been adapted and used in Indonesia since the 1940s, and its development has continued to progress to this day. The COVID-19 pandemic has led to more comprehensive use of elearning. Unfortunately, ABC University was not prepared for the drastic shift to fully online learning, and its e-learning system is not yet integrated with the academic information system (SIAKAD). Other challenges include system bugs and a lack of optimization in discussion forums. As a result, students and lecturers are not able to use e-learning to its full potential. To identify the root causes of these issues, this study aims to determine the influencing factors of e-learning usage at ABC University, with the findings intended to provide recommendations for the university to address the challenges it faces. The study uses a mixed-method approach, combining quantitative and qualitative methods to process and validate the data. The UTAUT (Unified Theory of Acceptance and Use of Technology) approach is applied to identify the factors that affect e-learning usage, with analysis conducted using PLS SEM (Partial Least Squares Structural Equation Modeling). The study results show that among the five factors examined—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI)—only Social Influence (SI) significantly impacts e-learning usage, while the other four factors do not have a significant effect. The recommendations from this study include the need for system integration between e-learning and SIAKAD to ensure optimal use, as well as conducting maintenance every three months, facilitating meeting accounts, and adding audio-video features in discussion forums.
Pengaruh Penerapan Algoritma Pemrograman Dalam Dunia Pekerjaan (Studi Kasus: Metode Deep Learning) Niken Rahma Diasri; Arini Winur Baeti; Ary Prabowo
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i1.8531

Abstract

Algoritma, khususnya yang berbasis pada pendekatan deep learning, telah memberikan dampak signifikan dalam berbagai bidang pekerjaan. Studi ini mengeksplorasi pengaruh penerapan algoritma deep learning dalam dunia kerja dengan menyoroti penerapannya di beberapa industri utama seperti manufaktur, kesehatan, keuangan, dan teknologi informasi. Penelitian ini menggunakan pendekatan studi kasus untuk mengidentifikasi bagaimana algoritma tersebut meningkatkan efisiensi, akurasi, dan automasi dalam proses bisnis. Hasil penelitian menunjukkan bahwa penerapan deep learning memberikan keuntungan signifikan dalam prediksi data, analisis keputusan, dan pemrosesan informasi, namun juga menimbulkan tantangan terkait dengan keterampilan tenaga kerja dan etika dalam penggunaannya. Studi ini memberikan wawasan mengenai peran penting algoritma dalam transformasi digital dunia kerja, serta tantangan yang harus dihadapi untuk optimalisasi keuntungannya di masa depan.
Rancang Bangun Sistem Perpustakaan Web Universitas Esa Unggul dengan Metode Scrum untuk Pengelolaan Digital Azzahra, Fayza; Dzikrya, Kaysa; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8207

Abstract

Conventional library systems that are still manual-based often face various obstacles, such as delays in the transaction process, the risk of recording errors, and low efficiency in collection management. This research aims to design and build an integrated web library system at Esa Unggul University by applying the Object Oriented Programming (OOP) approach and Scrum method. The development process is carried out iteratively through the stages of Sprint Planning, Execution, Review, and Retrospective. The system was developed using Python programming language with Flask framework and MySQL database. The main features include book data management, members, loan and return transactions, automatic notifications, and time-based fine calculations. Evaluation was conducted using the Black Box Testing method on 35 scenarios, including input validation, transaction processing, and system resilience to extreme conditions. The test results showed a 100% success rate and a 60% increase in transaction efficiency compared to the manual system. End-user validation showed that the system has a responsive interface, easy to use, and supports digital library management. This research contributes to the digital transformation of libraries and opens up opportunities for development towards mobile platforms and data analytics.
Optimalisasi Manajemen Perpusatakaan: Sistem Basis Data Terdistribusi Menggunakan Metode Rapid Application Development (RAD) PRABOWO, ARY; Qori Halimatul Hidayah; Sharen Ming Sianggara; Desmond Orix Khoermala
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 2 (2025): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i2.5356

Abstract

Library management in secondary schools often faces challenges with centralized database systems, including inefficiency, slow data access, and risk of information loss. This research aims to design a distributed database system to improve the library management performance of SMP XYZ Pekanbaru by addressing these limitations. Using the Rapid Application Development (RAD) method, the study successfully designed a system architecture that not only enhances data access speed and minimizes the risk of data loss but also enables effective data synchronization between library locations. Based on estimates, the system design improves data access speed by up to 30% compared to centralized systems and reduces data synchronization time to an average of 2 seconds per transaction. The proposed data replication architecture is also expected to reduce the central server load by 70% by distributing data requests to relevant locations, ensuring data consistency and increasing system reliability. This research demonstrates that transitioning from a manual to a distributed database system can significantly optimize library management, improve operational efficiency, and enhance the overall user experience.
Implementasi Gamifikasi untuk Sistem “Reward-Point” Pelanggan Berbasis Laman Kalagi, Richard Kevin Maruli; Prabowo, Ary; Anwar, Nizirwan; Sekti, Binastya Anggara
JUKI : Jurnal Komputer dan Informatika Vol. 7 No. 1 (2025): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2025
Publisher : Yayasan Kita Menulis

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

Abstract

Perkembangan teknologi informasi dan komunikasi yang pesat serta meningkatnya penetrasi internet di Indonesia telah mendorong pelaku usaha untuk mengadopsi strategi digital yang lebih inovatif. Salah satu pendekatan yang potensial adalah gamifikasi, yaitu penerapan elemen permainan dalam konteks non-permainan, guna meningkatkan keterlibatan dan loyalitas pelanggan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan metode gamifikasi berbasis website pada platform e-commerce CV Naraya Prima Jasa dengan menggunakan elemen point dan reward. Metode yang digunakan mencakup analisis kebutuhan, perancangan sistem, implementasi, dan evaluasi. Hasil penelitian menunjukkan bahwa penerapan gamifikasi mampu meningkatkan interaksi pengguna dan menciptakan pengalaman yang lebih menarik, sehingga berpotensi mendukung tujuan bisnis perusahaan dalam mempertahankan loyalitas pelanggan.
Classification of Indonesian Disasters with Decision Trees Based on Spatial and Text Data Ramadhan, Ridwan; Saputra, Ragil Raditya; Innova, Zacky; Prabowo, Ary
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2006

Abstract

Indonesia is one of the countries with a very high level of natural disaster vulnerability. The types of disasters that frequently occur include earthquakes, floods, landslides, volcanic eruptions, and others. This is because Indonesia is located at a geographical position where three world tectonic plates meet and has tropical climate conditions that make it prone to disasters. Therefore, Indonesia needs a system that can classify disaster types automatically and accurately to help the decision-making process quickly and accurately. This research aims to develop a natural disaster classification model based on information such as location (regency and province), time of occurrence (date), and causes that lead to disasters. The method used for classification in this research is the Decision Tree algorithm, because this algorithm can handle both numerical and categorical data and has high interpretability. Classification processing is also performed using textual cause data using Term Frequency-Inverse Document Frequency (TF-IDF) technique to convert text format into numerical form that can be processed by machine learning algorithms. The dataset obtained from the National Disaster Management Agency (BNPB) is open source. Test results show that the trained Decision Tree model can classify disaster types with an accuracy of 87%. This model also shows good precision, recall, and f1-score values in each disaster category. It is hoped that the results of this research can help in developing historical data-based disaster detection systems and assist government and society in responding to disasters more effectively and efficiently.
Optimasi Fuzzy Logic Menggunakan Genetic Algorithm (GA) dalam Menentukan Program Diet dan Bulking Pramuditya, Benno; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9459

Abstract

The increasing demand for accurate and personalized diet and bulking programs highlights the need for a reliable decision support system (DSS). This study aims to develop a fuzzy logic–based DSS optimized with a Genetic Algorithm (GA) to recommend diet, bulking, or maintenance programs tailored to individual conditions. The methodology involved designing fuzzy sets, formulating IF–THEN rules, applying the Mamdani inference method, and optimizing fuzzy parameters using GA. Data were collected from 50 adult respondents, and the system was tested using 10 input scenarios validated by fitness experts. The results revealed that the fuzzy system without GA achieved only 38% agreement with expert recommendations, whereas GA optimization significantly improved accuracy to 82%. Furthermore, GA refined membership functions and eliminated irrelevant rules, producing a more streamlined yet precise system. The web-based interface facilitated user interaction and interpretation of results, ensuring practical usability. In conclusion, integrating fuzzy logic with GA enhanced the accuracy and adaptability of the system for determining diet and bulking programs, establishing it as a promising decision-making tool that can be further expanded with additional personalization variables in the future.
Co-Authors Adigunawan, Adigunawan Aditya, Bintang Akbar, Habibullah Annisa Annisa Arini Winur Baeti Azzahra, Fayza Bambang Irawan Binastya Anggara Sekti Budi Tjahjono Desmond Orix Khoermala Dzikrya, Kaysa Herwanto, Agus Innova, Zacky Kalagi, Richard Kevin Maruli Kristy Wijaya, Marchello Kusdaryanto, Ardo Latumapayahu, Febrian Firmansyah Liu, Vico Andrean M. Rafi Maulana, Irfan Zidny Mohamad Rafi Naufal Aulio Sopian Niken Rahma Diasri Nizirwan Anwar Nurmala Nurmala Pasha, Muhammad Kemal Poppy Camenia Jamil Pramuditya, Benno Qori Halimatul Hidayah Ramadhan, Ridwan Ratna Yulika Go Saputra, Ragil Raditya Setiowati, Dewi Sharen Ming Sianggara Sinta Yulyanti Suryana, Septiara Pratama Verrel, Anggoro Widyantara Usat, Paskalis Dwi Wijanarko, Christoporus Dimas Wisnu Ananta Kusuma