Garuda - Garba Rujukan Digital

Pengujian Correctness Data Kartu Pembayaran pada Aplikasi E-commerce Menggunakan FitNesse Mahkota, Fachri Veryawan; Nugroho, Eddy Prasetyo
JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Vol 7, No 1 (2024)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jatikom.v7i1.31322

Seiring dengan kemajuan teknologi, penggunaan e-commerce sebagai tempat berbelanja sudah semakin melekat dengan keseharian masyarakat luas. Para perusahaan e-commerce pun memanfaatkan fenomena ini dengan menyediakan aplikasi untuk e-commerce mereka dengan berbagai fitur yang salah satunya merupakan pembayaran digital melalui kartu kredit atau debit. Hal ini mendatangkan resiko adanya kesalahan pemasukan data kartu pembayaran ataupun upaya penipuan dengan data kartu palsu. Maka dari itu, pada percobaan ini Penulis melakukan testing kepada mock-up fungsi pengujian keabsahan kartu pembayaran di dalam aplikasi e-commerce yang setelah pengujian disimpulkan sudah cukup baik untuk mendeteksi correctness pada data kartu pembayaran, memberikan jaminan transaksi yang aman baik untuk perusahaan e-commerce maupun pelapak yang berjualan pada platform e-commerce tersebut.

IMPLEMENTASI PENERAPAN METODE SCRAPING PADA PEMBUATAN CURICULUM VITAE Rachman, Irfan Haydar; megasari, Rani Megasari; nugroho, eddy prasetyo
JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Vol 4, No 1 (2021)
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jatikom.v4i1.27830

Curriculum vitae merupakan dokumen yang memberikan gambaran rinci tentang pengalaman, kualifikasi dan prestasi seseorang, terutama hal-hal yang berhubungan dengan akademis. CV berisi tentang Identitas pribadi, riwayat pendidikan, bidang/ spesifikasi keilmuan yang ditekuni, matakuliah yang diampu dalam 3 tahun terakhir, kegiatan pengabdian masyarakat yang dilakukan 3 tahun terakhir, buku teks yang diterbitkan oleh penerbit dalam 3 tahun terakhir, seminar dalam bidang keilmuan, kerjasama yang pernah dilakukan, Setiap dosen diharuskan untuk memiliki Curiculum Vitae. seorang dosen yang aktif dalam jangka 1 tahun dapat melakukan banyak kegiatan khususnya dalam bidang akademis, oleh karena itu setiap dosen harus memiliki CV yang terbaru untuk kebutuhan kegiatan dan lain-lain. Antisipasi yang dilakukan adalah membuat sebuah system yang akan membuat CV dengan cara Web Scraping dari sumber yang valid, aplikasi ini bekerja sesuai dengan kebutuhan dan menghasilkan CV dengan data yang dibutuhkan. Teknik Scraping yang dipakai yaitu HTML DOM Parser

Implementation of Signature based Intrusion Detection System with Snort Rule on E-Voting System Muhammad Adnan Khairi A.S.; Eddy Prasetyo Nugroho; Rizky Rachman J.
Journal of Computers for Society Vol 4, No 1 (2023): JCS: June 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v4i1.71176

Security is an important thing for everyone, including network security, which everyone needs, including the security at web server, there are problems encountered on the server one of which is on the E-voting site server, this server serves to store all the data storage of votes in an election between registered candidates. In this paper we propose a solution to detect these attacks using SNORT IDS. snort will detect an attack by adding a special rule to handle the attack. We tested the proposed solution by comparing the system against four different attacks, the result was that DDoS attacks had the greatest number of data packets compared to other attacks.

Pengembangan E-Marketing Menggunakan Model Double Diamond Berbasis Web Untuk Meningkatkan Minat Konsumen di Zenitland Robbani, Zuhal; Siregar, Herbert; Nugroho, Eddy Prasetyo; Kusnendar, Jajang
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.5044

Zenitland merupakan salah satu perusahaan yang bergerak pada bidang pengembangan properti. Proses bisnisnya berawal dari pemasaran melalui media iklan seperti Google dan Meta. Kemudian, konsumen yang tertarik akan menghubungi pihak Zenitland dan mengunjungi proyek hingga melakukan pembayaran. Setelah diteliti lebih lanjut, ketertarikan konsumen masih rendah sehingga jumlah orang yang survey dan melakukan pembayaran menjadi rendah. Berdasarkan masalah tersebut, peneliti akan mengembangkan website marketing dengan metode Double Diamond. Double Diamond dapat membantu dalam mengeksplorasi masalah dan mengembangkan solusi yang inovatif dengan berfokus pada pengguna. Karena itu, website yang dikembangkan akan memiliki pengalaman pengguna yang baik sehingga konsumen akan nyaman dan tertarik dengan produk yang ditawarkan. Untuk mengetahui tingkat keberhasilan website, penelitian ini akan diuji menggunakan User Experience Questionnaire (UEQ). UEQ dapat memberikan penilaian secara menyeluruh dengan cepat pada user experience sebuah produk. Hasil dari pengujian tersebut menunjukkan bahwa website yang dikembangkan dapat diterima dengan baik oleh calon konsumen.

Penerapan Komputasi Paralel dalam Pengembangan Model Random Forest: Untuk Memprediksi Coral Bleaching Kesuma, M Salman; Yudha, Apri Anggara; Nugroho, Eddy Prasetyo
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.14982

Coral bleaching is a critical environmental issue caused by environmental stressors, such as rising sea temperatures, which result in the loss of algae symbiosis within corals. However, predicting coral bleaching remains challenging due to the complexity of environmental conditions, the uncertainty of contributing factors, and the limited availability of accurate and consistent data. Additionally, managing large datasets and ensuring efficient training of predictive models with complex datasets pose significant challenges. This study explores the application of parallel computing in developing a predictive Random Forest model to forecast coral bleaching events based on environmental data, including sea surface temperature (SST), sea surface temperature anomalies (SSTA), depth, and location coordinates. Parallel computing is employed to enhance efficiency in training the model by utilizing multi-core processors, significantly reducing execution time. The results demonstrate that the model achieves a prediction accuracy of 95.19% with an R-squared value of 0.685. The application of parallel computing also shows a reduction in computation time, although not always linear due to the overhead associated with task management. This research is expected to support coral reef conservation efforts by providing a faster and more accurate predictive model. Keywords – Parallel Computing; Random Forest; Coral Bleaching.

Pemodelan Sistem Deteksi Intrusi pada Sistem Smart Home Pemantauan Konsumsi Energi Listrik Berbasis Machine Learning Nugroho, Eddy Prasetyo; Havid, Sabian Annaya; Nursalman, Muhammad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp42-49

The occurrence of electricity usage that exceeds the power capacity of the home requires a smart home system that can monitor electricity consumption efficiently. This smart home system is built based on the Internet of Things (IoT) which can help electricity users at home to evaluate usage more easily and in an integrated manner. The development of this IoT-based smart home system uses the ESP32 Micro Controller Unit (MCU) and the PZEM-004T v.3.0 sensor. The reading results from the system can be seen on the front end of the web-based application and the LCD module on the controller system. To obtain the efficiency of electricity usage, an electricity usage leakage detection system is needed or in this case, it is called an intrusion detection system or Intrusion Detection System (IDS). The development of IDS by identifying anomalies based on electricity usage. The IDS model utilizes Machine Learning with a labelling process pattern as a preprocess using the Isolation Forest unsupervised learning algorithm and the classification process using the Random Forest supervised learning algorithm with Anomaly and Normal status. Evaluation of the IDS model on the dataset that has gone through labelling gives quite good results with an accuracy value of 99.63 %. IDS Model is ready to be tested in the implementation of classifying recorded data in real-time against several electrical energy load scenarios in the future.

Pemodelan Sistem Monitoring Kualitas Udara Pintar Berbasis Internet of Things dengan Pendekatan Machine Learning Nugroho, Eddy Prasetyo; Anisyah, Ani; Al Fathin, Deva Shofa; Amadudin, Muhammad Nur Yasin; Ramadhani, Muhammad Satria; Yosafat
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 2: April 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129195

Penelitian ini bertujuan untuk merancang arsitektur model sistem pemantauan kualitas udara di Kota Bandung menggunakan empat parameter polutan utama: PM1.0, PM2.5, PM10, dan CO. Sistem ini dirancang dengan memanfaatkan algoritma Long Short-Term Memory (LSTM) untuk memprediksi kualitas udara harian berdasarkan data historis. Fokus penelitian meliputi perancangan desain arsitektur sistem, model data, dan metode prediksi, yang disusun berdasarkan analisis arsitektur sebelumnya serta kajian literatur. Salah satu elemen penting dan kebaruan dalam penelitian ini adalah penggunaan sensor ZH03B untuk pemantauan kualitas udara secara real-time yang memberikan solusi hemat biaya dan dapat diandalkan. Kombinasi antara sensor real-time dan algoritma LSTM menghasilkan tingkat akurasi prediksi kualitas udara sebesar 88%. Hasil evaluasi model menunjukkan nilai Root Mean Square Error (RMSE) sebesar 2,68 yang mencerminkan kinerja prediksi yang baik. Selain itu, pendekatan ini memberikan peningkatan signifikan dibandingkan metode konvensional yang sering kali kurang responsif terhadap perubahan kualitas udara secara dinamis. Penelitian ini memberikan dasar yang kuat untuk pengembangan sistem monitoring kualitas udara yang lebih akurat dan adaptif. Arsitektur yang diusulkan dapat menjadi acuan untuk pengembangan sistem monitoring kualitas udara di masa depan. Abstract This research aims to design the architecture of an air quality monitoring system model in Bandung City using four main pollutant parameters: PM1.0, PM2.5, PM10, and CO. The system is designed by utilising the Long Short-Term Memory (LSTM) algorithm to predict daily air quality based on historical data. The focus of the research includes the design of the system architecture, data model, and prediction method, which were developed based on previous architecture analysis and literature review. One important element and novelty in this research is the use of the ZH03B sensor for real-time air quality monitoring which provides a cost-effective and reliable solution. The combination of the real-time sensor and the LSTM algorithm resulted in an air quality prediction accuracy rate of 88%. The model evaluation results show a Root Mean Square Error (RMSE) value of 2.68 which reflects good prediction performance. In addition, this approach provides a significant improvement over conventional methods that are often less responsive to dynamic changes in air quality. This research provides a solid foundation for the development of a more accurate and adaptive air quality monitoring system. The proposed architecture can serve as a reference for the development of future air quality monitoring systems.

Development of an Automatic Summarization System based on Large Language Models for Annual Report Analysis Rizki, Muhammad; Wibisono, Yudi; Nugroho, Eddy Prasetyo
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6772

The increasing interest in stock market investment in Indonesia has highlighted a significant challenge for retail investors: the difficulty of analyzing lengthy and complex corporate annual reports. These documents, essential for fundamental analysis, are often hundreds of pages long and contain detailed narrative sections that require considerable time and effort to comprehend. This research addresses this issue by developing an automatic summarization system using a Large Language Model (LLM) to generate concise and insightful summaries of such reports. The primary objective was to develop and evaluate an LLM-based system specifically adapted for the structure and content of annual reports. The method involved creating a tailored dataset comprising 2,008 narrative text excerpts and their corresponding manual summaries sourced from the annual reports of companies listed on the Indonesia Stock Exchange (IDX). The open-source Llama-3.2-3B-Instruct model was then fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) technique, specifically Low-Rank Adaptation (LoRA). The research results demonstrated a significant improvement in the model's performance after fine-tuning. Quantitative evaluation using ROUGE metrics showed a relative increase of 18.63% in ROUGE-1, 44.45% in ROUGE-2, and 33.83% in ROUGE-L compared to the base model. Qualitative analysis confirmed that the fine-tuned model was capable of generating informative and relevant summaries aligned with the context of annual report analysis. In conclusion, this study demonstrates that fine-tuning LLMs with document-specific data is an effective approach for specialized tasks such as annual report summarization.

Pemodelan Sistem Deteksi Intrusi pada Sistem Smart Home Pemantauan Konsumsi Energi Listrik Berbasis Machine Learning Nugroho, Eddy Prasetyo; Havid, Sabian Annaya; Nursalman, Muhammad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp42-49

The occurrence of electricity usage that exceeds the power capacity of the home requires a smart home system that can monitor electricity consumption efficiently. This smart home system is built based on the Internet of Things (IoT) which can help electricity users at home to evaluate usage more easily and in an integrated manner. The development of this IoT-based smart home system uses the ESP32 Micro Controller Unit (MCU) and the PZEM-004T v.3.0 sensor. The reading results from the system can be seen on the front end of the web-based application and the LCD module on the controller system. To obtain the efficiency of electricity usage, an electricity usage leakage detection system is needed or in this case, it is called an intrusion detection system or Intrusion Detection System (IDS). The development of IDS by identifying anomalies based on electricity usage. The IDS model utilizes Machine Learning with a labelling process pattern as a preprocess using the Isolation Forest unsupervised learning algorithm and the classification process using the Random Forest supervised learning algorithm with Anomaly and Normal status. Evaluation of the IDS model on the dataset that has gone through labelling gives quite good results with an accuracy value of 99.63 %. IDS Model is ready to be tested in the implementation of classifying recorded data in real-time against several electrical energy load scenarios in the future.

Design Blockchain Architecture for Population Data Management to Realize a Smart City in Cimahi, West Java, Indonesia Nugroho, Eddy Prasetyo; Afrianto, Irawan; Piantari, Erna; Anisyah, Ani; Al Husaeni, Dwi Novia; Bisulthon, Ibrahim Danial; Jundurrahmaan, Irham
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27493

Smart city as a concept of city development which integrates information and communication technology with the intention of optimizing city management becomes a major goal for Indonesia, especially through the movement towards 100 Smart Cities. However, population data management is crucial in achieving this for optimal planning and management. Personal data protection becomes a crucial challenge with the rapid population growth and mobility in cities. The need for a more reliable protection system is very necessary. This research proposes a blockchain architecture that not only manages digital identities but also population data. The focus is population administration in Cimahi City, West Java, with the hope of providing security, transparency, and a strong audit trail for all population data. The contribution of this research is to design a blockchain architecture specifically for population data management, meeting the needs of population administration in cities, especially the city of Cimahi. Through a blockchain architecture development approach, this research considers the diverse administrative needs of the population and applies a blockchain model that enables data security and integrity. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This research also shows that the use of blockchain technology specifically for population data management can be a reliable and innovative solution in ensuring the security and reliability of data important for smart city development.However, this research has limited access to central data, so the data obtained is still very limited. Therefore, further research is needed to follow up on these limitations. Apart from that, this research is also expected to provide knowledge and solutions in securing data, especially population data in government environments.

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