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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTI Nusa Mandiri BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer IPSIKOM Innotech Journal of Informatics and Advanced Computing Journal of Applied Research In Computer Science and Information Systems
Hesananda, Rizki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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APPLICATION OF MACHINE LEARNING FOR BITCOIN EXCHANGE RATE PREDICTION AGAINST US DOLLAR Wiliani, Ninuk; Hesananda, Rizki; Rahmawati, Nidya Sari; Prianggara, Erdham Hestiadhi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 7 No 2 (2022): JITK Issue February 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1590.378 KB) | DOI: 10.33480/jitk.v7i2.2880

Abstract

Predicting a currency Exchange rate and performing analysis is an action to try to determine the price valuation of a currency or other financial instrument traded on an exchange platform. Bitcoin is a consensus network that enables new payment systems and fully digital money. Bitcoin is the first decentralized peer to peer payment network that is fully controlled by its users without any central authority or intermediary. From the user's point of view, Bitcoin is like cash in the internet world. Bitcoin can also be viewed as the most prominent triple bookkeeping system in existence today. The change in Bitcoin's behavior against the US dollar is influenced by many factors. Basic or economic factors that may be affected include inflation rates and money supply. In this study, data was collected by obtaining all data through the API provided by binance.com and labeled with the specified attribute. The modeling is done by using the rapidminer application. The process begins by taking training data that has been provided previously. The next stage is the data testing process, all operators that have been previously determined are connected and tested using the Linear Regression operator. The purpose of testing this data is to predict stock prices from the testing data that has been made by the Split Data operator, which is 19% of the total data that has been prepared.
CLOTH BAG OBJECT DETECTION USING THE YOLO ALGORITHM (YOU ONLY SEE ONCE) V5 Hesananda, Rizki; Natasya, Desima; Wiliani, Ninuk
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3019

Abstract

The use of plastic in modern life is increasing rapidly, causing the number of people who use plastic to increase, one of which is when shopping. The function of plastic bags as packaging for luggage is not comparable to the impact caused by plastic waste in the years to come. Plastic bags take a long time, even hundreds to thousands of years, to completely decompose. In order to support the government's program to reduce the use of plastic bags, this study will discuss how to detect cloth bags as a substitute for plastic bags. In this research, a system will be implemented to detect the use of cloth bags with Roboflow and Yolo v5. After carrying out all stages of the research, it can be concluded that the goodie bag detection model has been successfully created. The detection model was created using the YOLOV5 algorithm. The dataset used consists of 102 goodie bag images. The process model uses 100 epochs with the training result mAP@0.5 is 89.8%. So, in other words, it can be said that YOLO v5 can detect goodie bags very well.
TREND ANALYSIS AND CORRELATION OF TOURIST, RESTAURANT AND HOTEL VISITS IN KUNINGAN REGENCY Hesananda, Rizki; Trihandoyo, Agus; Wiliani, Ninuk; Rahmawati, Nidya Sari
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.4618

Abstract

This study conducts an in-depth analysis of the tourism sector in Kuningan Regency, focusing specifically on hotel stays, tourist arrivals, and restaurant visits. Utilizing forecasting models and correlation analyses, the research aims to uncover trends and interdependencies within the sector. The primary objective is to identify actionable insights that can inform data-driven decision-making. The study employs the FBProphet algorithm for forecasting future trends and conducts Kendall correlation analysis to examine relationships among key variables. Data collected spans a time series of 84 months, from January 2016 to December 2022. FBProphet accurately predicts trends in hotel stays, while variations exist in predictions for tourist arrivals and restaurant visits. Mean values for hotel stays, tourist arrivals, and restaurant visits are 21,098.67, 135,647.33, and 130,660.83, respectively. Kendall correlation analysis reveals a moderate positive correlation (0.214, p-value = 0.004) between tourist arrivals and restaurant visits, a strong positive correlation (0.324, p-value = 1.291e-05) between tourist arrivals and hotel stays, and a weaker positive correlation (0.176, p-value = 0.019) between restaurant visits and hotel stays. These findings underscore the intricate dynamics of Kuningan Regency's tourism sector, providing stakeholders with critical insights for strategic planning. The research contributes significantly to sustainable growth initiatives by guiding stakeholders in leveraging the interconnected elements of tourism and making well-informed decisions.
Implementasi YOLOv5 untuk Deteksi Objek Mesin EDC: Evaluasi dan Analisis Hesananda, Rizki; Noviani, Irma Ayu; Zulfariansyah, Muhammad
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.127

Abstract

The Electronic Data Capture (EDC) machine is essential for facilitating non-cash transactions, yet its efficient detection remains a challenge. This study explores the implementation of the You Only Look Once (YOLOv5) algorithm to enhance EDC machine detection. The objective is to improve accuracy and efficiency in detecting EDC machines in various environments, thereby enhancing transaction security and efficiency. The research methodology involved acquiring a diverse dataset from social media platforms and the internet, comprising 396 images after augmentation. Using Roboflow, the dataset was annotated and divided into training, validation, and testing sets. The YOLOv5 model was trained on Google Colab, achieving a Precision of 97.1%, Recall of 86.4%, and mean Average Precision (mAP50) of 92.0% on the validation set. The results demonstrate that YOLOv5 effectively detects EDC machines with high accuracy across different scenarios, validating its robustness in real-world applications. This research suggests that YOLOv5 can significantly improve transaction security and efficiency in retail and service industries. The implications of this research are substantial for industry stakeholders and decision-makers, offering a reliable solution to enhance transaction security and streamline non-cash payment processes. By integrating YOLOv5, businesses can optimize operational efficiency and customer service, paving the way for broader adoption of advanced computer vision technologies in commercial applications
IMPLEMENTASI MODEL YOLO V5 UNTUK DETEKSI KOREK API DALAM KEAMANAN PENERBANGAN Hesananda, Rizki
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5553

Abstract

Keamanan dalam transportasi udara merupakan prioritas utama, dengan regulasi ketat terkait barang-barang yang dapat dibawa ke dalam pesawat. Korek api adalah salah satu barang yang sering kali dibatasi karena potensinya sebagai sumber api yang berbahaya. Penelitian ini bertujuan mengembangkan model deteksi korek api yang akurat menggunakan YOLO v5 untuk meningkatkan keamanan penerbangan. Metodologi yang digunakan dalam penelitian ini adalah CRISP-DM (Cross Industry Standard Process for Data Mining), yang terdiri dari tahapan pemahaman bisnis, pemahaman data, persiapan data, pemodelan, dan evaluasi. Dataset yang digunakan adalah data primer yang terdiri dari 37 gambar potret korek api dengan resolusi tinggi, yang diambil sendiri menggunakan kamera HP. Gambar-gambar ini diunggah ke platform Roboflow untuk anotasi dan augmentasi, menghasilkan dataset yang tiga kali lipat dari jumlah gambar awal. Model YOLO v5 dilatih menggunakan Google Colab dengan 100 epoch, batch size 16, dan ukuran gambar 416 piksel. Evaluasi model menunjukkan hasil yang sangat baik dengan nilai precision, recall, mAP 0,5, dan mAP 0,95 yang meningkat mendekati 1 seiring bertambahnya epoch. Hasil ini membuktikan bahwa YOLO v5 memiliki kemampuan deteksi korek api yang sangat akurat, yang dapat diimplementasikan dalam sistem keamanan bandara untuk mendeteksi barang-barang berbahaya secara otomatis. Penelitian ini menyimpulkan bahwa model YOLO v5 efektif untuk deteksi korek api dan dapat meningkatkan keselamatan transportasi udara. 
Prediksi Pertumbuhan Jumlah UMKM di Jawa Barat Menuju Indonesia Emas 2045 Menggunakan Regresi Linear Issa Wirakusumah, Raden Mochamad; Hesananda, Rizki
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2025): Innotech Issue Januari 2025
Publisher : Universitas Siber Indonesia

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

Abstract

This research delves into the projection of the number of Micro, Small, and Medium Enterprises (MSMEs) in West Java, employing the methodology of linear regression. The study utilizes open data from West Java to analyze factors influencing MSME growth, such as government support, infrastructure, financial access, and demographic characteristics. The regression model is formulated, trained, and optimized to project MSME growth up to the year 2045, aligning with the vision of Indonesia Emas. The results reveal a significant upward trend in MSME numbers, particularly evident in regions like Bogor, under the auspices of West Java. The collaborative efforts of provincial and local governments, supported by effective policies, contribute to the optimistic projections. The study's findings underscore the pivotal role of sustained collaboration and adaptive policies in fostering a conducive environment for MSMEs. As Indonesia strives for economic growth and inclusivity, understanding the dynamics of MSMEs becomes instrumental. This research aims to guide policymakers in formulating strategic interventions for the sustainable development of MSMEs, ensuring their central role in the realization of Indonesia Emas by 2045.
IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES Hesananda, Rizki; Firmansyah, Vian
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6155

Abstract

This study aims to develop an object detection model based on YOLOv5 to classify debit card types. With the advancement of financial technology, the need for automated systems to identify debit cards has become essential to enhance transaction efficiency and security. The research methodology involves five main stages: dataset collection, data preprocessing through labeling and resizing to 640 x 640, dataset augmentation, YOLOv5 model training, and model evaluation. The dataset used consists of three categories of debit cards, with a total of 300 images. The results demonstrate that the YOLOv5 model achieves excellent performance with a mean average precision (mAP) of 92.7% and an object loss value of 0.08. The high mAP value indicates the model’s capability to accurately recognize objects, while the low object loss value reflects minimal detection errors during testing. In conclusion, YOLOv5 has proven to be reliable for application in debit card detection systems. This study provides significant contributions to the development of automation systems in the financial sector, particularly in improving the efficiency and accuracy of identification processes. It is hoped that this research will serve as a foundation for further studies with broader datasets, the application of more advanced augmentation techniques, and the utilization of more sophisticated hardware to enhance model performance.
Rancang Bangun Aplikasi Pengajuan dan Perhitungan Lembur Pekerja untuk Meningkatkan Efisiensi SDM di BRI Cabang Veteran Hesananda, Rizki; Surya Kencana, Nurrahman Putra
Journal of Informatics and Advanced Computing (JIAC) Vol 5 No 2 (2024): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v5i2.7766

Abstract

Pengelolaan perhitungan lembur secara manual pada bagian Penunjang Operasional Layanan SDM PT. Bank Rakyat Indonesia (Persero), Tbk Kantor Cabang Jakarta Veteran sering kali memakan waktu dan tidak efisien. Proses ini melibatkan pengumpulan dokumen lembur fisik, yang menyebabkan keterlambatan dan potensi kesalahan dalam penginputan data. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi perhitungan lembur berbasis web menggunakan PHP dan MySQL guna meningkatkan efektivitas proses pengajuan lembur. Metode pengembangan yang digunakan adalah Waterfall Model, dimulai dari analisis kebutuhan melalui observasi lapangan, perancangan sistem dengan UML dan High Fidelity Mockup, implementasi menggunakan PHP dan HTML, serta pengujian dengan metode Black Box Testing. Hasil evaluasi menunjukkan bahwa rata-rata waktu pengerjaan lembur oleh petugas SDM tanpa sistem adalah 186 menit, sedangkan dengan sistem informasi perhitungan lembur menjadi 33 menit. Dengan demikian, sistem yang dikembangkan mampu menghemat waktu pengerjaan hingga 153 menit atau sekitar 82,3%. Kesimpulannya, sistem informasi yang diusulkan dapat meningkatkan efektivitas dan efisiensi dalam proses pengajuan lembur serta meminimalkan kesalahan penginputan data. Sistem ini diharapkan dapat diintegrasikan dengan aplikasi web BRI Human Capital (BRIHC) untuk mendukung pengolahan data yang lebih lanjut.
Penerapan Klasterisasi K-Means terhadap Produktivitas Padi di Pulau Sumatera sebagai Strategi Pendukung Ketahanan Pangan Hesananda, Rizki
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 1 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/ve9jhz24

Abstract

Rice production is a crucial aspect of national food security, particularly in the Sumatra region, which plays a strategic role as Indonesia's food barn. This study was conducted to identify rice production patterns based on the characteristics of harvested area and annual production yields in order to produce a more informative segmentation of productivity areas. The main objective of this study is to segment rice production data using a clustering method, and evaluate the clustering results using statistical metrics and data mining visualization. The methodology used follows the CRISP-DM approach, with stages of data exploration, visualization, modeling, and evaluation. The algorithm used is K-Means Clustering with a total of three clusters. Data were obtained from Kaggle and BMKG, covering 8 provinces in Sumatra Island between 1995 and 2020, with a total of 208 annual observation data entries. The results show that the clustering model built has very good quality with a Silhouette Score of 0.7147 and a Davies-Bouldin Index (DBI) of 0.3752. The scatterplot visualization shows clear segmentation between clusters based on harvested area and production. In conclusion, the clustering approach is effective in classifying regions based on rice production characteristics. The implications of this research support the optimization of data-driven agricultural land management policies. For industry stakeholders and decision-makers, the results can provide a basis for prioritizing interventions, channeling agricultural assistance, and planning for sustainable food security at the regional level.
Customer Segmentation of Cash Management System Using K-Means Clustering Hesananda, Rizki; Apriliga, Patri
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.188

Abstract

The effective financial management is essential for running successful business operations. In the banking context, the Cash Management System (CMS) facilitates real-time, automated transaction management. PT Bank Rakyat Indonesia (Persero) Tbk., as one of Indonesia’s largest banks, has implemented CMS since 2009. Despite its benefits, challenges persist, such as customer transactions outside regular working hours and difficulties in segmenting customers based on transaction volume and frequency. This study aims to address these issues by clustering BRI CMS users using the K-Means Clustering method, following the CRISP-DM framework. The research utilized transaction data of 2,727 users from January 2021 to April 2022. Data preparation involved cleaning anomalies and converting non-numeric values to numeric formats. Using the Elbow method, the optimal number of clusters was determined, resulting in three distinct user segments. The clustering revealed actionable insights, such as identifying high-value customers for targeted marketing and improving service strategies. This research offers a novel application of K-Means Clustering and CRISP-DM to CMS data management, contributing to better customer segmentation and strategic decision-making. These findings can help banks optimize resources, improve customer satisfaction, and enhance overall transaction efficiency.
Co-Authors Agus Trihandoyo Apriliga, Patri Faizal Racma, Dhany Firmansyah, Vian Issa Wirakusumah, Raden Mochamad Mulyawan, Yudhi Natasya, Desima Noviani, Irma Ayu Prianggara, Erdham Hestiadhi Rahmawati, Nidya Sari Surya Kencana, Nurrahman Putra Tiara, Beby Widiyaningrum, Diyah Kiki Wiliani, Ninuk Zulfariansyah, Muhammad