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All Journal Journal of Information Systems and Informatics bit-Tech International Journal of Advances in Data and Information Systems Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Jurnal Impresi Indonesia Literasi Nusantara Journal of International Conference Proceedings Jurnal Ilmiah Edutic : Pendidikan dan Informatika Signal and Image Processing Letters
Diyasa, I Gede Susrama Mas
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Prototype of Integrated Pier Entrance Gate Access With QR-Code as An Iot-Based Manifest Recording System Diyasa, I Gede Susrama Mas; Putra, I Nyoman Dita Pahang; Merdana, I Gede Okta Budi; Sampurno, Ilham Ade Widya
Nusantara Science and Technology Proceedings 5th International Seminar of Research Month 2020
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.0942

Abstract

The pier is the place where the ships are being moored at the port. It is also a place for loading and unloading activities and for people or passengers to get on or get off from the ships. There is a need for a digital recording of vehicles or passengers entering the ships to speed up the port administration process. For this reason, in this study, a prototype of a Vehicle and Passenger Recording System (Manifest) at the dock access gate is integrated in an integrated manner based on the Internet of Thing (IoT), which consists of an Android and Web system. This system uses a QR-Code as a ticket that contains manifest data and is read with a QR-Code Reader to be compared with the data stored in the server. If it is appropriate, the dock entrance will be active, and the vehicles or passengers can enter the ships. Whereas if it does not fit, they cannot be open the pier access door. From the test results, the mechanical system can function as expected and can recognize as the entire QR-Code within an optimal distance of 3 cm. All registered users can open the door, as well.
Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas
Journal of International Conference Proceedings Vol 3, No 4 (2020): Proceedings of the 8th International Conference of Project Management (ICPM) Mal
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v3i4.1009

Abstract

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.
Application Development of Building Maintenance Periodization on Surabaya City Government Property Putra, I Nyoman Dita Pahang; Suryani, Erma; Mudjahidin; Trigunarsyah, Bambang; Diyasa, I Gede Susrama Mas
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4136

Abstract

Building maintenance is a very important activity and requires regular or scheduled implementation so that financial budgeting can be prepared and scheduled better. The building maintenance application is managed at the Regional Apparatus Work Unit through the Building Maintenance Section which inputs coding, building name, Final Hand Over (FHO) time, maintenance period, components and types of work, amount, unit cost, and construction cost. Observation data was collected on one hundred schools and government buildings for 5 years. Identify the components and types of work that require maintenance through interviews with the infrastructure division of each building that is surveyed. Observations and interviews are needed as a basis for determining the components and types of work that often occur in damage to each building after FHO. After the data is inputted, to prove the actual damage conditions to the buildings being reviewed, verification is required by the infrastructure division in each building. Verification consists of: building name, FHO time, maintenance period, components and types of work, quantity. The infrastructure division cannot see and change unit costs and development costs. Decision making on the implementation of building maintenance from the existing output is carried out by the Head of the Regional Apparatus Work Unit. Decisions taken by the Head of the Regional Apparatus Work Unit are expected to represent the priority scale of building maintenance that must be carried out by the Regional Apparatus Work Unit. In this building maintenance application, the total maintenance costs for each building and each month can be generated every year, and the total maintenance costs for the entire building and every month for each year, and the level of damage can be generated for each maintenance carried out on each building, and can the components and types of work to be carried out each month are known.
Classifying Legendary Pokémon with SF-Random Forest Algorithm Prayoga, Aji; Via, Yisti Vita; Diyasa, I Gede Susrama Mas
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.859

Abstract

Here’s an improved version of the abstract with better articulation: Accurate classification of legendary Pokémon is essential due to their distinct characteristics compared to regular Pokémon, impacting various domains such as research, gaming, and strategy development. This study employs the SF-Random Forest algorithm, an advanced variant of Random Forest, designed to effectively handle data heterogeneity and complexity. The dataset comprises 800 Pokémon samples, including attributes like type, base stats (HP, Attack, Defense, etc.), and other relevant features. To address the inherent imbalance between legendary and non-legendary Pokémon, the data preprocessing phase includes outlier removal, handling of missing values, normalization through Min-Max Scaling, and class balancing using the SMOTE (Synthetic Minority Over-sampling Technique) method. The preprocessed data is then used to train the SF-Random Forest model, with performance evaluated using metrics such as accuracy, precision, recall, and F1-score. The results reveal that SF-Random Forest achieves perfect scores across all metrics, demonstrating 100% accuracy, precision, recall, and F1-score. This highlights the algorithm's superior ability to identify key features and manage data imbalance compared to traditional classification methods. The study underscores the efficiency and robustness of SF-Random Forest as a classification tool, paving the way for the development of more advanced classification systems applicable to various fields requiring complex pattern recognition.
Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth Ali, Munawar; Gunawan, Anak Agung Ngurah; Prasetya, Dwi Arman; Ibrahim, Mohd Zamri Bin; Diyasa, I Gede Susrama Mas
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1277

Abstract

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.
Analysis Postponed VAT Feature on Invoicing Module of Odoo 16 using Rapid Application Development Permana, Eriko Indra; Diyasa, I Gede Susrama Mas; Swari, Made Hanindia Prami
EDUTIC Vol 12, No 1: 2025 (In Progress)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.28484

Abstract

The postponement of Value Added Tax (VAT) payment is a policy aimed at easing financial burdens for companies that frequently import goods, as it allows businesses to defer tax payments instead of prepaying them during imports, thereby improving cash flow and reducing operational costs. This study explores the implementation of VAT payment postponement in the Odoo 16 Invoicing module using the Rapid Application Development (RAD) method, chosen for its rapid iteration and prototyping capabilities to meet user needs and regulatory changes efficiently. By modeling an importing company’s business process in Odoo 16, the research implements and tests the VAT postponement feature, assessing its effectiveness in streamlining operations and enhancing financial flexibility. The study also evaluates the RAD method's efficiency in development and deployment, providing insights into the integration of fiscal policies with corporate IT systems to bolster operational performance and global competitiveness.
PENGGUNAAN K-MEANS DAN HIERARCHICAL CLUSTERING SINGLE LINKAGE DALAM PENGELOMPOKKAN STOK OBAT Sibarani, Michael Alexander Justin Audison; Diyasa, I Gede Susrama Mas; Sugiarto, Sugiarto
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.715

Abstract

Adequate and efficient availability of medicines is necessary to ensure patients receive optimal care. However, inefficient drug stock management can result in various problems, such as waste of resources, lack of necessary drugs, or even excessive stock. This study aims to improve the efficiency of the drug stock management process by using KMeans Clustering and Hierarchical Clustering methods on drug stock data. The data used includes information on initial stock, purchase, incoming distribution, service, outgoing distribution, outgoing adjustment, and final stock. Clustering analysis was performed to identify patterns in the drug stock data, which was then validated using Silhouette Score. The results showed that Hierarchical Clustering was able to achieve a Silhouette Score of 0.976, while KMeans achieved a Silhouette Score of 0.954
Identification of Abnormal Spermatozoa Motility Using the SVM Algorithm Karim, Mohammad Daniel Sulthonul; Puspaningrum, Eva Yulia; Diyasa, I Gede Susrama Mas
Literasi Nusantara Vol. 5 No. 1 (2025): Literasi Nusantara: November 2024- February 2025
Publisher : Yayasan Citra Dharma Cindekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56480/jln.v5i1.1324

Abstract

Spermatozoa motility is one of the key indicators in determining male fertility quality. Manual assessment of motility abnormalities often requires significant time and effort, thus necessitating a more efficient and accurate automated approach. This study aims to identify abnormalities in spermatozoa motility using the Support Vector Machine (SVM) algorithm, utilizing microscopic video data analyzed through TrackPy for spermatozoa trajectory tracking. The analysis process involves data acquisition, spermatozoa detection in each frame, sperm trajectory construction, and trajectory classification into normal or abnormal categories. The SVM model was trained using a dataset derived from spermatozoa trajectories classified based on parameters such as average velocity and trajectory linearity. The results show that the method achieved the highest accuracy of 89 percent in identifying spermatozoa motility abnormalities in HD resolution videos with a frame rate of 30 fps.
Enhanced Human Hitting Movement Recognition Using Motion History Image and Approximated Ellipse Techniques Diyasa, I Gede Susrama Mas; P, Made Hanindia; Zamri, Mohd; Agussalim, Agussalim; Humairah, Sayyidah; A, Denisa Septalian; Umam, Faikul
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1599

Abstract

Recognition of human hitting movement in a more specific context of sports like boxing is still a hard task because the existing systems use manual observation which could be easily flawed and highly inaccurate. However, in this study, an attempt is made to present an automated system designed for this purpose to detect a specific hitting movement commonly known as a punch using video input and image processing techniques. The system employs Motion History Image (MHI) to model trajectories of motions and combine them with other parameters to reconstruct movements which tend to have a temporal component. Thus, CCTV cameras set at different positions (front, back, left and right) enable the system to identify several types of punches including Jab, Hook, Uppercut and Combination punches. The most important aspect of this work is the proposal of MHI and the Ellipse approximation which is quicker in the integration of both than other sophisticated systems which take a considerable duration in computations. Therefore, the system classifies C_motion, Sigma Theta, and Sigma Rho parameters to distress hitting from non-hitting movements. Evaluation on a dataset captured from multiple viewpoints establishes that the system performs well achieving the goal of 93 percent when detecting both the hitting and the non-hitting motion. These results demonstrate the system’s superiority to the system based such detection methods. This study paves the way for other applications in real-time such as sports analysis, security surveillance, and healthcare requiring greater efficiency in and accuracy of human movement assessment. The focus of future work may be in the direction of improving the recognition of slower movements, also modifying the system for more dynamic conditions in the future.
Implementation of Convolutional Neural Network for Road Damage Detection and Classification in Surabaya City Kusuma, Nugraha Varrel; Diyasa, I Gede Susrama Mas; Anggraeny, Fetty Tri
Literasi Nusantara Vol. 5 No. 1 (2025): Literasi Nusantara: November 2024- February 2025
Publisher : Yayasan Citra Dharma Cindekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56480/jln.v5i1.1357

Abstract

Road damage is a significant infrastructural problem that impacts the safety of road users and economic efficiency. The current road damage detection system, which relies on manual inspection, has limitations in speed and accuracy. Therefore, this study proposes the use of a conventional Convolutional Neural Network (CNN) to enhance accuracy and efficiency in the detection and classification of road damage in Surabaya City. The methods applied include data preprocessing and basic data augmentation techniques such as rotation and flipping. The dataset used comes from CV. Wastu Kencana Teknik, consisting of four road damage classes: potholes, surface delamination, cracks, and edge cracks. The implementation of the CNN model with standard configurations shows potential for application in an AI-based road infrastructure monitoring system. The model evaluation was performed using a confusion matrix and ROC-AUC, indicating that the model has stable and accurate classification performance. With these results, the model has the potential to enhance the effectiveness of detection and decision-making in road maintenance.
Co-Authors A, Denisa Septalian A. A. Ngurah Gunawan Adila, Ma’ratul Agussalim, Agussalim Ali, Munawar Awang, Wan Suryani Wan Bambang Trigunarsyah, Bambang Basuki Rahmat Masdi Siduppa Damaliana, Aviolla Terza Dwi Arman Prasetya Erma Suryani Eva Yulia Puspaningrum Fahrudin, Tresna Maulana Faikul Umam Fetty Tri Anggraeny Hindrayani, Kartika Maulida Humairah, Sayyidah I Made Merdana Ibrahim, Mohd Zamri Bin Karim, Mohammad Daniel Sulthonul Kusuma, Nugraha Varrel Kuswardana, Dendy Arizki Made Hanindia Prami Swari Mudjahidin Najma P., Safira NYOMAN DITA PAHANG PUTRA, NYOMAN P, Made Hanindia Permana, Eriko Indra Prayoga, Aji Putra, Handie Pramana Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Sampurno, Ilham Ade Widya Saputra, Wahyu Syaifullah Jauharis Sibarani, Michael Alexander Justin Audison Sugiarto S Trimono Trimono, Trimono Viet Ha, Nguyen Yisti Vita Via Zahrah, Fathima Zamri, Mohd