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Jurnal ULTIMA Computing
Published by Universitas Multimedia Nusantara
ISSN : 23553286     EISSN : 25494007     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
urnal ULTIMA Computing merupakan Jurnal Program Studi Sistem Komputer Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang Sistem Komputer serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, organisasi dan arsitektur komputer, programming, embedded system, sistem operasi, jaringan dan internet, integrasi sistem, serta topik lainnya di bidang Sistem Komputer. Jurnal ULTIMA Computing terbit secara berkala dua kali dalam setahun(Juni dan Desember) dan dikelola oleh Program Studi Sistem Komputer Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 150 Documents
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Design of a Nutrient and Environment Monitoring IoT Device in Vertical Hydroponic System Linelson, Ricardo; Saputri, Fahmy Rinanda
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4145

Abstract

This study presents the design and performance evaluation of an Internet of Things (IoT)-based nutrient and environmental monitoring device for vertical hydroponic farming. The system employs multiple sensors to measure pH, Total Dissolved Solids (TDS), nutrient temperature, air temperature, and air humidity. Data are transmitted via ESP32 and integrated with the Arduino IoT Cloud, enabling real-time monitoring through a web dashboard and IoT Remote mobile application. A 10-day testing period was conducted to compare sensor outputs against standard calibrator references. The device demonstrated minimal bias (e.g., 0.20 for pH, 0.51"¯°C for nutrient temperature) and high precision (100.00%) across all parameters. Accuracy ranged from 92.33% (TDS) to 98.24% (nutrient temperature), while error rates were relatively low (e.g., 1.76% for nutrient temperature and 7.67% for TDS). These findings validate the system's reliability and consistency, supporting its potential for scalable implementation in precision-controlled, real-time monitoring applications within urban agriculture.
An Adaptive Stacking An Adaptive Stacking Approach for Monthly Rainfall Prediction with Hybrid Feature Selection: Hybrid Feature Selection Zulfa, Ahmad; Saikhu, Ahmad; Pradana, Hilmil; Budiawan, Irvan
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4157

Abstract

Rainfall is a critical climatic element for water resource management, agriculture, and hydrometeorological disaster mitigation. However, its nonlinear and fluctuating characteristics require a careful and adaptive predictive approach. This study aims to develop a monthly rainfall prediction model using an Adaptive Stacking Ensemble method combined with a hybrid feature selection framework. The feature selection integrates three techniques”correlation analysis, feature importance from Random Forest, and Recursive Feature Elimination (RFE)”through a voting mechanism. Three machine learning algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, are used as base learners. The meta-learner is adaptively selected based on the best-performing base model. Model performance is evaluated using R², RMSE, and MAE metrics. The proposed method is expected to produce a more accurate, stable, and reliable predictive model to support climate-based decision-making. By leveraging the hybrid feature selection framework, the model effectively identifies the most relevant weather variables related to monthly rainfall patterns, thereby reducing model complexity without sacrificing accuracy. The adaptive stacking approach also offers flexibility in capturing nonlinear relationships between features and targets, while enhancing model generalization across seasonally varying data. Experiments were conducted on long-term weather datasets, and the results demonstrate that the proposed model outperforms single models and conventional ensemble methods. This research contributes to the development of more robust, data-driven climate prediction systems that can be applied across sectors affected by rainfall variability.
Micro-Scale CPV Performance Enhancement through V-Trough Concentration and Passive Cooling Robert, Nicholas; Paramasatya, Johanes Dimas; Prastomo, Niki
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4206

Abstract

The global reliance on fossil fuels has driven the need for clean, renewable alternatives. Concentrated Photovoltaic (CPV) systems offer a promising solution by increasing energy yield per unit area, particularly in regions with high solar irradiance. This study investigates the performance enhancement of a micro-scale CPV system through the integration of a V-trough optical concentrator and passive thermal regulation mechanisms. Five system variants were developed and tested: a baseline with no enhancement, a standard CPV, and three CPV systems incorporating heat sinks, heat pipes, and a hybrid of both. Optical simulations were performed to achieve a 2× concentration ratio using planar mirrors angled at 60°, while all cooling systems relied on passive methods to maintain simplicity and low cost. Field tests conducted in a tropical environment revealed that all CPV systems outperformed the baseline, with the hybrid-cooled system delivering the highest average power output—138.76 mW, a 32.37% improvement over the baseline. Surface temperatures were also significantly reduced, with the hybrid system lowering temperatures by up to 6.8°C. These results highlight the synergistic potential of combining optical and passive thermal enhancements in compact CPV designs, providing a scalable, cost-effective solar energy solution suitable for rural and off-grid applications in high-irradiance regions.
Solar Radiation Intensity Imputation in Pyranometer of Automatic Weather Station Based on Long Short Term Memory Pahlepi, Richat; Soekirno, Santoso; Wicaksana, Haryas Subyantara
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3348

Abstract

Automatic Weather Station (AWS) experienced problems in the form of component damage and communication system failure, resulting in incomplete parameter data. Component damage also occurs in pyranometers. Decreased pyranometer performance results in deviations, uncertainty in measuring solar radiation intensity, and data gaps. Data imputation is one solution to minimize measurement deviations and the occurrence of missing AWS pyranometer data. This research aims to design and analyze the accuracy performance of the multisite AWS pyranometer solar radiation intensity data imputation model when a data gap occurs. This research attempts to utilize the spatio-temporal relationship of multisite AWS solar radiation intensity in the imputation model. Long-Short Term Memory (LSTM) algorithm is used as an estimator in the multisite AWS pyranometer network. Data imputation modeling stage includes data collection, data pre-processing, creating missing data scenarios, LSTM design and model testing. Overall, LSTM-based imputation model has ability of filling gap data on AWS Cikancung pyranometer with maximum missing sequence of 12 hours. Imputation model has MAPE 1.76% - 5.26% for missing duration 30 minutes-12 hours. It still it meet WMO requirement for solar radiation intensity measurement with MAPE<8%.
Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
An Automatic Internet of Things-Based System for Rabbit Cage Kharisma, Andrian; Sintawati, Andini
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3404

Abstract

Rabbits, low-maintenance mammals in terms of cost and space requirements, require meticulous care, encompassing disease control, feeding, and cage maintenance. To address these concerns, an automated system for feeding, drinking, temperature control, and monitoring rabbit manure gas levels within the cage was developed, all remotely accessible. The system comprises ultrasonic sensors, DHT11 sensors, MQ-135 gas sensors, a real-time clock (RTC), an Arduino Mega 2560 with built-in Wi-Fi, relays, servo motors, mini water pumps, mini fans, and a heat lamp. The feeding and drinking functions are automated, triggered by RTC sensor data or can be manually controlled through the Arduino IoT Cloud dashboard. Temperature regulation is managed based on data from the DHT11 sensor, and gas levels in the rabbit manure are monitored using the MQ-135 gas sensor. Conducting 30 tests for each primary function, including automatic and manual feeding/drinking, temperature control, and disinfectant spraying, these functions performed as designed. An exception occurred three times when the DHT11 microcontroller sensors lost connection, rendering the input from these sensors unusable. To address this issue, the addition of an extra voltage supply to the Arduino Mega 2560 microcontroller is proposed, mitigating this vulnerability.
Automatic Mass Waste Sorting System Using Inductive Proximity Sensor, Water Level Sensor and Image Processing using MobileNet Algorithm Pura, Megantara; Langko, Charles Hardi; Kho, Jason
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3421

Abstract

The global municipal solid waste is predicted to increase by threefold in 2050. Indonesia's most wastes are unsorted and only end up in landfill and the waste management is less than ideal. An automatic mass waste sorting system is proposed to answer such problems. The automatic mass waste sorting system is designed to be able to identify and separate metal, plastic and organic waste using electrical sensors and image processing. The electrical sensors was able to identify waste types with 65% accuracy and the image processing system was able to identify waste types with 86.67% accuracy. The result doesn't offer much advantage compared to other research on waste management system, however it is hoped that this research may inspire other researches on mass waste sorting systems.
Trajectory Planning of Spherical Pendulum Pattern for Application in Creating Batik Patterns Putri, Indah Radityo; Ekawati, Estiyanti; Budi, Eko Mursito; Juwandana, Alfisena; Mulyawan, Naufan Aurezan; Kho, Philip Inarta; Kudiya, Komarudin
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3425

Abstract

Batik Pendulum is a new batik pattern created by Rumah Batik Komar using a single-string pendulum filled with wax. However, current production is still manual, so it is impossible to re-manufacture in large quantities. This research is part of a machine and software development project to produce Batik Pendulum, where this research will only focus on software development. The designed software will have a spherical pendulum trajectory planning feature through parameter changes. The spherical pendulum path was chosen because it has the same pattern as the currently produced Batik Pendulum. In planning the spherical pendulum trajectory, an algorithm that receives input in the form of parameters to produce a spherical pendulum pattern has been designed. From these inputs, it is proven that the proposed parameters can provide a variety of spherical pendulum patterns. Implementing the spherical pendulum trajectory planning in the software shows that the time required to change parameters until the output trajectory is generated is 1 – 2 seconds. So, there is no need for any feedback to the user.
EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM Pratiwi, Monica
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3426

Abstract

Depression can induce significant anguish and impair one's ability to perform effectively in professional, academic, and familial settings. This condition has the potential to result in suicide. Annually, the number of deaths resulting from suicide exceeds 700,000. Among individuals aged 15-29, suicide has emerged as the fourth most prevalent cause of mortality. Challenges in treating depression include limited accessibility to mental health care in rural regions and misdiagnosis resulting from subjective evaluations, wherein insufficient expertise can contribute to inaccurate diagnoses. Electroencephalography (EEG) has gained popularity as a tool for the identification and study of a number of mental illnesses in the past several years. Therefore, an automated technique is required to precisely distinguish between normal EEG signals and depression signals. This research focused on developing an EEG-based depression detection system in the prefrontal cortex lobe area (Fp1, Fpz, and Fp2). One of the developments carried out in this research is the implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) as the model classification and minimum redundancy maximum relevance (mRMR) feature selection. Results suggest that the combination of mRMR feature selection with 25 features and the Bidirectional LSTM obtained 92.83% for the accuracy.
Implementation of the Fuzzy Logic Mamdani Method in the KUB Chicken Egg Incubator Apriyani, Indra; Yanti, Indri; Darsanto, Darsanto
ULTIMA Computing Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3587

Abstract

Poultry farming plays an important role in rural Indonesia's economy, with increasing demand for poultry meat and protein-rich eggs. One of the main challenges for farmers is the limited production of chicken seeds and suboptimal egg incubation methods. Modern egg incubators offer a solution with higher ease and efficiency compared to traditional methods. However, existing machines on the market have weaknesses, such as less accurate temperature and humidity control, and less optimal power source switching. The use of fuzzy logic methods in egg incubators has proven to be more efficient than manual methods, with a hatching success rate of 100% for 10 eggs. Fuzzy logic-based egg incubators start hatching earlier and more on days 18 and 19, while manual methods begin on days 19 and 20. The automatic egg-turning process in fuzzy logic machines saves labor and reduces the risk of error. This research highlights the importance of using accurate sensors and optimal temperature and humidity control systems to improve the success rate of chicken egg hatching. Index Terms”poultry farming; egg hatching; temperature; humidity; fuzzy logic

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