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INDONESIA
Jurnal Mandiri IT
Published by Institute of Computer Science
ISSN : 23018984     EISSN : 28091884     DOI : https://doi.org/10.35335/mandiri
Core Subject : Science, Education,
Computer Science & IT Library & Information Science Mathematics
The Jurnal Mandiri IT is intended as a publication media to publish articles reporting the results of Computer Science and related research.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 16 Documents
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Search results for , issue "Vol. 14 No. 2 (2025): Computer Science and Field" : 16 Documents clear
Sentiment analysis of privacy issues in the digital era using the naïve bayes method Ramadhan, Rio Fadli; Kurniawan, Rakhmat
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.450

Abstract

The development of information technology has triggered public concern about data privacy issues, especially on social media such as X (formerly Twitter). The rampant leaks of personal data have driven the need for a deeper understanding of public opinion. This study aims to analyze public sentiment towards data privacy issues by applying the Naïve Bayes algorithm. The formulation of the problem includes how the public perceives data privacy, how the algorithm performs in classifying sentiment, and how the evaluation results of the model used are. This study uses a quantitative method with a text mining and machine learning approach. Data were taken through crawling techniques on 1,500 tweets related to data privacy. The pre-processing stages were carried out through cleaning, tokenizing, normalization, stopword removal, and stemming. Furthermore, the data was labeled using the InsetLexicon dictionary and weighted using the TF-IDF method. The classification model was built using the Naïve Bayes algorithm and evaluated using accuracy, precision, recall, and f1-score metrics. The results showed that the majority of public opinion on data privacy issues was negative, reflecting concerns over the weak protection of personal data. The Naïve Bayes model performed quite well in sentiment classification. This research is useful in providing insight to the government and digital service providers in developing data protection policies that are more responsive to public opinion.
Reinforcement learning for bitcoin trading: A comparative study of PPO and DQN Prasetyo, Romadhan Edy; Sumanto, Sumanto; Chaidir, Indra; Supriyatna, Adi
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.455

Abstract

Bitcoin’s high volatility demands automated strategies that adapt to changing market regimes while managing risk. This study compares Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) for Bitcoin trading using hourly BTC/USDT data from 2019 to early 2025. The models are trained to generate buy and sell signals from technical indicators including the Relative Strength Index (RSI), MA20, volatility, Moving Average Convergence Divergence (MACD), volume trend, SMA200, and a weekly trend filter. All features are computed on hourly bars. The evaluation shows that PPO tends to trade more aggressively and delivers higher performance during bullish phases, though with greater risk in unstable markets. By contrast, DQN trades more selectively and maintains better stability in sideways or choppy conditions. These findings support the effectiveness of reinforcement learning for adaptive cryptocurrency trading and highlight complementary strengths between PPO and DQN across market regimes.
Implementation of binary search on website-based traffic data Najah, Novi Muhimmah Lailatun; Huda, Walidini Syaihul
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.452

Abstract

Fast and efficient data retrieval is a fundamental requirement in the development of web-based systems, especially in environments with large volumes of data. The goal of this study is to implement the Binary Search algorithm on the PT Jasamarga Pandaan Tol website to enhance the efficiency of the data retrieval process. System development was carried out using the waterfall method. The Binary Search algorithm was applied to a systematically sorted dataset. Then, the average time required for the search process was measured through testing. After conducting 100 searches on a dataset of 1,000 entries, we found that the average search time was 24.135 milliseconds. This finding indicates that the algorithm operates optimally and efficiently, making it suitable for implementation in large-scale information systems. The efficiency of the search algorithm is crucial to enhancing the system's overall performance.
Optimizing printer usage through data analytics for enhanced institutional efficiency Kadir, Fauwas Abdul; Sumanto, Sumanto
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.453

Abstract

The advancement of information technology had simplified various workplace processes, including document processing and printing. In an institution, the use of printers played a crucial role in daily operations. However, without proper management, printer usage often became inefficient, leading to increased operational costs and unnecessary waste of resources. Therefore, an analytical system was needed to monitor and optimize printer usage. Such a system provided valuable insights by analyzing data generated from printing activities. This data analysis revealed patterns in work habits and allowed institutions to make informed decisions. As a result, institutions were able to improve operational efficiency, reduce costs, and minimize environmental impact. Paper and ink waste were significantly reduced by implementing data-driven policies. Overall, the integration of data analytics into printer management contributed to sustainable practices and better resource allocation in institutional environments.
A realtime IoT-mobile-based system for mapping and monitoring water reservoirs of PDAM in Fakfak City Arridha, Riyadh; Roy, Andi; Saputra, Muhammad Faisal; Yusril
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.454

Abstract

The Regional Drinking Water Company (PDAM) of Fakfak plays a vital role in delivering clean water to the community. However, the current manual approach to monitoring water availability and quality in reservoirs can lead to delays in decision-making related to water distribution. This research aims to design and develop a mobile application integrated with a real-time reservoir mapping and monitoring system, utilizing Internet of Things (IoT) technology. The system employs ultrasonic sensors to measure water levels, pH sensors to assess water quality, and a microcontroller with a communication module to transmit data to a server. This data is then visualized on an Android-based application supported by integrated mapping features. The study follows a Research and Development (R&D) methodology using a prototyping model. The resulting prototype demonstrates the system’s ability to provide accurate and real-time insights on water reservoir conditions, thereby enhancing PDAM's capacity for efficient and responsive water distribution planning.
Development of a laravel-based web information system for network device maintenance management using the rapid application development method Bimorogo, Sembada Denrineksa; Lediwara, Nadiza; Heikhmakhtiar, Aulia Khamas; Aulia, Regifia Ningrum Nur; Sunami, Yoga; Priyani, Kadek Jana; Azahra, Manda Fatimah
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.456

Abstract

Information and Communication Technology (ICT) infrastructure is essential for government operations, particularly in managing network devices. Within the ICT Hardware Infrastructure Subdivision (Subbidang Harinfra TIK) of the Data and Information Center at the Indonesian Ministry of Defense, documentation of maintenance activities remains fragmented, making monitoring, analysis, and historical data storage less effective. This study developed a web-based information system using the Laravel framework and the Rapid Application Development (RAD) approach to address these issues. The system automates documentation, monitoring, and reporting, ensuring more structured, transparent, and efficient processes. Black Box testing confirmed reliable functionality, data validation, and improved efficiency in maintenance activities. Unlike previous studies that focused on general asset or helpdesk systems, this research emphasizes ICT infrastructure maintenance in a defense environment, highlighting security and adaptability for sensitive data. The implementation enhances systematic documentation and operational transparency, with future improvements directed toward intelligent notifications and platform integration in line with Industry 4.0 trends.
Implementation of PZEM-004T and LoRa for Internet of Things–Based Monitoring of Power Supply Sources in Laboratory Building Sirait, Regina; Pardede, Morlan; Hutajulu, Elferida; Junaidi, Junaidi; Pardede, Stephanie Ch Y; Pakpahan, Arnold
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.457

Abstract

This research develops an Internet of Things (IoT)-based system to monitor electrical voltage parameters in two rooms of the Telecommunication Laboratory at Medan State Polytechnic. The system employs two sensor nodes (PZEM-004T, ESP32, and LoRa SX1276) and a gateway node integrated with WiFi and the Blynk cloud. The sensors measure voltage, current, power, energy consumption, frequency, and power factor, which are processed by ESP32 and transmitted via a LoRa multi-point network to the gateway for online monitoring. An automatic cut-off mechanism and email notifications are provided when abnormal voltage or current conditions occur. Experimental results show high measurement accuracy with a maximum error of 0.29% for voltage and 2.52% for current. However, data transmission experienced 20% packet loss, with an average delay of 11 seconds on Blynk and 37 seconds for email notifications. These findings indicate that the proposed system is effective in protecting laboratory equipment from abnormal power sources and provides reliable online and offline monitoring, although transmission performance requires further optimization.
Comparison of MobileNetv2 and MobileNetv3 architectures in rice leaf disease classification using transfer learning Mifthauddin, Adlim; Lutfi, Moch.; Saadah, Zulfatun Nikmatus
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.459

Abstract

Rice is of the main food commodities in Indonesia that is susceptible to various leaf diseases, one of which is Bacterial Blight, Brown Spot, and Leaf Smut. Manual identification by farmers is often less accurate and time-consuming, thus requiring a technology-based detection system. The objective of this research is to categorize rice leafdiseases through the use of deep learning with a transfer learning approach based on MobileNetV2 and MobileNetV3 architectures. The dataset, comprising 4,684 rice leaf images, was divided into training and validation sets using an 80:20 ratio. Preprocessing included resizing images to 224×224 pixels, normalization, and augmentation to increase data variation. Training was carried out across 30 epochs with a mini-batch size set to 32. while applying an EarlyStopping mechanism to reduce the likelihood of overfitting. The result of the experiment indicate that MobileNetV2 reached an 96% accuracy, while MobileNetV3 outputperformed is with an accuracy of 99%. Therefore, MobileNetV3 is more effective for rice leaf disease classification.
Analysis and design of an inset-feed microstrip antenna for a LEO satellite IoT ground station at 921 MHz Taqwa, Rangga; Rimbawa, H.A. Danang; Miptahudin, Apip; Hasibuan, Bayu Nuar Khadapi; Sastradinata, Aria Kusumah; Bangun, Abbas Madani
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.464

Abstract

The evolution of the Internet of Things (IoT) demands global connectivity that terrestrial networks alone cannot provide1. Low Earth Orbit (LEO) satellites equipped with Long Range (LoRa) communication technology offer a promising solution to bridge this connectivity gap2. This paper presents a specific case study calculation for a LoRa-based IoT satellite mission, defining the system's operational constraints based on selected hardware3. This analysis is framed by the RFM95W LoRa transceiver for the ground station and the Satlab Polaris receiver for the satellite4. The datasheet specifications of these components establish the critical link parameters that dictate performance: a maximum Transmit Power (Pt) ) of 20 dBm from the RFM95W 5and a Receiver Sensitivity threshold of -130 dBm for the Satlab Polaris6. The objectives are: (1) to conduct a comprehensive link budget analysis to validate the communication viability between a LEO satellite and a ground station 77, and (2) to design and predict the performance of an inset-feed microstrip antenna operating in the 920-925 MHz Indonesian LoRa frequency band using an FR-4 substrate. The detailed link budget analysis, performed for an uplink to a 500 km orbit 9, reveals that these specific parameters create a stringent performance requirement: while a reliable link margin of $+7.8 \text{ dB}$ is achieved at a 90°  elevation (best case) 10101010, the system reaches its theoretical critical threshold (0.0 dB margin) at 19.1° and enters link failure with a -2.8 dB margin at the target 10°  elevation. This failure is directly linked to the preliminary simulation of the initial antenna design, which shows a suboptimal return loss (S11) of -9.41 dB. This paper concludes that the system's target for low-elevation communication has not been met. The performance gap, defined by the hardware constraints, confirms that the initial antenna design is insufficient15. Therefore, systematic optimization of the antenna design is identified as the crucial next step to achieve a positive link margin at the 10° target elevation and ensure a robust communication link across all operational scenarios.
Bayesian-Optimized XGBoost Model for Predicting Mushroom Toxicity Sastradinata, Aria Kusumah; Sunarta, Sunarta; Miptahudin, Rd. Apip; Abdurrahman, M. Daffa; Taqwa, Rangga
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.465

Abstract

Mushroom poisoning remains a significant public health concern due to the morphological similarities between edible and poisonous species, making traditional identification unreliable. This study aims to develop an accurate and interpretable machine learning framework for mushroom toxicity prediction using a Bayesian-Optimized Extreme Gradient Boosting (XGBoost) model. The dataset consists of morphological and ecological features derived from the secondary mushroom dataset, which underwent preprocessing through imputation, standardization, and one-hot encoding. Bayesian Optimization, implemented via the Hyperopt Tree-structured Parzen Estimator (TPE) algorithm, was employed to automatically fine-tune the XGBoost hyperparameters, thereby improving convergence and reducing manual experimentation. The model’s performance was evaluated using 10-fold cross-validation and standard metrics, including accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). Experimental results demonstrated that the proposed framework achieved an exceptionally high performance with an accuracy of 99.99% and an AUC of 1.0000, indicating near-perfect discrimination between edible and poisonous mushrooms. Feature importance analysis further revealed that habitat, veil color, and stem root were the most influential predictors of toxicity. The findings highlight the effectiveness of Bayesian-optimized ensemble learning in handling high-dimensional biological data, offering a reliable, transparent, and computationally efficient approach for biosafety assessment and ecological data analysis.

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