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Journal of Intelligent Computing, Systems, and Applications (JICSA)
Published by Institut Teknologi Del
ISSN : 31231705     EISSN : 77312317     DOI : https://doi.org/10.54074/jicsa.v2i1
Core Subject :
Journal of Intelligent Computing, Systems, and Applications (JICSA) publishes high-quality research and review articles emphasizing practical applications, interdisciplinary insights, and technological innovation. The scope of the journal includes, but is not limited to, the following themes: Artificial Intelligence (AI), Computing, Electrical and Electronics Engineering, Data Science and Analytics
Arjuna Subject : -
Articles 15 Documents
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A CTMC-Based Parameter Estimation and Optimal Control Framework for Covid-19 Transmission Andhika Dwi Anggara; Nila Prasetya Aryani
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 1 No. 02 (2026): March 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v1i02.29

Abstract

Covid-19 is a disease that infects the respiratory tract caused a corona virus. This disease has infected human populations around the world which causing a pandemic. The Covid-19 pandemic has become the focus of research on lately. This study aims to construct the latest mathematical model related to the transmission of Covid-19 in the human population. The model that is formed is constructed by considering several epidemiological parameters that are very identical to the actual conditions. In the formation of the model there are many unknown parameters. Therefore, the maximum likelihood method is used to estimate the parameters of the model that is formed. At the start the Covid-19 epidemic model was constructed without control with given assumptions and based on the facts obtained. Then from the model would formed equilibrium point, and basic reproduction number. The next discussion is to designed an optimal control using the Pontryagin Minimum Principle which is applied to reduce the number of people Covid-19 infected by using the backward-forward sweep algorithm. The results of the numerical simulation will later produce an optimal control strategy that is suitable to prevent Covid-19 to be more effective.
Energy-Aware Spatial Uniformity Assessment in a Container Farm Using a 27 Node Wireless Sensor Network Samuel Sormin; Raj David Francisco Damanik; Albert Sagala
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 2 No. 1 (2026): September 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v2i1.32

Abstract

Cooling is a major energy demand in container farms, but a suitable room-average temperature does not necessarily indicate spatially uniform growing conditions. This study examined whether air-conditioning energy consumption was associated with microclimate uniformity in a 10-ft container farm. Seven days of temperature, relative-humidity, and actuator-energy data were collected at 10-min intervals using a wireless sensor network deployed at three vertical levels. A total of 27 indoor sensor nodes were installed, but three nodes became non-operational during the observation period, leaving 24 valid nodes for spatial analysis. Spatial uniformity was quantified using vertical thermal and humidity gradients, spatial temperature standard deviation, and temperature range, while three-dimensional inverse distance weighting was used to visualize the spatial distributions. The AC consumed 28.37 kWh, representing 47.8% of the monitored actuator energy. However, the spatial temperature standard deviation remained nearly unchanged between AC OFF and ON conditions at 1.205 and 1.197 °C, while the temperature range increased from 4.539 to 4.680 °C. The vertical thermal gradient was also higher during AC ON periods. Daily AC energy showed negligible correlations with spatial temperature dispersion. These results indicate that greater AC energy consumption was not consistently associated with improved spatial uniformity. Improving air circulation and redistribution should therefore be investigated as a more relevant engineering intervention.
Experimental Comparison of Deployed DQN and Rule-Based Climate Controllers in Indoor Farming: Climate Performance and Electrical Energy Trade-offs Raj Damanik; Samuel Abednego Sormin; Albert Sagala
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 2 No. 1 (2026): September 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v2i1.33

Abstract

Indoor farming requires coordinated environmental control while limiting electrical energy use. This experimental case study compares two deployed controller configurations on the same physical platform using separate 24-hour records: a fixed Deep Q-Network (DQN) policy and a hysteresis rule-based controller. The DQN used an 11-feature state, an 11-64-64-8 neural network, eight discrete actuator combinations, and operational action constraints. The deployed control intervals were 10 min for DQN and 5 min for rule-based control. Performance was evaluated using target-band compliance, midpoint mean absolute error (MAE), excursion magnitude, measured actuator electrical energy, and actuator-status telemetry. Temperature compliance was 79.86% for DQN and 77.37% for rule-based control, with midpoint MAE values of 1.10 and 1.28 °C. During lamp-on periods, CO2 compliance was 45.31% and 18.23%, with MAE values of 119.31 and 221.69 ppm, respectively. Relative-humidity regulation was limited by the absence of active dehumidification. Controller-dependent electrical energy was 4.508 kWh in the DQN window and 3.788 kWh in the rule-based window. CO2 gas consumption could not be quantified because no flow meter was installed. Because the deployments differed in date, outdoor conditions, update interval, and auxiliary operational constraints, the findings are interpreted as configuration-level field observations rather than isolated causal effects of the decision algorithms.
Performance Evaluation of a Self-Hosted Edge-IoT Data Pipeline for Hydroponic pH and EC Monitoring Josh Delon; Samuel P; Albert Sagala
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 2 No. 1 (2026): September 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v2i1.34

Abstract

Many hydroponic Internet-of-Things prototypes display sensor readings but provide limited evidence that each record remains complete and traceable across the full local data path. This paper evaluates a self-hosted edge-IoT pipeline in which an ESP32 publishes pH and electrical conductivity (EC) measurements through Mosquitto MQTT to Telegraf, InfluxDB, and Grafana on a Raspberry Pi 4. Four traceable datasets were used: 80 calibration observations, 2,881 paired pH–EC records over approximately 48 h, three 30-min high-rate storage trials, and 43 broker-to-dashboard matched observations. The pH mean absolute percentage error (MAPE) was 0.775% and 0.386% at pH 4.00 and 7.00; EC MAPE was 2.173% and 0.986% at 1,413 and 12,880 μS cm−1. The 48-h export contained all 2,881 expected one-minute records. The high-rate trials stored 1,018 of 1,020 expected records, corresponding to 99.80% completeness. All 43 broker values were matched in Grafana. Their absolute timestamp-difference proxy had a mean of 345.91 ms and a 95th percentile of 763.6 ms; it is not claimed as true end-to-end latency because Grafana timestamps were exported at one-second resolution. The resultsdemonstrate a compact and auditable local monitoring pipeline while identifying the instrumentation still requiredfor defensible delay and loss attributio.
Evaluation of Gen-AI (LLM) Results on Prompts in the Batak Language Ade Siahaan; Angelina Nadeak; Jeremi Samosir; Jesica Siburian; Boy Hutajulu
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 2 No. 1 (2026): September 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v2i1.45

Abstract

The rapid advancement of Large Language Models (LLMs) hassignificantly enhanced natural language processing capabilities;however, their performance on low-resource regional languagesremains underexplored. The Toba Batak language, a regionallanguage spoken in North Sumatera, Indonesia, faces limitedrepresentation in global AI training datasets, raising concerns aboutlinguistic accuracy and cultural preservation in AI-generatedcontent. This study evaluates the performance of four LargeLanguage Models—ChatGPT, Gemini, Perplexity, and Toba-LLM—in generating responses to prompts written in the Toba Bataklanguage. The evaluation follows the CRISP-DM methodology andemploys a structured dataset consisting of categorized prompts andcorresponding ground truth answers. Model outputs are assessedusing six quantitative metrics: lexical similarity, keyword overlap,length ratio, cosine similarity, cultural relevance, and fluency. Theresults indicate that general-purpose models such as ChatGPT andGemini outperform the local Toba-LLM in terms of semanticsimilarity and fluency, while Toba-LLM demonstrates higher lexicalalignment and response conciseness. However, cultural relevanceremains a challenge across all models, highlighting the difficulty ofcapturing culturally grounded knowledge in low-resource languagesettings. These findings underscore the need for culturally awareevaluation metrics and enriched local language datasets to improvethe applicability of LLMs for regional language preservation and AI-based educational applications.

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