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SMARTICS Journal
Published by Universitas Kanjuruhan Malang
ISSN : -     EISSN : 24769754     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
SMARTICS Journal's aims is to disseminate research on applied computer science or information technology by publishing the original articles. The scope of SMARTICS are electrical, electronics, controls, information system, and applied technology.
Arjuna Subject : -
Articles 127 Documents
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Pengembangan Chatbot untuk Layanan Konsultasi Hukum dengan OpenAI GPT-4 Purnama, Arif Pria; Akbar, Mutaqin
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13817

Abstract

The development of information and communication technology has encouraged the use of artificial intelligence in various fields, including legal consultation services. In Indonesia, legal consultation services still face several challenges, such as a limited number of legal experts, relatively high consultation costs, and unequal access, making it difficult for the public to obtain basic legal understanding. This study aims to develop an intelligent chatbot for legal consultation services using the OpenAI GPT-4 Application Programming Interface (API). The research methodology includes problem scope determination, user needs analysis, legal data collection, data exploration and pre-processing using Natural Language Processing (NLP) techniques, GPT-4-based chatbot modeling, and system evaluation. The evaluation focuses on measuring the chatbot’s response time in delivering legal information to users. The results indicate that the chatbot developed using the OpenAI GPT-4 API is able to provide basic legal information quickly and interactively. However, human supervision is still required to ensure the accuracy and reliability of the legal information provided.
Prediksi Tingkat Keamanan Terhadap Pencurian Menggunakan Naive Bayes di Wilayah Sektor Kepolisian Merapi Barat Rahmi, Sutria; Permatasari, Indah; Purnamasari, Evi
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13919

Abstract

Theft remains one of the most prevalent forms of criminal activity in the jurisdiction of the West Merapi Police Sector, significantly impacting public safety and community security. Historically, the handling and securing of this region has been reactive in nature, lacking a predictive system capable of estimating theft risks preventively. This study aims to develop a predictive model for regional security levels related to theft cases using a machine learning approach. The data utilized in this research comprises secondary data obtained from 459 theft case reports documented by the West Merapi Police Sector from 2021 to 2024. Ten relevant variables were selected as features, while three security level categories (Low, Medium, and High) served as target classes. Data preprocessing included data cleaning, variable transformation, and label encoding. The Naive Bayes algorithm was employed with a 70% training data and 30% testing data split. The results demonstrated that the Naive Bayes method achieved an accuracy of 76.09% in predicting regional security levels. The model exhibited optimal performance for the High security level class, while the Low class showed lower performance due to imbalanced data distribution. This research demonstrates that police case report data can be effectively utilized to support data-driven risk analysis and has the potential to serve as a decision-making tool for preventive measures by law enforcement agencies.
Analisis Struktur Tulisan Tangan melalui Deteksi Zona Spasi Antarkata Menggunakan CNN Mendalam Mufti, Nabilah; Heriansyah, Rudi; Irfani, Muhammad Haviz
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13937

Abstract

Advances in image processing and deep learning technology enable more accurate handwriting analysis, including the detection of interword spacing, which exhibits high complexity due to variations in writing styles. This study aims to implement a Convolutional Neural Network (CNN) algorithm using the You Only Look Once version 11 (YOLOv11) framework to detect and classify interword spacing zones into three classes: Narrow Word Spacing (NWS), Medium Word Spacing (MWS), and Wide Word Spacing (WWS). The dataset comprises 150 handwritten images with a total of 4.117 annotated interword spacing objects. The research methodology involves testing the model across variations of learning rates (0.1, 0.01, 0.001, and 0.0001) and data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using Precision, Recall, F1-Score, and mean Average Precision (mAP) metrics. Based on 12 experimental trials, the best configuration was achieved with a learning rate of 0.001 and a 90:10 data split. This configuration produced an mAP@50 of 0.455, an mAP@50–95 of 0.261, and an F1-Score of 0.49. These results indicate that the YOLOv11 model is capable of detecting interword spacing zones with reasonably good performance, despite remaining classification errors due to visual similarities between classes.
Efektivitas Parsons Problem 2D terhadap Pemahaman Logika dan Struktur Kontrol pada Pembelajaran Pemrograman Berorientasi Objek Larasati Amalia, Eka; Wibowo, Dimas Wahyu; Bahroin Gana, Muhamad Saifulloh; Damayanti, Retno
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13994

Abstract

Students’ understanding of logic and control structures in object-oriented programming remains a major challenge in programming education. Based on a survey conducted by the researcher involving 30 third-semester students of the Informatics Engineering Study Program from the 2024 cohort, 69,7% of students stated that they experienced difficulties in understanding OOP materials. These difficulties are generally caused by the complexity of syntax and students’ limited focus on program logic flow. Therefore, appropriate learning media are needed to help students understand programming concepts in a more structured manner. This study aims to evaluate the effectiveness of the 2D Parsons Problem feature in improving students’ understanding of logic and control structures in object-oriented programming. This study employed a quantitative approach using a one-group pretest–posttest design. The research participants consisted of 42 students who had completed the Object-Oriented Programming course. Data were collected through pretests and posttests and analyzed using the Normalized Gain (N-Gain) method to measure improvements in students’ understanding. The results showed an increase in the average score from 57,9 in the pretest to 89,5 in the posttest, with an average N-Gain value of 0.715, which falls into the high improvement category. A total of 28 students were categorized as having high improvement, 12 as moderate improvement, and 2 as low improvement. These findings indicate that the 2D Parsons Problem feature is effective in enhancing students’ understanding of logic and control structures in object-oriented programming.
Analisa Perbandingan Kinerja Kontrol PID dan Fuzzy Logic Pada Sistem Load Frequency Control di Steam Power Plant Prabowo, Yuliyanto Agung; Rizky, Pingky Tri Nur; Pambudi, Wahyu Setyo; Waskito, Lugas Jabbar
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13995

Abstract

Load Frequency Control (LFC) plays an essential role in maintaining frequency and power stability in power generation systems under load disturbances. Variations in load demand, such as increases or decreases, may lead to power deviations that affect system reliability; therefore, an effective control strategy is required. This study evaluates the performance of an LFC system by comparing a Proportional–Integral–Derivative (PID) controller and a Fuzzy Logic Controller (FLC). The system is modeled based on the dynamic characteristics of the governor, turbine, and generator of a coal-fired steam power plant. System performance is analyzed under three operating conditions: no disturbance, load increase, and load decrease. The evaluation focuses on maximum power deviation and settling time as performance indicators. Simulation results show that both controllers are able to maintain system stability but with different dynamic characteristics. Under no-disturbance conditions, the PID controller reaches stability faster with a settling time of 18.126 s, while the FLC requires 28.038 s. During load increases, the PID-controlled system stabilizes at 194.652 s, whereas the FLC reaches stability at 200.524 s. For load reductions, the PID controller stabilizes at 194.378 s and the FLC at 196.559 s. In terms of maximum power deviation, the FLC produces a smaller deviation (0.41%) compared to the PID controller (0.47%). These results indicate that the PID controller provides faster recovery, while the FLC offers smoother responses with smaller deviations, showing potential for improving LFC performance.
Implementasi K-Means Clustering Kelompok Provinsi Penghasil Bawang Merah di Indonesia Tahun 2023 Ibrahim, Anton; Fauzi, Yan Akhmad
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

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

Abstract

Shallots are a strategic horticultural commodity that plays a vital role in food security and price stabilization in Indonesia. However, inter-provincial production data is still in the raw form, making it difficult to identify the distribution patterns of high, medium, and low-production areas. This study aims to implement the K-Means Clustering algorithm to group 38 provinces in Indonesia based on 2023 shallot production and productivity data sourced from the Central Statistics Agency (BPS). The method used is a data mining approach through the stages of data selection, preprocessing (Min-Max normalization), data transformation, determining the number of clusters using the Elbow method, and evaluation using the Silhouette Score with the assistance of Orange Data Mining software. The results show the formation of three clusters: a low-medium production cluster dominated by most provinces outside the main centers, a high production cluster consisting of Central Java and East Java as national centers, and a very high production cluster encompassing several provinces such as West Java and West Nusa Tenggara. The results of this grouping provide an overview of the inequality in production distribution between regions and can be the basis for formulating development policies, production equality, and shallot distribution planning in a more targeted and data-based manner
Performance Evaluation of Agentic Workflow-Driven Trend-Aware Rule Mining for Dynamic Menu Bundling Triyono, Andri Triyono; Santoso, Kartika Imam; Al Haq, Rohman Hadi
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.14079

Abstract

Digital transformation in the culinary industry currently demands moving beyond writing static lines of code, instead acting as an AI orchestrator adaptive to real-world conditions. This research focuses on addressing significant challenges in traditional data mining methods, such as the Apriori and FP-Growth algorithms, which often lack the flexibility to handle dynamic variables like ambient temperature fluctuations.Through the innovative orchestration of the Trend-Aware Rule Mining (TARM) algorithm and a LangGraphbased Agentic Workflow, this study transforms raw association rules into strategic business decisions via an iterative reasoning process and self-correction mechanism. Experimental results on a dataset of 52,494 rows demonstrate TARM's computational superiority, with memory usage of only 8.04 MB , significantly more efficient than Apriori's 127.44 MB. Furthermore, the synergy between the Strategy Agent and Evaluator Agent achieved a logic consistency score of 100% , validated by an independent audit with an average score of 96.25%.These findings confirm that the developed system is in a ready-to-use state to support precise and adaptive decision-making automation in production environments.

Page 13 of 13 | Total Record : 127

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