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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 13 Documents
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Search results for , issue "Vol. 7 No. 3 (2024)" : 13 Documents clear
Web Service Integration: Data Exchange among Area Sampling Framework, Paddy Sampling, and CAPI Cropping Systems Finmansyah Akbar, Edo; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.411

Abstract

Statistics Indonesia (BPS) is responsible for providing agriculture data. BPS collects statistics on paddy paddy production by performing a survey that involves sampling paddy plots using the Area Sampling Framework (ASF). The ASF survey is conducted monthly. The ASF System receives information from the Paddy Commodity Cropping Sampling System to prepare the sample frame and withdraw samples. This is done by the Sub-Directorate of Sample Frame Development (PKS Sub-Directorate). The existing system requires human processing of ASF results to modify the paddy observation code. This processing is carried out by the Sub-Directorate of Food Crops and the data is prepared by the Sub-Directorate of Sample Frame Development (PKS) before being uploaded into the Paddy Commodity Cropping Sampling System. The findings of sample retrieval by the Paddy Commodity Cropping Sampling System will be transmitted to the Sub-Directorate of Data Processing Integration (Sub-Directorate of IPD) and thereafter uploaded into the CAPI System for Paddy Cropping. The PKS Sub Directorate has identified many processes in the existing system that are deemed to be less efficient. The current inefficiency of the business process is caused by the manual execution of various tasks in the ASF system, such as sending data via email, modifying the paddy observation code, and sending the modified code results. Additionally, the data preparation process relies on additional applications, and sample documents from the Paddy Crop Sampling System are manually sent to the CAPI Cropping Sampling System. Hence, there is a requirement for enhancing the process flow of paddy harvesting sample. The lack of integration across systems necessitates manual execution of the process. This research proposes enhancing the Paddy Commodity Crop Sampling System by introducing new functionalities for modifying the paddy observation code and data preparation. Additionally, it suggests utilizing web services to integrate the ASF System, Paddy Commodity Crop Sampling System, and CAPI Cropping System
Image Classification of Seasoning Package Completeness in Noodle Products Using WEKA Analysis: Klasifikasi Citra Kelengkapan Paket Bumbu Pada Produk Mie Menggunakan Analisis WEKA Rendi Priyatna
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.425

Abstract

This research develops an intelligent system that utilizes vision camera technology to detect completeness of noodle packages consisting of noodle blocks, oil, and seasoning. Multi-Layer Perceptron (MLP) and Naïve Bayes are used to classify images in recognizing the shape and color of the seasoning that should be present in noodle packages using Weka. The system's input is the captured data of noodle package completeness taken in real-time with randomly positioned oil and seasoning. A total of 486 random data points were used, with 70% for training and 30% for testing. The testing results show that MLP outperforms Naïve Bayes in almost all evaluation metrics, with an accuracy of 98.48% for MLP, compared to 74.32% for Naïve Bayes. In terms of construction time, Naïve Bayes is superior with a construction time of 0.01 seconds
Sentiment Analysis of Hate Speech Against Presidential Candidates of the Republic of Indonesia in the 2024 Election Using BERT Amalia, Fahriza Rizky; Nisa Hanum Harani; Cahyo Prianto
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.432

Abstract

The issue of hate speech on social media has become a matter of growing concern, particularly in the context of political discourse, as evidenced by the 2024 elections in Indonesia. Online platforms such as YouTube represent a primary medium for political discourse, frequently accompanied by negative or hateful commentary directed towards presidential candidates. The objective of this study is to analyze the sentiment of YouTube comments related to Indonesian presidential candidates in the 2024 General Election using the BERT algorithm. The data was obtained through scraping using the YouTube API and subsequently categorized into three distinct categories of hate speech: The categories of hate speech are as follows: OFP (offensive personal), OFG (offensive group), and OFO (offensive others). The CRISP-DM method was employed in this research, which included the following stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results demonstrate that the BERT algorithm is capable of classifying comments with a satisfactory level of accuracy. This algorithm can be utilized to develop predictive applications that assist in identifying and managing hate speech on social media.
Hybrid Model of Artificial Neural Networks and Flower Pollination Algorithm for Stock Price Prediction Farhatuaini, Lia; Kurniawan, Heru Purnomo; Muslihah, Isnawati
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.433

Abstract

Predicting the future behavior of the stock market is a difficult task due to its complex and ever-changing nature. This study focuses on predicting BBRI stock prices using an Artificial Neural Network (ANN) improved with the Flower Pollination Algorithm (FPA). We found that the model works well with a 9-100-1 setup, achieving accurate predictions with a Root Mean Square Error (RMSE) of 0.127579154. While FPA effectively reduces errors in the initial 10 iterations, it faces challenges in further improvement, especially in responding to sudden changes in stock prices. Despite performing well overall, the model tends to have a wider margin during unexpected market shifts, indicating a need for additional fine-tuning. This research provides valuable insights into stock price prediction, highlighting the importance of refining models to handle unexpected market changes.
Implementation of Naïve Bayes Algorithm to Predict Food Crop Production Results in Garut Regency Oktapiani, Vini; Agustin, Yoga Handoko
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.455

Abstract

The ups and downs of food crop production each year are caused by changes in the area of land planted each year. These changes are influenced by several factors, including crop rotation, government policies, changes in agricultural practices, environmental factors such as climate, and economic pressures. In an effort to improve the efficiency and productivity of food crop production in Garut Regency, the use of technology and data analysis methods is becoming increasingly important. This research aims to predict food crop production in Garut Regency with Naïve Bayes algorithm and evaluate influential factors. This modeling is analyzed using Feature Forward selection and SMOTE techniques to determine the most influential attributes and overcome class imbalance. The method used is Cross-Industry Standard Process For Data Mining (CRISP-DM). Where the use of SMOTE successfully handles unbalanced classes, and the application of Feature selection results in the 5 most influential factors, namely crop type, added planting, realized harvest area, realized production and production. The results showed that the Naive Bayes model with Cross validation and Xgboost resulted in an Accuracy value of 82.54%, Recall value of 81.67%, Precision value of 83.34%. And the AUC value is 0.904% with the Good Classification category.
The Digital Transformation of Land and Building Tax Payments with Predictive Systems Wibisono, Bagas; Rohman, Abdul
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.459

Abstract

Digital transformation in the tax payment sector is an essential step to enhance transparency and accountability. This research aims to develop and implement a predictive information system that can predict the payment behavior of Land and Building Tax in Village. The main problem faced is the often slow and non-transparent payment process, as well as the risk of errors or payment defaults. The developed information system uses historical data analysis methods with predictive algorithms to project the future behavior of taxpayers. This system is also equipped with a real-time validation feature to improve accuracy and efficiency in managing payment data. The results show that the predictive information system can provide early warnings related to payment delays and reduce potential errors in recording Land and Building Tax payments. In conclusion, the application of predictive technology in the Land and Building Tax payment system in Village successfully improves transparency, accountability, and efficiency, providing an innovative solution for village tax administration issues.
Design of Monitoring and Fire Extinguishing System Based on the Internet of Things in Unmanned Vehicles Agustin, Sarah; Muhammad Suranegara, Galura; Setyowati, Endah
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.461

Abstract

Currently, people are often unaware of the existence of hotspots or gas leaks in the house which can trigger large fires in the house. Therefore, this research focuses on designing a fire monitoring and extinguishing system that can detect early IoT-based fires using the SIM800L GSM module on unmanned vehicles to detect fires and immediately extinguish them, accompanied by sending emergency messages via the SMS application on smartphones. As a warning, a fire has been detected in the house so that people can be more alert. In addition, this system is designed so that the vehicle can be controlled remotely using the HC-06 Bluetooth module and Bluetooth RC Car. This early fire detection process will be tested 30 times, each sample will be tested for the time speed obtained in detecting and extinguishing a fire. Thus, this system has the potential to prevent fires which can cause major losses.
Expert System For Identifying Diseases In Native Chickens Using The Certainty Factor Method Arifin, Abdullah; Novita, Rice; Permana, Inggih; M. Afdal
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.465

Abstract

Farming is the business of breeding and raising animals, divided into two groups: large animals (cows, buffaloes, horses) and small animals (chickens, ducks, birds). The demand for livestock, especially poultry like free-range chickens, is on the rise. However, many small to medium-sized free-range chicken farms still rely on conventional methods for disease treatment, which depend on the experience of the farmers. An expert system is a piece of computer software that mimics the choices and behaviors of a person or group with in-depth knowledge and expertise in a certain field. The objective of this study is to enhance the effectiveness of disease treatment for free-range chickens and streamline the diagnosis procedure. Farmers can determine which diseases are harming their free-range hens by using the Certainty Factor approach. Experts were surveyed to provide the data used in this study. Accurate diagnosis of diseases in free-range chickens and suitable treatment recommendations are provided by the system's diagnostic results.
Decision Support System Decision Support System (DSS) For Determining Scholarships Using The MOORA Method M Yogi; Saputra, Eki; Fronita, Mona; Muhammad Lutfi Hamzah
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.468

Abstract

A decision support system is a system that uses computer technology to assist decision making. Every year, Baznas Riau Province conducts a selection process for scholarship acceptance. Scholarships are financial assistance for individuals, both students and students, for educational purposes. In the scholarship selection process, there are several criteria and sub-criteria, including KTP, educational status (not yet a graduate), GPA, parents' income, and housing conditions. The existence of quotas and specific criteria that must be met makes it difficult for Baznas Riau Province to select prospective scholarship recipients. This research implements the MOORA method, which was chosen for its simplicity, effectiveness, stability, and ability to produce accurate judgments in decision-making when compared to other methods. This method is simple and practical to use, and optimizes various objectives based on ratio analysis by giving values or weights to each predetermined criterion. After testing using the MOORA method, results were obtained from 13 students tested, where the highest value was obtained by A12 (Maya Sari) with a value of 0.2454 and the lowest value by A11 (Rizka Syaputri) with a value of 0.1987. The final result of this research produces a decision support system to determine scholarship recipients at Baznas Riau Province, which is expected to help Baznas in the scholarship selection process and increase efficiency in its implementation.
Adaptive Nutrient Management for Vegetable Cultivation: A Fuzzy Rule-Based Approach Pohan, Sry Dhina; Arwani, Muhammad
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.471

Abstract

The availability of foodstuffs, especially vegetables in Indonesia, is highly dependent on seasonal changes, making it necessary to implement precision agriculture to improve the efficiency of vegetable cultivation. The accuracy in fulfilling plant nutrient requirements is a key factor in the effectiveness of vegetable cultivation, hence a nutrient solution irrigation control system is essential. The main challenge in developing such a control system is the variation in the duration of nutrient solution irrigation, which is highly dependent on soil fertility levels and the environmental conditions of the vegetable cultivation area. This research proposes a fuzzy rule-based algorithm to determine irrigation duration based on temperature, air humidity, soil moisture, and light intensity. The fuzzy algorithm is implemented in the nutrient solution irrigation control system through a wireless sensor network (WSN). This research resulted in the design of an application for the nutrient solution irrigation control system in vegetable plant growth, capable of determining irrigation duration accurately and clearly with the implementation of the fuzzy rule-based algorithm, resulting in an irrigation duration of 48 seconds/500ml categorized as long for nutrient solution irrigation. The fuzzy rule-based algorithm was tested using Mean Square Error (MAPE) based on the irrigation duration results, yielding an error percentage of 0.25%, which is considered highly accurate in conducting nutrient solution irrigation for vegetable plants. This automated control system has the potential to increase vegetable crop productivity by minimizing fertilizer and water wastage.

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