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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,170 Documents
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Design and Analysis of Load Frequency Control for a Two-Area Power System Using Conventional PID and FPID Controllers Win, Khin Thu Zar; Hlaing, May Su; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4066

Abstract

Power system stability is required to maintain a continuous balance between power generation and load demand. Frequency control is also a major function of automatic generation control and one of the most important control problems in power system design and operation. So, a robust control system must be implemented for controlling the actual power and frequency. This paper mainly focuses on the design of a two-area power system with a rated frequency of 50 Hz, which is used in Myanmar, using the fuzzy-PID method to control the load frequency. It also aims to imply from one area to another when demand suddenly increases or decreases. It involves applying mathematical formulas and models to analyze how an automatic generation control works. The plan is first implemented in a single area before being modified to a two-area power system with and without controllers. The output results are simulated with MATLAB/ SIMULINK.
Transfer Learning in Machine Learning: A Review of Methods and Applications Ali, Ali Hamad; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4068

Abstract

Transfer learning has gained significant traction and popularity in the field of machine learning due to its wide range of potential applications. This review article offers a thorough examination of transfer learning techniques and their wide-ranging applications in several fields. This text provides a thorough evaluation of the literature, focusing on important research and the methodology used. Furthermore, a comparative table highlighting transfer learning research across several areas provides valuable insights into the wide range of applications. The inclusion criteria were centred on recent articles published within the past five years that comprehensively examined transfer learning methodologies, applications, frameworks, problems, and future directions. The review articles highlight the widespread use of transfer learning models, the effectiveness of data augmentation strategies, and the capability of transfer learning to tackle issues particular to different domains. Nevertheless, some constraints like as biases in the dataset, difficulties in interpreting the model, and problems with scalability have been recognised. These limitations provide opportunities for future research to focus on creating transfer learning algorithms that are more resilient and easier to read.
Dinamika Opini Publik Indonesia terhadap Krisis Rohingya dalam Perspektif Waktu menggunakan Traditional Machine Learning dan Deep Learning Istiqomah, Relaci Aprilia; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4069

Abstract

The Rohingya are an ethnic minority who currently still face persecution and discrimination in Myanmar, so they have to flee to neighboring countries, such as Indonesia. However, the polemic regarding the issue of the existence of Rohingya refugees in Indonesia still shows that there are differences of opinion between groups who support and oppose it. For this reason, this research aims to determine the dynamics of Indonesian public opinion regarding the Rohingya from 2015-2023 via Twitter, as well as find out the topics that are often discussed each year using LDA. This research compares classification methods using traditional machine learning algorithms (NB, SVM, LR, and DT) and deep learning algorithms (LSTM, GRU, LSTM-GRU, and GRU-LSTM). The research results show that the traditional machine learning algorithm, LR, has the highest accuracy. There has been a change in sentiment from initially being dominated by positive sentiment to negative sentiment which is more dominant in the last five years. The topics that are often discussed for positive sentiment are the support of the Indonesian people for the Rohingya in providing assistance and shelter, while the negative topics are related to concerns about the social, economic, and security impacts that may be caused by the presence of Rohingya refugees.
Quality of Service Pengiriman Data dengan Menggunakan Wireless Sensor Network pada Prototype Greenhouse Budiyarto, Aris; Hadiani, Dini; Ardiyanto, Hery
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4070

Abstract

For agriculture that uses greenhouses, environmental health, especially soil and air, is very important. Therefore, soil and air quality must be managed properly. One way to manage this is by using a wireless sensor network system to facilitate monitoring by greenhouse farmers. This wireless sensor network concept uses NRF24L01+ devices as communication modules and ESP32 as microcontrollers on the master/receiver and 5 nodes/transmitters with each node using DHT22, Soil Moisture Sensor, MH-Z19, MQ-9, and MQ-2 sensors. The focus and purpose of this research is to analyze Quality of Service on wireless sensor network systems with latency/delay test, throughput test, jitter test, packet loss test parameters applied to a greenhouse monitoring system. The final results prove that the performance of the wireless sensor network is very good, so the system can be applied to monitoring environmental quality in greenhouses because the system can work in real-time.
IMPLEMENTASI METODE DEMPSTER SHAFER PADA SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT TROPIS Siregar, Ratu Mutiara
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4071

Abstract

Tropical diseases are various infectious diseases that occur frequently in tropical and subtropical regions. These diseases can be caused by infections from viruses, bacteria, fungi, and parasites, and are usually transmitted through vectors or direct contact. In Indonesia, some common tropical diseases include dengue fever, malaria, elephantiasis, tuberculosis, worm infections, and fungal infections. Understanding tropical diseases is crucial to finding ways to diagnose and treat them. Therefore, one method that can be used in this research is the expert system based on the Dempster-Shafer method. This method can be used to diagnose tropical diseases with high accuracy, thus enabling more effective treatment and prevention. The expert system using the Dempster-Shafer method is designed using symptom data of tropical diseases collected from an expert. The result obtained from this research is a system that functions to solve problems and provide information about diseases along with symptoms experienced by the user. By using a web-based system as access for the public, it becomes easier for them to obtain accurate results and information.
Mengeksplor Dampak Interaksi Siswa dengan ChatGPT terhadap Berpikir Kritis dan Pemecahan Masalah Elsa Sabrina; Yulianti, Rosi; Ambiyar; Rizal, Fahmi
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4073

Abstract

Penelitian ini mengeksplorasi dampak integrasi ChatGPT, sebuah kecerdasan buatan berbasis teks, dalam konteks pendidikan modern. Dengan melibatkan 215 peserta dari berbagai pemangku kepentingan, studi ini menyelidiki pengaruh interaksi dengan ChatGPT terhadap keterampilan kognitif, seperti berpikir kritis dan pemecahan masalah. Hasil menunjukkan variasi persepsi terhadap dampak ChatGPT, dengan beberapa peserta melaporkan peningkatan yang signifikan sementara yang lain merasakan dampaknya terbatas. Kesimpulan menekankan pentingnya penggunaan teknologi kecerdasan buatan secara bertanggung jawab dalam pendidikan, sambil menyadari keterbatasan metodologis dan memperhitungkan perspektif yang beragam dalam mengoptimalkan pengalaman belajar.
ANALISIS MODEL PREDIKSI UNTUK LAYANAN BUS SEKOLAH JAKARTA MENGGUNAKAN PENDEKATAN MACHINE LEARNING Wahyuni, Sri; Passarella, Rossi
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4074

Abstract

This research aims to predict the types of school buses in Jakarta using machine learning methods. Data from 2017 to 2019 includes the number of passengers, number of schools, and bus types. Exploratory data analysis identified patterns and trends, with feature engineering generating three main variables. Seven machine learning models were tested, including SVM, Logistic Regression, KNN, Gaussian Naive Bayes, Decision Tree, AdaBoost, and Gradient Boosting, with a focus on f1-score to handle data imbalance. The evaluation shows that Gradient Boosting has the best performance with the highest accuracy, precision, recall, and f1-score. The results provide insights into the factors that influence school bus types and offer an effective predictive model to support decision-making in school transportation management in Jakarta. Gradient Boosting proved to be the most reliable in predicting school bus types, providing a basis for strategies to improve the safety and efficiency of school transportation.
Transformation of Students' Career Orientation in the Era of Artificial Intelligence: A Systematic Literature Review Yupelmi, Mimi; Ganefri; Giatman, Muhammad; Krismadinata; Syah, Nurhasan
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4078

Abstract

This research revolves around the challenges faced by students in aligning their career orientation with demands and changes, as many traditional jobs are threatened by AI technology. The aim of this study is to identify trends in the transformation of students' career orientation in the era of Artificial Intelligence (AI), map the career challenges for students in the AI era, analyze the skills and competencies required, and assess the role of educational institutions in supporting this career transformation. The research method applied in this study is a systematic literature review. The initial stages involve collecting literature sources from scholarly databases and proceeding with a screening process to select literature relevant to the research focus. Finally, in-depth analysis of selected literature is conducted to identify patterns, trends, and key points related to the research topic. The results of the study describe that the development of AI technology has a significant impact on students' career orientation in higher education. Furthermore, students also face career challenges such as competition with technology, uncertainty about future employment, and skills gaps. To address these challenges, students need to develop technical AI skills, ethical AI understanding, problem-solving abilities, and continuous learning skills to succeed in the AI job market. On the other hand, higher education institutions should play a proactive role in addressing these challenges by developing relevant curricula, organizing training sessions, collaborating with industries, and enhancing AI learning facilities. Further research is suggested to focus on the implementation of these strategies in the specific context of higher education institutions and evaluate their impact on student career preparedness in the evolving AI era.
Sistem Penyiraman Otomatis berbasis Arduino Pada Kebun Tomat Menggunakan Sensor Soil Moisture Dimas Rizky Putra Pratama; Nunu Nugraha Pratama; Muhamad Riyad Ariwibowo
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4079

Abstract

Soil is a plant growing medium that contains soil minerals that can affect the condition of the soil, including soil temperature and moisture. Good soil conditions have moisture values above 50% - 80%. In addition to soil moisture, other factors that affect plant growth, one of which is temperature, the ideal soil temperature for some types of plants is in the range of 20ºC to 35ºC, tomato plants can grow well with a minimum of soil moisture condition above 50%, therefore, testing is needed to determine the condition of the soil, by making an Arduino – based Automatic Watering System Using Soil Moisture Sensor. The test results on the soil moisture sensor show that ideal soil conditions exist at night, with the average of each sensor used is 66,92%, 60,19%, 61,81%, 66,86%. Thus, pH sensors testing obtained an average error value of 3,89%, and a water flow sensor test value with an average value of 7,828 L/min.
Pemantauan dan Deteksi Penyakit Daun Tomat Berbasis IoT dan CNN dengan Aplikasi Android Pancono, Suharyadi; Indroasyoko, Narwikant; Asep Irfan Setiawan
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4083

Abstract

Tomatoes are a high-value commodity in agriculture, so farmers make various efforts to ensure the production of fresh and ready-to-consume tomatoes. However, farmers often face difficulties in monitoring tomato growth because they still use manual methods and have limited knowledge in detecting diseases on tomato leaves. This research offers a solution by utilizing transfer learning and fine-tuning Convolutional Neural Network (CNN) using DenseNet169 architecture, as well as Internet of Things (IoT) technology. The model is implemented in an Android application using TensorFlow on the Flutter platform after being converted to tflite format. The test results show that the accuracy of the model reaches 94%, while the accuracy of the application in detecting tomato leaf diseases reaches 92.80% and has a response time of about 1077.56 ms. In addition, the application can monitor plant conditions in realtime by having a delay of 1,998 ms.

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