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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 1,127 Documents
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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 (IJCS)
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 (IJCS)
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 (IJCS)
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 (IJCS)
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 (IJCS)
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 (IJCS)
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 (IJCS)
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.
Transformasi Karier Melalui Perancangan Sistem Informasi Bursa Kerja Pada SMKN 2 Padang Panjang Efmi, Efmi Maiyana; Giatman; Nurhasan Syah; Supratman Zakir
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4086

Abstract

Career transformation is becoming increasingly important in this digital era, especially in educational institutions such as Vocational High Schools (SMK). There are still many graduates who have not obtained employment opportunities at SMKN 2 Padang Panjang, which is the main reason for the school to establish a Job Exchange information system. An efficient and integrated labour market information system can help alumni find various career opportunities, understand what the labour market needs, and make better career plans. This study looks at ways to improve the availability of job information, increase alumni engagement in job search, and strengthen links between SMK and industry. This study provides practical guidance for SMKs to adopt and implement an effective job fair information system, using the Research and Development (Rnd) method, so that they can make a real contribution in mentoring alumni. This guidance is provided using an appropriate system design approach.
KLASFIKASI SENTIMEN APLIKASI X TERHADAP GUGATAN PEMILU 2024 MENGGUNAKAN NAÏVE BAYES DAN TEXTBLOB Suharyani Azisa, Nur'Ainun
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4087

Abstract

This study analyzes public sentiment towards the 2024 Election Results Dispute at the Constitutional Court through the X (Twitter) application using the Naïve Bayes and TextBlob methods. The dataset was collected through crawling and preprocessing to remove duplicate data, clean, and normalize the tweets. Labeling was done using TextBlob, followed by sentiment classification using the Naïve Bayes algorithm. The results show that out of 898 tweets analyzed, the TextBlob labeling identified 340 positive tweets, 427 neutral tweets, and 131 negative tweets. Meanwhile, the Naïve Bayes classification resulted in 515 positive tweets, 281 neutral tweets, and 102 negative tweets, demonstrating high accuracy with 95.29%. Data visualization through word clouds and bar charts helped map the sentiment distribution clearly. These findings provide valuable insights into public opinion on the election results dispute, with the majority of sentiments being positive and neutral.
Pengembangan Sistem Informasi Sekolah Berbasis Website Dengan Pengujian Website Vulnerability dan Acceptance Testing Firdaus, Falih Rizqullah
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4088

Abstract

SMA Negeri 1 Lirik terletak di Kecamatan Lirik, Kabupaten Indragiri Hulu, Riau, dan merupakan salah satu lembaga pendidikan tingkat Sekolah Menengah Atas. Sekolah ini dikenal memiliki berbagai pencapaian baik dalam bidang akademik maupun non-akademik, seperti program Tahfidz, musik, dan paskibra. Namun, untuk saat ini SMA Negeri 1 Lirik belum memiliki situs website untuk dipublikasikan karena tidak ada pemeliharaan situs website pada sekolah ini. Oleh karena itu, sistem informasi sekolah dibuat untuk memungkinkan setiap siswa mengakses situs website dengan mudah. Sistem yang akan dibangun menggunakan metode waterfall. PHP digunakan sebagai bahasa pemrograman, MySQL sebagai database, dan Framework codeigniter sebagai software pendukung. Penelitian ini bertujuan untuk dapat membantu SMA Negeri 1 Lirik dalam menyebarkan informasi positif kepada masyarakat dan memudahkan siswa, guru, dan warga sekolah dalam mengakses informasi terkait sekolah. Berdasarkan hasil pengujian sistem informasi sekolah berbasis website yang menggunakan metode Website Vulnerability Testing memiliki 2 peringatan tingkat sedang dan 2 peringatan informasi. Sedangkan Acceptance Testing pada pengujian Blackbox menunjukkan bahwa sistem informasi beroperasi dengan semestinya dan pengujian System Usability Scale (SUS) yang diperoleh dari kuisioner mendapatkan skor sebesar 71,583. Kesimpulan berdasaran hasil pengujian, bahwa sistem informasi berbasis website ini berfungsi dengan baik dan diterima oleh sekolah.

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