Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)

Design of Artificial Immune System - Models and Algorithms

2025-08-11T00:54:17+00:00

Artificial Immune Systems (AIS) belong to a group of computational intelligence methods inspired by the working mechanisms of biological immune systems to solve various computational problems. Artificial Neural Networks (ANNs) themselves are often used in various fields such as anomaly detection, pattern recognition, cyber and network security, task scheduling, process optimization, and data analysis, with the application of various ANN algorithms. In the AIS approach, there are four basic algorithms that serve as the main foundation, namely the Negative Selection Algorithm (NSA), Artificial Immune Networks (aiNet), Clonal Selection Algorithm (CLONALG), and Dendritic Cell Algorithm (DCA). The problem that occurs at this time is that there is still a lack of papers that discuss the main basic algorithms in AIS, resulting in difficulties in developing new models of basic algorithms. Apart from that, many other aspects of the natural immune system have not been touched due to not yet understanding the basic algorithm of AIS. This paper aims to explain the main models and algorithms in AIS above so that in future research, new algorithms can be developed based on the basic algorithm as a reference. The results of this paper are a review of the main basic models and algorithms in AIS.

Information Gain Feature Selection for Temporal Sentiment Analysis of Pedulilindungi Application Review using Naïve Bayes Classifier Algorithm

2025-08-01T01:37:56+00:00

Through the Instruction of the Minister of Home Affairs of the Republic of Indonesia Number 38 of 2021 concerning the Implementation of Restrictions on Community Activities (PPKM), all communities are required to use the Pedulilindungi application from August 31, 2021, to September 6, 2021, and updated regularly. Users can download and access the Pedulilindungi application through the Google Play Store application market. There, users can directly assess an application by providing reviews that can describe user responses and satisfaction with the application. The Naïve Bayes Classifier (NBC) algorithm is applied to perform modeling in classifying temporal sentiment analysis data. Prior to classification, a feature selection process with information gain is performed. Based on the experimental results, the best evaluation was produced on temporal data dated September 03, 2021, with an accuracy of 91.9% and precision and recall values of 99.9% and 91.9%, respectively.

Rancang Bangun Alat Monitoring Tanaman Hidroponik dengan Pemanfaatan Pupuk Cair Limbah Sampah Berbasis IoT

2025-08-11T00:51:42+00:00

Indonesia sebagai negara agraris menghadapi tantangan keterbatasan lahan pertanian akibat urbanisasi yang meningkat. Sistem hidroponik menjadi media untuk mengatasi permasalahan tersebut, seperti tanaman selada (Lactuca sativa). Mayoritas petani masih menggunakan cara manual dalam penanaman tanaman sehingga tidak konsisten untuk memantau tanaman. Penelitian ini bertujuan untuk mengembangkan alat monitoring yang diteliti sebelumnya. Pengembangan yang dilakukan pada alat ini dengan menambahkan fitur kamera berbasis Internet of Things. Metode yang dilakukan meliputi pembuatan pupuk cair melalui fermentasi limbah rumah tangga dengan cairan EM4 dan molase, serta perancangan sistem berbasis sensor ESP32 yang terhubung dengan sensor pH, sensor TDS, sensor DHT11, dan modul kamera ESP32-CAM. Data sensor ditampilkan pada LCD dan dikirim secara real-time ke aplikasi Blynk. Hasil dari penelitian menunjukkan bahwa sistem mampu bekerja dengan baik dalam monitoring tanaman dengan koneksi WiFi sensor ESP32 terhubung hingga jarak 10 meter, nilai sensor pH memiliki rata-rata error 4.6% sebelum pemupukan dan 5% sesudah pemupukan, sedangkan sensor TDS memiliki rata-rata error 6% sebelum pemupukan dan 10.8% sesudah pemupukan. Sensor DHT11 mencatat suhu lingkungan antara 27–31°C dan kelembapan 67–71%.