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International Journal Of Computer, Network Security and Information System (IJCONSIST)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 26863480     DOI : https://doi.org/10.33005/ijconsist.v3i1
Core Subject : Science,
Computer Science & IT
Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 7 Documents
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Search results for , issue "Vol 5 No 1 (2023): September" : 7 Documents clear
Prediction of Birth Rates Using the Naive Bayes Algorithm in the North Sumatra Region Yudha Kartika, Dhian Satria; putra, brillyan; Wibawa Syahalam, Aji Qolbu
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.109

Abstract

The birth rate constitutes a significant metric within the field of demographics. It serves as a crucial element influencing the strategic planning and development of a region, particularly in provinces characterized by notably large populations, such as North Sumatra. The process of forecasting an optimal birth rate necessitates the involvement of multiple agencies and services to effectively devise policies pertaining to health, education, and enhanced infrastructure for the future. This study employs modeling techniques utilizing the Naive Bayes algorithm. This particular algorithm represents a probabilistic classification method within the realm of data mining, aimed at predicting birth rates across all districts and municipalities in North Sumatra, leveraging demographic and socio-economic datasets commencing from the year 2022. The dataset encompasses variables such as population statistics, demographics of women of reproductive age, levels of educational attainment, accessibility to health services, and incidences of poverty, all of which were sourced from the Central Statistics Agency (BPS) over a five-year timeframe. The research methodology is executed through several phases, including data preprocessing, feature selection, partitioning of training and test datasets, and a validation testing process to affirm the reliability of the proposed model. The dataset is partitioned into training and test components utilizing a distribution ratio of 70:30. The outcomes of the proposed model's testing are computed, employing a confusion matrix to derive metrics such as accuracy, precision, recall, and F1 scores. The results yield an accuracy value of 85%, a precision of 87%, and an F1 score of 86%. These findings indicate a favorable outcome in regional mapping and reflect an appropriate birth rate. Subsequently, the results are visualized within a geographic information system (GIS) to elucidate the spatial patterns of the predicted birth rate, thereby facilitating local government interventions in specific areas.
Optimizing Red Onion TSS (True Shallod Seed) Production in the Lowlands Based on Smart Sensors Moeljani, Ida Retno; Rahajoe, RR Ani Dijah; N, Pangesti
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.115

Abstract

Onion cultivation technology using seeds still needs to be developed and socialized at the farm level. to be socialized at the farmer level, considering that until now farmers still cultivate shallots with consumption seed bulbs because there are still not many TSS produced, especially in the lowlands.In principle, not all shallot varieties are capable of flowering, some shallot varieties are capable of flowering. flowering, some shallot varieties are only 30% capable of flowering. This problem can be solved by optimizing flowering with an automation system. The advancement of Internet of thing (IoT) technology can be applied to optimize flowering by using smart sensors on the onion. flowering by using smart sensors on several varieties of shallots. The lanchor blue variety had no flower bulbs that set fruit and produced TSS seeds. This is because all the flower bulbs of the lanchor blue variety were rotten/damaged by disease due to the use of high watering during the growth period that led to flowering, fertilization, and sprouting. There was no interaction between varieties and application of gibberellic acid + and packlobutrazol on seed yield of TSS (Table4). In Bauji and Maaserati varieties, the percentage of flower bulbs that bear fruit and seed (harvested) is still better than BiruLanchor, only about 59.68 to 70% of the total number of flower bulbs that grow (Table 4). This indicates that the process of fertilization and seed formation of shallots is not optimal.
Design of Industrial Practice and Thesis Monitoring Information System (SIPIKRI) In Informatics Engineering Study Program, State University of Surabaya Alit, Ronggo; Agus Prihanto; Aditya Prapanca
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.116

Abstract

Abstract— Industrial Practice Activities are a place for students to gain experience in the world of work and research experience in the form of a thesis by practicing the theories they have learned in lectures. In addition, it is also a place where students express their ideas, scientific ideas and produce research results and products before they become a Bachelor. The mechanism for appointing supervisors and examiners as well as scheduling Industrial Practice and thesis in the Informatics Engineering study program is carried out by the study program through the administration while the schedule and examination team are informed via messages to each examiner. Previously, there was a system that only solved the problem of industrial practice activities, namely the Web-based Industrial Practice Monitoring Information System, which is a system that can provide information about industrial practice programs online. This system has advantages in terms of the speed of presenting the information produced. In addition, this system is web-based so that it can be accessed at any time. This process takes a long time and good management, so that students get supervisors who are in accordance with their fields of expertise and the right exam schedule so that it does not interfere with the lecture schedule and lecturer schedule, but the same problem arises related to student thesis activities. then a system is needed that can solve problems that arise related to thesis activities. The conclusion that can be drawn in this study is the production of the Industrial Practice and Thesis Monitoring Information System (SIPIKRI) which can help the Informatics Engineering study program, Faculty of Engineering, Surabaya State University to run smoothly. In addition, it is hoped that this information system can assist the work of informatics engineering study program managers in carrying out the administration of these activities. Keywords— Information systems, Industrial Practice and Thesis, Monitoring
Control and Monitoring System of IOT-Based Orchid Culvivation Tunggadewi, Elsyea Adia; Imansyah, Muhammad Hizbullah
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.120

Abstract

Abstract—This study focuses on orchid plants, specifically the Phalaenopsis species, commonly known as the moth orchid, which is popular in Indonesia due to its beauty and stable flower prices. Orchid cultivation requires special attention to environmental factors such as light intensity, temperature, air humidity, and soil moisture. To enhance the efficiency of plant care, this research designs an Internet of Things (IoT)-based control and monitoring system. The system utilizes DHT22 sensors to monitor temperature and air humidity, BH1750 sensors to measure light intensity, and soil moisture sensors to measure soil moisture. The data collected by these sensors are transmitted in real-time to a NodeMCU ESP32 connected to the Firebase platform. Users can monitor and control plant conditions through an Android application linked to Firebase. Testing indicates that the sensors used provide accurate results in monitoring soil moisture. The water pump control system based on soil moisture and air humidity has proven effective, with consistent responses to environmental changes. This system is expected to assist cultivators in improving water usage efficiency and plant condition monitoring. The implementation of IoT technology in orchid cultivation can be an innovative solution to enhance the quality and quantity of ornamental plant production in the future.
Comparison of C4.5 Decision Tree and Naive Bayes Algorithms for Classification of Nutritional Status in Stunting Toddlers Ishak Febrianto; Anggraini Puspita Sari
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.122

Abstract

Stunting is a condition where growth and development of children under 5 years of age is impaired due to chronic malnutrition. Data mining with classification techniques on the nutritional status of stunting toddlers can be performed to help identify toddlers experiencing stunting and provide objective measurements of their nutritional status. There are several classification methods, but this research will compare the performance of the C4.5 decision tree algorithm, which is included in the decision tree approach, and naive Bayes, which uses a probability-based approach of class occurrence in classifying nutritional status of stunting toddlers, with discretization performed in the preprocessing stage. The data used in this research was obtained from Jagir Health Center, Surabaya, in the form of secondary data on toddler nutrition in 2021, totaling 2,801 records. The labeling of stunting or normal in the dataset uses the reference of child anthropometric standards in Indonesia as stated in the Republic of Indonesia Minister of Health Regulation number 2 of 2020. The best method based on the AUC (Area Under the Curve) value was the C4.5 decision tree with a value of 86% (good classification), while naive Bayes achieved 77% (fair classification) using a 70:30 training and testing data ratio.
Nutritional Status; Infants and Toddlers; LightGBM; Posyandu; Nutrition Prediction; Doko Village; Monitoring Application Muhammad Thoriqulhaq; Idhom, Mohammad; Maulida, Kartika
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.136

Abstract

This study aims to implement a nutritional status index for infants and toddlers in Doko Village, Kediri Regency, using the LightGBM algorithm. Child health issues in Indonesia, particularly stunting, are a serious concern due to chronic malnutrition, recurring infections, and insufficient psychological stimulation during early developmental stages. Doko Village was selected as the research location due to significant challenges related to child nutrition in the area. The LightGBM algorithm was chosen for its ability to process large and imbalanced datasets while providing accurate predictions. The data used in this study comes from weight and height measurements of children at the local Posyandu. The main objective of this research is to develop a predictive model that can help healthcare workers identify children at risk of malnutrition, enabling more precise interventions. Additionally, this study developed a web-based application to monitor nutritional status in real-time, which is expected to improve the quality of life for children in Doko Village and nearby areas facing similar challenges.
Machine Learning for Password Strength Classification Using Length and Entropy Tanjung Arswendo Yudha; Reyhan, Muhamad; Mutmainnah, Dianisa; Hakiem, Nashrul
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.139

Abstract

Password security is a critical cybersecurity challenge due to the prevalence of user-generated weak credentials, so automated evaluation methods are needed. This paper develops a Random Forest classification model to predict password strength based on two main features, namely password length and Shannon entropy, trained on a large-scale public dataset. The model achieved a classification accuracy of 91.5% on the test data, where feature importance analysis identified entropy as the most significant predictor. The resulting high-accuracy model is suitable for integration into real-time password strength feedback systems and provides a quantitative basis for formulating stronger security policies.

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