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JOIN (Jurnal Online Informatika)
Published by Universitas Islam Negeri Sunan Gunung Djati Bandung
ISSN : 25281682     EISSN : 25279165     DOI : 10.15575/join
Core Subject : Science,
Computer Science & IT
JOIN (Jurnal Online Informatika) is a scientific journal published by the Department of Informatics UIN Sunan Gunung Djati Bandung. This journal contains scientific papers from Academics, Researchers, and Practitioners about research on informatics. JOIN (Jurnal Online Informatika) is published twice a year in June and December. The paper is an original script and has a research base on Informatics.
Arjuna Subject : -
Articles 490 Documents
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Modified Hash to Obtain Random Subset-Tree (MHORST) Using Merkle Tree and Mersenne Twister Ahmad, Faidhil Nugrah Ramadhan; Barmawi, Ari Moesriami
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1471

Abstract

The development of quantum computing triggers new challenges in data security, particularly in addressing attacks that can solve complex mathematical problems on the fly. Several hash-based data security methods have been proposed to deal with this threat, one of them being Hash to Obtain Random Subset-Tree (HORST). However, HORST has drawbacks, such as low security, because it only uses one hash round. The security of HORST is already improved by Hash to Obtain Random Subset and Integer Composition (HORSIC). However, HORSIC’s execution time is significantly increased. The problem of this research is the low-security HORST and the high execution time of HORSIC. This research proposes a new method, Modified Hash to Obtain Random Subset-Tree (MHORST), which aims to improve the security of HORST and reduce the execution time to less than HORSI’s. MHORST uses Merkle tree, SHA-256 hashes, and Mersenne Twister to build public keys and digital signatures. Based on the experiment results, MHORST reduces the signing time by more than 3.3 times compared to HORST. MHORST reduces the verification time by more than 1.1 times HORST and 17 times HORSIC. Although the security level of MHORST decreases slightly compared to HORSIC, this method is still more secure than HORST against signature forgery.
The Application of AI Technology in Vocational High School Curriculum Design Based on Individual Student Skills in Facing the Challenges of the 21st Century Industry Liza Efriyanti; Firdaus Annas
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1516

Abstract

Vocational high schools (SMK) are confronted with the challenge of adapting their curricula to align with the demands of industrial development in the 21st century. An inappropriate curriculum may result in students being inadequately prepared to navigate the demands of the professional world. Consequently, the objective of this research is to optimize the SMK curriculum through the utilization of an AI-based system, thereby enabling students the curriculum to be tailored to the specific skill requirements of individual. The methodology employed is Design-Based Research (DBR), which entails the analysis of student skill data, the design of an adaptive curriculum, and the evaluation of said curriculum through trials. The research comprised several phases, beginning with data collection and student skills analysis and concluding with an evaluation of student satisfaction with the implemented curriculum. The findings indicated that the introduction of an AI-assisted personalized curriculum resulted in an average improvement of 15% in students' practical skills over a six-month period. Furthermore, student satisfaction with the implemented curriculum increased by 25%, from 70% at the outset of implementation to 95% following the introduction of the AI-based system. This research can serve as a reference point for the development of more adaptive and responsive SMK curricula.
K-Means-Based Pseudo-Labeling Technique in Supervised Learning Models for Regional Classification Based on Types of Non-Communicable Diseases Surbakti, Herison; Munandar, Tb Ai
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1609

Abstract

Non-Communicable Diseases (NCDs) pose a critical threat to global public health, with Indonesia experiencing significant challenges due to high mortality rates and uneven regional distribution. In Banten Province, limited access to labeled health data hampers effective, data-driven intervention strategies. This study proposes a semi-supervised learning approach to develop a regional classification model for NCDs. The methodology begins with K-Means clustering applied to data from 254 community health centers (Puskesmas) to generate pseudo-labels. Various cluster configurations (k=2 to 8) were evaluated, with the optimal result being two clusters based on a silhouette score of 0.735. These clusters were then used to create a semi-labeled dataset for supervised learning. Eight classification algorithms—CN2 Rule Inducer, k-Nearest Neighbor (kNN), Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine (SVM), and Decision Tree—were trained and compared. Among them, the Neural Network model achieved the highest performance, with an AUC of 0.999 and an MCC of 0.976, indicating excellent stability and predictive accuracy. The findings validate the effectiveness of semi-supervised learning for health classification tasks when labeled data is scarce. This approach can serve as a valuable decision-support tool for regional health planning and targeted interventions, enhancing the precision and efficiency of public health responses.
Enhanced Agricultural Decision-Making: Machine Learning Approaches for Crop Prediction and Analysis in India Gupta, Sandeep; Hamid, Abu Bakar Abdul; Nyamasvisva, Tadiwa Elisha; Tyagi, Nitin; Jain, Vishal; Mun, Ng Khai; Ather, Danish
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1610

Abstract

This paper addresses the critical aspects of agriculture in the Indian economy and the challenges faced by this sector, including soil quality decline, unpredictable weather, and the need for efficient decision-making. It presents machine learning as a transformative approach for improved agricultural decision-making, enabling enhanced crop prediction and productivity. Machine learning (ML) algorithms are shown to effectively analyze vast datasets to generate predictive models that aid in crop selection optimization, disease outbreak prediction, and market fluctuation anticipation, thus leading to increased yields and profitability. Focusing on crop prediction, the paper discusses models leveraging historical data and advanced algorithms to forecast crop yields. Additionally, the application of machine learning in precision farming, such as optimizing fertilizer application, is explored. The paper uses a mixed-method approach on a dataset encompassing various crops and environmental parameters. In this paper the various techniques such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) algorithms have been employed to demonstrate the utility of ML in the agricultural fields. The KNN at the value of K=4 and SVM with polynomial kernel resulted the accuracy of 0.982 and 0.989 respectively. Whereas DT and RT gave the results in terms of accuracy of 0.987 and 0.970 respectively. Overall, it can be said that all these techniques used in the present work showed the better accuracy for agricultural sustainability.
Random Forest-Based Classification of Greywater Filtration Media for Intelligent Biofiltration Systems Sujadi, Harun; Subandi, Dipa; Nurdiana, Nunu
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1623

Abstract

The increasing volume of domestic wastewater, particularly greywater, has raised the demand for intelligent and adaptive treatment systems to support efficient water reuse. This study aims to develop a classification model for filtration media types (physical, chemical, and biological) based on water quality data using the Random Forest algorithm. Initial labeling was conducted using the K-Means Clustering method on a publicly available dataset simulated as greywater, based on ten key water quality parameters relevant to irrigation and environmental standards. Model evaluation demonstrated excellent classification performance, with a macro F1-score reaching 0.97 and consistent results in both 5-fold and 10-fold cross-validation. These findings indicate that the proposed model can be integrated into an IoT-based biofiltration system as an automated classification logic to support adaptive, efficient, and reusable household wastewater treatment in the context of irrigation.
Hybrid Squeeze-and-Excitation Convolutional Neural Network with Elastic Weight Consolidation for Longitudinal Learning in High-Accuracy Waste Classification Tiwari, Raj Gaurang; Shukla, Vinod Kumar
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1628

Abstract

Waste management has become a global issue. Increased urbanization and per capita consumption have caused unprecedented garbage growth. Sustainability has always been about proper waste management within the ecological framework. Recently, numerous studies have been conducted on automating the identification of waste items. In this study, a Convolutional Neural Network (CNN) model equipped with Squeeze and Excitation (SE) module is proposed based on hybrid squeezing methods for waste item classification. The core aim of this research is to improve the accuracy of classification by highlighting intricate relations between various features encoded within the dataset. Based on extensive tests on a waste dataset, the CNN model with the SE module using hybrid squeezing outperforms all other models. The suggested method's 99.63% accuracy proves its efficacy and robustness. Furthermore, we incorporate Elastic Weight Consolidation (EWC) to enable longitudinal learning, allowing the model to adapt to emerging waste types (e.g., e-waste, biodegradable materials) while retaining prior knowledge with minimal forgetting (<1%). Ablation studies validate the critical role of hybrid squeezing, showing a 1.5% accuracy drop when spatial-wise components are omitted. This revelation affects automated recycling, waste sorting, and intelligent waste management. The proposed technology's accuracy shows its applicability and dependability, advancing sustainable waste management. By automating waste classification with unprecedented precision, the proposed framework can reduce landfill reliance, enhance recycling rates, and inform policy decisions for sustainable urban planning.
Cyberbullying Detection in the Libyan Dialect Using Convolutional Neural Networks M. Elgoud, Sara; Abuzaraida, Mustafa Ali; S. Attarbashi, Zainab; Saip, Mohamed Ali
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1631

Abstract

ecently, the widespread use of social media has increased, leading to increased concerns about cyberbullying. It has become imperative to intensify efforts and methods to detect and manage cyberbullying through social media. Arabic has recently received increasing attention to improve the classification of Arabic texts. Given the multitude of Arabic dialects used on social media platforms by Arabic speakers to express their opinions and communicate with each other, applying this approach to Arabic becomes extremely challenging due to its structural and morphological complexity. Analyzing Arabic dialects using Natural Language Processing (NLP) tools can be more challenging than Standard Arabic. In this paper, the impact of using stopword removal and derivation techniques on detecting cyberbullying in the Libyan dialect was presented. The efficiency of text classification was compared when using a Libyan dialect word list alongside pre-generated Modern Standard Arabic (MSA) lists. The texts were classified using Convolutional Neural Network (CNN) classifiers, and the experiments showed that when using Libyan dialect words, the accuracy results were 92% and 83%, and when using only Standard Arabic stop words, the accuracy results were dropped to 91% and 77%. Based on these results, the higher accuracy was obtained when using the presented stop words list which it is specific to the Libyan dialect, and they had a positive impact on the results, better than Standard Arabic stop words.
A Trust-Based Reputation System for Security in the Internet of Vehicles (IoV) Nozha, Dhibi; Makhlouf, Amel Meddeb; Zerai, Faouzi
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1632

Abstract

The Internet of Vehicles (IoV) integrates with different nodes, like for example connected vehicles, roadside units, etc. Due to communication exchange, they are exposed to various attacks on the network, which poses a security risk. Nevertheless, security is a major concern in IoV networks, especially during data transmission. To address this issue, our team suggest an innovative approach. reputation management schema in an IoV environment to detect attacks at an early stage based on vehicle and driver behavior along with network state. Our algorithm combines direct and indirect trust with various metrics like Packet Lost Rate (PLR), vehicle speed distance between neighbors, alert content, and link quality. These metrics are used to compute a reputation score to identify malicious nodes. Based on its reputation, vehicles communicate with only trusted nodes. After assessment, we see that our solution surpassed the others solution and has demonstrated superior effectiveness in detecting abnormal vehicles. Furthermore, the computed delay, equal to 4.7 ms, does not affect the network communications, which is interesting for the introduced safety features.
A Data Science Approach to Exploring the Relationship Between TikTok Engagement and Revenue in Malaysia: A Case Study of the Beauty and Personal Care Sector Ahmad Asmawi, Muhammad Akmal Hakim; Isawasan, Pradeep; K.S. , Savita; Shamugam, Lalitha; Ahmad Salleh, Khairulliza
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1633

Abstract

TikTok has reshaped digital marketing in the beauty and personal care sector, yet the relationship between engagement metrics and revenue outcomes remains unclear. This study aims to examine how public engagement metrics (likes, comments, shares, and live interactions) relate to revenue performance among TikTok influencers. Using the Data Science Trajectories (DST) framework, data from 17 Malaysian influencers across Celebrity, Macro, Meso, and Micro categories were analyzed through descriptive statistics and machine learning models implemented in Python. The findings reveal that high engagement does not consistently lead to higher revenue. Live sessions were more effective than standard videos in driving sales due to real-time interaction. While Celebrity influencers led in revenue, Meso influencers recorded the highest engagement rates. A Random Forest regression model showed strong predictive power (R² = 0.94), demonstrating that public-facing metrics can be used to estimate revenue. The study also introduces category-based engagement rate benchmarks and highlights the unique value of live content in converting engagement into sales. This research contributes to the growing body of work on TikTok marketing by combining statistical and predictive techniques to link engagement behavior with commercial outcomes, offering actionable insights for both practitioners and scholars.
Blockchain-Enabled Secure Healthcare Data Management with Modified Gazelle Optimization and DLT-Trained RNN-BILSTM Approach Saxena, Rolly; D, Srinivasa Rao
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1638

Abstract

The growth of the healthcare system has posed challenges in safeguarding patient privacy amidst the storage, distribution and management of medical data. Blockchain (BC) offers a promising result by securely enabling the exchange of medical information. Utilizing block chain technology ensures the security of individuals' confidential health information.  The use of a decentralized, immutable ledger using blockchain technology provides a secure, impenetrable platform for storing and retrieving private medical information, protecting patient privacy.   The application of Modified Gazelle Optimization enables the determination of the shortest path for efficient data transfers within the block chain network. By adopting a specialized routing protocol called Modified Gazelle Optimized Routing, this approach minimizes latency and maximizes throughput, facilitating continuous and expedited transfer of health data across the network. To assure the data confidentiality and integrity of network nodes, a Distributed Ledger Technology (DLT) trained Recurrent Neural Network with Bidirectional Long Short Term Memory (RNN-BILSTM) approach is implemented. This advanced Deep Learning (DL) technique enhances the security and reliability of the network by detecting and preventing unauthorized access and tampering attempts. The proposed RNN-BILSTM based Intrusion Detection System (IDS) efficiently detects different types of attacks with high accuracy. By analyzing network traffic and patterns in real-time, the IDS have the ability to identify and mitigate harmful Internet of Things (IoT) requests and various stealthy attack types, including previously unknown threats. The outcomes of this research prove an efficacy and consistency of the proposed strategy in enhancing the security, efficiency and performance matrix with an accuracy of 97% and comparative analysis is done with traditional methods, thereby ensuring an availability and integrity of healthcare data.

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