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Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id
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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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Adoption of Artificial Intelligence and Digital Resources among Academicians of Islamic Higher Education Institutions in Indonesia Suwendi, Suwendi; Mesraini; Bakti Gama, Cipta; Rahman, Hadi; Luhuringbudi, Teguh; Masrom, Maslin
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1549

Abstract

This study aimed to assess the readiness, attitudes, knowledge, and skills of lecturers in using artificial intelligence (AI) and electronic resources (ER) to enhance academic capacity. Understanding this adoption level is crucial for effectively integrating AI and ER into educational practices. In addition, this study contributes both theoretically and practically to digital scholarship by enhancing digital adoption and competence in education. This mixed-method study captured individual experiences and statistical trends related to digital scholarship in higher education. The qualitative method includes interviews, while the quantitative method involves survey questionnaires. The study focuses on lecturers from Islamic higher education institutions (IHEIs) in Indonesia. The results indicate that while lecturers rarely use AI and ES, they recognize the potential of digital technology in academic tasks. Despite limited exposure to AI and ER, IHEI lecturers in Indonesia can define these technologies accurately. Most lecturers actively update their knowledge and consider bias and ethical aspects in AI and ES usage. Regarding skills, over 60% of respondents reported proficiency in using AI and ES, suggesting a growing level of digital competence. These findings suggest that while many IHEI lecturers in Indonesia are prepared to adopt AI and ER, further support may be needed to ensure widespread acceptance.
Pyramid Quantum Neural Network Based Resource Allocation with IoT: A Deep Learning Method Singh, Khushwant; Yadav, Mohit; Kirti; Kumar, Sunil; Sobirov, Bobur
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1578

Abstract

As more smart devices are connected and collecting massive quantities of data, the Internet of Things is growing rapidly. Resource management is another crucial issue since IoT networks are very diverse and often built and rebuilt dynamically. This study introduces a new kind of deep learning model known as the Pyramid Quantum Neural Network (PY-QNN) to solve the problem of resource allocation in Internet of Things systems. PY-QNN builds on quantum computing to improve the accuracy, scalability, and computation performance of Deep Learning. Because of superposition and entanglement, which increase generalization and provide faster convergence, QNNs enhance learning capabilities. The pyramid structure also helps manage the hierarchy of IoT networks. In order to forecast efficient resource assignment and implement this as soon as feasible to lower latency and boost efficiency, PY-QNN uses simulated resource and network requirements. Experimental findings demonstrate that PY-QNN outperforms baseline common deep learning techniques by reducing resource waste and offering online solutions, especially in large and complex IoT networks.
Anatomy of Sentiment Analysis in Ontological, Epistemological, and Axiological Perspectives Fadli Hidayat, M. Noer; Dwi Prasetya, Didik; Widiyaningtyas, Triyanna; Patmanthara, Syaad
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1228

Abstract

The aim of this article was to examine sentiment analysis methods from the perspective of the philosophy of science with three approaches, ontological, epistemological and axiological. This research used a qualitative research method (descriptive-analysis) with an ontological, epistemological and axiological approach that uses library research and document studies of previous research results. Data collection was carried out through books and reputable scientific journals on Scopus, ScienceDirect, IEEEXplore and Springer Link. The results of this research showed that sentiment analysis from an ontological perspective describes the definition, development and relationship of sentiment with social reality. Meanwhile, from an epistemological perspective, sentiment analysis is viewed from how the source of knowledge is obtained, explaining the production of sentiment analysis knowledge, and several ways of working that can be applied in studies. Axiologically, sentiment analysis can see the function and value resulting from sentiment analysis, as well as discussing the results of interpretation from sentiment analysis studies. These findings showed the development of sentiment analysis in answering various problems to improve the quality of sustainable services in various fields.
Simulation and Empirical Studies of Long Short-Term Memory Performance to Deal with Limited Data Khikmah, Khusnia Nurul; Sadik, Kusman; Notodiputro, Khairil Anwar
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1356

Abstract

This research is proposed to determine the performance of time series machine learning in the presence of noise, where this approach is intended to forecast time series data. The approach method chosen is long short-term memory (LSTM), a development of recurrent neural network (RNN). Another problem is the availability of data, which is not limited to high-dimensional data but also limited data. Therefore, this study tests the performance of long short-term memory using simulated data, where the simulated data used in this study are data generated from the functional autoregressive (FAR) model and data generated from the functional autoregressive model of order 1 FAR(1) which is given additional noise. Simulation results show that the long short-term memory method in analyzing time series data in the presence of noise outperforms by 1-5% the method without noise and data with limited observations. The best performance of the method is determined by testing the analysis of variance against the mean absolute percentage error. In addition, the empirical data used in this study are the percentage of poverty, unemployment, and economic growth in Java. The method that has the best performance in analyzing each poverty data is used to forecast the data. The comparison result for the empirical data is that the M-LSTM method outperforms the LSTM in analyzing the poverty percentage data. The best method performance is determined based on the average value of the mean absolute percentage error of 1-10%.
Forecasting Shallot Prices in Indonesia Using News-Based Sentiment Indicators Salsabila, Atikah; Nooraeni, Rani
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1422

Abstract

The volatile price changes of shallots are a challenge in controlling their prices. The fluctuation in the price of shallots is always reported in the media because it affects people's lives. The news is released online via the internet and has beneficial information so it can be utilized. This study aims to provide a comparative analysis of forecasting models for shallot prices in Indonesia, evaluating the impact of using the most effective sentiment indicators derived from four lexicon-based methods. Data were collected by scraping method on three news portals and one food price information source website during the period from 2020 to 2023. The correlation and causality analysis was conducted to determine the relationship between food prices and sentiment indicators that was obtained using four sentiment analysis methods. The selected sentiment indicators for each day were used as an additional variable in forecasting using ARIMA, SARIMA, and BSTS models. The results showed that the use of news sentiment could reduce RMSE, MAPE, and MAE in forecasting shallot food prices.  
Multi-Platform Detection of Melon Leaf Abnormalities Using AVGHEQ and YOLOv7 Ishak, Sahrial Ihsani; Priandana, Karlisa; Wahjuni, Sri
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1441

Abstract

This research develops a multiplatform system for detecting abnormalities in melon leaves, integrating an Internet of Things (IoT) approach using Jetson Nano, a Streamlit-based website, and a mobile application for real-time monitoring. The system employs preprocessing with Average Histogram Equalization (AVGHEQ) to enhance image quality, followed by modeling with the YOLOv7 algorithm on a dataset of 469 training images and 52 test images, validated through 5-fold cross-validation. The model achieved a mean Average Precision (mAP) of 84% with an inference detection time of 4.5 milliseconds. Implementation on Jetson Nano resulted in a 25% increase in CPU usage (from 25% to 50%) and a 20% increase in RAM usage (from 70% to 90%). By combining these platforms and leveraging robust data preprocessing and modeling techniques, the system provides an accessible, efficient, and scalable solution for agricultural monitoring, enabling farmers to address plant health issues promptly and effectively.
Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts Wiratmoko, Galih; Thamrin, Husni; Pamungkas, Endang Wahyu
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1506

Abstract

Automatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. Indonesian presents unique linguistic challenges, including its agglutinative structure, borrowed vocabulary, and limited availability of high-quality training data. This study conducts a comparative evaluation of extractive, abstractive, and hybrid models for Indonesian text summarization, utilizing the IndoSum dataset which contains 20,000 text-summary pairs. We tested several models including LSA (Latent Semantic Analysis), LexRank, T5, and BART, to assess their effectiveness in generating summaries. The results show that the LexRank+BERT hybrid model outperforms traditional extractive methods, achieving better ROUGE precision, recall, and F-measure scores. Among the abstractive methods, the T5-Large model demonstrated the best performance, producing more coherent and semantically rich summaries compared to other models. These findings suggest that hybrid and abstractive approaches are better suited for Indonesian text summarization, especially when leveraging large-scale pre-trained language models.
Reviewing the Blockchain’s Framework and its Role in Sustainable Industries Ahamed, N. Nasurudeen; Alam, Tanweer; Benaida, Mohamed
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1545

Abstract

Blockchain technology is often regarded as a highly advanced and pioneering breakthrough in modern times. Blockchain technology is a distributed ledger that uses encryption to prevent security breaches and securely stores data across many systems. This facilitates collaborative transactions by providing a solitary, dependable reference point, revealing the purported trust intermediaries. This study aims to investigate the core principles of blockchain technology and assess its potential to support sustainability across various sectors. It seeks to examine how blockchain technology enhances reliability, effectiveness, and transparency in industries such as supply chain management and the energy sector. This study addresses these concerns by assessing the valuable applications, advantages, and drawbacks of blockchain in promoting sustainable industrial practices. Bitcoin and other cryptocurrencies rely on hashing as the foundation of their blockchain technology. Blockchain is a digital ledger that documents and tracks financial transactions. Blockchain technology has become prevalent across several sectors, encompassing artificial intelligence, machine learning, and the Internet of Things. Therefore, once the blockchain is prepared for dissemination, the data cannot be modified by anyone. This implies that it is immutable. Hyperledger offers a neutral platform for facilitating collaborative operations among organisations that frequently engage in competitive activities. Hyperledger is specifically designed to provide explicit support for blockchains as a means of business agreements. Authorisation is a prerequisite for a framework, ensuring that only those with proper authorisation can join the organisation. The ability of the manager to impose limitations on user access to the blockchain enhances security measures. Moreover, instead of being universally accessible through online platforms, trades are maintained secretly, limiting access to only essential participants. Using distributed code bases and open-source record upgrades facilitates enhanced efficiency in corporate activities. The fast expansion of blockchain technology has led to its widespread adoption across several industries worldwide. Illustrations encompass various domains, including logistics, copyright, finance, medicine, and supply chain management. Furthermore, we offer an introductory overview of blockchain technology, encompassing topics such as different types of blockchains and their utilisation across many sectors.
Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM Fadillah, Maulana Ahsan; Angraini, Yenni; Anisa, Rahma
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1571

Abstract

Agricultural productivity in East Java is under threat from unpredictable and harsh weather patterns, particularly rapid variations in sunlight length and rainfall intensity.  These abnormalities can interrupt agricultural cycles, lower yields, and make farming communities more vulnerable to climatic calamities.  However, current weather monitoring systems frequently fall short of detecting small anomalies in time series weather data that could serve as early warning signs of such disasters.  This study seeks to close this gap by creating a robust anomaly detection methodology adapted to time-dependent weather variables important to agriculture. In this study, a hybrid model combining Long Short-Term Memory (LSTM) autoencoder and One-Class Support Vector Machine (OCSVM) is proposed. The LSTM autoencoder's structure reconstructs time series data and signifies anomalies through reconstruction errors (MSE), while OCSVM validates these anomalies to reduce false positives. The model was applied to daily weather data from East Java spanning 2015–2024. The results showed that the model effectively detected 11 anomalies in sunlight duration and 7 in rainfall, with F1-scores of 0.71 and 0.82, respectively. Several of these anomalies corresponded to actual disaster events such as floods, landslides, and droughts. This research contributed to the field by demonstrating the effectiveness of combining deep learning and machine learning for weather anomaly detection. The proposed framework offers valuable insights for early warning systems and can support local governments and farmers in improving disaster preparedness and enhancing agricultural resilience in East Java.
Generative Adversarial Networks In Object Detection: A Systematic Literature Review Mat Raffei, Anis Farihan; Suakanto, Sinung; Hamami, Faqih; Ismail, Mohd Arfian; Ernawan, Ferda
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1576

Abstract

The intersection of Generative Adversarial Networks (GANs) and object detection represents one of the most promising developments in modern computer vision, offering innovative solutions to longstanding challenges in visual recognition systems. This review presents a systematic analysis of how GANs are transforming these challenges, examining their applications from 2020 to 2025. The paper investigates three primary domains where GANs have demonstrated remarkable potential: data augmentation for addressing data scarcity, occlusion handling techniques designed to manage visually obstructed objects, and enhancement methods specifically focused on improving small object detection performance. Analysis reveals significant performance improvements resulting from these GAN applications: data augmentation methods consistently boost detection metrics such as mAP and F1-score on scarce datasets, occlusion handling techniques successfully reconstruct hidden features with high PSNR and SSIM values, and small object detection techniques increase detection accuracy by up to 10% Average Precision in some studies. Collectively, these findings demonstrate how GANs, integrated with modern detectors, are greatly advancing object detection capabilities. Despite this progress, persistent challenges including computational cost and training stability remain. By critically analyzing these advancements and limitations, this paper provides crucial insights into the current state and potential future developments of GAN-based object detection systems.

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