cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
 44   45   46   47   48 
Evaluating the Performance of an LBS-Based Waste Reporting Application for Digital Waste Management Umar, Najirah; Asrul, Billy Eden William; Wabula, Yuyun
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9945

Abstract

The escalating volume of urban waste in Indonesia presents a serious challenge, exacerbated by conventional reporting mechanisms that are slow and inefficient. This study aims to develop and evaluate E-Trash, a Location-Based Services (LBS) application designed to accelerate the workflow of participatory waste reporting, handling, and monitoring in Makassar City. The novelty of this research lies in the synergistic integration of citizen reporting, real-time bidirectional notifications between reporters and field officers, and a spatial monitoring dashboard for policymakers, validated through direct, real-world implementation. The research methodology employs a software engineering approach utilizing a prototype model. System validation was conducted in three stages: black-box testing on 24 core features, performance testing under various bandwidth conditions, and a two-week field trial involving community members and sanitation personnel in two sub-districts. The findings robustly conclude that the E-Trash application effectively leverages a digital, Location-Based Services (LBS) approach to significantly enhance citizen participation in waste reporting and improve the response efficiency of sanitation personnel. The system demonstrated optimal functionality across diverse network conditions and device types, with stable response times and a high data transmission success rate affirming its reliability. Field implementation notably yielded a reduction in illegal waste accumulation and an increase in overall handling efficiency, primarily facilitated by the bidirectional notification system between citizens and sanitation teams. Consequently, E-Trash emerges as a highly viable candidate for replication in other urban settings, serving as a robust, community-participation-centric smart solution for sustainable urban sanitation management.
A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification Kumala, Raffa Adhi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9958

Abstract

Effective waste management represents a global challenge with significant environmental and public health impacts. Despite existing waste classification systems achieving high accuracy rates, a critical research gap exists in determining optimal CNN architectures for real-world deployment constraints, particularly regarding computational efficiency versus classification accuracy trade-offs. We compared two Convolutional Neural Network (CNN) architectures MobileNetV2 and VGG16 for classifying ten types of waste using image-based analysis. Using transfer learning approach, both models were modified for waste classification tasks by adding custom layers to pre-trained models. The dataset contained 19,762 images balanced to 9,440 samples through under-sampling techniques and enhanced with data augmentation to increase variation. Results demonstrated that MobileNetV2 achieved 95.6% test accuracy with precision 0.93, recall 0.93, and F1-score 0.93, significantly outperforming VGG16's 89.13% accuracy with precision 0.91, recall 0.90, and F1-score 0.90. Beyond superior accuracy, MobileNetV2 also demonstrated higher computational efficiency with 350ms/step training time compared to VGG16's 700ms/step, and more consistent performance across all waste categories.
IoT-Based Prediction of Ornamental Plant Water Needs Using Sugeno Fuzzy Algorithm Dwitama, Reiza Hersa; Ningrum, Novita Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9999

Abstract

Urban plant care is increasingly important amid growing concerns about air pollution and limited time for manual maintenance. In Indonesia, air quality has deteriorated significantly, with PM2.5 pollution levels exceeding World Health Organization standards, particularly in major cities like Jakarta. Ornamental plants play a crucial role in improving air quality; however, urban residents often struggle to consistently water them. This study addresses that problem by developing an Internet of Things (IoT)-based smart irrigation system that utilizes the Sugeno fuzzy algorithm to predict the water needs of ornamental plants. The system combines a capacitive soil moisture sensor and a DHT11 temperature-humidity sensor with an ESP8266 microcontroller to monitor environmental conditions. Data is transmitted to Firebase and visualized in an Android application, which provides real-time monitoring and specific volume recommendations ranging from 10 ml to 240 ml, calibrated for medium-sized plant pots which is also based on 27 fuzzy rules derived from three input parameters: air temperature, humidity, and soil moisture. Real-world testing with the Aglaonema Snow White plant confirmed that the system functions reliably, helping users optimize water usage and support sustainable, data-driven plant care in urban environments. The system achieved an average prediction accuracy of 89.14% and a mean absolute error of 7.6% in guiding soil moisture toward a 70% target, confirming its practical effectiveness. While the system was tested on Aglaonema Snow White, the fuzzy rule base can be recalibrated for other ornamental plant species with different water needs.
Web-Based F&B Lazatto Product Sales and Stock Prediction System with Double Moving Average (DMA) Method Zarasky, Dzira Faza; Alda, Muhamad
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10012

Abstract

This study aims to develop a web-based sales and stock prediction system for Lazatto, a Food and Beverage (F&B) company, using the Double Moving Average (DMA) method. The background of this research is based on issues stock requirement planning is still done conventionally, where the head of the restaurant places stock orders solely based on personal experience and intuition, without utilizing past sales data as a basis for decision-making, which often result in overstocking or stockouts. By implementing a web-based forecasting information system, the company can obtain real-time and structured data. This study uses sales data from April 2024 to March 2025. The prediction results show a downward trend in sales for the "Kentang" (Potato) product, with a forecasted value of 107.33 for April 2025, compared to an actual value of 95. Model evaluation indicates an average MAPE of 21.19%, which is considered a "fair" level of forecasting accuracy. Additionally, the time required for weekly stock planning was reduced, and interviews with staff revealed increased user satisfaction and ease of use. The developed system has proven to support more accurate and efficient decision-making in inventory management.
Few-Shot Learning for Classifying Genuine and Bot Comments on YouTube Using Transformer Models Fikriah Nst, Nahdah; Hamdhana, Defry; Qamal, Mukti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10023

Abstract

This study aims to develop a comment classification system on the YouTube platform to distinguish between real accounts and bot accounts, addressing the challenge of limited labeled data through a few-shot learning approach. The issue of bot accounts masquerading as real users in comment sections is becoming increasingly prevalent and has the potential to spread spam, misinformation, and influence public opinion. In this study, a Transformer-based model, DistilBERT, is used, which is known for its efficiency in understanding natural language context. The model is trained in a few-shot scenario (N5 to N50) using a very limited amount of training data. Testing results show that the model maintains high and stable performance even with minimal data (N5), achieving an F1-score above 0.90. In addition, this system is implemented into a web application using Flask to enable direct and interactive comment detection. The main contribution of this research is the proof that the combination of few-shot learning and the DistilBERT model can provide a practical and efficient solution for classifying YouTube bot account comments even with limited data conditions, as well as providing a replicable approach for similar problems on other digital platforms.
Evaluation of a Virtual Reality-Based Introduction to Hazardous and Toxic Waste Management Using the Technology Acceptance Model Rananda Saputra, Denaldi; Tranggono, Tranggono; Annisa Islami, Mega Cattleya Prameswari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10040

Abstract

The management of hazardous and toxic waste (B3 waste) demands innovative educational approaches to improve technical comprehension and environmental awareness, particularly among internship students. This study presents a formative evaluation of an early-stage virtual reality (VR) application prototype, aimed at assessing initial user perceptions and gathering feedback to guide further development, using the Technology Acceptance Model (TAM) framework. The prototype was tested on six internship students involved in the supervision of B3 waste in collaboration with the Environmental Agency (Dinas Lingkungan Hidup) of East Java Province. Data were collected through a questionnaire focusing on three TAM dimensions: Perceived Ease of Use, Perceived Usefulness, and Attitude Toward Technology. The results showed that the VR application was perceived as highly useful (87.5%), easy to use (89.2%), and positively received by users (92.5%). These findings indicate that VR technology holds strong potential as an interactive learning tool for introducing hazardous and toxic waste management practices. The study recommends continued content development and broader testing with a larger respondent base to validate these initial results.
Detection and Localization of Brain Tumors on MRI Images Using the YOLO Algorithm Bayu Satria, Zaky Indra; Supriyanto, Catur
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10047

Abstract

This study addresses the critical need for early and accurate brain tumor diagnosis on MRI images by comparing five versions of the YOLO algorithm (YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv12) with consistent parameters. Utilizing a pre-annotated Kaggle MRI brain dataset, the research meticulously verified annotations and employed data augmentation (flipping, rotation, blurring, noise) to expand the dataset from 801 to approximately 1362 images, enhancing model generalization and robustness. Models were trained and evaluated on metrics including precision, recall, mAP@0.5, mAP@0.5:0.95, and inference time. YOLOv12 demonstrated superior overall performance, achieving the highest recall (97.32%), mAP@0.5 (92.2%), and mAP@0.5:0.95 (76.57%), establishing its robustness for accurate detection and object localization. While YOLOv7 achieved the highest precision (96.89%) and excellent inference speed, its overall mAP and recall were surpassed by other iterations. YOLOv9 and YOLOv8 also showed strong competitive performance, indicating significant advancements in the newer YOLO generations. The findings confirm the efficacy of the YOLO algorithm for brain tumor detection and localization in MRI images, with YOLOv12 proving to be the most effective variant in this comparative analysis.
Design of an Internet of Things (IoT)-Based Fish Feeder System Using an Android Application Ariyandi, Zulham; Taufiq, Taufiq; Nunsina, Nunsina
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10072

Abstract

Fish farming plays a crucial role in aquaculture, where feed management is a key factor affecting productivity and operational costs. This research presents the design and implementation of an Internet of Things (IoT)-based automatic fish feeder system, integrated with a custom Android application. The system uses an ESP32 microcontroller to control a load cell sensor for accurate feed weighing, an ultrasonic sensor to monitor feed availability, servo motors for feed release mechanisms, and a DC motor for feed dispersion. Firebase Realtime Database serves as the data communication medium between the hardware and mobile application, enabling real-time control and monitoring. A rule-based control logic is implemented to execute scheduled or manual feeding processes. Experimental results show a feed weight accuracy of ±5 grams, with feeding operations completed within 1.5 minutes and an average throw distance of 287.8 cm. The system supports automatic alerts, scheduling, feed history logging, and remote access via the application. Compared to conventional manual methods, the system reduces feed waste, increases portion accuracy, and decreases feeding time by over 75%. These features demonstrate the system’s capability to enhance feeding efficiency, reduce labor dependency, and support sustainable and scalable fish farming practices through automation and real-time monitoring.
Topic Clustering of Student Complaints Based on Semantic Meaning Using the indoBERT and K-Means Models Setiawan, Gede Herdian; Pranata, Made Doddy Adi; Arimbawa, Ida Bagus Alit; Giri, I Wayan Paramarta; Carisa Dayani, Ni Putu Leona
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10080

Abstract

This study applies Natural Language Processing (NLP) technology to extract and cluster information from student complaint text data. The model used is IndoBERT, a variant of BERT (Bidirectional Encoder Representations from Transformers) that has been adapted for the Indonesian language. The main objective of this research is to perform topic clustering based on semantic similarity. The process begins with data collection and cleaning, followed by tokenization and text normalization. Each complaint is transformed into a vector representation through IndoBERT embeddings, which are then used as input for the K-Means clustering algorithm. Evaluation is conducted using various metrics, and the results of the Silhouette Score and Elbow Method indicate that the optimal number of clusters is four. Cluster visualization using the t-distributed Stochastic Neighbor Embedding (t-SNE) method reinforces these findings by displaying four fairly distinct groups of complaints, although one cluster appears dispersed and less well-defined, indicating possible topic overlap. The quality of topics within each cluster is evaluated using the Topic Coherence (c_v) metric, where Cluster 3 achieved the highest score of 0.7084. The topics in this cluster highlight critical issues such as campus facilities, lecturer quality, and information delivery systems. Overall, the four resulting clusters reflect central themes: Facilities, Expectations or Impressions, Services, and Academic Lectures. These results are expected to serve as a reference for institutions in formulating service improvement policies based on student complaint analysis.
Mental Health Classification Using Naïve Bayes and Random Forest Algorithms Faisti, Muhammad Jazum; Kusumodestoni, R. Hadapiningradja; Wibowo, Gentur Wahyu Nyipto
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10144

Abstract

Mental health is a crucial issue affecting individual and societal well-being. This study aims to investigate and compare the performance of Machine Learning algorithms, namely Naïve Bayes and Random Forest, for text-based mental health classification. The dataset used is the Mental Health Corpus from Kaggle, consisting of 27,977 English text messages from online forums, with binary labels (0: no indication of mental disorder, 1: indication of mental disorder) pre-annotated by the dataset creators. Text preprocessing involved lowercasing, negation handling, stopword removal, slang normalization, tokenization, and stemming. Data transformation was performed using TF-IDF. Model evaluation utilized accuracy, precision, recall, and F1-score metrics, along with 5-Fold Cross Validation. Evaluation results indicate high performance for both algorithms. Naïve Bayes achieved 88.7 % accuracy, 84.2 % precision, 95.2 % recall, and 89.3 % F1-score on the test data. Random Forest demonstrated more balanced performance with 89.3 % accuracy, 88.1 % precision, 90.5 % recall, and 89.3 % F1-score. The 5-Fold Cross Validation for Naïve Bayes yielded average scores of 88.8 % accuracy, 84.4 % precision, 94.9 % recall, and 89.3 % F1-score. In contrast, Random Forest showed averages of 89.2 % accuracy, 88.8 % precision, 89.5 % recall, and 89.3 % F1-score. While Naïve Bayes had higher recall, Random Forest exhibited the best overall performance, considering the combination of accuracy, precision, and stable generalization, making it more effective for mental health text classification.

Page 46 of 70 | Total Record : 695

 44   45   46   47   48 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 6 (2025): December 2025 Vol. 9 No. 5 (2025): October 2025 Vol. 9 No. 4 (2025): August 2025 Vol. 9 No. 3 (2025): June 2025 Vol. 9 No. 2 (2025): April 2025 Vol. 9 No. 1 (2025): February 2025 Vol. 8 No. 2 (2024): December 2024 Vol. 8 No. 1 (2024): July 2024 Vol. 7 No. 2 (2023): December 2023 Vol. 7 No. 1 (2023): July 2023 Vol. 6 No. 2 (2022): December 2022 Vol 6 No 1 (2022): July 2022 Vol. 5 No. 2 (2021): December 2021 Vol 5 No 2 (2021): December 2021 Vol 5 No 1 (2021): July 2021 Vol. 5 No. 1 (2021): July 2021 Vol 4 No 2 (2020): Desember 2020 Vol. 4 No. 2 (2020): Desember 2020 Vol 4 No 1 (2020): Juli 2020 Vol 3 No 2 (2019): Desember 2019 Vol 3 No 1 (2019): Juli 2019 Vol 2 No 2 (2018): Desember 2018 Vol 2 No 1 (2018): Juli 2018 Vol. 2 No. 1 (2018): Juli 2018 Vol 1 No 2 (2017): Desember 2017 Vol 1 No 1 (2017): Juli 2017 More Issue