Jurnal Teknik Informatika is a peer-reviewed academic journal published by the Department of Informatics, Faculty of Science and Technology, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia. The journal is officially registered with ISSN 2549-7901 (online) and ISSN 1979-9160 (print), and is nationally accredited as Sinta 2, affirming its recognition and quality within Indonesia’s academic landscape. Published twice a year in April and October, it provides open and advance access to scholarly works in the fields of Information and Communication Technology (ICT) and Islamic studies. The journal’s scope encompasses a wide range of topics, including software engineering, system design methodology, artificial intelligence, big data and data mining, human-computer interaction, mobile computing, computational linguistics, cybersecurity, and computer networking. Through its consistent publication and commitment to academic rigor, Jurnal Teknik Informatika serves as an influential platform for researchers, educators, and students to exchange ideas and findings that contribute to the advancement of informatics and computing. Its affiliation with the Indonesian Association of Higher Education in Informatics and Computing (APTIKOM) further enhances its role as a collaborative medium that promotes innovation, interdisciplinary research, and scientific dialogue across Indonesia’s higher education institutions. By bridging academic and technological perspectives, the journal continues to strengthen the national and global discourse in the development of informatics and its applications in various domains.
Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah
Implementation of IoT Technology on MySmartTrash Waste Bank Application
The effectiveness of Waste Bank in addressing national waste management challenges is limited by inherent shortcomings. Conventional processes, which heavily rely on manual labor and record-keeping, often face logistical challenges and inefficiencies that limit the effectiveness of waste banks. This paper presents the MySmartTrash application, a solution that integrates IoT technology to enhance waste management practices through a smart waste bank system. By utilizing IoT-enabled sensors, the application allows users to monitor waste levels in real time, thereby optimizing waste collection processes and promoting effective waste segregation. This study employed IoT Design Methodology and Prototyping. Through a SWOT analysis of existing waste management applications, the research identifies strengths and opportunities for enhancing waste management systems. Usability testing also highlighted the significance of various features. This study offers insights for future research into IoT applications in environmental sustainability and waste management systems.
Copyright (c) 2025 Viva Arifin, Siti Ummi Masruroh, Rizka Amalia Putri, Fitri Mintarsih, Nenny Anggraini, Nurhayati, Dewi Khairani https://creativecommons.org/licenses/by-sa/4.0
Use Case Point Activity-Based Costing and Adjusted Function Point for Software Cost Estimation
Effective software development planning is vital across various industries, and inadequate planning can lead to project failures. A key part of this planning is accurately estimating effort and costs, which is crucial for staying within budget and meeting deadlines. This research compares two methods – Use Case Point Activity Based Costing (with 21 complexity factors) and Adjusted Function Point (with 16 complexity factors) – for estimating costs versus actual values. The analysis reveals that the Use Case Point method had a 23.52% deviation from actual costs, while the Adjusted Function Point method had a 33.35% deviation. These findings provide essential reference points during software project planning, ensuring estimates closely align with actual values based on project-specific attributes. This deviation underscores the importance of precision and selecting appropriate methodologies tailored to each project’s unique characteristics. Ultimately, this research equips businesses and project managers with a robust financial prudence framework and enhances the likelihood of project success.
Copyright (c) 2025 Ahmad Fauzan Haryono, Andria Farhan, Dewi Khairani, Supardi Razak, Fitri Mintarsih https://creativecommons.org/licenses/by-sa/4.0
Uncovering Hidden Themes in Indie Music: Crisp-Dm Guided LDA Topic Modeling on a Kaggle-Based Lyric Generation Dataset
The development of music has produced many works in the form of data, especially lyrical data, which provide insight into the semantic structure of music. This study explores latent thematic patterns in the indie lyric dataset from Kaggle by applying Latent Dirichlet Allocation (LDA), which is the first LDA study of indie music lyrics in the Indonesian context with the interpretation of love, emotional needs, romance, and inner conflict. The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology can be effectively applied to unstructured data, opening up opportunities for better music classification. The methodological stages include business and data understanding, data preparation, modelling, evaluation, and dissemination. In the early stages, the Kaggle dataset implemented Natural Language Processing, which was done with case folding, punctuation removal, stopword removal, stemming, and tokenization. The LDA model is trained by identifying five topics with different interpretations. Visualization in WordClouds, with topic distribution on datasets and title-based topic mapping. This model yielded a coherence value of 0.3044, which indicates limited semantic consistency, which means the words in the topic have a reasonably good relationship, but there is still potential for refinement in subsequent studies. The limitations of this study include the limited size of the dataset, with only 347 rows and slight variation in interpretation. For future research, it is recommended to use larger datasets and more diverse interpretations and apply more machine learning models.
Copyright (c) 2025 Thoyyibah T , Yan Mitha Djaksana https://creativecommons.org/licenses/by-sa/4.0
VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems
Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.
Copyright (c) 2025 Yunita Sartika Sari, Kusrini, Ema Utami, Ferry Wahyu Wibowo https://creativecommons.org/licenses/by-sa/4.0
Sentiment Classification in Imbalanced Data: Trade-Offs Between Metrics and Real-World Relevance
Sentiment analysis plays a crucial role in assessing public perception, particularly in healthcare services like BPJS Kesehatan, Indonesia’s national health insurance program. However, sentiment classification faces a challenge due to class imbalance, where negative feedback dominates positive responses. This study investigates whether sentiment classification should prioritize traditional evaluation or maintain real-world data representation by preserving the original sentiment distribution. Two feature extraction methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), were evaluated using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression with varying maximum feature counts (100–300) to examine the impact of feature dimensionality. Model performance was evaluated using traditional metrics, while sentiment distribution fidelity was assessed by comparing predicted proportions with the dataset. Results show TF-IDF achieves higher precision and recall but fails to capture positive sentiments, leading to a skewed representation of real-world trends, while BoW offers a more balanced distribution with slightly lower accuracy. Paired t-tests and Wilcoxon signed-rank tests confirmed differences in accuracy and recall are significant, but not in precision and sentiment distribution. These findings highlight a trade-off between performance and sentiment diversity, vital in healthcare services and other fields with imbalanced datasets, emphasizing the need to align evaluation metrics with real-world objectives. Future research should investigate advanced models, such as deep learning and transformer-based approaches, to enhance both accuracy and fairness when analyzing imbalanced data.
Copyright (c) 2025 Indra Swanto Ritonga, Wanayumini, Dedy Hartama https://creativecommons.org/licenses/by-sa/4.0
Back-End Development of an Interactive Dashboard with Real-Time API Integration for Chili Plant Monitoring in Precision Agriculture
This research focuses on the development of an interactive web-based dashboard to support a precision agriculture system for chili plants. The primary focus of this research is on the back-end development of the system. The system integrates several internal and external APIs, including the Flask API (internal) for plant disease classification and growth prediction, and the Google Gemini API for the AI-powered chatbot that provides consultation to farmers (external). These features allow farmers to receive automatic disease diagnosis and growth predictions, improving decision-making and crop management. The dashboard also presents weather information, environmental data, and nanobubble data, along with Echarts gauge charts for seven essential metrics: Electrical Conductivity (EC), temperature, humidity, pH, nitrogen, phosphorus, and potassium. Data for the environmental and nanobubble data is retrieved from the ThingSpeak API (external), while weather information is fetched from the OpenWeatherMap API (external). The system was thoroughly tested using Postman to ensure all API endpoints function correctly. The results confirmed that all endpoints responded with status code 200 OK, indicating stable back-end performance. Performance testing showed response times stabilizing at 2000 ms after initial 4500 ms peaks, confirming efficient handling, reliable endpoints, and deployment readiness.
Copyright (c) 2025 Azwar Farrel Wirasena, Hanif Fakhrurroja, Dita Pramesti https://creativecommons.org/licenses/by-sa/4.0
Real-Time Retail Shelf-Stock Detection with YOLOv7
This study developed a real-time shelf stock monitoring system for retail environments, leveraging the You Only Look Once version 7 (YOLOv7) deep learning-based object detection framework. The system effectively addresses the inefficiencies, delays, and errors inherent in manual stock auditing processes. The underlying model was trained on a comprehensive dataset comprising 15,397 annotated object labels across fifteen distinct retail product categories. The fully trained model was then integrated into a web-based platform designed to capture real-time shelf images via a webcam. These captured images undergo automated processing for product detection and counting. The detection results are dynamically displayed on an interactive dashboard and securely stored in a backend database. The system also incorporates voice alerts, which are triggered automatically when stock levels fall below predefined thresholds, thereby facilitating immediate restocking. Experimental validation indicates high performance, with both precision and recall exceeding 96%, and an average processing latency of less than one second per frame. The model achieved an mAP@0.5 of 0.996 and an mAP@0.5:0.95 of 0.86. These findings underscore the system's effectiveness in providing a rapid, accurate, and efficient monitoring solution specifically tailored for small to medium-sized retail businesses. The primary contribution of this research lies in its comprehensive, end-to-end system integration, combining robust YOLOv7-based object detection with real-time web visualization and automated voice alerts, successfully addressing existing gaps in prior implementations.
Copyright (c) 2025 Annies Alquratu SeptriaPS, Ade Silvia Handayani, Nasron Nasron https://creativecommons.org/licenses/by-sa/4.0
Food Image Classification and Recipe Recommendation for South Sumatran Cuisine Using EfficientNetB1
Visual-based food classification and recipe recommendation systems remain underexplored in the context of local culinary traditions. To address this gap, a system was developed using the EfficientNetB1 architecture of Convolutional Neural Networks (CNN), integrated with a Large Language Model (LLM) to generate South Sumatran recipes from food images, adapting suggestions to classification results. The model was trained using transfer learning on eight food ingredient classes selected for their prevalence in local cuisine. It achieved a validation accuracy of 98.2% and a test accuracy of 98%, with average precision, recall, and F1-score all exceeding 98%, indicating consistent and reliable performance. The system was deployed as a web-based application, DapoerKito, allowing users to upload food images, receive classification results, and obtain generated recipe suggestions. LLM-generated recipes are produced instantly, matched to ingredients, and shown in a clear format. These findings demonstrate the value of integrating computer vision and language generation in an AI-based platform that supports usability and cultural relevance. In addition to its technical capabilities, the system contributes to the digital preservation of regional culinary heritage through interactive AI. This CNN–LLM integration offers a novel approach for advancing food AI with diverse ingredients, personalized nutrition, and multilingual support.
Copyright (c) 2025 Farhah Salsabillah, Ade Silvia Handayani, Nurhajar Anugraha https://creativecommons.org/licenses/by-sa/4.0
Public Sentiment and GoTo Stock Price Movement in Indonesia: A Null-Relationship Study Using Naïve Bayes and Non-Parametric Measures
The expiration of the lock-up period for PT GoTo Gojek Tokopedia Tbk's shares led to a sharp stock price decline and public discourse on Twitter. This study aims to examine the statistical relationship between public sentiment and GoTo’s stock price movement in Indonesia. Tweets were classified into positive or negative sentiment using the Naïve Bayes classifier, selected for its computational efficiency on large-scale textual data. The model achieved 70% accuracy, with a precision of 82% and F1-score of 75%. The sentiment polarity was then compared with stock trends across 39 distinct trading periods using four non-parametric statistical tests: Chi-Square (p = 0.6398), Cramer’s V (0.014), Goodman-Kruskal’s Lambda (0.053), and Mann-Whitney U test (p = 0.8994). None of these tests showed a statistically significant association between sentiment polarity and stock price movement. These findings highlight that while public sentiment may reflect short-term public interest, it does not reliably capture the market’s behavioral dynamics—especially in cases of investor decisions driven by broader macroeconomic or institutional factors. Sentiment data, therefore, should be considered as a complementary, rather than primary indicators in stock price analysis.
Copyright (c) 2025 Dita Pramesti, Hanif Fakhrurroja, Rahma Karina M. https://creativecommons.org/licenses/by-sa/4.0
Small Object Detection and Object Counting for Primary Roe Dataset Based on Yolo
This research offers an initial exploration into the effectiveness of three variations of the YOLOv8 model original, trimmed, and YOLOv8n.pt in combination with two distinct datasets characterized by tight and loose distributions of roe, aimed at enhancing small object detection and counting accuracy. Utilizing a primary roe dataset across 776 images, the research systematically compares these model-dataset configurations to identify the most effective combination for precise object detection. The experimental results reveal that the YOLOv8n.pt model combined with the loosely distributed dataset achieves the highest detection performance, with a mean Average Precision (mAP) of 53.86%. This outcome underscores the critical impact of both model selection and data distribution on the detection accuracy in machine learning applications. The findings highlight the importance of tailored model and dataset synergies in optimizing detection tasks, particularly in complex scenarios involving small, densely clustered objects. This research contributes valuable insights into the strategic deployment of neural network architectures for refined object detection challenges.
Copyright (c) 2025 Wahyu Andi Saputra, Nicolaus Euclides Wahyu Nugroho, Dany Candra Febrianto, Andi Prademon Yunus, Muhammad Azrino Gustalika, Yit Hong Choo
Detection of Vulgarity in Anime Character: Implementation of Detection Transformer
Vulgar and pornographic content has become a widespread issue on the internet, appearing in various fields include anime. Vulgar pornographic content in anime is not limited to the sexuality genre; anime from general genres such as action, adventure, and others also contain vulgar visual. The main focus of this research is the implementation of the Detection Transformer (DETR) object detection method to identify vulgar parts of anime characters, particularly female characters. DETR is a deep learning model designed for object detection tasks, adapting the attention mechanism of Transformers. The dataset used consists of 800 images taken from popular anime, based on viewership rankings, which were augmented to a total of 1,689 images. The research involved training models with different backbones, specifically ResNet-50 and ResNet-101, each with dilation convolution applied at different stages. The results show that the DETR model with a ResNet-50 backbone and dilation convolution at stage 5 outperformed other backbones and dilation configurations, achieving a mean Average Precision of 0.479 and of 0.875. The other result is dilated convolution improves small object detection by enlarging the receptive field, applying it in early stages tends to reduce spatial detail and harm performance on medium and large objects. However, the primary focus of this research is not solely on achieving the highest performance but on exploring the potential of transformer-based models, such as DETR, for detecting vulgar content in anime. DETR benefits from its ability to understand spatial context through self-attention mechanisms, offering potential for further development with larger datasets, more complex architectures, or training at larger data scales.
Copyright (c) 2025 Amalia Suciati, Dian Kartika Sari, Andi Prademon Yunus, Nuuraan Rizqy Amaliah
Classification of Coconut Fruit Quality Using The K-Nearest Neighbour (K-NN) Method Based on Feature Extraction: Color, Shape, and Texture
In 2021, Indonesia was the world's largest coconut producer, with production reaching 17.1 million tons, according to the Food and Agriculture Organization (FAO). However, due to the long distribution time from farmers to consumers, the quality of coconuts often decreases, mainly due to manual classification. Coconuts that meet consumption standards are considered suitable, while coconuts that are overripe, damaged, or unripe are considered Non-standard. To overcome this problem, an automatic classification system was developed using machine learning with the K-Nearest Neighbor (K-NN) algorithm. The total required dataset is around 500, comprising 250 standard coconut datasets and 250 non-standard coconut datasets. The dataset was taken from coconut Images from Indragiri Hilir, Riau Province. Coconut features colour, shape, and texture.. The development process used the Cross Industry Standard Process for Data Mining (CRISP-DM). The evaluation used a confusion matrix .This study explores five training-test ratio data split scenarios of 90:10, 80:20, 70:30, 60:40, and 50:50. The highest accuracy, 96%, is achieved with a data split of 90:10 and a K value 5. Then, the K-NN model will be compared with other models, for Support Vector Machine (SVM) with RBF kernel accuracy of 94%, SVM with Linear kernel of 90%, Random Forest with accuracy of 92%, and Convolutional Neural Network (CNN) with accuracy of 86%.
Copyright (c) 2025 Sucinda Kardena, Fildza Izzati,Rusdah
Feature Extraction Using Mel-Frequency Cepstral Coefficients (MfCC) Technique For A Tajweed Guess Based on Android Application Development
The development of information and communication technology today has had a significant impact on various aspects of life, including education. One notable example is the increasing number of applications designed for learning to recite the Quran with proper tartil. The growing trend of tahfidz (Quran memorization) is undoubtedly a positive development from a religious perspective. However, many individuals focus solely on memorization without acquiring the ability to recite the Quran properly and accurately. One discipline that supports proper Quran recitation is the knowledge of tajweed. Numerous applications have been developed in this field, especially on Android platforms. However, applications that utilize artificial intelligence (AI) to recognize tajweed rules and involve users in guessing tajweed readings are still in need of further development. The aim of this research is to develop a tajweed learning application using the concept of Automatic Speech Recognition (ASR). This study employs data collection methods such as literature review, quantitative methods, and testing. The design is represented using Unified Modeling Language (UML), while the application is tested using the Black Box Testing method. For data analysis and testing of the speech recognition model, the Hidden Markov Model (HMM) algorithm is employed, with Mel-Frequency Cepstral Coefficients (MFCC) used for feature extraction. The output of this research is an Android-based tajweed learning application that integrates speech recognition and allows users to guess tajweed rules interactively.
Copyright (c) 2025 Khodijah Hulliyah, Lilik Ummi Kultsum, Wahyu Hendarto Wibowo, Anif Hanifa Setianingrum, Arini, Yusuf Durachman
Evaluation of An Existing System Using The System Usability Scale (SUS) as A Guideline for System Improvement
The e-Polvot system at the University of Science and Technology Indonesia (USTI) is a digital platform used for student elections, replacing traditional paper-based voting to enhance efficiency and minimize election fraud. This study evaluates the system using the System Usability Scale (SUS) to assess its usability, including efficiency, effectiveness, and user satisfaction. However, SUS alone does not determine failure points but provides a usability score that reflects user perception. A survey was conducted with 88 respondents from three different academic programs, which showed that while the system generally received a "Good" usability rating, certain areas require enhancement to improve user engagement and satisfaction. Based on the findings, this study recommends enhancing the user interface, providing targeted user training, and introducing additional features to broaden the system’s application across academic units. Additionally, the study highlights the potential for expanding the system's functionality beyond student elections, supporting activities such as departmental voting and organizational decision-making processes. These improvements aim to increase user satisfaction and usability, making the system a more effective tool for various academic and institutional contexts.
Copyright (c) 2025 M. Khairul anam, Susanti Susanti, Nurjayadi Nurjayadi, Fransiskus Zoromi, Atalya Kurnia Sari
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk
Hypertension is a major cause of cardiovascular disease, making early risk prediction essential. According to WHO, hypertension cases are estimated to reach 1.28 billion by 2023. This study aims to optimize the K-Nearest Neighbor (KNN) algorithm for predicting hypertension risk through hyperparameter tuning. Three methods Grid SearchCV, Bayes SearchCV, and Random SearchCV are compared to determine the best parameter configuration. The dataset, obtained from Kaggle, consists of 520 balanced samples (260 positive and 260 negative) with 18 health-related features such as age, gender, blood pressure, cholesterol, glucose, and others. After preprocessing, the KNN model is tuned using each method by testing combinations of neighbors (k), weight types, and distance metrics. Results show Bayes SearchCV achieved the highest accuracy of 92%, outperforming the baseline KNN model, which had 85% accuracy. The ROC AUC score of 0.96191 also indicates excellent classification performance. In conclusion, Bayes SearchCV significantly improves KNN's predictive ability in hypertension risk classification.
Copyright (c) 2025 Dimas Trianda, Dedy Hartama, Solikhun Solikhun
Adaptive Hint Generation for Educational Games Using Fuzzy Logic
The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.
Copyright (c) 2025 Anggina Primanita, Hadipurnawan Satria, Muhammad Qurhanul Rizqie, Ananda Haykel Iskandar, Wibisena Nugraha
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification
This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
Copyright (c) 2025 Muhammad Nasri Gea, Wanayumini, Rika Rosnelly
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining
The rapid growth of network data has increased the complexity of detecting anomalies, which are crucial for ensuring the security and integrity of information systems. This study investigates the use of the Isolation Forest algorithm for anomaly detection in network traffic, utilizing the Luflow Network Intrusion Detection dataset, which contains 590,086 records with 16 features related to network activities. The methodology encompasses data preprocessing (cleaning, normalization, and feature scaling), feature selection (bytes in, bytes out, entropy, and duration), model training, and performance evaluation. The results demonstrate that Isolation Forest can effectively identify anomalies based on feature patterns, isolating suspicious data points without the need for labeled datasets. However, performance metrics, such as accuracy (42.92%), precision (14.37%), recall (2.87%), and F1-score (4.79%), reveal challenges such as high false-positive rates and low sensitivity to true anomalies. These findings highlight the potential of the algorithm for dynamic, high-dimensional datasets but also indicate the need for further improvements through hyperparameter tuning, feature engineering, and alternative approaches. This study contributes to the development of adaptive anomaly detection frameworks for network security and suggests future integration into real-time systems for proactive threat mitigation. The study's findings are particularly relevant for enhancing network security in environments such as corporate and governmental networks, where real-time anomaly detection is crucial.
Copyright (c) 2025 Hartati Tammamah Lubis, Roslina, Lili Tanti
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm
This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
Copyright (c) 2025 Erica Rian Safitri, Lili Tanti, Wanayumini
Design and Development of the Koperasi Bintang Tapanuli (KBT) Ticket Ordering System
The transportation industry has undergone a major transformation with the widespread adoption of online ticketing systems. However, Koperasi Bintang Tapanuli (KBT), a major player in regional transport, relies on a traditional manual booking system for its buses. The system suffers from inefficiencies such as long queue times and limited access to information. The project used a rigorous requirements gathering process, including stakeholder interviews to ensure the system met user needs and functionality. Passengers can conveniently search routes, compare timetables, book tickets and manage bookings online without the need for a physical ticket counter. The team built a website consisting of 28 functions. They are: registration, authentication (login and logout), profile viewing, profile editing, information viewing, adding information, information editing, information deleting, ticket viewing, ticket adding, ticket editing, ticket deleting, vehicle detailed information viewing, dashboard viewing, customer data viewing customer package information viewing, package payment viewing, ticket approval, review viewing, payment viewing, notification viewing, history viewing, ordering method viewing, payment viewing, ticket ordering, package delivery, check ticket order and add review. This website is built using the laravel framework and the waterfall software development methodology. The application we built helps KTB admins in managing ticket orders.
Copyright (c) 2025 Hernawati Samosir, Monica Silaban, Resa Halen Manurung, Elisabeth Uli Tambunan, Juan Saut Pandapotan Sitorus
Challenges and Strategies in Forensic Investigation: Leveraging Technology for Digital Security Using Log/Event Analysis Method
Cybersecurity threats continue to evolve, necessitating advanced techniques for network anomaly detection. This study developed a comprehensive methodology for detecting network anomalies by leveraging sophisticated log and event analysis using machine learning algorithms. By employing a Naive Bayes classification approach on a synthetic cybersecurity dataset comprising 40,000 entries with 25 unique features, the research aimed to enhance anomaly detection precision. The methodology involved meticulous data preprocessing, feature selection, and strategic model validation techniques, including cross-validation and external benchmarking. Comparative analysis with K-Nearest Neighbors and Support Vector Machine algorithms demonstrated the Naive Bayes method's superior performance, achieving a classification accuracy of 94.8%, an Area Under the Curve (AUC) of 0.949, and a Matthews Correlation Coefficient of 0.896. The study identified critical parameters influencing anomaly detection, such as source port characteristics and attack signatures. These findings contribute significant insights into machine learning-based network security strategies, offering a robust framework for early threat identification and mitigation.
Copyright (c) 2025 Ammar Yasir Nasution, Hartono Hartono, Rika Rosnelly
Syllable-Based Javanese Speech Recognition Using MFCC and CNNs: Noise Impact Evaluation
Javanese, a regional language in Indonesia spoken by over 100 million people, is classified as a low-resource language, presenting significant challenges in the development of effective speech recognition systems due to limited linguistic resources and data. Furthermore, the presence of noise is a significant factor that impacts the performance of speech recognition systems. This study aims to develop a speech recognition model for the Javanese language, focusing on a syllable-based approach using Mel Frequency Cepstral Coefficients (MFCC) for audio feature extraction and Convolutional Neural Networks (CNNs) methods for classification. Additionally, it will analyze how different types of colored noise: white gaussian, pink, and brown, when added to the audio, impact the model's accuracy. The results showed that the proposed method reached a peak accuracy of 81% when tested on the original audio (audio without any synthetic noise added). Moreover, in noisy audio, model accuracy improves as noise levels decrease. Interestingly, with brown noise at a 20 dB SNR, the model's accuracy slightly increases to 83%, representing a 2.47% improvement over the original audio. These results demonstrate that the proposed syllable-based method is a promising approach for real-world applications in Javanese speech recognition, and the slight accuracy improvement in noisy conditions suggests potential regularization effects
Copyright (c) 2025 Hermanto, Tjong Wan Sen
Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data
Logistic regression is a model commonly used for predicting data with large sample sizes. However, in real-world scenarios, many cases involve small datasets that need to be addressed using logistic regression. The aim of this research is to develop a hybrid logistic regression model to address issues with small sample sizes by combining the Newton Raphson and Super Cubic methods. This hybrid model is applied to predict student dropout at Universitas Duta Bangsa Surakarta. The performance of the hybrid model is evaluated using two main metrics: the convergence of the parameter approximation to measure the precision of parameter estimation, and the ROC curve to assess prediction accuracy. Experimental results show that the Hybrid Logistic Super Newton model outperforms the logistic regression Newton Raphson model, requiring only three iterations to converge, thus improving computational efficiency. Moreover, this model achieves higher accuracy, with an AUC of 0.8833. These findings suggest that the developed model has the potential to be applied in various fields, such as healthcare, finance, and others, offering an effective solution for accurate, real-time predictive analytics. Further research could focus on optimizing the model’s computational efficiency and exploring its application in other domains with small dataset challenges, such as healthcare and finance.
Copyright (c) 2025 Nurmalitasari Nurmalitasari, Zalizah Awang Long, Nurchim Nurchim
Optimizing Naïve Bayes Method for Felder-Silverman Learning Style Model Identification
One important issue in education institusion is the differences in students learning styles, which requires educators to pay attention to individual learning preferences. The manual learning style identification method is considered less effective in terms of time and data accuracy. This study aims to develop a student learning style identification system using the Felder-Silverman model and the Naïve Bayes method, This system is designed to assist lecturers in adjusting learning strategies according to student learning preferences, thus increasing the effectiveness of the learning process. The Naïve Bayes method was applied by analyzing student datasets and determining the accuracy of learning style identification. The validation results showed significant identification accuracy: 85% for the active-reflective dimension, 96% for the sensitive-intuitive dimension, 98% for the verbal-visual dimension, and 91% for the sequential-global dimension. The results of user validation show the effectiveness of the learning style identification application that has been tested based on the percentage value of each statement, and an average percentage value of 85.6% was obtained for all statements, indicating that the system functions well in identifying students' learning styles, while the results of expert validation state that the statements are in accordance with the indicators, the statements use simple and easy-to-understand language, and the identification results are appropriate. This study is expected to contribute to helping universities identify student learning styles efficiently, improve the quality of learning in higher education, and contribute to supporting an inclusive learning approach in higher education environments.
Copyright (c) 2025 Hanatyati Nur Asmi, Slamet Risnanto, Othman Bin Mohd
Application of the ELECTRE I and ELECTRE IS Method to Optimize Maize Seed Selection in Cameroon: A Multi-Criteria Approach
This study aims to help Cameroonian farmers choose the best maize seeds to improve their yields. To achieve this, we evaluated 15 varieties based on five essential criteria: cycle length, yield per hectare, cob quality, stem height, and grain weight. Using the ELECTRE I and ELECTRE IS multi-criteria decision-making methods, we selected four particularly high-performing varieties: CLH103, CMS8602, CMS9015, and CMS 8501. These seeds offer a good balance between productivity and adaptation to local conditions, with potential yields of up to 10 tons per hectare. In-depth analyses have confirmed the reliability of these results, assuring farmers of a robust and effective choice. These recommendations can help improve food security and the profitability of farms in Cameroon
Copyright (c) 2025 Tanone Demas, Guidana Gazawa Fréderic, Yaboki Elisabeth
Addressing Class Imbalance in Machine Learning for Predicting On-Time Student Graduation at The Islamic University of Riau
Timely graduation is an important indicator of academic performance in higher education. However, many students still fail to graduate on time, prompting the need for predictive models to support academic decision-making. This study aims to analyze the impact of class imbalance on machine learning algorithm performance in predicting student graduation at the Islamic University of Riau. Data were obtained through questionnaires and labeled into “graduated on time” and “not on time” classes, which were initially imbalanced. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied during preprocessing to balance the dataset. Four machine learning algorithms were compared: Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The evaluation was conducted with and without SMOTE, using accuracy, precision, recall, F1-score, and confusion matrix. Results showed significant performance improvements after applying SMOTE, with all models achieving around 99% accuracy. SVM achieved the most stable results across both conditions. The study highlights the effectiveness of SMOTE in improving classification fairness and reliability, especially in datasets with class imbalance. This work may assist universities in early intervention for students at risk of late graduation.
Copyright (c) 2025 Akmar Efendi, Sarjon Defit https://creativecommons.org/licenses/by-sa/4.0
Evaluating BiLSTM Performance with BERT, RoBERTa, and DistilBERT in Online Bullying News Detection
This study examines the performance of BiLSTM combined with three transformer-based word embeddings—BERT, RoBERTa, and DistilBERT—in classifying bullying news in online media. BiLSTM was chosen for its significant advantages in processing text sequences compared to traditional RNN and LSTM models. The study used a dataset of 2,800 articles from three major Indonesian news portals, with 2,000 articles for training and 800 for testing, labeled using the lexicon method. The testing results showed that the combination of BiLSTM and RoBERTa achieved the best performance, with an accuracy of 94% and a near-perfect precision of 99%. Statistical significance tests confirmed that BiLSTM with RoBERTa performs significantly better than with BERT or DistilBERT. These findings suggest that the BiLSTM and RoBERTa combination is the most effective for classifying bullying news, especially for new or unseen data. This research contributes to the development of automatic bullying content detection systems to enhance content moderation on news platforms.
Copyright (c) 2025 Moh. Rosidi Zamroni, Miftahus Sholihin, Erna Hayati, Rahayu A Hamid, Nurul Aswa Omar https://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of Pospay Application Reviews Using the Bert Deep Learning Method
E-money usage in Indonesia has grown significantly due to increasing internet penetration and smartphone adoption. Digital transactions are becoming more common, with platforms like GoPay, OVO, and Dana leading the market. The government and financial institutions actively support this shift through regulations and initiatives. This study analyzes user sentiment on the Pospay application using the BERT deep learning method, based on 16,760 Google Play Store reviews. To the best of our knowledge, this is the first study to apply BERT for sentiment analysis of Pospay user reviews in Indonesia. The goal is to understand user perceptions and satisfaction. BERT helps capture subtle nuances in reviews, including informal expressions and abbreviations like "gk" for negative sentiment. The model achieves high accuracy, with precision scores of 0.82 (negative) and 0.93 (positive), and recall scores of 0.92 (negative) and 0.93 (positive). Findings suggest PT Pos should enhance application stability, security, transaction processing, and customer service. Regular updates are recommended to improve performance and user satisfaction.
Copyright (c) 2025 Erna Mulyati, Muhammad Ibnu Choldun Rachmatullah, Adri Sapta Firmansyah https://creativecommons.org/licenses/by-sa/4.0
Evaluating User Satisfaction in The Halodoc Application Using a Hybrid CNN-BiLTSM Model for Sentiment Analysis
The growing demand for digital healthcare services in Indonesia has driven the adoption of Online Healthcare Applications (OHApps) such as Halodoc. Despite over 65 million users, maintaining user satisfaction remains a challenge. This study employs sentiment analysis using a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to classify user review ratings. A dataset of 10,000 Google Play Store reviews was divided into COVID-19 and post-pandemic segments. The methodology includes data collection, pre-processing, and dataset segmentation for training, validation, and testing. Results indicate that the CNN-BiLSTM model surpasses traditional machine learning by combining CNN’s feature extraction with BiLSTM’s long-term dependency capture, achieving 98.71% accuracy on COVID-19 data and 98.16% post-pandemic. Additionally, the model demonstrates strong performance across other key evaluation metrics, with precision, recall, and F1-score. Misclassification analysis highlights minor errors, particularly in ratings 4 and 5. These findings help healthcare providers enhance digital services by identifying user concerns, improving platform features, and optimizing customer engagement. Beyond healthcare, this approach has real-world applications in e-commerce and financial services, where sentiment analysis informs user experience improvements.
Copyright (c) 2025 Dian Kurniasari, Arif Su'admaji, Favorisen Rosyking Lumbanraja, Warsono https://creativecommons.org/licenses/by-sa/4.0
Voice Spoofing Classification Using Residual Bidirectional Long Short Term Memory
Voice spoofing attacks are a major security concern for speech-based biometric systems. Detection and classification of spoofed voice are essential steps for preventing unauthorized accesses. This study proposes a novel approach to voice spoofing classification using a Residual Bidirectional Long Short Term Memory (R-BLSTM) network. The goal is to enhance the accuracy and robustness of voice spoofing detection using the power of deep learning and residual connections. The current proposed approach based on bidirectional LSTM with residual connections is designed to capture long-range dependencies and latent characteristics of speech signals. Experimental evidence that the R-BLSTM model is superior to classic ML techniques is also demonstrated by observing an accuracy of 95.6% on the ASVspoof 2019 collection. The designed system can be further utilized for enriching the security of speech-based biometrics modalities and making anti-voice spoofing attacks ineffective.
Residual Bidirectional Long Short Term Memory (R-BLSTM)
Copyright (c) 2025 Fatan Kasyidi, Rifaz Muhammad Sukma, Annisa Mufidah Sopian, Dhika Rizki Anbiya https://creativecommons.org/licenses/by-sa/4.0