Journal of Information Technology and Informatics Engineering
Journal title : Journal of Information Technology and Informatics Engineering Initials : JITIE Frequency : 4 issues per Year P-ISSN : XXXX-XXXX E-ISSN : 3123-7827 Editor in Chief : Ninuk Wiliani, S.Kom., M.Kom.., PhD. Publisher : PT Jurnal Cendekia Indonesia Akreditasi : - Journal of Information Technology and Informatics Engineering (JITIE) is published by PT Jurnal Cendekia Indonesia as a platform to support academics, researchers, and practitioners in disseminating their research findings in the fields of Information Technology and Informatics Engineering. JITIE is a peer-reviewed journal dedicated to publishing high-quality research articles relevant to current developments in information technology, software engineering, data science, system development, and informatics applications. All publications in JITIE are open access, allowing articles to be freely accessible online without subscription fees. JITIE is a national journal with e-ISSN: 3123-7827, and publication fees are applied during the submission and review process. The journal is published six times a year, in the months of February, June and October. To maintain the integrity and quality of manuscripts, JITIE uses Turnitin for plagiarism checks, Mendeley for reference management, and is supported by Crossref (DOI) for the identification and indexing of scholarly works.
Creative Commons Attribution 4.0 International (CC BY 4.0).
TinyLLM-Assisted Real-Time MQTT Intrusion Detection with Conformal Alerting, Cross-Attack Generalization, and Evidence-Based Forensic Narratives
MQTT intrusion detectors are often evaluated with random row partitions even when packet order and capture identifiers make those partitions optimistic. This study evaluates a leakage-aware, real-time pipeline on 222,567 labelled packets from four MQTTEEB-D acquisition loops. Nine packet fields were combined with strictly past context over 8- and 32-packet windows, while absolute time and message-order proxies were excluded. Model selection used an earlier validation interval; a separate calibration interval supported temperature scaling and split-conformal prediction sets and alert thresholds; the last 33,404 packets formed the test interval. The selected Causal HGB achieved macro-F1 0.761 (95% block-bootstrap interval 0.719–0.788) and attack-versus-legitimate AUROC 0.851. At a calibration budget of 25 false alerts per day, observed test recall was 4.3%; raw packet-level false alerts were 7.8 per day and 5-second-collapsed false incidents were 3.9 per day, normalized over the 6.1-hour test interval. Loop holdout, leave-one-attack-out, imbalance, field-corruption, drift, and leakage analyses exposed the gap between within-interval discrimination and deployment generalization. TinyLLM, defined here as a Tiny Language Layer for MQTT, paired a protocol-token SGD classifier with a deterministic evidence renderer. Across 600 audited alerts, exact slot fidelity was 100.0% with unsupported numeric-claim rate 0.0%. The resulting design joins measured detection, explicit uncertainty, and packet-verifiable forensic prose without free-form report generation.
Copyright (c) 2026 Logan Smith, Diana Qi, Riley Scott, Alan Zheng (Author) https://creativecommons.org/licenses/by/4.0
Therapist-Facing Evidence-Linked Session Copilot with Progressive-Context Retrieval, Support-Strategy Control, Safety-Oriented Escalation, and Explanation Cards
Conversational language models can organize large collections of prior counseling exchanges, yet unsupported advice, opaque ranking, and unsafe automation limit their use in mental-health settings. This study evaluated a therapist-facing copilot that retrieves source-linked behavioral-health-coach responses, exposes operational support strategies, and routes safety cues to review. The primary corpus comprised 6,310 question–response pairs derived from anonymized caregiver interviews in MentalChat16K. A separate synthetic component contributed 9,747 usable pairs only in an auxiliary feature-fitting condition. A component-grouped 60/20/20 split compared BM25, word and character TF–IDF, 128-dimensional latent semantic analysis, and four-way reciprocal-rank fusion. Progressive-context tests revealed the original description cumulatively, while strategy-controlled maximal-marginal-relevance extraction assembled cards from the real training library. Reciprocal-rank fusion achieved MRR@10 of 0.4491 and Recall@5 of 0.6220; character TF–IDF performed best at 0.6007 and 0.7071. In grouped out-of-fold evaluation, the character model reproduced narrow and broad phrase proxies with recall 0.5000 and 0.4324. Source-linked cards attained 1.000 exact attribution coverage and three-excerpt completeness. These findings establish an offline retrieval, provenance, confidence-routing, and policy-proxy profile, not therapeutic efficacy. Interpretation and action remained under professional control.
Copyright (c) 2026 Ethan Lewis, Joyce Yuan, Harper Walker, Tony Mao (Author) https://creativecommons.org/licenses/by/4.0
Calibration-Light Cross-Subject Signal-Readiness Screening for Motor-Imagery BCI with Self-Supervised Conformers, Selective Prediction, and Evidence-Grounded LLM Feedback
Low-cost electroencephalography can widen access to brain-computer interface research, but cross-subject deployment is limited by device heterogeneity, electrode saturation, and scarce calibration data. We examined the six continuous recordings in the OpenBCI multidirectional mouse-cursor collection as a signal-readiness benchmark. The repository describes four motor-imagery activities, eye blinking, and rest, yet the files contain no event boundaries and all marker values are zero. Accordingly, the study does not assign motor-imagery classes or report task-decoding accuracy. All 450,651 rows were audited, harmonized to eight channels at 125 Hz, and divided into 506 five-second windows. A deterministic target marked the upper within-subject artifact-burden quartile. Subject-held-out evaluation compared bandpower-LDA, descriptor-map CNN, descriptor Conformer, masked-descriptor self-supervision, covariance alignment, and eight-window target calibration. Unlabeled-target CORAL-logistic achieved 0.823 mean balanced accuracy. The supervised Conformer obtained 0.689, whereas masked pretraining decreased it to 0.579. At 80.44% realized coverage, confidence-based withholding reduced accepted-window error from 0.135 to 0.093. A persistent-rail hard gate prevented lower-relative-burden predictions from certifying a defective recording. The structured output layer rendered 18 rule-verified, nonmedical feedback cards from measured quality and confidence fields. The study establishes what these files support directly and identifies the event metadata required for valid activity decoding.
Copyright (c) 2026 Mason Robinson, Helen Sun, Amelia Clark (Author) https://creativecommons.org/licenses/by/4.0
Language-Guided Cross-Subject Wearable Activity Recognition with Self-Supervised Transfer, Selective Prediction, and Edge Coaching Explanations
Cross-subject wearable activity recognition often fails when a model trained on one person encounters another person's motion range, pace, or sensor placement. This paper evaluates a compact language-guided transfer pipeline on two complete participant archives from the OpenPack v1.1.0 Zenodo record. Four arm and wrist inertial sensors and two wrist physiological devices produced 184 one-second statistics; 4,072 nonoverlapping five-second sequences represented ten packaging operations. The evaluation reverses source and target participants. Source sessions S01–S03 train the models, S04 selects models and calibration parameters, and S05 provides an internal test. Target sessions S01–S02 form a label-blind correlation-alignment pool after operation-window filtering, while S03–S05 remain an external cross-subject test. The method combines a 20% masked linear reconstruction objective, a transparent TF-IDF operation-to-sensor gate, target-to-source CORAL, BiGRU and compact attention branches, temperature scaling, and confidence-based abstention. The LG-SSL-CORAL ensemble reached external macro-F1 scores of 0.490 and 0.524 in the two directions, compared with 0.450 and 0.409 for BiGRU. Box-block paired bootstrap gains were 0.040 (95% CI 0.012–0.068) and 0.115 (0.089–0.145). Extra Trees remained a strong comparator with mean macro-F1 0.519 versus 0.507 for the proposed ensemble. Masked reconstruction improved the forward Conformer-lite result from 0.385 to 0.443, whereas CORAL and source-only temperature scaling were direction-sensitive. At 50% coverage, selective accuracy rose to 0.669 and 0.677, but a source-validation 10% risk rule did not transfer to the target subject. The language branch required 55.6 KiB of FP32 weights and 1.13 ms per sequence in a TensorFlow.js host-CPU proxy. The results establish both the benefit and the limits of language-guided sensor selection, covariance transfer, and confidence rejection in a strict two-participant cross-subject setting.
Copyright (c) 2026 Oliver Thompson, Rachel Lu, Sophia Garcia, Kevin Li (Author) https://creativecommons.org/licenses/by/4.0
Numerical-Reasoning Guardrails for Financial Chart Vision-Language Models with Evidence Extraction, Calibrated Abstention, and Institutional Risk Dashboards
Financial chart questions require a model to align labels, periods, units, visual marks, and arithmetic before returning a compact answer. This study evaluates a backbone-agnostic numerical guardrail on the archived FinChart-Bench release containing 7,019 annotations over 1,202 chart keys. The system extracts OCR evidence, executes a restricted arithmetic trace, estimates answer correctness, and routes low-confidence cases to review. All questions were assigned to five source-document-grouped folds; each fold served once as test data, while a separate fold calibrated confidence and the remaining folds trained textual baselines. On 2,285 numerical QA records, the evidence executor reached 69.85% strict exact match and 93.39% precision-aware accuracy. Isotonic calibration reduced adaptive expected calibration error from 0.144 to 0.063 and area under the risk-coverage curve from 0.190 to 0.179. A threshold selected under a 10% calibration-fold risk budget answered 43.02% of QA records with 85.25% selective accuracy on held-out source documents. OCR recovered all operands for 30.28% of QA records and at least one operand for 68.80%, showing that arithmetic verification can remain useful even when chart text extraction is incomplete. The resulting dashboard exposes evidence completeness, calculation status, calibrated correctness, and review priority. These findings support selective, auditable assistance rather than unqualified automation in financial chart analysis.
Copyright (c) 2026 Benjamin Harris, Derek Guan, Charlotte Martin (Author) https://creativecommons.org/licenses/by/4.0
Event-Sourced Memory and State-Consistency Verification for Dual-Control Enterprise Agents: Recovery, Rollback, and Human Escalation
Enterprise language-model agents operate through conversations, mutable records, external tools, and user actions that can change the same environment. Reliability therefore depends on authoritative execution provenance, duplicate and conflict detection, boundary-resume behavior, rollback, and a state-verified escalation decision when automation cannot continue. This study evaluates four coupled controller-policy packages—Stateless, Full History, Summary Memory, and Event-Sourced Memory—on all 114 telecom tasks in the fixed τ²-bench v0.2.0 release. The telecom corpus contains 516 listed actions, including 393 user-side and 123 assistant-side actions, plus 209 environment assertions. An oracle-plan protocol holds semantic planning constant while native tools execute paired clean, recoverable-fault, and persistent-failure runs. The main campaign contains 13,960 runs and the component campaign contains 12,080 runs. Separately, 12 release-supplied primary result files provide 4,448 model trajectories across airline, retail, and telecom, with 56.83% pooled success. Across 2,450 recoverable-fault runs per controller, task success was 45.92% for Stateless, 89.55% for Full History, 80.37% for Summary Memory, and 100.00% for Event-Sourced Memory. The event package also recorded complete safe resolution, no unresolved failures, and no controller-induced violations. Under persistent failure, each stateful package made a state-verified escalation decision in every case; no person was invoked or scored. Removing the durable idempotency index, precondition policy, or rollback reduced recoverable success to 94.04%, 90.94%, and 82.33%. The results support separating semantic context from transaction-aware execution state while limiting claims to the evaluated policy packages and deterministic interventions.
Copyright (c) 2026 James Jackson, Vincent Tan, Isabella White, Fiona Shi (Author) https://creativecommons.org/licenses/by/4.0
Confidence-Calibrated Camera–LiDAR Fusion under Sensor Shift: Synchronized-Pair Association, Anomaly Detection, and Proposal-Tracklet Continuity
Camera–LiDAR fusion can fail when sensor quality, synchronization, or extrinsic calibration departs from the conditions used to construct the fusion rule. This study evaluates whether confidence calibration makes those failures more visible and reduces overconfident association decisions. Experiments used the DAVE inland-waterway sequence, comprising 128 synchronized 1280 × 720 images, 128 LiDAR point clouds containing 5,719,139 XYZ points, and 781 scored two-dimensional privacy proposals. A chronological 64/32/32-frame train/calibration/test split prevented future frames from entering model fitting. Pair association distinguished synchronized camera–LiDAR frames from frames offset by 1, 2, 4, 8, or 16 s. Image-only, LiDAR-only, early summary-fusion, late evidence-rule, learned-fusion, and confidence-gated models were compared. Held-out discrimination was near chance: learned fusion obtained AUROC 0.4971 and confidence-gated fusion obtained 0.4865. Relative probability quality nevertheless improved on this block. The Platt-calibrated, confidence-gated pipeline reduced Brier score from 0.1768 to 0.1571 and expected calibration error from 0.1743 to 0.0968 relative to the uncalibrated learned model; paired frame-block bootstrap differences were −0.0197 (95% interval −0.0218 to −0.0177) and −0.0730 (−0.0827 to −0.0465), respectively. The fixed severe blur, camera-noise, LiDAR-dropout, and modality-loss conditions were perfectly separated, whereas temporal offsets and fixed extrinsic perturbations remained difficult. Because DAVE provides neither track identities nor manually verified object boxes, temporal experiments report continuity, forward–reverse agreement, edge retention, and spurious-link proxies rather than MOTA, IDF1, or HOTA. JPDA-style affinity produced the highest clean link rate, whereas confidence gating gave the best edge-retention and spurious-link proxies under bounding-box jitter. Calibration can therefore improve the meaning of confidence without creating discriminative evidence absent from the sequence.
Copyright (c) 2026 William Moore, Brian Ye, Mia Anderson, Sabrina Hou (Author) https://creativecommons.org/licenses/by/4.0
Auditing Dark-Pattern Risk in LLM Interaction Prompts: Leakage-Controlled Routing, Confidence-Aware Review, and an Autonomy-Centered Audit Design on DarkBench
Large language model interfaces can steer users through brand preference, retention pressure, agreement-seeking behavior, anthropomorphic framing, harmful assistance, or silent changes to user intent. DarkBench organizes these risks into 660 prompts and six balanced categories. This study asks a narrower but operationally important question: how reliably can an audit gateway recognize the risk category of a prompt before a model generates a reply? The released records were audited at field, ordering, duplication, and source-code levels. A training-prior baseline, a fixed description lexicon, Multinomial Naive Bayes (NB), logistic regression, and a linear support vector machine (SVM) were evaluated with repeated five-fold cross-validation over five repeats. The primary protocol kept connected components of prompts with character n-gram cosine similarity of at least 0.80 in the same fold; a conventional repeated-stratified protocol estimated the performance difference associated with near-duplicate grouping. All learned features were word unigram–bigram TF–IDF fitted within each training fold. Under grouped evaluation, NB obtained 0.9742 accuracy and 0.9741 macro-F1, followed by SVM at 0.9721 and 0.9720 and logistic regression at 0.9697 and 0.9694. Random splitting raised SVM macro-F1 to 0.9930, a 2.11 percentage-point gain that did not survive template-aware grouping. Brand bias was the hardest NB category (F1 0.9334). Confidence ranking was useful despite underconfident probabilities: the highest-confidence 80% of prompts were classified without error by both probabilistic models across all five repeats, while 90% coverage retained 0.9980 accuracy for NB and 0.9993 for logistic regression. The corpus contains prompts and risk targets but no model responses, benign controls, response annotations, or model identities. Accordingly, the measured task is pre-response risk routing rather than detection of manipulative behavior in generated text. The resulting workflow sends uncertain or high-stakes requests to evidence-based response review and retains ordinary routing for high-confidence cases as part of an autonomy-centered audit design.
Copyright (c) 2026 Victor Xie, Noah Davis, Alice Gao (Author) https://creativecommons.org/licenses/by/4.0
Constraint-Verified LLM Graph Workflow Synthesis with Execution-Guided Self-Repair, Parallel Optimization, and Trajectory Failure Prediction
Large language model agents increasingly express multi-step work as directed graphs, yet a plausible action list does not guarantee correct dependencies, structural executability, or useful parallelism. This paper presents CVR-Graph, a verification layer combining dependency scoring, typed priors, diagnostic-guided repair, transitive reduction, graph-quality failure prediction, and critical-path scheduling. Evaluation used 18,679 WorFBench training records and all 2,146 official test workflows from nine domains. After overlap removal, de-duplication, and structural filtering, 17,128 workflows remained for development. A topology-controlled protocol held reference task nodes fixed and measured edge construction directly. Linear and parallel-optimized methods achieved edge F1 0.875, path F1 0.963, and strict graph success 0.754. Reduction removed 3,836 redundant predicted edges and increased edge F1 by 0.083 without changing path F1. Validator feedback also restored structural executability for all 31 released graphs with incomplete START-to-END coverage. Gradient-boosted trajectory screening achieved ROC-AUC 0.944, PR-AUC 0.952, Brier score 0.102, and expected calibration error 0.051; accepting the lowest-risk 50% reduced failure risk by 68.7%. Gold-DAG scheduling produced mean speedup 1.369 with eight workers, whereas predicted graphs remained chains. The results show that verification and calibrated control are effective, while non-linear dependency induction remains the limiting factor under domain shift.
Copyright (c) 2026 Liam Miller, Jason Peng, Ava Taylor, Melody Ding (Author) https://creativecommons.org/licenses/by/4.0
Critical-Path and Uncertainty-Aware Dynamic Scheduling for Resource-Constrained LLM Multi‑Agent Teams
Large-language-model multi-agent systems can decompose complex work, but adding agents does not guarantee shorter completion time: precedence constraints, uneven task sizes, and cross-agent handoffs can erase the benefit of parallelism. This study evaluates six deterministic scheduling policies on the released MultiAgentBench/MARBLE coding corpus. All 100 tasks were converted into requirement-level directed acyclic graphs using temporal, integration, testing, adaptive-feedback, and final-stage dependency evidence in the task specifications. The resulting corpus contains 621 requirement nodes, 780 edges, 40 application topics, and four coordination categories. Word counts serve as normalized work units; consequently, the study measures scheduling behavior rather than live model latency or software correctness. We executed 1,800 schedules spanning FIFO, Round-Robin, static-role, critical-path, uncertainty-weighted critical-path, and a critical-path policy with uncertainty and communication control (CP-UC), each at 2, 4, and 8 concurrent workers. Critical-path scheduling produced modest mean improvements over FIFO of 0.80%, 0.96%, and 0.51%, respectively. CP-UC reduced mean communication work by 7.27%-9.27% and propagation exposure by 1.66%-2.26% relative to FIFO, but increased mean makespan by 2.10%-3.39% on the paired percentage measure. Category analysis locates this cost mainly in test-case and dependency-heavy tasks. The results show that critical-path priority is a useful low-risk default, whereas communication control should be treated as an explicit latency-coordination trade-off rather than a universally faster scheduler.
Copyright (c) 2026 Shawn Tan, Emma Wilson, Vera Lu (Author) https://creativecommons.org/licenses/by/4.0
Design and Development of a Digital Attendance Software Application with Data Security for Jakarta International Polytechnic
This research was conducted by researchers to utilize design according to technological developments. Design is a process that defines something that will be done using various techniques and includes a description of the architecture and component details as well as the limitations that will be experienced in the process [1]. Design is a series of procedures to translate the results of analysis of a system into a programming language to describe in detail how the system components are implemented [2]. Data security is an effort to protect information from unauthorized access, use, disclosure, change, or destruction (M. Hadiwintata, 2020). According to Kristanto (2020/3) a Database Management System (DBMS) contains a collection of interrelated data and a set of programs to access that data. A database is a collection of interrelated files, the relationship is usually indicated by the key of each existing file (Kristanto, 2020). MySQL is a software or SQL database management system or DBMS that is multithreaded and multiuser (Solichin, 2005). Hypertext Preposessor is an abbreviation of PHP created by Rasmus Ledof in 1994, at the beginning of PHP development it was called an abbreviation of Personal Home Page (Antonius Nugraha, 1994). This research aims to design a fingerprint software for digital attendance with data security for the Jakarta International Polytechnic to make it easier for students, admins, lecturers and the director to do digital attendance. vehicle washing service business. application with Data Security for Jakarta International Polytechnic with Data Security for Jakarta International Polytechnic
Rancang Bangun, Audio Web Recording, Internet Market, TAM, Beyoursound
Copyright (c) 2025 Lanny Catrin Dale (Author) https://creativecommons.org/licenses/by/4.0
Medical Decision Making Based on K-nearest Neighbor (Knn) Algorithm in Stroke Patient Classification
Stroke is a non-communicable disease and one of the leading causes of death and disability worldwide. Early detection of potential stroke risk is crucial to support effective prevention and management efforts. This study aims to develop a stroke risk classification system using the K-Nearest Neighbor (KNN) algorithm implemented through the RapidMiner platform. The dataset analyzed consists of 932 patient records with various medical and demographic attributes. The research process includes data preprocessing, variable transformation, normalization, and splitting the data into training and testing sets. Model evaluation shows an accuracy rate of 82.35%; however, the model has not performed well in identifying stroke cases due to data imbalance. These findings highlight the importance of addressing class imbalance in medical data and the need to consider alternative algorithms to improve detection of minority classes.
Stroke, K-Nearest Neighbor, KNN, RapidMiner, Data Mining
Computer Science; Data Science; Medical Informatics; Information Systems
Ilmu Komputer; Sains Data; Informatika Medis; Sistem Informasi
Copyright (c) 2025 Risma Ananta Maulida, Suci Anisa Aulia, Ridho, Satrio Dzulfahmi Yulianto, Shania Clara Efendi, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Forest Fire Characteristics Segmentation In Montesinho National Park Using K-means Clustering Algorithm In Rapidminer
his study aims to analyze the characteristics of forest fires using the K-Means Clustering algorithm in RapidMiner software. Forest fires are disasters that significantly impact ecosystems and human life, making data-driven analysis of their causal patterns crucial. The dataset includes critical variables such as the Fire Weather Index (FWI) system components (FFMC, DMC, DC, ISI), weather conditions (temperature, humidity, wind speed, rainfall), and spatial coordinates from the Montesinho National Park in Portugal. The research methodology involved data preprocessing, feature normalization, and the implementation of the K-Means algorithm with three clusters to classify fires based on risk levels.The analysis revealed that Cluster 1 was dominated by high-temperature and low-humidity fires (high risk), Cluster 2 was characterized by higher rainfall (low risk), and Cluster 0 exhibited large-scale fires with significant wind influence. The clustering demonstrated the effectiveness of K-Means in identifying forest fire patterns based on environmental factors, supported by a Silhouette Score of 0.62, indicating reasonably well-separated clusters.These findings provide a foundation for developing more accurate early warning systems for forest fires and support data-driven prevention and mitigation strategies
Copyright (c) 2025 Vira Yuniarti, Syaepul Rahmat Dani, Tegar Winata, yogi wardana saputra, zaky Ramadhan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Data Mining Using Decision Tree Algorithm to Determine Student Graduation with RapidMiner
Student graduation prediction is an important aspect in higher education management which is intended to project students' chances of completing their studies on schedule. Accurate prediction results can support educational institutions in formulating strategic policies to improve the quality of academic services and provide more effective interventions and provide more effective support to students at risk of experiencing delayed graduation. This study applies the Decision Tree algorithm with the help of the RapidMiner application to build a student graduation prediction model, using data such as age, graduation status, and cumulative achievement index as the main variables. The results of the analysis show that the developed model is able to achieve a prediction accuracy level of 96.57%. This finding confirms that data mining techniques have great potential in helping educational institutions identify students who need special attention in order to complete their studies on time. Therefore, the results of this study not only play a role in the development of prediction models in the academic realm, but the results of this study can also be used as an initial basis for subsequent research that focuses on graduation prediction in the higher education environment.
Copyright (c) 2025 Kevin April Akhmallahudin, Divia Cahyani, Dwiky Rachmatullah, Dzikrully Akbar, Hilmi Malik, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Heart Attack Risk Sentiment Analysis Using K-means Clustering With Rapidminer
This study discusses the analysis of patient grouping based on heart attack risk by applying the K-Means Clustering algorithm using RapidMiner software. In this modern era, patient health data is very important for early identification and prevention of serious diseases such as heart attack. To understand the patterns of patient characteristics related to this risk, a clustering process was carried out on a heart attack risk dataset obtained from Kaggle, consisting of 8,763 patient data entries. The research stages began with data collection, data preprocessing, and the implementation of the K-Means algorithm with a certain number of clusters (e.g., three), which will group patients based on their risk profiles (e.g., low, moderate, and high risk). The research results are expected to show the distribution of patient data into these clusters, for example, how many patients fall into the high, moderate, and low-risk clusters. With these results, the K-Means algorithm proves effective in identifying groups of patients with similar characteristics, as well as providing useful insights for early detection and intervention of heart attack risk automatically. This research is expected to serve as a basis for the development of a more accurate and adaptive risk identification system for the dynamics of health data
Copyright (c) 2025 Bima Aditiya, Ade Kurniaty, Adi Muslim, Aryazeyla Rachayudiza, Diana Manullang, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Utilization of Data Mining for Credit Card Customer Segmentation Using the K-Means Method
This study aims to cluster credit card users based on demographic information and card usage behavior using K-Means clustering algorithms. The BankChurners.xlx dataset, which contains over 10,000 customer data, was analyzed using RapidMiner software. The analysis process includes data preprocessing steps, including normalization, attribute selection, and categorical data encoding. The K-Means algorithm is then used to group customers into two clusters. The results of this clustering show the existence of two main segments with different characteristics, where the majority of customers fall into one larger group. Cluster quality assessment using the Davies-Bouldin index shows satisfactory separation results. This result can serve as a basis for strategic decision-making, particularly in designing marketing plans and developing services that are more precise and suited to the characteristics of each customer segment.
Copyright (c) 2025 Intan Pramesta Nurhayati, Helmayana, Adis Tiani, Kezia Maruenci, Yuriana Sari Harahap, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Naive Bayes Algorithm in Categorical Data Classification Prediction of Tennis Playing Decisions Based on Weather
Decision-making based on weather factors is often subjective and inconsistent. This research applies data mining classification methods to build an objective predictive model regarding the decision to play tennis based on weather conditions. The objective of this study is to analyze the performance of the Naive Bayes algorithm in predicting this decision. The methodology involves applying the Naive Bayes algorithm to the classic "Play Tennis" dataset, which consists of 14 instances with four categorical predictor attributes: outlook, temperature, humidity, and wind. The modeling and evaluation process was conducted visually using the Altair AI Studio (RapidMiner) platform, employing the cross-validation technique to test model stability. The test results show an average model accuracy of 57.14%. A deeper analysis of the confusion matrix reveals that the model has a strong bias towards predicting the 'Yes' class, yet is very weak in identifying the 'No' class (20.00% recall). Specifically, the model exhibits a high number of False Positive errors, where 4 out of 5 'No' cases were misclassified. In conclusion, the Naive Bayes model in its current configuration is not yet fully reliable for practical application due to its biased performance. This study recommends further optimization, such as applying data balancing techniques or using more complex alternative algorithms, to significantly improve predictive performance.
Copyright (c) 2025 Feriandri Lesmana, Athila Defian Rizkimu, Muhamad Ridwan Nurrulloh, Maulana Farras Fathurrahman, Abdul Habib Hasibuan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Negative Comments (Hate Speech) on Twitter with K-means Clustering Algorithm Using RapidMiner
This study discusses sentiment analysis of negative comments (hate speech) on the social media platform Twitter by applying the K-Means Clustering algorithm using RapidMiner software. In today’s digital era, Twitter has become one of the main platforms for the open dissemination of public opinion, including negative comments that may lead to hate speech. To understand the sentiment patterns in these comments, clustering was carried out on a dataset consisting of 27,325 tweets obtained from Kaggle. The research stages included data collection, preprocessing, and the implementation of the K-Means algorithm with three clusters, categorizing the comments into negative, neutral, and positive groups. The results showed that most of the comments fell into the negative cluster, comprising 14,032 entries, followed by 9,924 neutral entries and 3,369 positive entries. These findings demonstrate that the K-Means algorithm is effective in identifying the distribution of hate speech on social media and provides valuable insights for mitigating and monitoring negative content automatically. This study is expected to serve as a foundation for developing more accurate and adaptive sentiment analysis systems in response to the dynamics of digital communication.
Copyright (c) 2025 Nazwa Alfira, Muhammad Refa Tsalits Ramdhani, Muhammad Ridho Putra Budika, Muhammad Virgi Santoso, Nyla Zahry (Author) https://creativecommons.org/licenses/by/4.0
Data Mining Using K-means Algorithm To Determine The Most Popular Games On The Steam Platform With Rapid Miner
With the number of games increasing every year, it is a challenge to determine which games are the most popular on the Steam platform. This study uses the K-Means clustering algorithm in RapidMiner to group games based on their popularity. Ratings and estimated number of game downloads are the variables used in this study. Data were collected from the top game sales dataset on the Steam platform. Clustering produces two clusters: less dan most populer, indicate the level of game popularity. This study can help game developers and publishers understand what features users are most interested in in a game.
Copyright (c) 2025 Deryl Iman Condro Baskoro, I Putu Ganesa Weda Pratama, Aryo Chandra Ray Hash, Muhammad Fakih, Muhammad Fauzan, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Student Clustering Based on Academic Performance Using the K-Means Algorithm in RapidMiner: A Case Study with the Student Academic Performance Dataset
One of the primary indicators used to convey the effectiveness of the learning process and to create more efficient teaching methods is student academic performance. This study uses the RapidMiner application to use the K-Means Clustering method in order to group students according to their academic performance. The synthetic data, which includes details about student involvement, attendance rates, and academic grades, is taken from the Kaggle platform. This study was carried out in a number of steps, including cluster quality assessment, attribute selection, algorithm application, and data pre-processing.Based on the results, three student groups with characteristics of high, medium, and low academic performance were examined. The Davies-Bouldin Index examination indicated that the clustering results were optimal. These findings are expected to serve as a guide for educational institutions to develop more appropriate and successful teaching strategies.
Copyright (c) 2025 Siti Khodijah, Athaya Rima Hariyanto, Berliani Salsabiilah, Winona Septi Aulia, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Weather Factor Analysis on Badminton Playing Decisions Using Naive Bayes Algorithm
By utilizing the Naive Bayes algorithm as a classification method, this study investigates how weather factors influence a person's decision to play badminton. The Badminton dataset, including attributes such as weather conditions, air temperature, humidity levels, and wind conditions, was collected and processed using RapidMiner software. The preprocessing stage involved data cleaning and transforming the attributes to be suitable for analysis. To predict the decision to play badminton based on weather conditions, the Naive Bayes algorithm was chosen due to its capability to compute class probabilities easily and effectively.This study found that weather factors significantly influence a player's decision to play badminton, and the Naive Bayes model demonstrated the ability to make reasonably accurate predictions. In conclusion, the Naive Bayes algorithm can be effectively used to predict players' decisions in playing badminton
Copyright (c) 2025 Gusti Alfian, Ageng Samudro Ndiko Laksono, Ardiansyah, Mahis Duhan, Raffa Nurprasetyo Araya, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Prediction of Physiotherapy Patient Satisfaction Level Using Naive Bayes Algorithm
This study aims to predict patient satisfaction levels in physiotherapy services using the Naive Bayes algorithm. Patient satisfaction is a key indicator of healthcare service quality, and this prediction is based on attributes such as age, gender, session duration, and therapist expertise. The dataset, consisting of 31 entries, was analyzed using RapidMiner software. The classification process applied the Naive Bayes model, known for its simplicity, computational efficiency, and strong performance even with limited data. Evaluation results showed an accuracy rate of 90%, with balanced precision and recall between the "satisfied" and "dissatisfied" categories. These find-ings demonstrate that data mining techniques can serve as valuable tools to support continuous improvement in physiotherapy service quality.
Copyright (c) 2025 Kaila Nazuwa Kaila (Author) https://creativecommons.org/licenses/by/4.0
Diabetes Prediction Based on Patient Medical Factors Using Decision Tree Algorithm
Early detection of diabetes risk is crucial to prevent severe complications. This study develops a predictive model for diabetes using the Decision Tree algorithm based on patient medical data. The dataset consists of 768 records with eight health-related attributes, of which 99 labeled instances are used to train the model. The process includes data cleaning, target attribute assignment, and model construction using RapidMiner. Results indicate that variables such as age and glucose levels significantly influence diabetes classification. Although the initial findings show promising potential, further validation with larger and more balanced datasets is needed to improve the model's accuracy.
Copyright (c) 2025 Ahmad Reza, Amar Naufal Al-kharits, Muhamad Bustomi, Nazar Maulana, Taupik Abdul Rahman (Author) https://creativecommons.org/licenses/by/4.0
Implementation Of K-nearest Neighbor (KNN) Algorithm Using Rapid Miner For Diabetes Disease Prediction Based On Indian Pima Dataset
The objective of this research is to use the publicly accessible Pima Indian dataset to use the K-Nearest Neighbor (KNN) algorithm for diabetes prediction. A straightforward yet powerful classification technique, the KNN method is particularly useful for processing medical data. RapidMiner software was utilized for this study's analysis method, which included data pre-processing, training and test data separation, and classification model validation. Numerous health indicators, including age, blood pressure, body mass index, and glucose levels, are included in the Pima Indian dataset and are utilized as predictive features. The test results demonstrate that the KNN algorithm can categorize patients with or without diabetes with a reasonably high degree of accuracy. Accuracy, precision, recall, and confusion matrix metrics were used to assess the model's performance. As a result, using KNN to this dataset may be a way to help the decision support system for diabetes early diagnosis.
Copyright (c) 2025 Tetta Thirza Herdyawan, Dimas Cahyo Saputra, Gabriel Carol Aldosion, Salsha Sabilla Nurhidayat, Sukrinah (Author) https://creativecommons.org/licenses/by/4.0
Development of Cloud Computing Based Office Asset Management Information System at Pamulang University for Operational Efficiency
This study aims to develop a cloud computing-based canteen asset management information system at Pamulang University to improve operational efficiency and optimize asset management. The rapid development of information technology drives digital transformation in various sectors, including campus facilities such as canteens, which demand high efficiency and accuracy in asset management. Using the System Development Life Cycle (SDLC) approach through needs analysis, system design, conceptual implementation, and theoretical evaluation based on literature studies, this study identifies problems such as manual recording, difficulty in real-time monitoring, and lack of automated reporting. The proposed system utilizes a Software as a Service (SaaS) architecture for high scalability and multi-platform access, providing digital asset catalog features, inventory management, real-time asset tracking, and analytical dashboards to monitor canteen operational performance. The implementation of cloud computing is expected to improve data security, reduce IT infrastructure costs, and facilitate access to asset information from anywhere. The concept of this system is designed to support various user roles, such as canteen staff and university management, and provides a framework that can be applied to other similar facilities.
Copyright (c) 2025 Femilda Aprilia, Veivi Novi lestari, Junjung Adi Prasetyo Lenggogeni (Author) https://creativecommons.org/licenses/by/4.0
Application of K-Means Clustering Method Using RapidMiner for Student Achievement Classification in Private Schools
This study discusses the application of the K-Means Clustering algorithm in grouping the level of academic achievement of students in private schools with the help of RapidMiner software. The data analyzed include assignment scores, midterm exams, final exams, and attendance. The K-Means algorithm was chosen because of its ability to group unlabeled numeric data and recognize hidden patterns in the dataset. The analysis was carried out on data from 5,000 students obtained through the Kaggle platform. The clustering results produced two main groups, namely students with high academic achievement and students with lower achievements. This process allows schools to understand the characteristics of each group of students and develop more effective coaching strategies and educational policies. The use of RapidMiner has been proven to help the data analysis process efficiently and intuitively, without the need for advanced programming skills.
Copyright (c) 2025 Eko Andri Wibowo, Ririn Aryanti (Author) https://creativecommons.org/licenses/by/4.0
Prediction and Classification of Best Sales Transactions in Building Supply Stores Using the K-Nearest Neighbors (K-NN) Method
This study uses the K-Nearest Neighbors (K-NN) method to predict and classify the best-selling products in a hardware store. With the current development of information technology, sales trend analysis and prediction have become an important part of the business decision-making process. The popular K-NN classification algorithm is used to analyze sales data from a public dataset to determine which products are most in demand by consumers. The process of data collection, selection, preprocessing, transformation, data mining, and evaluation of results are all part of the Knowledge Discovery in Database (KDD) stages. The analysis results show that products in the “active” category sell more than products in the “passive” category. Out of the total data, 56 were successfully categorized as active data, and the remaining 29 were categorized as passive data. This study is expected to provide deeper insights into consumer behavior and assist building material store management in making better decisions using the data they possess. This is anticipated to enhance the company's competitiveness and improve operational efficiency.
Copyright (c) 2025 Adrian Chandra Kusumah, Nandi Adi Nugroho, Genta Aldora Leopriandis, Achmad Khautsar Rizaldi, Firmansyah, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Algoritma K-means Clustering Data Penjualan Pada Warung Sembako Isan Menggunakan Rapidminer
This study aims to apply the K-Means Clustering algorithm with the help of RapidMiner software on sales data at Warung Sembako Isan. In managing small businesses such as grocery stores, processing sales data manually often faces various challenges, such as errors in recording and difficulties in identifying sales trends. Therefore, data mining techniques, especially clustering methods, are used to categorize products based on their sales capabilities. This process is carried out using RapidMiner, which allows analysis without the need for programming through a visual interface. The data were analyzed using the K-Means algorithm with parameter k = 3, which produces three categories: products with high potential, medium potential, and low potential. The results of this clustering make it easier for shop owners to understand product performance, develop storage strategies, and plan more efficient promotions. This study shows that the use of simple technology can improve operational efficiency and assist MSMEs in data-based decision making.
Copyright (c) 2025 Muhammad Azriel, Daviqia Fadel, Fajri Maulana Azzam Harahap, Irsad Fauzan, Muhammad Fadlan Jabbar, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0
Application of K-Nearest Neighbor Algorithm Using Rapidminer on Life Satisfaction of Commuter Workers in Indonesia
The level of life satisfaction of commuter workers in Indonesia is classified using the K-Nearest Neighbor (K-NN) algorithm using the RapidMiner application. This study aims to provide a better understanding of the social and economic conditions of workers who have to travel long distances every day. To collect data, a questionnaire covering various information such as income, number of dependents, location of residence, travel time, and level of life satisfaction was sent. Before being entered into the model, the data is then processed through a cleaning stage, normalizing numeric values, and dividing into test data and training data. One of the reasons for RapidMiner is its visual interface, which allows users to create classification models without writing programming code. The test results show that the K-NN algorithm can accurately classify the level of life satisfaction of commuter workers. Model performance is greatly influenced by the selected variables, namely the K value, and data quality. This study is expected to help related parties, this approach is considered effective in helping data-based decision making.
Copyright (c) 2025 Muhammad Fadli Juliana Putra, Bayu Pangestu, Sopyan Hidayat, Bintang Ardian Nugroho, Dastin Ramadhani, Maulana Fansyuri (Author) https://creativecommons.org/licenses/by/4.0