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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 876 Documents
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Prediksi Risiko Penyakit Jantung Menggunakan Support Vector Machine dengan Seleksi Fitur dan Optimasi Hyperparamete Nurhadi Surojudin; Sufajar Butsianto; Rizal Ainun Yaqin
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10518

Abstract

Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accurate prediction models to support early diagnosis and clinical decision-making. This study proposes a heart disease risk prediction model based on Support Vector Machine (SVM) integrated with Recursive Feature Elimination (RFE) for feature selection and GridSearchCV for hyperparameter optimization. The study utilized the Cleveland Heart Disease Dataset, consisting of 303 patient records, 13 predictive attributes, and one target variable. The research workflow included dataset collection, data preprocessing, feature selection, hyperparameter optimization, model development, and performance evaluation using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Experimental results demonstrate that the proposed model achieved an Accuracy of 86.89%, Precision of 85.71%, Recall of 85.71%, F1-score of 85.71%, and ROC-AUC of 93.18%. Feature selection successfully reduced irrelevant attributes, improving model efficiency, while hyperparameter optimization produced a more effective parameter configuration than the default settings. These findings indicate that integrating RFE with GridSearchCV enhances the predictive performance of SVM and provides a promising approach for supporting heart disease diagnosis using machine learning techniques.
Sistem Lost and Found Berbasis Vision-Language Retrieval dengan Explainable AI untuk Lingkungan Smart Campus Enrico Samuel Djimesha; Regi Fernando Najoan; Argha Orion Silitonga; Wilsen Grivin Mokodaser; George Morris William Tangka
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.10990

Abstract

Evidence for combined image-text retrieval in campus lost-and-found systems remains limited, and zero-shot CLIP's ability to distinguish item attributes and identities is uncertain. This study evaluates CLIP ViT-B/32 on an item-image collection covering eight categories. The controlled multimodal evaluation uses 404 images, 85 metadata-defined relevance groups as proxy ground truth, and 237 leave-one-out queries. Separate image-only validation uses 387 images from 79 provenance-based Object IDs after duplicate removal and quality control; each Object ID contains multiple images of the same physical item. In Object-ID validation, CLIP achieved Recall@1 of 0.674, Recall@10 of 0.935, and mAP of 0.419 (95% object-cluster bootstrap CI: 0.369–0.469). In multimodal evaluation, image-only achieved mAP 0.624, text-only 0.409, and image+text reached the best mAP of 0.732 at α=0.5, selected in four of five group cross-validation folds. Ablation showed that the single fused-index outperformed the three-index configuration (mAP 0.715 versus 0.692); therefore, three-index is not claimed as an accuracy improvement. Direct Indonesian input to English CLIP achieved mAP 0.163, compared with 0.363 using multilingual CLIP and 0.409 through translation. Attention Rollout is positioned only as a supporting visualization because its deletion-curve AUC of 0.871 lacks a random-occlusion baseline. These results indicate the benefit of image-text fusion under controlled evaluation but do not establish performance for free-form user descriptions or production readiness.
Integrasi Analisis Butir Soal dan Klasifikasi Kognitif Untuk Personalisasi Rekomendasi Remedial Adaptif Menggunakan K-Nearest Neighbor Karimah Agustin; Alun Sujjada; Muhammad Ikhsan Thohir
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11066

Abstract

In many secondary schools, classroom assessments still use tests that are written by hand, which makes it hard for teachers to ensure each question matches the right level of thinking from Bloom's taxonomy or to check if the questions are statistically reliable. This study tackles that issue by creating a web-based testing system for a madrasah tsanawiyah, which automatically sorts multiple-choice questions, checks their quality using psychometric methods, and offers custom learning suggestions to help students improve. Cognitive-level tags (C1 to C6) are assigned by matching keywords that describe actions, and a kNearest Neighbor classifier uses TF-IDF features to sort items into groups that show more or less complex thinking. Item quality is checked using Classical Test Theory module automatically computes the difficulty index and discrimination index of each item from students answer patterns, and materials that help improve learning are chosen based on each student's weak areas through content-based filtering. The system was developed using the Waterfall model and was tested on 600 items taken from three different subjects. The classifier achieved 86.67% accuracy when using k=11, which was verified through 10- fold cross-validation showing 81.83% ± 5.19%, and all 23 black-box test cases were successfully passed. The results show that using automatic item analysis along with adaptive remediation is possible to implement in assessment practices at the madrasah level. The main contribution of this study is the integration of automatic cognitive classification, CTT-based psychometric analysis, and content-based filtering remedial recommendation into a single real-time examination platform, a combination that has not been found in previous similar studies, which generally address only one of these three aspects separately.
Klasifikasi Serangan DDOS Pada Arsitektur Software-Defined Network Menggunakan Stacked Ensemble Learning Berbasis Deep Neural Network Dicopran Sisco; Edi Surya Negara
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11099

Abstract

Software-Defined Networking (SDN) architecture offers a flexible network management paradigm through the centralization of the control plane. However, this centralization creates a single point of failure that is highly vulnerable to Distributed Denial of Service (DDoS) attacks, particularly those originating from Internet of Things (IoT) botnets. Traditional intrusion detection systems are no longer sufficient to recognize modern attack tactics, while the implementation of Machine Learning and Deep Learning is often hampered by high rates of false positives that degrade the Quality of Service (QoS) for legitimate users. This research proposes the development of a two-tier stacked ensemble learning-based DDoS detection framework to classify traffic with precision. The preprocessing stage drastically reduces data dimensions by extracting the top five features using the Gini Importance metric (covering 71.9% of the information variance). The classification architecture then integrates Random Forest (RF) and Support Vector Machine (SVM) as base learners, along with a Deep Neural Network (DNN) as a meta-learner tasked with synthesizing probability distributions. Experimental results demonstrate superior model performance with Accuracy, Precision, Recall, and F1-Score of 99.97%, as well as an ROC-AUC value of 0.99999976. This architecture successfully achieved zero false negatives and generated only five false alarms out of 19,750 test samples. The main contributions of this research lie in the proposal of a multi-level Stacked Ensemble architecture to distribute the detection load, the application of feature dimension reduction to optimize computation while reducing the False Positive Rate, and empirical evidence of the model’s robustness in maintaining the stability of the SDN network controller without degrading QoS.
Penerapan Algoritma YOLOV5 Berbasis Computer Vision Untuk Pemantauan Real Time Child Part Assembly Bumper Pada Industri Otomotif Abby Muhammad Titan; Dendy K. Pramudito; Muhamad Fatchan
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11131

Abstract

The assembly process of small-scale components, widely known as the bumper child part assembly stage in the automotive manufacturing industry, is highly vulnerable to human error and daily quality inspection inconsistencies. This vulnerability is primarily driven by excessive operational reliance on manual visual inspection conducted by human operators over extended periods. Such dependency frequently leads to defective products escaping to subsequent production lines, ultimately causing financial losses for the company. To address this critical issue, this study aims to design and implement an intelligent real-time monitoring system based on computer vision technology and the You Only Look Once version 5 (YOLOv5) deep learning algorithm to automatically detect anomalies in vehicle bumper component assembly. The image preprocessing stage is comprehensively performed using the OpenCV library and matrix manipulation to normalize variations in artificial lighting intensity on the production floor. The testbed in this study is focused on an assembly workstation equipped with ring illumination and a controlled conveyor speed to ensure visual data validity. This artificial intelligence system is then integrated into an interactive web monitoring interface that provides instantaneous feedback along with automated inspection logging into a database. Empirical test results in an actual production environment demonstrate that the system achieves an average detection accuracy of 98.6%, a precision of 98.9%, a recall of 99.8%, and an F1-score of 99.3%. The inference time is remarkably short at 12.5 milliseconds, enabling a processing speed of up to 65 frames per second. The principal contribution of this research lies in the development of an integrated inspection framework that synergizes a lightweight YOLOv5 detection model with a real-time Fatigue Analytics instrument and automated database logging. This integrated solution functions not only as a passive visual quality verifier but also as an empirical ergonomics diagnostic tool that suppresses component defect rates below two percent in actual manufacturing environments.
Perbandingan Algoritma Random Forest dan SVM untuk Prediksi Tren Harga Saham BBCA Muhammad Hasan Rizky; Dian Hafidh Zulfikar; Hermanto Hermanto
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11198

Abstract

A lack of financial literacy and a fear of investment risk often lead university students to engage in stock speculation without objective decision-making tools. This study aims to develop a machine learning-based predictive model to classify daily stock price movement trends for PT Bank Central Asia Tbk (BBCA) during the 2020-2026 period, a popular investment choice among students. A comparative experimental method is employed to evaluate and compare the performance of the Random Forest (RF) algorithm against the Support Vector Machine (SVM) algorithm using a Radial Basis Function (RBF) kernel. SMA-10, RSI-14, MACD, and Signal Line technical indicators were used to extract 1,151 rows of historical data from 2020 to 2026. The data was then divided chronologically using an 80:20 ratio. According to experimental data, the SVM model outperformed Random Forest (accuracy: 52.41%; precision: 43.15%) in terms of global accuracy (57.88%) and precision (47.69%). Nevertheless, the SVM model turned out to be unduly cautious, producing a poor recall of 24.22% and, as a result, losing many chances to spot rising (bullish) patterns. Random Forest, on the other hand, showed better balanced generalization ability, with an F1-score of 45.99% and a recall of 49.22%. This study introduces the integration of momentum indicators to mitigate FOMO among novice investors. As a result, Random Forest is suggested as the decision engine architecture for creating a system that helps students make investment decisions.

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