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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,078 Documents
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Evaluation of the Impact of Labeling Quality and Class Imbalance on Sentiment Classification of the Palestine–Israel Conflict Salvia Devi Muhshanah; Evi Maria
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6304

Abstract

This study aims to evaluate the performance of sentiment classification on social media data related to the Palestine–Israel conflict, with a particular emphasis on the role of labeling quality and data distribution. The proposed approach combines TF-IDF text representation with lexicon-based labeling using InSet, along with two classification algorithms: Support Vector Machine (SVM) and Random Forest. The dataset was collected from the social media platform X and consisted of 2,831 Indonesian-language tweets that had undergone preprocessing. The results indicate that the sentiment distribution was dominated by the negative class (39.35%), followed by neutral (38.43%) and positive (22.21%) classes, indicating the presence of class imbalance. The labeling validity evaluation produced a Cohen’s Kappa value of 0.0175, indicating a low level of agreement between automatic labeling and manual annotation. The SVM model achieved an accuracy of 0.69 and a weighted F1-score of 0.68. However, both models demonstrated poor performance on the positive class as the minority class. These findings suggest that the limitations in model performance are not solely caused by the classification algorithms themselves, but are also significantly influenced by labeling quality and data distribution characteristics. This study contributes by emphasizing the importance of comprehensive evaluation throughout the sentiment analysis pipeline, particularly when dealing with complex and uncontrolled data sources such as social media.
Classification of Diabetes Mellitus using the K-Nearest Neighbor (KNN) Algorithm: A Case Study of Patient Data at Salatiga Regional Hospital Gwen Theresia Grandis Aritonang; Magdalena A. Ineke Pakereng
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6306

Abstract

One of the most common metabolic diseases in Indonesia, including at RSUD Salatiga, is Diabetes Mellitus. Early diagnosis of this disease is crucial to prevent the development of more severe complications; however, this process is often challenging because the initial symptoms are difficult to recognize. This study implements the K-Nearest Neighbor (KNN) algorithm as a classification technique for predicting diabetes mellitus risk based on clinical data from patients at RSUD Salatiga. The dataset used in this research included variables such as gender, age, hypertension history, glucose level, HbA1c level, smoking status, liver disease history, and Body Mass Index (BMI). The research stages consisted of data collection, preprocessing (data cleaning, normalization, and variable encoding), feature selection, and optimization of the k parameter. Model evaluation was conducted using a confusion matrix with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that the KNN algorithm achieved the highest accuracy of 92.08% when feature selection was applied with k = 6. This improvement demonstrates that both k-parameter optimization and feature selection significantly affect model performance. Therefore, the KNN algorithm can serve as an effective early prediction tool to assist medical personnel in identifying diabetes mellitus patients, enabling faster and more accurate interventions.
Application of Natural Language Processing for Emotion Detection and Motivational Response Generation in Indonesian Text using the CRISP-DM Method Jefri Yushendri; Purnawarman Musa
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6334

Abstract

The rapid growth of social media has encouraged teenagers to express their emotions through text-based social media posts. However, existing systems still face limitations in understanding emotional meaning and providing appropriate responses. This study aims to develop a Natural Language Processing (NLP)-based system for detecting emotions in Indonesian-language text and generating contextual motivational responses. The research methodology employed the Cross Industry Standard Process for Data Mining (CRISP-DM), which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. Text processing was conducted through tokenizing, filtering, stemming using the Enhanced Confix Stripping algorithm, negation checking, and matching with an emotion lexicon based on Plutchik's Wheel of Emotions classification. The dataset consisted of 14,182 emotion lexicon entries used as reference data and 100 emotional expression sentences collected from 100 respondents as testing data. Evaluation using the Weighted F1-score produced a result of 87%. These findings indicate that the proposed system is capable of identifying emotions and generating relevant motivational responses. The integration of emotion detection and response generation within a single system enables outputs that are adaptive to users’ emotional conditions. However, the system still has limitations in detecting non-standard language and slang expressions, indicating that further lexicon enrichment and more adaptive methods are still required.
Feature Importance Analysis of SMV Gap and Manpower Variables on Garment Production Output based on Ensemble Learning Heni Candra Kirana; Eka Ardhianto
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6364

Abstract

In the labor-intensive garment manufacturing industry, there is a conventional assumption that achieving production targets largely depends on increasing the number of workers (manpower), causing evaluations of operational time efficiency to often be overlooked. To examine this assumption, this study aims to identify the dominant factors affecting garment production output using a Machine Learning approach based on Ensemble Learning methods, namely Random Forest and Gradient Boosting. The dataset consisted of 700 observations collected at 20-minute intervals, including variables such as actual Standard Minute Value (SMV), SMV gap, actual manpower, and manpower gap. The evaluation results indicate that the Random Forest model outperformed Gradient Boosting, achieving a Mean Absolute Error (MAE) of 4.55, Root Mean Square Error (RMSE) of 6.85, Mean Absolute Percentage Error (MAPE) of 19.12%, and an R² value of 0.758. In comparison, Gradient Boosting obtained an MAE of 4.88, RMSE of 7.21, MAPE of 20.78%, and an R² value of 0.733. Based on the best-performing model, the feature importance analysis revealed that actual SMV was the most dominant factor (>0.70), followed by the SMV gap (>0.20). In contrast, manpower variables had a very limited influence (
Optimization of Support Vector Machine for the Classification of Nutritional Status in Children Emanuell Deftavalandra Rahmanto; Charitas Fibriani (SCOPUS ID=57192643331)
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6192

Abstract

Toddlerhood is a critical developmental period that requires precise nutritional monitoring. However, automated classification systems are often challenged by imbalanced data, which makes minority classes difficult to detect accurately. This study aims to optimize a Support Vector Machine (SVM) using a polynomial kernel to improve detection sensitivity for critical classes. By excluding BMI features to avoid redundancy, the proposed model achieved an accuracy of 98%. The main novelty of this research lies in its achievement of an F1 Macro Score of 0.86, confirming that the model provides balanced and reliable classification performance across all nutritional status categories. These results demonstrate the model’s superiority in identifying the minority classes of Severe Malnutrition and Undernutrition more effectively than previous studies. Therefore, the model is highly recommended as an objective decision support system for the early detection of stunting.
Design and Development of a Hybrid NLP and Rule-based QA Assistant for Indonesian User Stories Cheria Sevani Apiani; Ichsan Ibrahim
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6383

Abstract

This study aims to address challenges in Agile-based software testing, where manually creating test cases from user stories is often time-consuming and produces inconsistent quality. Although Natural Language Processing (NLP) techniques and rule-based systems have been proposed, each approach has limitations in handling linguistic ambiguity and variations in sentence structure, particularly in the Indonesian language context. This research proposes a hybrid Quality Assurance (QA) assistant that integrates an IndoBERT-based Named Entity Recognition (NER) model with a deterministic rule-based system. The NER model is used to extract functional elements, including actors, actions, objects, conditions, and expected outcomes, while the rule-based system maps these elements into structured test case templates. Qualitative evaluation conducted by QA practitioners showed that the hybrid approach achieved an average score of 4.67 on a 5-point Likert scale, outperforming both the NLP-only approach (3.87) and the rule-only approach (4.60). The proposed system was proven to improve testing efficiency by more than 99% while generating test cases that are more complete, readable, and traceable. These findings confirm that integrating the flexibility of NLP with the consistency of rule-based systems is highly effective for automating Quality Assurance processes in the Indonesian local context.
Enhanced Network Intrusion Detection and Classification based on Ensemble Learning Techniques: A Study on the NSL-KDD Dataset Ammar Adel Ahmed; Mahmood Mohammed Mahmood; Omar Abdulmunem Ibrahim Aldabbagh
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6308

Abstract

This research explores an improved Network Intrusion Detection System (NIDS) on the NSL-KDD dataset using machine learning, deep learning and ensemble learning methods. Our approach involves essential steps such as data preparation, feature engineering with Random Forest, feature reduction, model building, hyperparameter tuning with GridSearchCV, and evaluation. We perform binary and multiclass classification tasks with Naïve Bayes, Logistic Regression, Random Forest, LightGBM, CNN, and LSTM approaches. The findings show ensemble techniques enhance classification accuracy. Random Forest and LightGBM models in binary classification, and CNN and LSTM models in multiclass classification achieved up to 99% and 97.99% and 97.80% accuracy, respectively. Additionally, the proposed stacked ensemble model, with XGBoost as the meta-learner, achieved a final test accuracy of 99.03%, and improved precision, recall, F1-score and ROC-AUC compared to the individual models. Tuning the hyperparameters also improved model stability and accuracy. This research is novel in combining feature selection, hyperparameter-tuned deep learning models and a stacking ensemble to enhance accuracy and stability in intrusion detection. The research also emphasizes the need for interpretability, real-time considerations and transfer learning in future NIDS research.
Design and Development of a File Sharing System with Integrity Assurance and Audit Trail using Permissioned Blockchain and IPFS: A Case Study of DISPUSIPDA West Java Sansan Syahrul Hidayah; Ichsan Ibrahim
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6410

Abstract

Digital transformation in government institutions requires file-sharing systems capable of ensuring document integrity, secure access control, user activity transparency, and traceable audit trails. This study designs and implements a file-sharing system based on Hyperledger Besu and InterPlanetary File System (IPFS) for document management at the Dinas Perpustakaan dan Kearsipan Daerah Jawa Barat (DISPUSIPDA). The research employed the Design Science Research Methodology (DSRM), which includes problem identification, system design, implementation, demonstration, and evaluation stages. The proposed system integrates IPFS for off-chain document storage, blockchain technology for immutable metadata recording and audit trails, and smart contracts to permanently record access rights modifications. Documents are encrypted using AES-256 before storage, while integrity verification is performed through SHA-256 hash comparison. The testing results demonstrate that all core system features operated according to the specified requirements. Document manipulation attempts were successfully detected across all testing scenarios, and the revocation mechanism effectively restricted access for users whose permissions had been revoked. Blockchain transaction latency remained within an acceptable range, while IPFS upload time increased proportionally with file size. This study contributes to the development of a file-sharing system architecture that integrates encryption, Role-Based Access Control (RBAC), revocation mechanisms, IPFS, and permissioned blockchain technology within a unified framework to support document integrity and institutional auditability in government environments.
Comparative Analysis of TinyBERT, SVM, and Char-CNN Models for Phishing URL Detection Haeranisa Bella Krisanti; Chaerul Umam
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6345

Abstract

Phishing is one of the most prevalent cybersecurity threats that exploits malicious URLs to deceive users and steal sensitive information. This study proposes a URL-based phishing detection method using the lightweight Transformer model TinyBERT and compares its performance with three baseline models: SVM based on character n-grams, Random Forest based on lexical URL features, and Char-CNN. The dataset used in this study consists of 49,750 URLs with multi-class labels (benign, defacement, malware, and phishing), which were subsequently binarized into phishing (label 1) and non-phishing (label 0). The data were divided using a stratified split into training, validation, and testing sets with a ratio of 70%–15%–15%. To address class imbalance, the TinyBERT model was trained using a weighted loss approach based on class weights. The evaluation was conducted using a confusion matrix, accuracy, precision, recall, F1-score, as well as ROC and Precision–Recall curves. Experimental results demonstrate that TinyBERT achieved the best performance, with an accuracy of 0.9925, phishing recall of 0.9512, and an F1-score of 0.9387. In addition, the model produced the lowest number of false negatives (22) compared with the baseline models. These findings indicate that TinyBERT is more effective in minimizing phishing URLs that are incorrectly classified as benign, making it more suitable for implementing URL-based phishing detection in cybersecurity systems.
Improving Identity Validation in a Flutter-based Attendance System Carlita Masaccio Mauren; Abdussalam Abdussalam
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6365

Abstract

This study aims to improve identity validation in a Flutter-based attendance system that remains vulnerable to attendance manipulation, such as buddy punching and fake GPS usage. The primary issue in the previous system was that location-based validation mechanisms were unable to ensure that attendance activities were genuinely performed by authorized users. As a system implementation and software engineering study, this research applies a multi-feature similarity approach based on face embeddings, where appearance similarity serves as the primary component calculated using cosine similarity. Supporting features include geometry similarity, quality score, color similarity, and texture similarity. The system was developed using the FAST methodology, with implementation based on Flutter, Google ML Kit for face and landmark detection, and MobileFaceNet for face embedding extraction. Testing was conducted through direct implementation trials, API testing, and black-box testing involving 655 employees using a similarity threshold of 0.80. The results from 14 testing scenarios showed that all system outputs matched the expected outcomes, resulting in 100% scenario accuracy. Compared to the previous GPS-based attendance system, indications of attendance manipulation decreased from 70 cases (10.7%) to only 1 case (0.15%). In addition, the False Acceptance Rate decreased from 12.8% to 0.2%, with an average verification time of 1200 ms. These findings demonstrate that the multi-feature similarity approach based on face embeddings is capable of improving the validity and integrity of real-time attendance data on mobile devices.

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