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Sisfo: Jurnal Ilmiah Sistem Informasi
Published by Universitas Malikussaleh
ISSN : 2598599X     EISSN : 25990330     DOI : https://doi.org/10.29103/sisfo.v6i1.7950
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering
Jurnal Sistem Informasi Merupakan bidang keilmuan sistem informasi dan teknologi informasi dengan memuat artikel ilmiah penelitian murni dan terapan serta ulasan mengenai metode dan perkembangan teori, serta ilmu-ilmu terapan yang terkait dengan teknologi informasi serta informatika.Jurnal Sistem Informasi diterbitkan oleh Program Studi Sistem Informasi. Redaksi mengundang para peneliti, praktisi untuk menulis artikel ilmiah di bidang yang berkaitan dengan sistem informasi dan teknologi informasi serta informatika.Jurnal Sistem Informasi diterbitkan 2 (dua) kali dalam 1 tahun pada bulan Mei dan Oktober.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 267 Documents
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Improving the Accuracy of Halal Tourism Sentiment Classification Using a Stacking Ensemble Method Based on Machine Learning Algorithms Nadya Rahmi; Ilmawati Ilmawati
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.27962

Abstract

Halal tourism has become an increasingly important sector due to the growing diversity of tourist preferences and the rapid development of digital platforms that generate large amounts of opinion data. However, sentiment classification in halal tourism remains challenging because tourist opinions often contain complex patterns and class imbalance. This study aims to improve the accuracy of halal tourism sentiment classification by applying a stacking ensemble approach based on machine learning algorithms. The proposed model combines Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and AdaBoost as base learners, while Logistic Regression is used as the meta-learner to integrate the prediction results of the base models. The research process includes data preprocessing, class balancing using Synthetic Minority Over-sampling Technique (SMOTE), and model validation using 5-Fold Cross-Validation. The evaluation was conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve. The experimental results show that the stacking ensemble model achieved an average accuracy of 80.55%, outperforming the individual base models, namely SVM with 79.77%, MNB with 79.27%, and AdaBoost with 70.77%. In addition, the stacking model obtained an AUC value of 0.8795, indicating strong discriminative ability in distinguishing sentiment classes. These results demonstrate that the stacking ensemble approach can improve classification performance and provide a more robust and reliable model for halal tourism sentiment analysis. Therefore, the proposed method can support data-driven decision-making in halal tourism management and promotion.
Usability Evaluation of Customer Information System Application Interface Design Using System Usability Scale (SUS) Method Kezia Anatasya Caroline Manalu; Muhammad Fajar Wahyudi Rahman; Muhammad Febrilian Dwi Syahputra; Riska Dhenabayu
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28340

Abstract

Abstract The Customer Information System (CIS) application of Perumda Air Minum Surya Sembada Surabaya is one of the digital services supporting public service delivery. However, no systematic usability evaluation has been conducted to date, while users continue to report various complaints through Google Play Store reviews, including slow application performance, confusing navigation, and inconsistent self-meter reading schedules. This study aims to produce interface improvement recommendations for the CIS application through the Design Thinking approach and to evaluate the usability level of the resulting interface design using the System Usability Scale (SUS). Data collection was carried out through content analysis of 20 Google Play Store reviews and in-depth interviews with 20 customers. The evaluation results indicate that the main issues include slow application performance (60 percent) and inconsistent self-meter reading schedules (25 percent). Based on these findings, interface improvement recommendations were developed through the Design Thinking stages, resulting in a Figma-based interactive prototype featuring simplified navigation, billing information cards, water consumption graphs, meter reading reminder features, and the AIRA chatbot. The prototype was then evaluated using SUS with 30 general respondents and 5 expert respondents, obtaining SUS scores of 86 for general respondents and 81 for expert respondents, both of which fall into the Excellent category. The results demonstrate that usability evaluation combined with the Design Thinking approach is capable of producing interface recommendations with a high usability level, which can serve as a reference for future CIS application development.
Sentiment Analysis of Lombok Tourism Destinations with Automatic Labeling Using Bidirectional Encoder Representations from Transformers (BERT) and Naïve Bayes Nora Ananda Putri; Lalu Mutawalli; Maulana Ashari
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28494

Abstract

Reviews on Google Maps can be used to determine travelers' perceptions of the destination, but the large amount of data makes manual analysis less effective. This study aims to analyze the sentiment of tourist reviews of beach destinations on the island of Lombok using BERT and Naïve Bayes. The data was obtained through Google Maps scraping and generated 12,040 data after the cleaning process. The research stages include preprocessing, translation into English, sentiment labeling using BERT, TF-IDF feature extraction and data splitting, and classification using Naïve Bayes. The results showed that positive sentiment dominated with 8569 data (72.03%), followed by neutral sentiment as many as 1,824 data (15.33%) and negative sentiment as many as 1,504 data (12.64%). The Naïve Bayes model obtained an accuracy of 76.13% and showed a fairly good performance, although it was more optimal in classifying positive sentiment than neutral and negative sentiment. Overall, the results of the study show that beach destinations on Lombok Island have a positive image in the eyes of tourists and can be an evaluation material for managers in improving the quality of tourism services and facilities.
Evaluation of Information Gain Feature Selection on Support Vector Machines Performance in Craniometric Sex Classification Sonya Aulia Febriana; Iis Afrianty; Jasril Jasril; Eka Pandu Cynthia
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28622

Abstract

Sex classification is a fundamental stage in forensic anthropology because it provides the basis for constructing an individual's biological profile during the identification process. Although Support Vector Machine (SVM) has demonstrated high performance for craniometric sex classification, previous studies have primarily focused on classification performance using the complete feature set, with limited evaluation of the trade-off between feature reduction and predictive performance. Therefore, this study aims to evaluate the influence of Information Gain feature selection on SVM performance, identify the optimal combination of Information Gain threshold and SVM kernel configuration, and analyse the trade-off between feature reduction and classification performance. The proposed approach was evaluated using the William W. Howells Craniometric Dataset consisting of 2,524 skull samples. After removing identification attributes, 82 predictor features were used for classification. The research process included data preprocessing, label transformation, Z-score normalization, Information Gain feature selection using threshold values of 0.01, 0.05, and 0.1, followed by classification using SVM with Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid kernels. Model performance was evaluated using 10-fold cross-validation based on Accuracy, Precision, Recall, and F1-score. The baseline SVM model without feature selection achieved an accuracy of 89.59% using 82 features. Experimental results show that Information Gain effectively reduced feature dimensionality while maintaining competitive classification performance. The best feature-selection result was obtained using an Information Gain threshold of 0.01, which retained 68 features. The optimal configuration used the RBF kernel with C = 1 and gamma='auto', achieving an Accuracy of 89.42%, Precision of 90.73%, Recall of 89.69%, and F1-score of 90.17%. Compared with the baseline model, the Information Gain-based model reduced the number of features by 17.07% while producing an accuracy difference of only 0.17 percentage points. These findings indicate that Information Gain primarily contributes to feature reduction and model simplification rather than improving classification accuracy. The results demonstrate that a moderate reduction in craniometric features can maintain classification performance close to the baseline model and highlight the trade-off between feature reduction and predictive performance in computer-assisted forensic sex classification.
Development of a Machine Learning-Based Clean Water Demand Forecasting Model for Decision Support at the Lubuklinggau City Water Utility yogi primadasa; Ihsan Verdian; Yufitri Yanto
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28748

Abstract

Clean water demand forecasting is essential for supporting production planning and distribution management in municipal water utilities. This study developed an integrated machine learning and rule-based Decision Support System (DSS) for forecasting clean water demand at the Lubuklinggau Municipal Water Utility. The study used 60 monthly observations from 2021–2025, which resulted in 58 observations after preprocessing and feature engineering. Random Forest Regression (RFR) was evaluated using five-fold TimeSeriesSplit and compared with Support Vector Regression (SVR) using the same temporal validation framework. The results showed that RFR outperformed SVR, achieving an overall out-of-fold R² of 0.7153, with lower prediction errors than the comparative model. Feature importance analysis indicated that water production, lagged demand, population, and Non-Revenue Water were among the most influential predictors. The final RFR model was subsequently retrained using all 58 observations to generate a baseline forecast for January–December 2026, with monthly clean water demand ranging from 675,563.98 to 691,313.64 m³. The forecast outputs were integrated into a rule-based DSS to classify demand conditions and generate corresponding operational recommendations. The proposed framework demonstrates the integration of temporally validated machine learning forecasting with interpretable rule-based decision support. However, the DSS remains a prototype and requires expert validation, user acceptance testing, and operational evaluation before implementation.
Implementation of an IoT-Based Automation and Monitoring System for Pempek Production Using ESP32 at Pempek Ashifa MSME in Sematang Borang Muhammad Zaid Akbar; Suroso Suroso; Irma Salamah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.27928

Abstract

The production process of pempek at small and medium enterprises (SMEs) generally involves repetitive activities, particularly during the dough mixing and molding processes, which require considerable time and operator effort. This study aims to develop a semi-automatic pempek production system based on the Internet of Things (IoT) to assist the mixing, molding, and cutting processes at Pempek Ashifa MSME in Sematang Borang. The system uses an ESP32 as the main controller and integrates DC motors, an MG996R servo motor, manual push buttons, limit switches, and a Telegram Bot for remote control. System evaluation was conducted through functional testing, servo cutting testing, Telegram command response testing, and performance comparisons between conventional hand operation, manual machine control, and IoT-based control using 1 kg, 2 kg, and 3 kg of dough. The results showed that all manual control functions operated successfully, while the servo cutting mechanism successfully produced 50, 100, and 150 pieces from 1 kg, 2 kg, and 3 kg of dough, respectively, at a 90° servo angle and 3-second cutting time. The Telegram commands /aduk_on and /aduk_off achieved a response time of 3 seconds, while /cetak_on achieved 4 seconds. In the mixing process, the total processing time decreased from 47 minutes using the conventional hand method to 39 minutes using both manual-button and IoT control. In the molding process, the total time decreased from 180 minutes to 130 minutes. The production quantity remained consistent across all tested methods. These results indicate that the developed system can improve pempek production efficiency through reduced processing time and provide operational flexibility through local and remote IoT control.  
Implementation Of An Internet Of Things (IOT)-Based Security System For Oil Palm Fertilizer Warehouses Using Rfid Authentication And Load Cell Stock Monitoring Steven Immanuel Simarmata; Andi Prayogi; Ratu mutiara Siregar
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28326

Abstract

Fertilizer management in oil palm plantations still faces security and stock-recording accuracy challenges because it is largely carried out manually using mechanical locks and paper-based records. This study designs and implements a prototype Internet of Things (IoT)-based security and stock monitoring system that integrates RFID authentication to control warehouse access with a load cell sensor to monitor changes in fertilizer weight as a stock indicator. The entire process is controlled by an ESP32-S microcontroller, with data transmitted in real time to a Firebase Realtime Database, displayed on a Kodular-based Android application, and automatically logged to Microsoft Excel. The research method used is Research and Development (R&D) through the stages of needs analysis, hardware and software design, implementation, and system testing. The test results show that the system is able to perform RFID authentication correctly, accurately read fertilizer weight changes in the 2-10 kg range, display information on the LCD and Android application in real time with an average data update time of 1.1-1.3 seconds, and automatically log all access activities. All ten hardware and software components tested functioned according to the design. This system is expected to improve the security, transparency, and efficiency of fertilizer management in oil palm plantation warehouses

Page 27 of 27 | Total Record : 267

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