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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 260 Documents
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Determining Optimal Service Facilities Using a Simulation Approach at Restaurant Riva Bahari; Cut Ita Erliana; Fatimah Fatimah
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.v10i2.27723

Abstract

The rapid growth of the fast-food industry has intensified competition, requiring continuous service quality improvements to maintain customer satisfaction and loyalty. At a popular restaurant, customers often face long queues, with waiting times ranging from 8 to 35 minutes, potentially reducing satisfaction and repeat visits. This study examines the operational conditions of a restaurant that implements a single-channel service system with two process stages: cashier service and food preparation and serving in the kitchen. Observations show that total service time ranges from 8 to 35 minutes, consisting of cashier time of approximately 2–15 minutes and kitchen processing time of 6–20 minutes, with significant variation leading to the formation of long queues. This situation indicates a major bottleneck in the system, particularly in the kitchen during surges in demand, which results in customers leaving the queue before being served and potentially leading to lost revenue. Therefore, this study applies a simulation approach to represent the real conditions of restaurant operations, identify bottlenecks, and evaluate alternative service facility improvements in a structured manner without disrupting operational activities, in order to obtain a more optimal system configuration in terms of waiting time, service capacity, and cost efficiency. This study aims to identify the most suitable queuing model and optimal service facility configuration to reduce both waiting time and queuing costs. Data were collected over three consecutive days through direct observation (1:00 PM–9:00 PM WIB), covering customer arrival, cashier service, food preparation, and order pickup times. Analysis was performed using Arena software supported by distribution testing and scenario simulations. The best improvement scenario involved adding one kitchen station staffed by two employees, reducing the average waiting time from 21 to 13 minutes and achieving a total queuing cost of Rp 227,237.4 per hour. Clarity in cost calculations is necessary so that scenario selection truly reflects the consideration between increasing service capacity and costs that must be borne. However, the lack of explanation regarding the method for assessing customer waiting time costs, assumed labor wage rates, and the basis for calculating operational costs makes the results of the economic analysis difficult to retest and verify. Thus, although the simulation results indicate a decrease in waiting time, the validity of decisions in optimizing service facilities still needs to be supported by a more detailed, measurable, and systematic description of the cost structure. This adjustment resulted in a multi-channel, multi-phase system, enhancing operational efficiency and customer satisfaction. The urgency of this research is important because restaurants experience an imbalance between service capacity and high demand during peak hours. This condition causes long queues and increased customer waiting times. This has an impact on decreased satisfaction and the potential loss of customers who leave the queue. A simulation approach is needed to test various scenarios and determine the most optimal service configuration efficiently.
Application of the Mamdani Fuzzy Logic Method in an Expert System for Determining Oil Palm Fertilizer Dosage Based on Soil Condition and Plant Age Rizki syawaluddin; Raden aris sugianto; Ritna wahyuni
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

Fertilization accounts for 40-60% of total oil palm maintenance costs, yet smallholder farmers who manage roughly 41% of Indonesia's oil palm area still determine dosages from habit and visual estimation rather than from measured soil condition or plant age, a gap linked in field surveys to dosage errors of 25-35% relative to PPKS-recommended values. Prior decision-support tools, including web-based lookup tables and static recommendation charts, lacked dynamic input handling, did not model the inherent uncertainty in soil nutrient data, and were validated only internally without independent field verification. To close this gap, this study designed, implemented, and empirically validated SawIT PalmExpert, a Mamdani Fuzzy Logic-based expert system that recommends macro-fertilizer dosages (Urea, TSP/SP-36, KCl/MOP, Kieserite/Dolomite) from five inputs soil pH, Nitrogen (N), Phosphorus (P), and Potassium (K) obtained from accredited laboratory analysis (pH H2O 1:5; Kjeldahl N; Bray-1 P; NH4OAc K), together with plant age. The specific contribution of this research is threefold: (1) the first documented web-based expert system to integrate soil chemical status and plant age simultaneously as continuous fuzzy inputs for oil palm fertilizer dosing, rather than treating them as independent lookup criteria; (2) a 24-rule Mamdani knowledge base explicitly calibrated against PPKS agronomic standards through structured expert interviews and three rounds of rule validation, rather than derived from generic literature; and (3) a multi-dimensional empirical validation combining functional, accuracy, usability, and field simulation testing on a single deployed system. Concretely, black-box functionality testing across 10 scenarios all passed; fuzzy output accuracy testing on 15 cases against PPKS references yielded 100% conformity within a +/-10% tolerance (maximum deviation 2.9%, confined to fuzzy-set transition zones); usability testing with 10 purposively sampled respondents (6 smallholder farmers, 4 extension workers), using a validated instrument (content validity index 0.88; Cronbach's alpha 0.84), produced an overall score of 4.24/5.0 ("Very Good") across five dimensions ease of use, information clarity, response speed, recommendation relevance, and overall satisfaction; and field simulation testing on three representative soil scenarios received full agronomist approval. These results demonstrate that a laboratory-calibrated Mamdani fuzzy expert system can deliver both algorithmic accuracy and practical usability, positioning SawIT PalmExpert as a scientifically grounded, field-ready decision-support alternative to experience-based fertilization for smallholder oil palm farmers.
Classification of Personal Account Identification via Mobile NFC Devices Using the K-Means Algorithm Iwan Syaputra; Very Kurnia Bakti; Abdul Basit
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

The use of Near Field Communication (NFC) in membership systems has commonly been limited to automated identification, attendance recording, or access verification, whereas K-Means clustering studies generally analyze stored datasets without direct integration with real-time operational data acquisition. This separation limits the ability of fitness center systems to transform attendance records into behavioral information for managerial use. This study proposes Gymku, a mobile-based gym membership system that integrates NFC-based attendance acquisition with K-Means-based member segmentation. The main objective is to evaluate whether attendance and membership data collected through an NFC-enabled mobile system can be processed into meaningful member segments. The system was developed using the Waterfall Software Development Life Cycle, covering requirement analysis, system design, implementation, and evaluation. The clustering process used 700 customer records obtained from membership and transaction data, consisting of attendance frequency, membership status, customer type, package type, payment method, and transaction-related attributes. Data preprocessing was conducted through attribute selection and categorical-to-numerical transformation before applying the K-Means algorithm within the Knowledge Discovery in Databases framework. The system evaluation included NFC processing performance, database response time, application performance, clustering validation, and functional access-control testing. The NFC-based attendance process achieved an average processing latency of 0.256 seconds, with average database storage and retrieval times of 0.47 seconds and 0.25 seconds, respectively. The K-Means model produced three member segments representing active, inactive, and pending membership patterns, with a Davies-Bouldin Index of 0.874 and a Silhouette Score of 0.856, indicating relatively compact and well-separated clusters. Cluster-label mapping against reference categories resulted in 81.81% accuracy. These findings show that integrating NFC and K-Means can extend a gym membership system from simple digital attendance recording into operational member segmentation. However, the segmentation results remain specific to the dataset used in this study, and broader validation across different fitness center environments is still required.
Biometric Based Personal Identification for Cashless Payment Systems in Gym Memberships Alya Dwi Rahma; Very Kurnia Bakti; Abdul Basit
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

The increasing consciousness regarding healthy living has triggered a marked rise in gym memberships. Nonetheless, most facilities still depend on traditional payment and documentation methods. The main issue associated with these outdated systems is their vulnerability to human mistakes, data redundancy, loss of physical records, and a significant deficiency in digital integration. Although the use of digital payment methods such as QRIS has expanded, these systems are still prone to fraudulent activities and delays. This research intends to create an organized, secure, and extremely effective membership and payment management system. The approach taken includes the design and execution of a software structure built on Near Field Communication (NFC) technology, which is directly linked to a payment gateway. The findings from the implementation indicate that the newly developed system effectively resolves traditional operational challenges. The NFC "tap and go" feature allows transactions to be completed in less than 30 seconds, protected by limited range communication and data encryption from the payment gateway. In summary, the incorporation of NFC technology into digital processes is essential for speeding up membership renewal, ensuring the accuracy of databases, and reducing security threats, thereby evolving the fitness service landscape into a contemporary, highly efficient digital framework.
Website-Based Information Management and Temperature and Air Quality Monitoring System Using ESP32 Eliza Fitri Ramadanti; Ahmad Taqwa; Adewasti Adewasti
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

The development of Internet of Things (IoT) technology has created new opportunities for improving information management and environmental monitoring in healthcare facilities. Puskesmas Makrayu Palembang currently relies on conventional information dissemination methods, resulting in limited accessibility and inefficient information updates. This study aims to design and implement a website-based information management and environmental monitoring system using Arduino Uno as the main controller and ESP32 as the communication module. The proposed system integrates a website-based information management platform, Firebase Realtime Database, P10 LED matrix display, DHT22 temperature sensor, MQ135 air-quality sensor, and buzzer notification mechanism within a unified IoT architecture. The research adopted a system development approach consisting of literature study, field observation, system requirement analysis, system design, implementation, and testing. The implementation results demonstrated that website data synchronization, Firebase communication, ESP32 connectivity, LED matrix display operation, sensor integration, and overall system integration were successfully achieved during prototype testing. Information entered through the website could be displayed correctly on the running-text display in real time, while environmental information obtained from the DHT22 and MQ135 sensors could be processed and displayed simultaneously. The evaluation focused on functional verification of communication, synchronization, display operation, and sensor integration within a prototype environment. However, the system was evaluated only at the prototype level due to limited deployment opportunities and research time constraints, and therefore does not yet fully represent continuous operational conditions in healthcare facilities. Consequently, aspects such as long-term ESP32–Firebase communication stability, data transmission latency, system consistency under prolonged operation, and performance under high communication loads were not evaluated in this study. The proposed system nevertheless provides a practical foundation for integrated healthcare information management and environmental monitoring applications in primary healthcare facilities.
Implementation of Convolutional Neural Network for Leaf Disease Detection in Cayenne Pepper Plantsaper Ayu Suningsih; Zara Yunizar; Rizki Suwanda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

Cayenne pepper (Capsicum frutescens L.) is an economically important horticultural crop in Indonesia; however, its productivity is frequently affected by leaf diseases, including leaf curl, leaf spot, and yellow leaf disease. Conventional disease identification mainly relies on visual inspection, making the diagnosis highly dependent on farmers’ experience and increasing the possibility of inaccurate identification and delayed treatment. This study proposes a Convolutional Neural Network (CNN)-based approach for automatic chili leaf disease classification and implements the trained model in an Android application for offline real-time detection. A total of 2,000 images were collected from chili plantations located in Lhokseumawe City and Aceh Tamiang Regency. The dataset was organized into five balanced categories, namely healthy leaf, leaf curl, leaf spot, yellow leaf, and non-leaf, with 400 images assigned to each category. The addition of a non-leaf category enables the application to distinguish chili leaves from irrelevant objects during mobile-based detection. Before training, all images were resized to 150 × 150 pixels, normalized, and partitioned into training, validation, and testing sets using an 80:10:10 ratio. The proposed CNN architecture comprised three convolutional layers followed by max-pooling layers, a flatten layer, a fully connected layer, a dropout layer, and a Softmax output layer. Experimental evaluation on the testing dataset produced an overall accuracy of 89.50%, while the macro-average precision, recall, and F1-score reached 90%, 89%, and 89%, respectively. The trained model was successfully converted into TensorFlow Lite (TFLite) format and integrated into an Android application capable of providing real-time disease prediction, confidence scores, disease descriptions, treatment recommendations, and detection history without requiring an Internet connection. These results indicate that the proposed system is suitable for practical field deployment to support early identification of chili leaf diseases.
Design of A Web-Based Operational Management Information System for Playmania LPJ Zahra Fahrani; Ahmad Ferdian Shobur
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

In daily operational activities, Playmania Lippo Plaza Jambi has utilized a computerized system for sales transactions. However, inventory management, petty cash recording, and operational reporting are still managed separately using spreadsheet files, while children’s accident records are documented manually using paper forms. In addition, playtime monitoring is still supported by manual voice announcements through a microphone. These conditions result in inefficient data management and increase the risk of recording errors. This study aims to design and develop a web-based Operational Management Information System for Playmania LPJ using the Waterfall method. The system is developed to integrate sales transactions, inventory management, petty cash recording, incident reporting, and playtime monitoring within a single platform. The implementation process utilizes PHP and MySQL technologies, while system functionality is evaluated using Black Box Testing. The developed system consists of integrated modules for inventory management, customer management, sales transactions, petty cash recording, incident reporting, user management, and real-time playtime monitoring equipped with an automatic voice announcement feature. The test results indicate that all system functions operate according to the expected requirements and successfully support operational activities in a coordinated and efficient manner. Therefore, the proposed system is capable of improving the effectiveness and accuracy of operational data management, facilitating monitoring activities, accelerating report generation, and supporting decision-making processes at Playmania LPJ.
IoT-Based Soil Moisture Monitoring and Automatic Irrigation System Using ESP32 and YL-69 Sensors for Oil Palm Seedlings in the Main Nursery Septia Dwi Pratiwi; Raden Aris Sugianto; Muhammad Akbar Syahbana Pane
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
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Abstract

This study developed an Internet of Things (IoT)-based soil moisture monitoring and automatic irrigation system for oil palm seedlings in the main nursery using an ESP32 microcontroller and a YL-69 soil moisture sensor. The proposed system integrates a relay module, a water pump, and the Blynk Cloud platform to provide real-time soil moisture monitoring, threshold-based automatic irrigation, and cloud-based remote monitoring within a single IoT platform. A prototype-based development approach consisting of problem identification, system design, implementation, testing, and evaluation was employed. Experimental evaluation conducted over a four-week period demonstrated that the developed prototype successfully differentiated dry, moist, and wet soil conditions, transmitted soil moisture data to the Blynk dashboard through a Wi-Fi connection in real time, automatically controlled irrigation based on a predefined soil moisture threshold, and maintained an average daily irrigation volume of 498.75 mL. The system also implemented a consecutive-reading confirmation mechanism to improve operational stability by reducing unnecessary relay switching caused by temporary sensor fluctuations. These findings indicate that the proposed system successfully integrates real-time soil moisture monitoring, threshold-based automatic irrigation, and cloud-based remote monitoring into a single IoT platform for supporting automated irrigation management of oil palm seedlings in the main nursery.
Development of a Web-Based Village Administration Information System With an AI Chatbot Feature Putri Arum Sari; Irma Salamah; Lindawati Lindawati
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
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Abstract

The rapid development of information technology has encouraged government institutions to improve administrative efficiency and public service quality through digital transformation. However, many village administrations still rely on manual data management practices, resulting in inefficient information retrieval, data redundancy, and limited public access to administrative services. This study aims to develop a web-based village administration information system integrated with an Artificial Intelligence (AI) chatbot feature to support administrative management and public information services. The research employed a system development approach consisting of problem identification, literature study, data collection, system analysis, system design, implementation, and testing. The system was developed using HTML, CSS, JavaScript, PHP, and MySQL, while the chatbot functionality was implemented through Application Programming Interface (API) integration. The developed system integrates several administrative modules, including employee management, population data management, family card management, correspondence services, statistical information reporting, and AI-based information services. System evaluation was conducted using the Black Box Testing method to verify functional correctness. The testing results showed that all implemented modules operated according to the specified requirements and successfully performed their intended functions. The AI chatbot was able to provide administrative information in real time and support citizen interaction without requiring direct assistance from village officers. The developed system contributes to improving administrative efficiency, facilitating information retrieval, increasing transparency, and supporting the digital transformation of village public services through the integration of administrative management and intelligent information services within a unified web-based platform.
Evaluasi Model Machine Learning untuk Prediksi Diagnosis Cancer Payudara Berdasarkan Data Wisconsin Diagnostic Rudi Setiawan; Mira Febriana Sesunan
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

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Abstract

Cancer payudara merupakan salah satu penyakit dengan tingkat kejadian dan kematian yang tinggi pada perempuan sehingga membutuhkan metode diagnosis yang cepat dan akurat. Penelitian ini bertujuan mengevaluasi dan membandingkan performa lima algoritma machine learning dalam mengklasifikasikan cancer payudara sebagai jinak dan ganas menggunakan dataset Breast Cancer Wisconsin Diagnostic yang terdiri atas 569 observasi dan 30 fitur numerik karakteristik inti sel. Algoritma yang dibandingkan meliputi Logistic Regression, Decision Tree, Random Forest Classifier, Support Vector Machine, dan K-Nearest Neighbors. Evaluasi dilakukan pada 171 data uji menggunakan accuracy, precision, recall, F1-score, confusion matrix, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Logistic Regression memberikan performa terbaik dengan accuracy sebesar 98,83%, precision 0,98, recall 0,98, F1-score 0,98, dan ROC-AUC 1,00. Model ini hanya menghasilkan satu false negative dan satu false positive. Support Vector Machine menempati urutan kedua dengan accuracy 97,66% dan ROC-AUC 1,00, diikuti K-Nearest Neighbors sebesar 95,91%, Random Forest sebesar 93,57%, dan Decision Tree sebesar 91,81%. Hasil tersebut menunjukkan bahwa Logistic Regression memiliki keseimbangan terbaik antara kemampuan mendeteksi cancer ganas dan mengenali cancer jinak. Model ini dapat digunakan sebagai model acuan dalam klasifikasi cancer payudara pada dataset WDBC. Namun, penerapan klinis masih memerlukan validasi eksternal menggunakan data yang lebih besar dan beragam.

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