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Mutammimul Ula
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Prodi Sistem Informasi Fakultas Teknik Universitas Malikussaleh Kampus Utama Cot Tengku Nie Reuleut Muara Batu, Aceh Utara, Provinsi Aceh, Indonesia Telp : +62.645.41373, Fax : +62.645.44450
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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 238 Documents
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Application of Fuzzy Inference System Sugeno Model for Forecasting Antam Gold Prices in Indonesia: A Case Study of Monthly Data 2023–2025 Irwanda Syahputra; Alfa Saleh; Khairul Anam
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27233

Abstract

Antam gold prices represent one of the most volatile investment indicators in Indonesia, influenced by macroeconomic factors including rupiah exchange rates, global inflation, and geopolitical uncertainty. The ability to accurately forecast gold prices has become a strategic necessity for investors and market participants. This study applies a Fuzzy Inference System (FIS) with the Sugeno model to forecast Antam gold prices using monthly data from January 2023 to December 2025, comprising 36 data points. The input variables are gold prices from the previous month (t-1) and two months prior (t-2), while the output variable is the predicted price for period t. Data is split 75:25 for training and testing. Evaluation using Mean Absolute Percentage Error (MAPE) yields 2.14%, categorized as excellent accuracy. This study provides empirical evidence that the simple yet interpretable Fuzzy Sugeno method achieves high accuracy in commodity price time series forecasting.
Implementation of Edge Intelligence in an IoT-Based Automatic Plant Watering System Using a Classification Algorithm Mulyadi Mulyadi; Zulfan Khairil Simbolon; Hendrawaty Hendrawaty; Hasyimi Abdullah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27263

Abstract

This study discusses the application of Edge Intelligence in an Internet of Things (IoT)-based automatic plant watering system by utilizing the Decision Tree algorithm to support the concept of precision agriculture. The system was developed using several environmental parameters, such as soil moisture, air temperature, humidity, light intensity, and plant age as input data to determine watering decisions. All data processing is carried out directly on the ESP32 microcontroller so that the system can work faster, reduce dependence on cloud computing, and increase network and power usage efficiency. The Decision Tree method is applied through Information Gain calculations to determine the attributes that most influence watering decisions. Based on the analysis results, air humidity obtained the highest Information Gain value of 0.8813 bits at a threshold of 57.5%, making it considered the most effective in the watering classification process. However, soil moisture was chosen as the root node because it has a higher level of agronomic relevance and is easier to implement in the plant monitoring system. Data processing is carried out locally using the ESP32 microcontroller, while the monitoring data is sent via the MQTT protocol to a smartphone application for real-time monitoring. Test results show that the system is capable of carrying out automatic watering accurately, increasing water efficiency, and adapting to changing environmental conditions adaptively. Thus, this system can be an effective, intelligent, and sustainable solution in supporting IoT-based plant cultivation management. The results show that the Decision Tree algorithm can determine the right watering decisions through Information Gain calculations. The system is also connected to the MQTT protocol that allows real-time monitoring of plant conditions via a smartphone application. Based on the test results, the system is capable of carrying out automatic watering effectively, saving water, and adapting to changing environmental conditions. Therefore, this system can be an innovative and sustainable solution to support the development of IoT-based smart agriculture.
Optimizing CNN-Based Transfer Learning through Fine-Tuning and Adaptive Augmentation for Chili Plant Disease Detection Bawazir Fadhil Mohammad; Ani Dijah Rahajoe; Agussalim Agussalim
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27270

Abstract

Chili peppers (Capsicum annuum L.) are a strategic horticultural commodity in Indonesia, but their productivity is often hampered by pathogen infections that cause leaf diseases such as anthracnose, leaf spot, and yellow virus. Early detection by farmers is still dominated by subjective visual observation and prone to misdiagnosis due to the similarity of symptoms between diseases. Although Deep Learning technology through Convolutional Neural Networks (CNN) offers an automated solution, implementation in real-world conditions still faces significant challenges such as lighting variations, complex backgrounds, and limited local datasets. This often leads to a drastic decrease in model performance compared to testing in a controlled environment. To address these issues, this study proposes an optimization of the transfer learning strategy on the MobileNetV2 architecture by integrating progressive layer-wise fine-tuning and adaptive data augmentation techniques. The fine-tuning method is carried out gradually on the pre-trained model layers, while adaptive augmentation dynamically manipulates images based on environmental characteristics to improve model robustness. The results of this study, which include multi-class classification on cross-location image data, are projected to be able to boost the accuracy and generalization ability of the model in heterogeneous field conditions. Practically, this research provides a framework for a more precise and robust disease detection system to accelerate the implementation of precision agriculture in the future.
Analysis of Machine Learning-Based Classification Models for Determining Fertilizer Types for Rice Crop Growth: Machine Learning Approach for Optimizing Fertilizer Selection in Rice Cultivation Mira Humaira; Almuna Ramadhani; Uchti Nuzul Qhinanti Lubis; Fadhliani Fadhliani; Septiarini Zuliati; Usnawiyah Usnawiyah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27283

Abstract

Determining the appropriate fertilizer type is essential for supporting rice plant growth and optimizing agricultural productivity. However, conventional fertilization practices still rely heavily on empirical judgment and often neglect dynamic soil and plant growth characteristics. This study aims to analyze and compare the performance of several machine learning classification models for fertilizer type determination in rice cultivation. The study employed a computational experimental approach adapted from the CRISP-DM framework using a dataset of 480 records consisting of soil and rice growth parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, moisture, and plant height. Five classification algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. Model performance was assessed using accuracy, precision, recall, and F1-score, combined with Stratified k-Fold Cross Validation. The results showed that Random Forest achieved the best performance with an accuracy of 95.83%, precision of 95.54%, recall of 95.12%, and F1-score of 95.33%. These findings indicate that ensemble learning methods are more effective in handling heterogeneous and multivariable agricultural data than conventional classification approaches. This study contributes to the development of machine learning-based classification analysis for more accurate and data-driven fertilizer determination in rice cultivation.
Design and Development of a Mobile Application for Palm Oil Harvest Recording and Reporting Using a User-Centered Design (UCD) Kisah Tiara Sihombing; Ritna Wahyuni; Ratu Mutiara Siregar
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27284

Abstract

The digitization of oil palm harvest recording is necessary to improve data accuracy, reporting efficiency, and operational transparency in plantation environments. The manual recording process currently used often leads to reporting delays, data inconsistencies, and errors in harvest data recording. This study aims to design and develop a mobile application for oil palm harvest recording and reporting using a User-Centered Design (UCD) approach to meet user needs and field conditions. The research methods included observation, interviews, user needs analysis, system design, prototype development, and system evaluation. Functional testing was conducted using black-box testing, while usability testing utilized the System Usability Scale (SUS). The developed application provides features for recording fresh fruit bunches (FFB), loose fruit recording, photo documentation, offline data storage, automatic synchronization, foreman validation, and periodic reporting. The test results indicate that all system features functioned properly and achieved an average SUS score of 77.75, which falls into the “Good” category. These results demonstrate that the application is easy to use and accepted by users. Thus, the User-Centered Design approach successfully produced a practical, efficient, and user-friendly application to support oil palm harvest data management.
Predictive Analysis of Palm Oil Biodiesel Production Using a Naive Bayes Algorithm Based on Data Mining Techniques: A Machine Learning Approach for Renewable Energy Production Prediction Khaidir Khaidir; Nelly Fridayanti Fridayanti; Muhamad Yusuf; Nazimah Nazimah; Safrizal Safrizal; Teuku Multazam
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27291

Abstract

The increasing demand for renewable energy has encouraged the optimization of palm oil biodiesel production to improve product quality and process efficiency. Biodiesel production is strongly influenced by operational parameters, including Free Fatty Acid (FFA) content, moisture content, methanol-to-oil molar ratio, catalyst concentration, reaction temperature, and reaction time, which may lead to quality variability and off-spec products. This study aimed to develop a predictive analysis model for palm oil biodiesel production using the Gaussian Naive Bayes algorithm based on a data mining approach. The study employed the Knowledge Discovery in Databases (KDD) framework using a secondary dataset consisting of 250 observations and six operational variables. Data preprocessing included missing value handling, Min-Max normalization, and Random Over-Sampling (ROS) to address class imbalance. The results showed that the model achieved an accuracy of 86.0%, weighted F1-score of 0.86, and cross-validation accuracy of 86.3 ± 2.4%. The analysis identified FFA, reaction temperature, and moisture content as the main factors influencing biodiesel quality. In addition, the model demonstrated high computational efficiency with a total processing time of 0.070 seconds, indicating its potential for real-time quality monitoring applications in biodiesel production systems.
Analysis of the Implementation of a Health Service Quality Monitoring Information System in Improving the Quality of Hospital Services Esar Alkautsar; Eri Saputra; Rahmadi M. Ali
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Improving the quality of healthcare services is a top priority in hospital management. Utilizing information technology through a Healthcare Quality Monitoring Information System (HSMS) is a strategy that can be used to support the ongoing monitoring and evaluation of healthcare quality. This system enables more systematic, rapid, and accurate management of healthcare data, enabling the resulting information to be used as a basis for managerial decision-making. This study aims to examine the implementation of a Healthcare Quality Monitoring Information System and its impact on improving the quality of healthcare services in hospitals. The method used is a qualitative descriptive approach, with data collection through field observations, interviews with relevant parties, and analysis of supporting documents. The study focused on the effectiveness of the system, the quality of the information generated, ease of operation, and its contribution to improving healthcare performance. The results indicate that the use of a HSMS provides benefits in accelerating the process of monitoring quality indicators, facilitating the identification of service problems, and increasing the accuracy of decision-making based on available data. In addition, the system also supports efforts to increase patient satisfaction through more measurable service improvements oriented to user needs. However, several factors remain challenges in implementation, such as limited human resource capabilities, the need to improve user competency, and obstacles to data integration between departments within the hospital.
Gamifikasi Edukasi Rumah Adat Nusantara Berbasis Android Menggunakan Algoritma Fisher-Yates Shuffle Said Fadlan Anshari; Rizki Suwanda; Rizky Putra Fhonna; Muh Fahrudin Alawi
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia memiliki kekayaan budaya yang sangat beragam, salah satunya berupa rumah adat yang mencerminkan identitas dan kearifan lokal masyarakat di berbagai daerah. Namun, minat generasi muda untuk mempelajari warisan budaya tersebut cenderung menurun akibat kurangnya media pembelajaran yang menarik dan interaktif. Penelitian ini bertujuan untuk mengembangkan aplikasi gamifikasi edukasi rumah adat Nusantara berbasis Android dengan menerapkan algoritma Fisher-Yates Shuffle sebagai metode pengacakan elemen permainan. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan pendekatan kualitatif dan eksperimental. Data penelitian diperoleh melalui studi literatur dari Perpustakaan Kota Lhokseumawe, Perpustakaan Program Studi Antropologi Universitas Malikussaleh, serta sumber daring yang relevan. Aplikasi yang dikembangkan memiliki tiga fitur utama, yaitu permainan susun gambar rumah adat, kuis interaktif, dan ensiklopedia rumah adat Nusantara. Algoritma Fisher-Yates Shuffle digunakan untuk mengacak susunan puzzle dan soal kuis sehingga menghasilkan variasi permainan yang lebih menarik dan tidak repetitif. Hasil penelitian menunjukkan bahwa aplikasi yang dikembangkan mampu menjadi media pembelajaran yang interaktif, edukatif, dan menyenangkan bagi anak-anak usia sekolah dasar hingga sekolah menengah pertama. Selain meningkatkan keterlibatan pengguna dalam proses belajar, aplikasi ini juga berkontribusi dalam memperkenalkan dan menumbuhkan kecintaan terhadap budaya Indonesia melalui pemanfaatan teknologi digital.

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