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Jusikom : Jurnal Sistem Komputer Musirawas
Published by Universitas Bina Insan Lubuklinggau
ISSN : 25411896     EISSN : 26148714     DOI : https://doi.org/10.32767/jusikom.v9i1
Core Subject : Science,
Computer Science & IT
JUSIKOM is a place of information in the form of research results, literature studies, ideas, application of theory and critical analysis studies in the fields of research in the fields of Computer Systems, Computer Science, and Electronics. Focus and Scope: Embedded system, Intelligent control system, Software engineering, Computer network, Mobile computing, Artificial Intelligent, Internet of Things, and Information system.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 237 Documents
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STRATEGI MITIGASI KEBAKARAN HUTAN DAN LAHAN BERBASIS AI DI RIAU Ali, Edwar; Djahara, Khairani; Pradewanta, Rian
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 2 (2025): Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i2.2833

Abstract

Forest and land fires (Karhutla) are a significant environmental threat in Riau Province with substantial ecological, health, and economic impacts. This research develops an integrated artificial intelligence (AI)-based application for Karhutla mitigation. The method uses a quantitative approach with a system development design. The dataset includes 87,600 spatiotemporal data items (2020-2024) from MODIS/VIIRS, BMKG, and Sentinel-2. Machine learning models (Random Forest and XGBoost) were trained on the data for real-time hotspot prediction. The XGBoost model achieved an accuracy of 91.2% (AUC 0.871), outperforming RF (88.1%, AUC 0.847). The results are integrated into a Geographic Information System through three main modules: (1) Prediction and Visualization, (2) Early Warning, and (3) Reporting and Analysis. A usability test involving 15 field users resulted in a System Usability Scale score of 82.5 (Excellent). A 4-week implementation pilot achieved a detection rate of 88.9% and a suppression rate of 86.7%, reducing the response time from 4.2 hours to 1.1 hour. The application integrates solutions for real-world AI challenges: model drift (automated retraining), black box (SHAP interpretability), and knowledge gap (training program). The research demonstrates AI technology for disaster mitigation operations with an ROI of 480% and an (investment) payback period of 10.3 months.
DETEKSI KADAR HBA1C BERBASIS SINYAL PHOTOPLETHYSMOGRAPHY (PPG) Mahfudhoh, Eny; Anggraeni, Dinda Wahyu; Adilla, Axl
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 2 (2025): Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i2.2858

Abstract

Diabetes is a chronic disease with a high prevalence in Indonesia, requiring routine blood glucose monitorin1. However, the standard method for measuring Glycated Hemoglobin (HbA1c) is invasive, painful, and costly. This study aims to summarize and discuss the non-invasive estimation of HbA1c levels using Photoplethysmography (PPG) signals. PPG, a non-invasive optical technique, detects microvascular blood volume changes. Its pulse wave morphology is affected by biomechanical and hemodynamic alterations due to HbA1c accumulation, such as increased arterial stiffness. Various studies have explored the extraction of PPG signal features (statistical, physiological, and AC/DC ratio), which are then processed using machine learning and deep learning algorithms like 1D-CNN, XGBoost, Random Forest, and QSVM. The results demonstrate promising performance, with some models achieving Pearson correlation coefficients up to R = 0.96 and a clinical accuracy of 100% estimation points falling within Zone A of the Clarke Grid Analysis (CGA). The non-invasive approach based on PPG and artificial intelligence offers an accurate, fast, and comfortable solution for HbA1c monitoring, marking a crucial advancement in diabetes management.
OPTIMALISASI SMART AGRICULTURE MELALUI PREDIKSI HARGA SAYURAN BERBASIS DEEP LEARNING SEBAGAI UPAYA MENDUKUNG KETAHANAN PANGAN NASIONAL rahmadayanti, fitria; muntari, siti
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 2 (2025): Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i2.2872

Abstract

The agricultural sector plays a vital role in supporting national food security. However, farmers in many regions, including Pagar Alam—one of the main vegetable production centers in South Sumatra—continue to face significant challenges due to unpredictable price fluctuations. This instability makes it difficult for farmers to determine the optimal timing for planting, harvesting, and distributing their produce, which often results in economic losses and inefficiencies within the supply chain. Such conditions directly impact farmers’ welfare and the stability of market supply. This study aims to identify patterns of vegetable price fluctuations through data analysis and the development of a prediction model using a deep learning approach, specifically the Long Short-Term Memory (LSTM) algorithm. Evaluation of the model’s performance is conducted to determine the best predictive model based on accuracy and result stability. The findings are expected to provide data-driven policy recommendations to support Smart Agriculture initiatives and strengthen food security at both local and national levels.The research adopts the CRISP-DM framework, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The expected outcome of this study is the development of a predictive model that can offer valuable insights and recommendations to stakeholders, ultimately contributing to the improvement of farmers’ welfare.
MODEL ENSEMBLE TREE-BASED UNTUK PREDIKSI KELEMBABAN TANAH BERBASIS DAQ ARDUINO Ucky Pradestha Novettralita; Dinda Wahyu Anggraeni; Moeng Sakmar; M. Agus Syamsul Arifin
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3126

Abstract

Soil moisture is a critical parameter in agricultural and hydrological systems that requires accurate monitoring to support smart irrigation management. This study aims to develop a soil moisture forecasting model based on stacking ensemble machine learning using time series data from an Arduino-based Data Acquisition (DAQ) system. The dataset includes environmental variables such as atmospheric temperature, soil temperature, humidity, soil_moisture, and dew point with hourly temporal resolution over the 2017–2018 period. The research stages include data preprocessing (missing value handling, interpolation, outlier handling using the IQR method, and resampling), feature engineering (temporal feature extraction, lag features, and rolling windows), and modeling using six tree-based methods: Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, and CatBoost. The three best-performing models (CatBoost, LightGBM, and Gradient Boosting) were combined using Ridge Regression as a meta-learner. Evaluation was conducted through time-series cross-validation with MSE, RMSE, MAE, R², and MAPE metrics. Test results show the Ridge Ensemble achieved MSE of 52.21, RMSE of 7.23, MAE of 5.16, R² of 0.9652, and MAPE of 9.07%. The stacking ensemble approach outperformed individual models and shows strong potential for real-time deployment in IoT systems to support precision agriculture
SISTEM MONITORING SUHU, KELEMBABAN, DAN CAHAYA BUDIDAYA JAMUR TIRAM DI JAMUR MADANI UNGARAN ilham sazaly
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.2857

Abstract

Oyster mushroom cultivation requires controlled environmental conditions, especially temperature, humidity, and light intensity. Instability in these parameters can reduce productivity and mushroom quality. This research aims to design and implement an Internet of Things (IoT)-based environmental monitoring system to support real-time monitoring of oyster mushroom cultivation at Jamur Madani Ungaran. The system uses a DHT22 sensor to measure temperature and humidity, and an LDR sensor to measure light intensity, controlled by an ESP8266 microcontroller as the data processing unit. The collected data are transmitted and visualized on the Blynk IoT platform using graphs and indicators for easier monitoring. Additionally, the system provides notifications when environmental parameters exceed the ideal thresholds. The testing results show that the system operates properly, provides accurate and stable data, and improves monitoring efficiency. In conclusion, this monitoring system offers a practical solution to support enhanced oyster mushroom production quality through optimal environmental supervision
IMPLEMENTASI LOAD BALANCING PADA JARINGAN MENGGUNAKAN ROUTER MIKROTIK DI KANTOR DESA JUM’AT Hendri Alamsyah; Putra Dwi Nurdiyanto; Yoli Andi Rozzi
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3074

Abstract

The development of information technology requires government agencies to have a stable and efficient internet network. The Jum'at Village Office faced traffic congestion on a single ISP path. This study applies the Per Connection Classifier (PCC) Load Balancing method on a MikroTik Router to improve network stability and efficiency. Using an experimental quantitative method, the research compared Traffic Graph before and after implementation, sticky connection testing, and traffic distribution. Results show PCC reduced traffic congestion, improved download speed from 1.1 Mbps to 4.2 Mbps and upload from 2.3 Mbps to 8.5 Mbps. Traffic was balanced: 3 PCs used ISP 1 (Iconnet) and 2 PCs used ISP 2 (Indosat). PCC-based Load Balancing on a MikroTik Router effectively improved speed, reduced traffic load, and enhanced routing efficiency.
IMPLEMENTASI WAHTAPP CHATBOT BERBASIS KECERDASAN BUATAN (AI) UNTUK MENINGKATKAN EFISIENSI LAYANAN SERTIFIKASI DI LSP POLITEKNIK NEGERI SRIWIJAYA PALEMBANG Agus Setiawan; Yudistira Sira Permana; Meilyana Winda Perdana
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3110

Abstract

The Sriwijaya State Polytechnic Professional Certification Institute (LSP) faces the challenge of manual certification information services, causing response delays and high administrative burdens. This study aims to implement an artificial intelligence (AI)-based chatbot to improve the efficiency of certification services, in line with the Smart ASN concept and BerAKHLAK values. The development method includes FAQ research, flow design, technical construction with WhatsApp Business API integration, validation through User Acceptance Test (UAT), and full implementation. The chatbot utilizes Natural Language Processing (NLP) to provide information on certification schemes, requirements, and schedules automatically 24 hours a day.. The results show that the chatbot is able to handle routine questions independently, reduce staff workload, and significantly speed up response times. This implementation supports the digital transformation of innovative and responsive public service.
Platform Web Promosi Bisnis Mahasiswa Terintegrasi Firebase dan WhatsApp YUSRIDA JELIANTI SIHITE SIHITE; Jatmiko Althaf Aziz; Andika Veriando Saragih; Debi Yandra Niska
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3146

Abstract

This study was motivated by the challenges of student business promotion, which is still primarily conducted through social media and WhatsApp groups, making business information difficult to access and poorly organized. This study aims to develop a web-based student business promotion platform integrated with WhatsApp and a popularity-based recommendation system to help users discover the most preferred businesses. The research methods included observation, interviews, and literature review to analyze system requirements, while the system development adopted the Waterfall method consisting of analysis, design, implementation, and testing phases. The system was developed using Node.js, Firebase Realtime Database, and Bootstrap. The main features include user registration, business data management, business search, WhatsApp integration, and a popularity-based recommendation system utilizing user interaction data. The integration of Firebase and WhatsApp enables responsive and real-time data management and communication. System testing was conducted using Black Box Testing and usability testing involving 50 students from Universitas Negeri Medan. The results showed a 100% success rate for all main features, while usability testing achieved an average score of 4.17, categorized as high. The findings indicate that the system effectively supports structured student business promotion, simplifies business information access, and improves communication effectiveness between users and business owners.
PENERAPAN MACHINE LEARNING ALGORITMA RANDOM FOREST UNTUK PREDIKSI DIABETES BERDASARKAN DATA REKAM MEDIS PASIEN Aisyah Milaniyah R.J. Prastyo; Iftitaahul Mufarrihah; Hery Kristianto; Ahmad Heru Mujianto
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3148

Abstract

Diabetes mellitus is a chronic disease with a high prevalence that requires accurate and efficient early detection. The process of identifying diabetes risk at Hasyim Asy’ari Hospital, which is still carried out conventionally, is considered ineffective in handling large volumes of medical records, thereby potentially slowing down patient care. This study developed a web-based diabetes mellitus prediction system using the Random Forest algorithm with a configuration of 100 decision trees (n_estimators=100, random_state=42). The dataset consists of 900 medical records from patients at Hasyim Asy’ari Hospital (630 positive cases and 270 negative cases of diabetes), which were divided in a 70:30 ratio into 630 training data and 270 test data using a stratified split. The features used include Fasting Blood Glucose, HbA1c, Random Blood Glucose, Systolic Blood Pressure, BMI, Age, Diastolic Blood Pressure, and Gender. The system was implemented using PHP 8.2, Python 3.11.4 (scikit-learn 1.3.0), and Tailwind CSS 3.0. Evaluation results on the test data show an accuracy of 96.67% (95% CI: 94.12–98.89%), precision of 98.85% (95% CI: 96.55–100.00%), recall of 96.63% (95% CI: 92.86–100. 00%), and an F1-score of 97.73% (95% CI: 95.24–99.42%), calculated via bootstrap resampling with 1,000 iterations. This performance significantly outperformed SVM (accuracy 91.00%) and Naive Bayes (precision 89.00%) based on the McNemar test (α=0.05). Feature importance analysis using Mean Decrease in Impurity (MDI), cross-validated with Permutation Importance (n_repeats=30), revealed that Fasting Blood Glucose (19.60%), HbA1c (15.78%), and Random Blood Glucose (15.46%) were the three most influential indicators, consistent with the WHO and ADA diagnostic criteria.
DESAIN SMART FARMING KEBUN DURIAN DATARAN RENDAH BERBASIS SUSTAINABLE AGRICULTURE DI GROBOGAN : DESAIN SMART FARMING KEBUN DURIAN DATARAN RENDAH BERBASIS SUSTAINABLE AGRICULTURE DI GROBOGAN Ahmad Latif Utomo; Firdatun Nisa; Fiery Dhio Arya Pramudya; Wisaeni Intannia; Muhammad Zakki Irfani
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3159

Abstract

Durian cultivation is a horticultural sector with high economic potential; however, its development in the lowland areas of Grobogan Regency faces several challenges, particularly limited water availability, specific soil characteristics, and the need for more efficient land management. This study aimed to design a smart farming system for lowland durian cultivation based on sustainable agriculture through field condition evaluation and a literature review of Internet of Things (IoT) technology, automatic irrigation systems, and solar energy utilization. The research was conducted at a pilot-project durian plantation in Ngaringan District, Grobogan Regency. The research methods included field observation, analysis of soil chemical characteristics using soil test kits and SoilGrids data, land mapping based on GPS coordinates, and a literature review related to smart farming development. The results showed that the study area had suitable soil pH, organic carbon content, and cation exchange capacity for durian cultivation, although potassium levels were relatively low. Based on these findings, a smart farming system was designed by integrating solar panels, an energy management system, pH sensors, soil moisture sensors, nutrient sensors, an IoT-based microcontroller, a cloud server, and an automatic drip irrigation system. The proposed system design has the potential to improve water and energy use efficiency while supporting sustainable durian cultivation

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