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METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Published by Universitas Methodist Indonesia
ISSN : 25988565     EISSN : 26204339     DOI : 10.46880
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
Sistem Informasi Sistem Informasi Manajemen Sistem Informasi Akuntansi Manajemen Basis Data Pengembangan Aplikasi Web dan Mobile Sistem Pendukung Keputusan Desain Grafis dan Multimedia Audit Sistem Informasi Topik-topik lain yang Relevan dengan bidang ilmu Manajemen Informatika Topik-topik lain yang Relevan dengan bidang ilmu Kompuerisasi Akuntansi
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 350 Documents
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Klasifikasi Pola Konsumsi Energi Rumah Tangga Menggunakan Algoritma Machine Learning untuk Mendukung Implementasi Smart City Alfina, Ommi; M. Safii
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

Population growth in urban areas drives a significant increase in household energy consumption. This condition poses a major challenge for the implementation of the smart city concept, particularly in achieving energy efficiency and sustainability. This study aims to classify household energy consumption patterns based on household power consumption data to support intelligent decision-making in urban energy management. The research method includes data preprocessing, data cleaning, and aggregation of daily energy consumption by utilizing key attributes such as Global Active Power, Voltage, Global Intensity, and three sub-metering variables. Consumption pattern categories are formed using the tertile method into three classes: Low, Medium, and High. Several machine learning algorithms are applied to build the classification model, including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting. The test results show that the Random Forest model with hyperparameter adjustments produces the best performance with an accuracy value of 0.98 and an F1-macro value of 0.98, surpassing other models. These findings indicate that the ensemble learning approach is able to capture the complexity of household energy consumption patterns more effectively than conventional linear models. The contribution of this research lies in the development of a machine learning-based predictive model to support adaptive energy consumption monitoring and control systems in smart city implementations.
Penerapan Algoritma K-Nearest Neighbors dalam Mengklasifikasi Penyakit Multiple Sclerosis Sitompul, Andrew Efraim Nicholas; Margaretha Yohanna; Arina Prima Silalahi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

The central nervous system is impacted by multiple sclerosis (MS), a chronic autoimmune disease that requires early identification for successful treatment. Because of its many symptoms and similarities to other neurological disorders, MS can be difficult to diagnose. Artificial intelligence techniques like the K-Nearest Neighbors (KNN) algorithm can be used to help with quicker and more precise classification in order to solve this problem. The goal of this study is to classify MS using the KNN technique and assess how well it performs in this regard. The Kaggle platform provided the dataset, which consists of 273 patient records with 18 clinical characteristics. With k = 3 as the number of neighbors, the data was split into 80% for training and 20% for testing. The Python programming language was used to implement the classification procedure. According to the findings, the KNN algorithm classified MS with an accuracy of 81.82%. The precision, recall, and f1-score for class 1 were 0.83, 0.76, and 0.79, respectively, according to additional analysis utilizing a classification report, whereas the scores for class 2 were 0.81, 0.87, and 0.84. These findings suggest that the KNN method has the potential to serve as a supportive tool in the diagnosis of Multiple Sclerosis.
Implementasi Teknologi Cerdas Berbasis IoT dan Telegram untuk Monitoring Kesehatan Jantung Pangesti, Lintang Desy; Sandi A , Arif Setia; Ardianto, Rian
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp332-339

Abstract

The human heart acts as a vital organ that pumps blood throughout the body, and heart disease is the leading cause of death globally, including in Indonesia. To address this issue, this research proposes an Internet of Things (IoT)-based health monitoring system that can measure heart rate, oxygen saturation, and body temperature using MAX30100 and LM35 sensors. The system is equipped with real-time notification via Telegram and data display on an OLED screen. The method used is prototyping, with testing of sensor accuracy compared to conventional measuring instruments. The test results show good accuracy in BPM and SpO₂ measurements with an error of 5.78% and 3.6%, respectively, compared to the oximeter. However, body temperature measurement using the LM35 sensor showed an average error of 7.78%, due to the sensitivity of the sensor to ambient temperature.
Pendekatan Transfer Learning dan SMOTE untuk Klasifikasi Kanker Kulit pada Imbalanced Dataset Lutviana, Lutviana; Purwono, Purwono; Imam Ahmad Ashari
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp323-331

Abstract

Skin cancer is one of the most commonly diagnosed cancers worldwide, with the incidence increasing every year. While early detection is a key factor in reducing skin cancer mortality, conventional methods such as biopsy have limitations in terms of cost and invasiveness. This research applies a deep learning based approach for skin cancer classification with Convolutional Neural Networks (CNN) model using transfer learning method. 3 CNN architectures namely MobileNetV2, EfficientNetB0, and DenseNet121 are used to evaluate the performance of the model in detecting skin cancer. One of the main challenges in this research is the imbalanced dataset, which can cause bias in classification. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to improve the representation of minority classes. The dataset used comes from Kaggle and consists of 2,357 images classified into 9 skin cancer categories. The results show that the transfer learning method combined with SMOTE can significantly improve the accuracy of the model, especially in detecting classes with a smaller number of samples. The evaluation was conducted using accuracy, precision, recall, and f1-score metrics. This research is expected to contribute to the development of an artificial intelligence-based skin cancer detection system that is more accurate, efficient, and can be used as a tool for medical personnel in early diagnosis of skin cancer.
Penerapan Algoritma Decision Tree C4.5 Pada Test MBTI Berbasis Web: Studi Kasus: Universitas Katolik Musi Charitas Elvira, Redempta; Herdiatmoko, Fery
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp316-322

Abstract

A major problem in student personality assessment is the manual process of completing and interpreting test results, which leads to subjective bias and delays in counseling services. To address this, this study applies the Decision Tree C4.5 algorithm to a web-based MBTI test to produce an objective and efficient personality type classification. This study discusses the implementation of the Decision Tree C4.5 algorithm in a web-based Myers-Briggs Type Indicator (MBTI) test to classify students’ personality types at Musi Charitas Catholic University. The research objectives are (1) to apply and evaluate the Decision Tree C4.5 algorithm in personality classification based on MBTI test results, and (2) to develop a counseling support system capable of providing automatic, objective, and easy-to-understand classification results. The research method employed is development research (Research and Development) using the Waterfall model, including requirement analysis, system design, implementation, testing, and evaluation. The C4.5 algorithm was implemented to construct a classification model based on decision rules, which was then integrated into the web application. System testing using Black-Box and White-Box methods ensured that the system operates according to specifications and that all logical paths have been tested. Evaluation results indicate a classification accuracy of approximately 86% with consistent precision, recall, and F1-score values, demonstrating the effectiveness of the C4.5 algorithm in personality type classification. The system improves efficiency, accessibility, and objectivity in personality assessment compared to manual methods and can support sustainable student counseling and development services.
Penerapan Algoritma DBSCAN dalam Mengidentifikasi Risiko Stroke Istiqomah, Hani; Khoirun Nisa; Arif Setia Sandi A.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp340-349

Abstract

Stroke is a serious disease that can cause permanent disability and death. This study applies the DBSCAN algorithm to cluster Stroke risk using a public Kaggle dataset (n = 5,110), which contains demographic and clinical attributes such as age, gender, hypertension, heart disease, body mass index (BMI), glucose levels, and smoking status. Preprocessing steps included median imputation for BMI, categorical encoding, Z-score standardization, and PCA for visualization. Parameter selection was conducted using the k-distance plot and Silhouette evaluation, resulting in ε = 2.5 and min_samples = 3 with a Silhouette Score of 0.2158. The findings indicate that DBSCAN has potential to support Stroke prevention strategies, although further parameter tuning and feature optimization are required to improve clustering quality.
Analisis Sentimen K-Popers di Twitter (X) terhadap Harga Tiket Konser Menggunakan Metode Support Vector Machine: Studi Kasus: Golden Disk Awards 2024 Sutjiningtyas, Sri; Samsul Budiarto; Hutapea, Marlyna Infryanty; Nurulqolbi Mutmainnah
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp350-355

Abstract

Korean Pop (K-Pop) in Indonesia has grown rapidly, creating a large and influential community of fans, known as “K-Popers”. The popularity of K-Pop is supported by digital technology that allows easy access to South Korean music and entertainment content. Social media, including Twitter (X), became the main platform for K- Pop fans or K-Popers to interact and share opinions about the South Korean music industry. The ticket price of The 38th Golden Disc Awards (GDA) 2024 in Jakarta became a controversial hot topic, causing debate among fans regarding the overpriced ticket price. This study aims to analyze the sentiment of K-Popers towards GDA 2024 ticket prices using the Support Vector Machine (SVM) method to classify positive and negative sentiments related to GDA 2024 ticket prices. The analysis showed that negative sentiment was more dominant with 62.31% of 2698 tweets, indicating the need to evaluate the ticket price policy by the organizers. Sentiment classification with SVM method achieved the highest accuracy of 85.19% with polynomial kernel at the proportion of training and test data of 90:10, indicating that this method is good at classifying positive and negative sentiments. This research provides insights for event organizers regarding fan responses and can help in planning ticket pricing policies and marketing strategies so as to increase subsequent customer satisfaction.
Framework Logbook Monitoring Aset Peralatan Operasional Berbasis Blockchain untuk Optimalisasi Manajemen Aset Nardi, Nardi; Mochamad Rifki Rismawan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp356-362

Abstract

All equipment installed at BMKG Technical Implementation Units must be monitored in accordance with BMKG Head Regulation No. 7 of 2014. BMKG provides a web-based monitoring system called WebSIKI and a tunneling system to the BMKG Central network via VPN. However, these systems have been abandoned, and the current monitoring system at the South Tangerang Climatology Station UPT uses WhatsApp messages, email, and Google Spreadsheets to exchange data between agencies. The purpose of this research is to design a blockchain system to be implemented in the operational equipment monitoring system. The blockchain system will record monitoring data blocks and connect each previous monitoring data block that is related. The blockchain system is designed with the Hyperledger Fabric framework, the backend system is designed with Node.js using Hyperledger Fabric SDK for Node.js, and the frontend system is designed with React.js. This system was tested using the white box testing method to test the structural chaincode and black box testing to test the functionality of the system. The results of the structural and functional implementation and testing of the system show that the blockchain system is functioning properly and that the blockchain system can improve the security and integrity of equipment monitoring data.
Implementasi Deep Learning untuk Deteksi Dini Bencana Cuaca Ekstrem Berbasis Analisis Citra Awan Rumapea, Humuntal
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp313-318

Abstract

This study aims to implement Deep Learning methods for early detection of extreme weather disasters based on satellite cloud image analysis. The dataset consists of multi-spectral imagery obtained from the Himawari-8 satellite, covering various atmospheric conditions. The proposed approach employs two main models: Convolutional Neural Network as the baseline model and Vision Transformer as the comparative model. The research methodology includes data preprocessing, model training, evaluation using accuracy, precision, recall, and F1-score metrics, and model interpretation using Explainable AI techniques. The results indicate that the Vision Transformer outperforms the CNN model, achieving an accuracy of over 92%. Furthermore, Grad-CAM visualization demonstrates that the model effectively identifies cloud regions associated with extreme weather phenomena. This study contributes to the development of an accurate and interpretable cloud-based early warning system, with potential applications in disaster mitigation, particularly in regions prone to extreme weather such as Indonesia.
Evaluasi Kinerja CNN dan Vision Transformer pada Klasifikasi Citra Resolusi Tinggi Berbasis Deep Learning Rumapea, Humuntal
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp372-379

Abstract

This study aims to evaluate and compare the performance of Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in high-resolution image classification based on deep learning. The dataset consists of high-resolution images that undergo preprocessing and data augmentation, and is divided into training, validation, and testing sets. The CNN models used include ResNet50 and EfficientNet as baselines, while Vision Transformer is employed as a comparative model utilizing a self-attention mechanism. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, as well as training and inference time. The results indicate that Vision Transformer achieves superior classification performance compared to CNN, with an accuracy of up to 93.85%. However, CNN demonstrates better computational efficiency with lower training and inference time. Furthermore, increasing image resolution improves the performance of both models, albeit at the cost of higher computational complexity, particularly for Vision Transformer. This study highlights a trade-off between accuracy and efficiency, suggesting that model selection should be aligned with specific application requirements.

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