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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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Penerapan Algoritma K-Nearest Neighbors dalam Mengklasifikasi Penyakit Multiple Sclerosis Yohanna, Margaretha; Sitompul, Andrew Efraim Nicholas; Silalahi, Arina Prima
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.pp307-315

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.
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 | DOI: 10.46880/jmika.Vol9No2.pp300-306

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.
SISTEM PENDUKUNG KEPUTUSAN EVALUASI HASIL BELAJAR SISWA DI SMK PGRI 3 SIDOARJO MENGGUNAKAN METODE FUZZY AHP (ANALYTICAL HIERARCHY PROCESS) Ahmad Husain Abiyyu; Lilis Widayanti
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp158-174

Abstract

Evaluation of student learning outcomes have an important role for teachers in knowing students abilities and in determining how to guide students. However, in its application to vocational high school PGRI 3 Sidoarjo, the teachers have difficulty regarding the assessment system to evaluate students learning outcomes, the difficulty is in the ranking process. Vocational high school 3 Sidoarjo still used the old and manual systems so the result is that the time needed is inefficient time and made the teachers difficult in processing data. The decision support system of students learning result evaluation at vocational high school 3 Sidoarjo using AHP fuzzy method. The goal is to facilitate the teacher in the student ranking process. The process in this system is the admin inputing students data, classes and scores after that the admin determines the value of each criterion and sub-criterion, then the ranking process is based on class. This decision support system's output is the ranking of students' classes. Using the test results from 20 students, the old system and the new system will be compared. As the result, based on 20 students score data, there were incompatible data, the amount of data were 4 data, with a system accuracy rate of 80%. Unsuitable data due to the old system using 2 criterion while the new system using 5 criterion in ranking.
PENGGUNAAN CLOUD COMPUTING DALAM PERANCANGAN APLIKASI MOBILE BERKONSEP GAMIFIKASI BERKEBUN Korbafo, Adrianus Ragil Indrajaya; Nababan, Darsono; Risald, Risald
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp181-187

Abstract

Climate change causes an increase in global temperature, which results in rising sea levels and threatens many cities in the future, including Jakarta. Carbon emissions are the primary cause of climate change, which is difficult to reduce. Forests can act as a natural filter for carbon emissions, but deforestation still occurs for various reasons. The author and their team propose a solution by creating an application called "Eden" that uses gamification to encourage users to develop a habit of planting trees. This application encourages the public to participate in saving the earth from global warming and climate change by planting trees around their homes, which can filter some of the carbon emissions in their environment.
PENGEMBANGAN SISTEM INFORMASI UNTUK ADMINISTRASI LAYANAN SURAT DI KELURAHAN BUMIAJI Suci Cahya Amalia; Yusuf Sulistyo Nugroho
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp188-200

Abstract

One of the various types of written communication tools is a letter, which is divided into official letters and non-official letters. Official letters are used for formal purposes. However, the process of letter administration in Kelurahan Bumiaji, Kabupaten Sragen is still done conventionally, where people who need a letter have to come to the Kelurahan office and queue, which makes the process ineffective. The letter archiving process carried out by the Kelurahan office is also poorly organized. To overcome this problem, in this study, an information system for letter service administration based on a website was created for the Kelurahan Bumiaji office. The method used to develop the system is the software development life cycle (SDLC), with a waterfall process model. The result of this research is the creation of an information system that can assist the community in obtaining the letters needed and assisting in mail services by the Kelurahan Bumiaji. In addition, this information system also displays information such as announcements from Kelurahan and assets owned by the Kelurahan to the public. Black box testing shows that the system can function as it should, and SUS testing obtained an average score of 77.4 and a grade scale B+.
ANALISIS PERBANDINGAN PERFORMA VIRTUALISASI SERVER MENGGUNAKAN VMWARE ESXI, ORACLE VIRTUAL BOX, VMWARE WORKSTATION 16 DAN PROXMOX Ridho Akbar Nuryadin; Ramadhani, Tarisa A.; Karaman, Jamilah; Reza, Muhammad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp175-180

Abstract

In the era of advancing digitization, server infrastructure plays a key role in the development of applications and web services. To effectively and efficiently manage and develop virtualized servers, server virtualization techniques can be employed. There are several virtualization platforms available, such as VMware Workstation 16, VMware vSphere (ESXi), Oracle VirtualBox, and Proxmox, each with their own strengths and weaknesses. The objective of this research is to analyze the performance of these four virtualization platforms in developing and managing virtualized servers used for web services, taking into consideration response time, throughput, CPU performance, storage performance, and RAM performance. Experimental methods were used to test these four platforms and measure CPU performance, RAM performance, disk performance, throughput, and response time using Moodle benchmark. The data was then analyzed to draw conclusions about the performance of each platform. The research results show that VMware vSphere ESXi and Proxmox have better CPU performance and response time when handling multiple virtual machines, and are more efficient in disk and memory usage compared to VMware Workstation 16 and Oracle VirtualBox. Significant differences in data transfer speed were found among the four platforms. Overall, VMware vSphere ESXi and Proxmox can be considered better choices for running web servers.
PENERAPAN DATA MINING MENGGUNAKAN METODE K-MEANS UNTUK PENENTUAN REWARD PELANGGAN: Studi Kasus: UD. Penyubur Tani Indah, Sari; Larosa, Fati Gratianus Nafiri; Rumapea, Yolanda Y. P.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp201-207

Abstract

UD. Penyubur Tani is a trading business that sells various kinds of needs for farmers in running their business in agriculture such as fertilizers, pesticides, seeds, and others. UD. Penyubur Tani wants to increase customer loyalty by giving rewards in the form of discounts so that its business is increasingly trusted and increasing, but in giving rewards to customers is still not effective, because UD. Penyubur Tani has difficulty in calculating one by one which customers whose frequency of purchases and total purchase price are most in the category of very loyal and loyal. Therefore, an application is needed to classify customers so that in determining strategies in building loyalty on target. By using the concept of CRM and the K-Means method, the results obtained from data processing are able to group customers who must be prioritized and can determine which customers deserve a reward. From 100 customer data, the K-Means Clustering method succeeded in grouping very loyal criteria by 8%, loyal 34% and potential by 58%.
MODEL BIDIRECTIONAL LSTM UNTUK PEMROSESAN SEKUENSIAL DATA TEKS SPAM Siringoringo, Rimbun; Jamaluddin, Jamaluddin; Perangin-angin, Resianta; Harianja, Eva Julia Gunawati; Lumbantoruan, Gortap; Purba, Eviyanti Novita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp265-271

Abstract

This study examines the LSTM-based model for processing spam in text data. Spam poses several dangers and risks, both for individuals and organizations. Spam can be a nuisance that hampers both individual and organizational productivity. Much spam contains fraudulent or phishing attempts to obtain sensitive information. Spam detection using deep learning involves the utilization of algorithms and deep neural network models to accurately classify messages as either spam or not spam. Typically, spam detection systems use a combination of these methods to improve the accuracy of identifying spam messages. This study applies the Bi-LSTM deep learning model to sequentially process text (sequencing). The performance of the model is determined based on the loss and accuracy. The data used are the Spam SMS and Spam Email datasets. The test results show that the Bi-LSTM model demonstrates better performance on all tested datasets. Bi-LSTM is able to capture textual patterns from both the context and the text itself, as it can combine information from both directions. The test results prove that the Bi-LSTM model is more effective in text comprehension. So we need to use Snort to maintain network security. Snort is a useful software for observing activity in a computer network. Snort can be used as a lightweight Network Intrusion Detection System (NIDS). Detection is carried out based on the rules that have been described by the administrator in the directory rules contained in the configuration file. Snort can analyze real time alerts, where the mechanism for entering alerts can be in the form of a user syslog, file or through a database. So we can detect attacks on computer networks early.
PENERAPAN METODE NAÏVE BAYES CLASSIFIER PADA SENTIMEN ANALISIS APLIKASI INVESTASI KEUANGAN DIGITAL: Studi Kasus: Bareksa Dan Bibit Girsang, Jhon Vebrianto; Jaya, Indra Kelana; Simanullang, Harlen Gilbert
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp225-230

Abstract

Investing online is a very promising opportunity. There are many online investment enthusiasts who do not understand how to invest online correctly and be able to minimize risk. Lack of public understanding of the investment implementation process can lead to fraud by irresponsible parties. So understanding investing online is very necessary. There are many online investment applications on the Google Play Store, but these investment applications have their own advantages and disadvantages. The objects of research are the applications of Bareksa and Seeds because the news media often report on these applications at the top and selecting an application requires a collection of information obtained from previous user reviews. The method used is the Naïve Bayes Classifier. Based on the results, the classification is divided into 3 (three) sentiments, namely positive, negative and neutral. With a comparison of training data and testing data 70%:30% accuracy in the Bareksa application was obtained 54% and 44% in the seed application.
MEDIA PEMBELAJARAN BAHASA ISYARAT DENGAN METODE MULTIMEDIA DEVELOPMENT LIFE CYCLE Bustamin, Syamsumar; Hamdani, Ibnu Mansyur; Hadi, Abri
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp217-224

Abstract

Interactive learning media is one manifestation of the use of information and communication technology in the field of education. One part of the use of information technology is Augmented Reality, where the use of Augmented Reality in education is still lacking. Education is not only for those with normal physical abilities, but all people have the right to receive an education that is general, as well as children with special characteristics, such as deaf students who are at SLB Negeri 1 Palopo. Deaf students are students who have lost the ability to hear which hinders the process of language information through hearing, because of these limitations the teaching and learning process will become an obstacle, so other media are needed to visualize the alphabetic sign language code so that it can be learned easily and interactively. Augmented Reality is the right technology to overcome this. The implementation of Augmented Reality is used for Android mobile devices with the Multimedia Development life Cycle.

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