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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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Implementasi MYOB Accounting Plus V18 dalam Pengolahan Data Akuntansi pada CV. Tiga Raya Inovasi Ariesha Fen
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.pp258-263

Abstract

This research aims to determine the implementation of MYOB Accounting as a form of utilizing computerized accounting technology in recording and compiling financial reports for MSMEs. The research method used is descriptive qualitative with observation and interview techniques on CV. Tiga Raya Inovasi as the subject of research. The research results show that MYOB Accounting is able to produce financial reports that can be understood with a more effective and efficient work process for the company. However, several supporting factors are needed, such as awareness and ability of business actors to carry out accounting records using MYOB Accounting.
Sistem Pakar Diagnosis Anxiety Disorder Dengan Metode Forward Chaining Berbasis Web Shaela, Pamela; Sugianti, Devi; Syaifudin, Anas; Darmawan, Arief Soma; Risqiati, Risqiati
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (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.Vol9No1.pp32-41

Abstract

Anxiety Disorders are the most common mental health disorders in the world. The 2018 Indonesian Health Research Main Results showed a significant increase in the prevalence of mental disorders. However, the comparison of the number of Indonesian people with professional psychology personnel is still very unbalanced and far from the WHO standard which requires the ratio of experienced mental health personnel to the ideal population of 1:30,000. Pekalongan City only has 6 clinical psychologists with a population of 317,958 people. So many people with mental health disorders do not receive adequate treatment. To overcome this problem, a web-based expert system was developed that can diagnose anxiety disorders using the forward chaining method, which imitates the thought process of an expert in making decisions based on symptom data and certain rules. This system was developed using the waterfall method, which includes needs analysis, system design, implementation, integration, testing, and maintenance. Testing was carried out using the White Box, Black Box, and User Acceptance Test (UAT) methods. Data for the UAT test was obtained by involving 100 respondents from Pekalongan City, who were selected using the Simple Random Sampling method and calculated using the Slovin Formula to ensure adequate representation. UAT results showed that respondents “strongly agreed” that the system was easy to use, informative, and useful in providing an initial understanding of anxiety disorders and the importance of mental health.
Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia Sujacka Retno; Rozzi Kesuma Dinata; Novia Hasdyna
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.pp274-280

Abstract

This study evaluates the quality of K-Medoids clustering applied to criminal incident data in Indonesia from 2000 to 2023. The analysis compares the clustering performance on both original and normalized datasets using various evaluation metrics, including the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Calinski-Harabasz Index (CH). The findings reveal that the original dataset consistently outperforms the normalized dataset across all metrics. The optimal clustering was achieved in the seventh iteration of the original data, with the lowest DBI (0.438), the highest SS (0.683), NMI (0.916), ARI (0.984), and CHI (57.418). In contrast, the normalized data exhibited higher DBI values and, in some cases, negative Silhouette Scores, indicating less distinct clusters. These results suggest that for this dataset, K-Medoids clustering performs more effectively on the original data without normalization, providing more accurate and well-defined clusters of criminal incidents. This insight is crucial for future research and practical applications in crime data analysis, emphasizing the importance of dataset preprocessing in clustering methodologies.
Analisis Algoritma J48 Pada Pengambilan Keputusan Pemberian Pinjaman Kepada Calon Nasabah Silitonga, Agnes Irene; Ginting, Lukas; Sinaga, Enjelina; Zega, Elson; Sembiring, Samuel; Simamora, Yoakim
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.pp281-293

Abstract

This research aims to analyze the stages of decision making for granting loans to prospective customers using the J48 Algorithm. Using the "Loan-Approval-Prediction-Dataset" dataset obtained from Kaggle, this research will build a decision tree model that can provide insight into the key factors that influence the decision. It is hoped that the results of this research can contribute to financial institutions in increasing accuracy, efficiency and objectivity in the credit evaluation process, as well as helping prospective customers understand the factors that need to be considered to increase their chances of loan approval.
Klasterisasi Pemetaan Kedisiplinan Pegawai Berdasarkan Rekap Kehadiran menggunakan Algoritma Clustering K-Means Ashari, Imam Ahmad; Purwono, Purwono; Indriyanto, Jatmiko; Sandi A., Arif Setia
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (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.Vol9No1.pp12-18

Abstract

Employee discipline is one of the key success factors in a company. Work discipline has an important role in the formation of a positive work environment. One of the things that shows employee discipline is the time of attendance. Attendance time is usually recorded at the time the employee enters and leaves. Disciplinary information can be mapped into several groupings so that it is easy for decision makers to read. One of the computational methods that can perform data mapping is the K-Means Clustering method. The K-Means Clustering method can group data based on their characteristics. In this study, attendance data were analyzed using the K-Means method to obtain disciplinary groupings. The number of Clusters is calculated using the elbow method, 3 Clusters are obtained which are the best Cluster choices, namely Clusters 0, 1, and 2. The data analysis process shows Cluster 2 is the Cluster with the best level of discipline. From the analysis, it shows that the K-Means Clustering method can classify data based on employee discipline. Based on these results, decision makers can be helped in assessing employee discipline at Universita Harapan Bangsa using the disciplinary data grouping that has been made.
Sistem Pendeteksi Tingkat Kesegaran Daging Ayam pada Citra Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android Naturizal, Rayhan; Fuadi, Wahyu; Rosnita, Lidya
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.pp301-312

Abstract

This research develops a chicken meat freshness detection system based on image processing, implemented on an Android platform using the Convolutional Neural Network (CNN) method optimized with TensorFlow Lite. The system classifies chicken meat into three categories: fresh, less fresh, and rotten. The CNN model uses 32 filters to enhance feature extraction from the meat images. Testing on 30 samples, with each category tested 10 times, showed an accuracy of 90%, with 27 correct detections and 3 errors in the less fresh category. While the system effectively identifies fresh and rotten categories, there is a challenge in distinguishing the less fresh category due to its ambiguous visual characteristics. One limitation is the lack of a bounding box, causing the application to still provide detection results even when the scanned object is not chicken meat. This application is specifically designed to detect chicken meat pieces, so it is not recommended for use outside this context.
Penerapan Algoritma Sorting dalam Penentuan Pekerja Pada Aplikasi Cari Kerja Oleh dan Untuk Warga Satu Kelurahan Dataran Tinggi Binjai Siregar, Manutur Pandapotan; Jinan, Abwabul; Siagian, Akbar Idaman Prince Peter S.
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.pp294-300

Abstract

The current job search process often involves posting an announcement on paper or a banner on a notice board, or in front of the company’s location. Another common method is through job search applications, such as JobStreet and others. The first method has a drawback because people may not know when the job posting is published. Meanwhile, with the second method, many people hesitate to use these applications as they feel their skills may not be sufficient. To address these issues, an Android or web-based job search application is proposed to facilitate job sharing and job seeking within a nearby area, specifically within a single subdistrict. This application is targeted at individuals with a high school education level or lower, and the jobs shared are typically daily work requiring minimal skills, such as construction work, electrical repairs, gardening, cleaning, and similar tasks. A sorting algorithm will be implemented to help select the nearest and most suitable candidate for each job. To access the application, users must first register, enabling employers to post jobs and workers to find suitable positions.
Analisa Hubungan Penyakit Jantung Koroner Terhadap Penyebabnya Menggunakan Algoritma Frequent Pattern Growth Darnila, Eva; Nazira, Nazira; Fajriana , Fajriana
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (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.Vol9No1.pp19-31

Abstract

The increase in cases of coronary heart disease without detailed knowledge of the causes is a serious problem that requires immediate treatment. This study aims to analyze the relationship between causal factors and the incidence of coronary heart disease using the Frequent Pattern Growth (FP-Growth) algorithm. This algorithm is applied to medical data of inpatients at RSUD dr. Fauziah Bireuen to identify patterns of relationships that often arise between risk factors such as age, gender, diabetes, cholesterol, hypertension and uric acid on the diagnosis of coronary heart disease. There were 180 patient medical record data with 17 items used for analysis. The results show the three most significant relationship patterns: the combination of risk factors for diabetes and high cholesterol has a support value of 50% and confidence of 67%, the risk of diabetes in men has a support value of 47% and confidence of 63%, and the combination of cholesterol and hypertension shows a support value of 45 % and confidence 66%. These results are expected to provide better insight into the prevention, early detection and treatment of coronary heart disease, as well as improving health services in hospitals. This research also emphasizes the importance of applying data mining technology in the analysis of complex health data.
Leaf-Type Image Classification Using Deep Learning Method Convolution Neural Network Sopany, Mikael Reichi; Handhayani, Teny
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (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.Vol9No1.pp86-91

Abstract

One of the most important parts of an ecosystem is a plant, Plants life has given us many benefits from food, oxygen, and medicine. There are many species of plant each with its unique benefits and utilities. In this paper, we try to identify plants by their leaf using deep learning. For this research, we use the convolution neural network architecture Xception to classify 5 different types of leaves. We used 1075 images of leaves that can be classified into 5 different types of leaves. the classification model achieved an overall accuracy score of 74%. We hoped that the result of our research can help people's life by helping them to identify plants that they have so that they can use them for their benefit.
Pemodelan Sistem Deteksi Intrusi pada Sistem Smart Home Pemantauan Konsumsi Energi Listrik Berbasis Machine Learning Nugroho, Eddy Prasetyo; Havid, Sabian Annaya; Nursalman, Muhammad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (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.Vol9No1.pp42-49

Abstract

The occurrence of electricity usage that exceeds the power capacity of the home requires a smart home system that can monitor electricity consumption efficiently. This smart home system is built based on the Internet of Things (IoT) which can help electricity users at home to evaluate usage more easily and in an integrated manner. The development of this IoT-based smart home system uses the ESP32 Micro Controller Unit (MCU) and the PZEM-004T v.3.0 sensor. The reading results from the system can be seen on the front end of the web-based application and the LCD module on the controller system. To obtain the efficiency of electricity usage, an electricity usage leakage detection system is needed or in this case, it is called an intrusion detection system or Intrusion Detection System (IDS). The development of IDS by identifying anomalies based on electricity usage. The IDS model utilizes Machine Learning with a labelling process pattern as a preprocess using the Isolation Forest unsupervised learning algorithm and the classification process using the Random Forest supervised learning algorithm with Anomaly and Normal status. Evaluation of the IDS model on the dataset that has gone through labelling gives quite good results with an accuracy value of 99.63 %. IDS Model is ready to be tested in the implementation of classifying recorded data in real-time against several electrical energy load scenarios in the future.

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