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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 25 Documents
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Search results for , issue "Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika " : 25 Documents clear
Implementasi Sistem Informasi Akuntansi Penjualan dan Pembelian Menggunakan Metode Extreme Programming (Studi Kasus Toko Sumber Sayur) Patunisa, Riva Nursari; Sudrajat, Ari
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.pp1-11

Abstract

Sales and purchasing are the main activities of companies, especially trading companies. One of the techniques needed by trading companies to support business activities is the use of an appropriate accounting information system. Accounting information is closely related to business activities, because with accounting information the company can see the condition of the company's development so that it can be used as a source for decision making so that the company's goals can be achieved. The simple process of recording sales and purchase transactions using book media can cause various problems in reporting sales and purchase transactions and is ineffective. The Sumber Sayur Store in running its business still uses a conventional sales and purchase transaction recording system, namely recording using books. This is very detrimental and inefficient. The system was built using the Extreme Programming software development method. Based on the results of research and system testing, it can be concluded that this system can help overcome problems in sales and purchase transactions effectively with the results of system testing using the User Acceptance Test of 87,33%.
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.
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.
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.
Grade Classification of Diabetic Retinopathy Based on Single Model Convolutional Neural Network Fitriati, Desti; Nursari, Sri Rezeki Candra
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.pp50-55

Abstract

Diabetes Mellitus (DM) is one of the diseases that has attracted global attention because it ranks fourth as a non-communicable disease with the highest mortality rate after cardiovascular, cancer, chronic respiratory diseases. DR is a condition caused by diabetes that can cause permanent damage to the blood vessels of the retina which can lead to blindness. DR is divided into 2 stages, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (DR), where each stage has different characteristics. From several studies that have been conducted previously, Convolutional Neural Network (CNN) has been widely used in recent years to segment medical images with remarkably consistent results. However, it is still necessary to find a suitable model to be able to adapt to all existing variables. For this reason, this study proposes a method as a modified model of CNN using seven layer. From the results of the research conducted, the proposed method uses four class models, namely 5 classes, 3 classes, 2 classes (Healthy & DR), and 2 classes (Healthy & Moderate). This research produced accuracy rates of 52%, 68%, 92% and 84% respectively.
Analisis Sentimen Masyarakat Terhadap Pelayanan Jasa Ekspedisi JNE dan J&T Express Menggunakan Metode Lexicon-Based Mola, Sebastianus Adi Santoso; Mbatu, Dinda Permata; Sihotang, Dony Martinus
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.pp56-65

Abstract

JNE and J&T Express are two of the largest and most popular courier companies in Indonesia, leading to various public opinions regarding the quality of their services. This research employs a lexicon-based method using the InSet dictionary, a simple scientific approach where the system calculates the weight of words and classifies them as positive, negative, or neutral sentiments. The analysis process begins with data collection of reviews using scraping techniques, followed by text processing including cleaning, case folding, normalization, tokenization, stemming, and stopword removal. Out of 3,565 reviews for JNE and 3,967 reviews for J&T, the sentiment analysis indicates that the majority of the public holds negative opinions towards the services of both courier companies. The analysis accuracy reaches 82% for JNE data, with a precision value of 95% for negative sentiment, 54% for positive sentiment, and 7% for neutral sentiment. The sensitivity values are 83% for negative sentiment, 82% for positive sentiment, and 15% for neutral sentiment. Data for J&T shows an accuracy of 78%, with a precision value of 97% for negative sentiment, 28% for positive sentiment, and 4% for neutral sentiment. Sensitivity values are 80% for negative sentiment, 82% for positive sentiment, and 4% for neutral sentiment.
Implementasi ISO-IEC 25010 untuk Analisis Kualitas Sistem Informasi Manajemen Kerja Praktik (SIM-KP) Dewi, Rosanti; Satyareni, Diema Hernyka; Kurniawan, Eddy
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.pp76-85

Abstract

The rapid development of technology has influenced various sectors, including education, where technology supports academic processes such as the Internship Management Information System (SIM-KP) at Universitas Pesantren Tinggi Darul Ulum Jombang. Despite its benefits, several issues have been identified, such as user interface complexity and unused features, necessitating a quality analysis. This study aims to analysis the quality of SIM-KP using the ISO/IEC 25010 standard. Compared to other methods, ISO/IEC 25010 offers the most comprehensive approach to analysis software system quality, focusing on aspects such as usability, functional suitability, reliability, efficiency, security, portability, compatibility, and maintainability. Data collection was conducted through questionnaires distributed to 53 Information Systems students via WhatsApp, using a Likert scale for assessment criteria. The research results show an average quality score of 71.3%, categorized as good. Performance efficiency achieved the highest score (80.7%), followed by functional suitability (77.2%), while maintainability (65.2%) and usability (65.0%) scored lower. Other scores include security (70.2%), portability (74.3%), and compatibility (69.2%). These findings indicate that the eight variables of ISO/IEC 25010 used in the analysis of SIM-KP achieved a good score.
Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko, Prihandoko; Rumapea, Sri Agustina; Pratama, Abdul Hanif
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.pp179-187

Abstract

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.

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