cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jamaluddin
Contact Email
jamaluddin@methodist.ac.id
Phone
+6281397181985
Journal Mail Official
jamaluddin@methodist.ac.id
Editorial Address
Jl. Hang Tuah No. 8 Medan Sumatera Utara - Indonesia Kode Pos: 20152
Location
Kota medan,
Sumatera utara
INDONESIA
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 391 Documents
 36   37   38   39   40 
Segmentasi Pasar Terhadap Generasi Z Berdasarkan Perilaku Belanja Online Menggunakan Metode K-Means Clustering Meri Nova Marito; Ratna Wati Simbolon; Duma Lasmaria Siagian; Bertha Nerpy Siahaan; Ade Linhar P.; Anugrah Zai
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp244-248

Abstract

The advancement of digital technology greatly affects consumption patterns, especially among Gen Z, towards e-commerce platforms. Gen Z has a high affinity for digital technology. This condition creates a need for more precise market segmentation so that marketing strategies can be tailored to consumer characteristics. This study aims to conduct market segmentation based on online shopping behavior using the K-Means Clustering method. The data used in this study is the Online Retail Dataset obtained from the UCI Machine Learning Repository. The research stages include data preprocessing, attribute selection, data normalization, determination of the number of clusters, and the clustering process using the K-Means algorithm. The variables analyzed include transaction frequency, product purchase quantity, customer transaction value, and transaction time interval. The research results show that customer data can be grouped into several segments with different characteristics, such as active customers with high purchase levels, customers with moderate transactions, and customers with low purchasing activity. Thus, the resulting segmentation can help business actors understand consumer behavior and develop more targeted marketing strategies. In addition, the K-Means Clustering method has been proven effective in grouping customer data based on online shopping patterns. 
Rekomendasi Kata Kunci Produk Variasi Motor Menggunakan Metode Analytical Hierarchy Process Hafidz Aby Pratama; Ni Gusti Ayu Putu Harry Saptarini; Ni Ketut Pradani Gayatri Sarja
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp278-285

Abstract

The rising trend in motorcycle modification drives business owners to optimize digital marketing strategies through relevant product keywords. However, keyword determination is often conducted subjectively without a structured approach. This study aims to determine keyword recommendations for motorcycle variation products using the Analytical Hierarchy Process (AHP) method. The AHP method works by breaking down multi-criteria problems using pairwise comparison matrix calculations based on Relevance and Search Volume criteria. The results indicate that the AHP method is capable of determining keyword priorities objectively and systematically. The final results show that the keyword "Shockbreaker" ranks first with a score of 0,415. Consistency testing demonstrates a Consistency Ratio (CR) value of 0,00 for the criteria matrix and 0,081 for the search volume matrix. Since the CR values are CR ≤ 0,1, the weighting and decision-making results in this system successfully meet the standards established in the AHP method.
Analisis Efektivitas Algoritma Cosine Similarity dan Boyer-Moore dalam Sistem Pencarian Dokumen Digital Wawan Ade Saputra; Lidya Wati
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp269-277

Abstract

This study analyzes the effectiveness of the cosine similarity and Boyer-Moore algorithms in digital document retrieval within the SIPANDOK system developed for the Bengkalis Regency Health Office. Cosine similarity measures semantic document relevance via TF-IDF vector weighting, while Boyer-Moore performs direct pattern matching through string heuristics. The system was built using the Rapid Application Development (RAD) methodology and evaluated against 56 documents and 55 test queries using precision, recall, F1-score, accuracy, and execution time metrics. Results indicate that Boyer-Moore achieves higher average recall (66.7%) and F1-score (33.3%), demonstrating superiority in retrieving relevant documents, whereas cosine similarity offers faster execution time (average 0.31 seconds) compared to Boyer-Moore (0.91 seconds). Each algorithm presents distinct advantages depending on whether precision-orientation or recall-orientation is prioritized in document retrieval scenarios.
Implementasi XGBoost dan Logika Reverse Calculation pada Sistem Estimasi Harga Beli Mobil Bekas Berbasis Web Ida Bagus Aditya Cahya Wiraguna; Putu Indah Ciptayani; I Putu Bagus Arya Pradnyana; Ni Gusti Ayu Putu Harry Saptarini
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp357-365

Abstract

The movement of used car prices is highly dynamic and subjective, making the manual estimation process prone to bias and financial risk. This study aims to develop a web-based used car purchase price estimation system using the Extreme Gradient Boosting (XGBoost) algorithm combined with a reverse calculation logic. The dataset was obtained from secondary market data and primary showroom transaction records, totaling 12,324 clean data after passing the Grouped-IQR outlier filtering process. The XGBoost model was optimized using Grid Search and validated through 10-Fold Cross-Validation. The results showed that the optimal model configuration achieved a Mean Absolute Percentage Error (MAPE) of 11.23%, a Root Mean Squared Error (RMSE) of Rp 54,779,437, and a Coefficient of Determination (R2) of 0.8386. This performance indicates a highly accurate forecasting capability. The model was successfully integrated into a Laravel-based web application via a Python REST API, allowing users to obtain fair market price predictions and maximum purchase bids to improve the efficiency and objectivity of decision-making.
An Explainable Clinical Variant Risk Assessment Framework for Genomic Decision Support Fahmi Izhari; Hanna Willa Dhany; Syarif Hidayat Matondang; Rizki Khairani Nasution
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp350-356

Abstract

Clinical variant interpretation is essential for precision medicine; however, conventional machine learning approaches often focus on prediction accuracy without providing sufficient interpretability and decision-support capabilities. This study proposes a hybrid framework integrating Light Gradient Boosting Machine (LightGBM), SHapley Additive exPlanations (SHAP), and a Fuzzy Decision Support System (FDSS) for clinical variant risk assessment using the ClinVar dataset. A stratified sample of 200,000 genetic variants was utilized for model development and evaluation. LightGBM was employed to predict variant pathogenicity, while SHAP was applied to identify feature contributions and improve model transparency. The resulting prediction probabilities were subsequently processed through fuzzy inference to generate interpretable risk categories and recommendation-oriented outputs. Experimental results showed that the proposed framework achieved an Accuracy of 95.89%, Precision of 95.58%, Recall of 82.97%, F1-Score of 88.83%, and ROC-AUC of 98.73%. Explainability analysis revealed that variant-type representation was the most influential predictor of pathogenicity. The proposed framework extends conventional classification by transforming predictive outputs into actionable risk assessments, thereby enhancing transparency and supporting informed genomic decision-making. These findings demonstrate the potential of integrating predictive analytics, explainable artificial intelligence, and fuzzy reasoning for clinical variant assessment in precision medicine.
Deteksi Kecurangan pada Kompetisi Capture the Flag Menggunakan Two-Stage Similarity Analysis dan Tiered Weighted Risk Scoring Dimas Maulana; I Wayan Candra Winetra; I Nyoman Rai Widartha Kesuma
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp366-372

Abstract

Flag sharing, inter-team cooperation (teaming), and prohibited tools such as AI undermine how validly a Capture the Flag (CTF) event measures skills, corrupting the integrity of its scoring information system. GZCTF's only cheating signal is the dynamic flag that catches flag theft. Nothing else feeds a per-team risk profile. This study adds a detection module to the GZCTF backend built on two components. The first is Two-Stage Similarity Analysis: pairwise Longest Common Subsequence and Jaccard scores are blended into a Relative Sequence Index (RSI), after which Confidence Screening Detector (CSD) screening confirms suspicious groups. The second is a tiered Weighted Risk Scoring model that assigns 38 indicators to four evidence tiers (Hard, Strong, Behavioral, Context), caps every non-Hard tier, and gives network or identity correlations no direct score. Evaluation used a controlled simulation of ten team participations on a live instance, with ground-truth labels fixed at design time. Precision reached 1.000 with zero false positives, accuracy 0.900 and F1 0.909, and recall 0.833. The single miss came from collusion evidence attributed to only one member of a pair. An RSI threshold of 0.85 split every colluding pair from benign ones, and teams with purely network or identity correlation scored zero.
Analisis Sentimen Ulasan Pengguna Aplikasi Grab Mobile Menggunakan Metode K-Nearest Neighbor dan Lexicon-Based Maulana Bakti; Pitrasacha Adytia; Bartolomius Harpad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp402-411

Abstract

Grab is one of the most widely used online transportation and digital service applications in Indonesia. As the number of users grows, reviews provided on the Google Play Store have become an important sourc of information to understand what users think and how satisfied they are. This study aims to study customer feelings about app reviews. Grab uses the K-Nearest Neighbor (KNN) method combined with a Lexicon -Based approach for automatic labeling. The dataset consists of 300 reviews in Indonesian sourced from the Google Play Store. The preprocessing process includes cleaning, case folding, tokenizing, stopword removal, and stemming using the Sastrawi library. Emotion labeling is done automatically using a Lexicon-Based sentiment dictionary. Text attributes are extracted using the TF-IDF (Term Frequency-Inverse Document Frequency) method, then classified using KNN with K = 5 and a training and experimental data sharing ratio of 80: 20. The research findings emphasize that the majority of users (80.67%) give positive reviews to the Grab app. The KNN model achieved 90% accuracy with a precision of 0.92, a recall of 0.96, and an F1-score of 0.94 for the good class. This study demonstrates that the combination of KNN and Lexicon-Based methods can be used effectively in sentiment classification of Indonesian-language reviews.
Sistem Navigasi Keselamatan Pelayaran Perairan Darat Berbasis Web Anton Widodo; I Putu Putra Wira Sarwa Yudha; Maulana Putra; Agustina Rachmawardani; Nardi Nardi
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.pp363-371

Abstract

Navigation safety in inland waters is often threatened by extreme weather conditions such as strong winds and high waves. This study developed SIGADANA, a web-based navigation system that provides early warnings of weather and water conditions in seven major ports: Ajibata, Ambarita, Balige, Muara, Simanindo, Sipinngan, and Tigaras. The system integrates real-time data with hourly temporal intervals from the OpenMeteo API, including wind speed, wind direction, temperature, and humidity, and utilises the JONSWAP model with Boussinesq corrections to estimate significant wave height (Hs) based on local topography and water depth. Furthermore, to assist the analyst team in understanding weather and water patterns in the test area, the system also stores early warning data for a five-year period, 2020-2024. SIGADANA has three main features: (1) historical monitoring of water condition trends, (2) real-time predictions for early warnings up to two hours in advance, and (3) navigation route advisories. To date, particularly in Indonesia, there has been no project estimating wave conditions prior to SIGADANA. Therefore, the system was only evaluated using OLS regression, with results showing that the model was able to explain more than 70% of the data variation. However, there are indications of residual autocorrelation and potential multicollinearity that need to be considered in the interpretation. In the future, SIGADANA is expected to improve navigation safety and has the potential to become a model for other inland waters in Indonesia.
Prediksi Evaporasi Berbasis Mesin: Perbandingan ANN, KNN, Random Forest dan Regresi Linier Muchamad Rizqy Nugraha; Haidar Amru Rusdan; Marzuki Sinambela
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.pp380-386

Abstract

Evaporation plays a key role in water allocation, yet data limitations are often encountered. This study evaluates four regression models (Linear Regression, K-Nearest Neighbors, Random Forest, and Artificial Neural Network—ANN) to predict evaporation rates at the Banten Climatology Station. Models were assessed using R-squared (R²) and Root Mean Squared Error (RMSE). The results show that the ANN achieved the best accuracy with RMSE = 0.122 and R² = 0.475 (47.5%), followed by Linear Regression (RMSE = 0.123, R² = 0.460), K-Nearest Neighbors (RMSE = 0.126, R² = 0.437), and Random Forest (RMSE = 0.129, R² = 0.406). Other models also provided acceptable predictions, but the ANN stood out as the most accurate and reliable for applications at the Banten Climatology Station. These findings offer valuable insights for water resources management and agricultural planning, highlighting the potential of machine learning techniques to overcome evaporation data limitations.
Prediksi Harga Saham Apple Inc (AAPL) Menggunakan Metode Single Exponential Smoothing Gomgom Triputra Sinaga; Jimmy Febrinus Naibaho; Jhoni Maslan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp373-379

Abstract

The stock price movement of Apple Inc. (AAPL) exhibits high levels of fluctuation and volatility, necessitating adaptive forecasting methods to assist investors in decision-making. This study aims to implement the Single Exponential Smoothing (SES) method to predict the closing price of AAPL stock based on historical data. The research methodology involves processing 1,256 daily data points from Investing.com for the period October 2020 to October 2025 using the Python programming language. The results indicate that using a smoothing parameter of $\alpha = 0.9$ yields the most optimal performance with a Mean Absolute Percentage Error (MAPE) of 1.29% and a Mean Absolute Error (MAE) of $2.2267. With a MAPE value below 10%, the accuracy of this model is classified as Highly Accurate. The system generates an estimated AAPL stock price for the next period of $258.87, proving that the SES method is effective for short-term forecasting on volatile data

Page 39 of 40 | Total Record : 391

 36   37   38   39   40 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 6 No. 1 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 5 No. 2 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 5 No. 1 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 4 No. 2 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 4 No. 1 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 3 No. 2 (2019): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 3 No. 1 (2019): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 2 No. 2 (2018): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 2 No. 1 (2018): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi Vol. 1 No. 1 (2017): METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi More Issue