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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 383 Documents
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Pengembangan Intelligent Web-Based System untuk Diagnosa Kerusakan Sepeda Motor Listrik Menggunakan Naive Bayes Wirhan Fahrozi; Linda Wahyuni; Abdul Meizar
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.pp151-160

Abstract

The rapid growth in the use of electric motorcycles has increased the need for a damage diagnosis system that is fast, accurate, and easily accessible. The process of identifying faults, which still relies on manual inspection, often requires considerable time and depends heavily on the expertise of technicians. This study aims to develop a web-based intelligent system capable of automatically diagnosing electric motorcycle faults using the Naïve Bayes method. This method is chosen due to its ability to perform probabilistic classification with good accuracy, even when dealing with limited data. The developed system utilizes symptom data and types of faults as the basis for probability calculations to determine the most likely damage. The system development process includes data collection, model design, algorithm implementation, and system testing. The testing results indicate that the system is able to provide fast and consistent diagnostic recommendations based on the symptoms input by users. With the implementation of this system, it is expected to assist both users and technicians in conducting initial identification of electric motorcycle faults more efficiently, thereby accelerating the repair process and reducing the potential for diagnostic errors.
Perancangan Aplikasi Mobile untuk Rekomendasi Kuliner Lokal Efrans Michael Harahap; Jamaluddin Jamaluddin; Eva Julia Gunawati Harianja
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.pp161-167

Abstract

The rapid advancement of information technology has shifted the paradigm of tourism and culinary exploration towards mobile platforms. However, local culinary searches in Medan City are currently still conventional and random. Scattered culinary information across various social media without a comprehensive summary often confuses customers and complicates the promotion of micro-culinary businesses (UMKM) in the digital era. Existing commercial platforms often burden local sellers with high platform taxes. This research aims to design and implement an Android-Based Mobile Application for Local Culinary Recommendations as an effective solution to these problems. This application is built natively using the Java programming language for the Android client side, integrated with a PHP Application Programming Interface (API) and MySQL relational database. The system design involves a Client-Server architecture and includes three distinct access rights: User, Culinary Admin, and Super Admin. Implemented features include dining place search, interactive reviews, ratings, popularity levels based on view counts, and Google Maps API integration for precise culinary navigation routes. The result of this research is a self-managed mobile application that accelerates navigation access for users while assisting business owners in managing their stalls independently, objectively, and expanding their promotional reach without internal transaction fees.
Penerapan Association Rule Menggunakan Algoritma Apriori untuk Rekomendasi Strategi Penjualan pada UMKM Toko Pempek Putri Salsabilah; Ummu Farida Muthmainnah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Alsella Meiriza
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.pp192-197

Abstract

Pempek Ceria SME has a growing number of daily sales transactions; however, these data have not been optimally utilized to support sales strategies. This situation highlights the need for transaction data analysis to understand customer purchasing patterns and develop more effective promotional strategies. Therefore, this study focuses on applying association rules using the Apriori algorithm to provide sales strategy recommendations for Pempek Ceria SME. The analysis was conducted using RapidMiner software on 317 transactions from October to December 2025, with a minimum support of 15% and a minimum confidence of 65%. The results show two association rules that meet these criteria: the combination of Pempek Adaan and Orange Juice, with a support of 28% and confidence of 72%, and the combination of Pempek Kapal Selam and Sweet Iced Tea, with a support of 27% and confidence of 70%. These findings indicate that the association rule method based on the Apriori algorithm can identify relationships between menu items frequently purchased together. By understanding these purchasing patterns, Pempek Ceria SME can optimize bundling strategies and product recommendations to improve promotional effectiveness and sales.
Analisis Sentimen Pelanggan Kopi Kenangan pada Media Sosial Instagram Menggunakan Metode Lexicon Based Riani Sela; Wahyuni; Ivan Haristyawan
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.pp198-207

Abstract

This study aims to analyze customer sentiment towards Kopi Kenangan on Instagram using a lexicon-based method. The rapid growth of Kopi Kenangan as a local coffee brand in Indonesia has made Instagram a key platform for building brand image and interacting with customers. The main problem faced is the large volume of unstructured comment data that cannot be processed manually, so an efficient and systematic automated approach is needed. This study uses a case study methodology and quantitative descriptive techniques. The object of research in this study is the official Kopi Kenangan Instagram account, and the dataset used to conduct this study comes from that account. The dataset that can be used to test this research will be created by developing processing stages. The Lexicon-Based method is used in this research approach. The purpose of the dictionary-based Lexicon-Based method is to determine the weight of sentences in the dataset to identify sentiment class labels. The data used in this study comes from 1689 records that have been preprocessed to produce 1438 patent records by removing empty comment records and verifying duplicates up to a certain threshold. The next step is to identify comments based on their sentiment: 25 negative, 1347 neutral, and 317 positive. The results show that negative sentiment reached 1,5%, neutral 79.8%, and positive 18.8%. Based on this presentation, the majority of the text examined was consumer responses to Kopi Kenangan's Instagram posts, and most of them were negative. This indicates that the Lexicon-Based model developed is capable of classifying sentiment with good accuracy.
Analisis Perbandingan Akurasi Metode Single Exponential Smoothing (SES) dengan Optimasi Parameter Alpha dan Single Moving Average Pada Sistem Peramalan Kebutuhan Stok Produk Bangunan: Studi Kasus: UD Ema Kencana Abadi I Made Sesa Putra; Ni Gusti Ayu Putu Harry Saptarini; Ni Nyoman Harini Puspita
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.pp217-225

Abstract

Manual inventory management at UD Ema Kencana Abadi has the potential to cause recording errors and stock imbalances. The primary focus of this study is to implement a web-platform-based inventory system that integrates forecasting techniques as a decision-support instrument. The method used is Single Exponential Smoothing (SES) with dynamic optimization of the alpha (α) parameter through iteration to obtain the lowest Mean Absolute Percentage Error (MAPE), which is then compared with the Single Moving Average (SMA-3). Testing was conducted using nine months of historical sales data on six building material products. The results showed that SMA-3 is more optimal for data with stable fluctuations, yielding a MAPE of 11.91%, whereas the optimized SES is more accurate for data with medium to high fluctuations. The implementation of this system improves forecasting accuracy and supports more objective stock procurement decision-making.
Pengembangan Aplikasi Mobile Berbasis Android untuk Penilaian Kinerja Guru di MTs Baiturrohim Menggunakan Metode Multi-Objective Optimization on the Basis of Ratio Analysis Afrizal Fanani; Iftitaahul Mufarrihah
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.pp208-216

Abstract

This study integrates an Android-based mobile application system for teacher performance evaluation with the development of the MOORA method. This system is designed to transform conventional evaluation mechanisms into a digital platform that is more objective, efficient, and transparent. The Agile model was used for its development. The MOORA algorithm is implemented to process four main criteria of teacher competence pedagogical, personal, social, and professional which are broken down into 78 performance indicators. Test results show that the system successfully identified a Pearson correlation of 0.9999, which falls into the very strong category.
Prediksi Kepatuhan Wajib Pajak PBB-P2 Berbasis Kecerdasan Buatan dengan Algoritma Gradient Boosting pada Badan Pendapatan Daerah Kabupaten Badung Caca Natasya Sugiana; I Putu Oka Wisnawa; 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.pp234-243

Abstract

Rural and Urban Land and Building Tax (PBB-P2) is an important source of Regional Original Income (PAD). However, suboptimal taxpayer compliance often results in under-targeted regional tax revenues. Conventional taxpayer compliance management encourages the need for Artificial Intelligence (AI) and machine learning-based approaches to support more proactive, data-driven decision-making. This study uses the Extreme Gradient Boosting (XGBoost) algorithm to predict PBB-P2 taxpayer compliance based on historical tax data. The research steps include data preprocessing, splitting the training and testing datasets with a 70:30 ratio, model training, hyperparameter tuning, and model evaluation using precision, recall, F1-score, and confusion matrix. The results showed that the best model produced an F1-Macro value of 0.7108 with a learning rate of 0.2, max depth of 12, n_estimators of 400, min_child_weight of 1, subsample of 0.8, and gamma of 0. The most influential variables in the prediction included the District Code, Principal Payment Amount, and Village Code. The XGBoost model was able to provide quite good classification performance in supporting the identification of PBB-P2 taxpayer compliance more effectively.
Prediksi IHSG Berbasis Web Menggunakan Metode Long Short-Term Memory (LSTM) I Komang Agus Arta Cahyana; Ni Gusti Ayu Putu Harry Saptarini; Putu Desiana Wulaning Ayu
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.pp249-259

Abstract

The Indonesia Composite Stock Price Index (IHSG) is one of the primary indicators used to measure the performance and stability of the capital market in Indonesia. The dynamic and non-linear characteristics of IHSG data make stock market prediction a challenging task when using conventional statistical methods. This study aims to develop a web-based IHSG prediction system using the Long Short-Term Memory (LSTM) method to improve prediction accuracy and provide an interactive forecasting platform for users. Historical IHSG data were collected from Yahoo Finance API using OHLCV (Open, High, Low, Close, Volume) variables. The data preprocessing stage included data cleaning, normalization using Min-Max Scaling, and sequence generation with the sliding window technique. Hyperparameter tuning was conducted by testing several configurations of window size, hidden units, learning rate, and epoch values. The best model configuration was obtained using a window size of 60, hidden units of 128, a learning rate of 0.01, and 74 epochs, resulting in RMSE of 51.9821, MAE of 39.8241, and MAPE of 0.559 %. Unlike previous studies that mainly focused on offline model evaluation, this research integrates the LSTM model into an interactive web-based prediction system equipped with visualization, AI forecasting, statistical evaluation, and batch prediction simulation features. The results indicate that the LSTM model is capable of producing accurate IHSG predictions and can be effectively implemented in a real-time web-based forecasting system.
Pengembangan Sistem E-Learning Berbasis Decision Tree untuk Analitik dan Klasifikasi Nilai Siswa di SDK Harapan Denpasar Joan Jasmine Malelak; I Nyoman Eddy Indrayana; Yessi Aprilia Waluyo
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.pp226-233

Abstract

Monitoring student academic performance at the elementary school level remains a challenge because most grade management is still performed manually, slowing the identification of students who need special attention. This study developed a Laravel 10-based e-learning system integrating online examination with security features, automatic grade recapitulation, and academic performance classification using the CART Decision Tree algorithm. The system was built using the waterfall model and evaluated on 60 students from grades 1A to 6A at SDK Harapan Denpasar. Final scores were computed using a 40% assignment and 60% examination weighting formula, then classified into four categories: Very Good, Good, Sufficient, and Needs Guidance. Model evaluation yielded an accuracy of 86.7%, macro precision of 89.6%, macro recall of 86.3%, and macro F1-score of 86.2%, all exceeding the 80% threshold. Black box testing passed 100% of test cases (15/15) and User Acceptance Testing produced a score of 4.68 out of 5.00. The system enables teachers to identify students requiring further guidance directly from the dashboard without relying on external analytical tools.
Pengembangan Aplikasi Virtual Tour Kampus Berbasis XR Menggunakan Unity untuk Meta Quest 2 Dengan Chatbot AI LLaMA Farhan Maulana; Ni G. A. P. Harry Saptarini; I Putu Astya Prayudha; Gde Brahupadhya Subiksa
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.pp260-268

Abstract

The development of Extended Reality (XR) technology creates new opportunities in delivering more immersive and interactive digital information, particularly in higher education environments. However, current campus promotion media are still dominated by static visual presentations and have not been able to provide natural two-way interaction for users. This study aims to develop an XR-based campus virtual tour application using Unity for the Meta Quest 2 device integrated with an Artificial Intelligence chatbot based on the Large Language Model (LLaMA). The application enables users to explore the campus environment in a three-dimensional virtual space, interact with campus objects, and ask questions through voice input to obtain real-time responses. The research method used is Research and Development (R&D) with an experimental software engineering approach and Agile iterative incremental development model. System development includes XR environment implementation, speech-to-text integration, prompt delivery to the LLaMA model through REST API using LM Studio as a self-hosted AI server, and text-to-speech response generation. System evaluation was conducted using black box testing, performance testing on AI response latency and frame rate stability, and usability evaluation using the User Acceptance Testing (UAT). The expected result of this research is an interactive, stable, and responsive campus virtual tour application capable of providing a more immersive information experience while functioning as an independent digital promotion medium without dependence on cloud-based AI services.

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