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METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Published by Universitas Methodist Indonesia
ISSN : 24427861     EISSN : 26143143     DOI : https://doi.org/10.46880/mtk
Core Subject : Science,
Computer Science & IT
JURNAL METHODIKA diterbitkan oleh Program Studi Teknik Informatika dan Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Methodist Indonesia Medan sebagai media untuk mempublikasikan hasil penelitian dan pemikiran kalangan Akademisi, Peneliti dan Praktisi bidang Teknik Informatika dan Sistem Informasi. Jurnal ini mempublikasikan artikel yang berhubungan dengan bidang ilmu komputer, teknik informatika dan sistem informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 271 Documents
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SISTEM PREDIKSI VOLUME PENUMPANG HARIAN KRL YOGYAKARTA-SOLO MENGGUNAKAN MODEL HYBRID SARIMAX-PROPHET Muhammad Ilham 'Aziiz Alfarobi; Nurmalitasari; Ratna Puspita Indah
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5721

Abstract

The operation of the Yogyakarta-Solo Commuter Line (KRL) since 2021 has become the backbone of transportation in the Yogyakarta Special Region and Central Java. However, highly dynamic fluctuations in passenger volume pose challenges for operational optimization. This research aims to develop an accurate daily passenger volume prediction system with a 30 day forecasting horizon to mitigate overcrowding and fleet inefficiency. The methodology employed is CRISP-DM, proposing a layered hybrid architecture based on residual modeling. In this model, SARIMAX serves as the primary pattern modeler (Layer 1), while Facebook Prophet acts as a residual corrector (Layer 2), optimized with selective correction mechanisms and daily adaptive weights. The research data covers the period from January 2025 to January 2026, totaling 396 observations. The test results show that the hybrid model provides the best performance compared to single models, achieving a Mean Absolute Percentage Error (MAPE) of 9.66% and a Mean Absolute Error (MAE) of 2,739 passengers per day. Utilizing historical data from January 2025 to January 2026, the modeling results are integrated into an interactive Streamlit dashboard as a practical decision support tool for KAI Commuter's proactive operational planning
ANALISIS POLA PEMBELIAN PRODUK MENGGUNAKAN ALGORITMA APRIORI PADA DATA TRANSAKSI RETAIL Romadona
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5730

Abstract

The growth of transaction data in the retail sector increases the need for analytical methods capable of identifying consumer purchasing patterns efficiently. This study applies the Apriori algorithm within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework to perform market basket analysis on retail transaction data. The dataset consists of 934,974 transaction records, including 407,171 unique transactions and 27,344 unique products collected between July 2021 and September 2025. After the data cleaning process, 189,724 valid transactions were obtained. To improve computational efficiency, the analysis was limited to the 300 best-selling products, resulting in 90,718 transactions for the modeling stage. Frequent itemset generation was performed using a minimum support value of 0.1% and a maximum itemset length of three, producing 570 frequent itemsets consisting of 300 1-itemsets, 213 2-itemsets, and 57 3-itemsets. Association rule generation using a minimum confidence threshold of 70% and a lift ratio greater than 1 produced 52 valid rules. The best rule achieved a lift ratio of 161.32 and a confidence value of 93.88%, indicating a strong purchasing relationship among school supply products. The results demonstrate that the selected support and confidence parameters are effective in identifying meaningful purchasing patterns. Furthermore, the resulting association rules can support practical retail strategies, including product bundling, shelf arrangement optimization, and inventory management.
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN BONUS KARYAWAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING Fahmi Prima Yasa; Ifan Junaedi; Anton Zulkarnain Sianipar
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5747

Abstract

Employee bonus allocation is one form of corporate appreciation for employee performance in supporting organizational goals. However, the bonus determination process, which is still carried out manually at a private company that is the object of this study, often leads to subjectivity and inaccuracy in decision-making. This study aims to design and build a Decision Support System (DSS) for employee bonus allocation using the Simple Additive Weighting (SAW) method. The SAW method is used for multi-criteria calculation through the weighting and ranking of alternatives. The system was developed as a web-based application using the PHP programming language and MySQL database, applying six assessment criteria: attendance, discipline, work quality/competence, teamwork, loyalty, and administrative violations. The research was conducted using the System Development Life Cycle (SDLC) waterfall model, consisting of requirement analysis, system design, implementation, and testing. The system was tested on 20 employee alternative data and was able to perform objective employee assessment and ranking based on the highest preference value, which was then mapped into four bonus-recommendation zone classifications. Black Box Testing on authentication security, data integrity, computational accuracy, and interface usability showed a 100% success rate with a 0% computational error margin, indicating that all system features functioned according to user requirements. With this system, the employee bonus determination process becomes more effective, transparent, and efficient.
WEBSITE INFORMASI PARIWISATA DAERAH KABUPATEN MANGGARAI DENGAN SISTEM REKOMENDASI DESTINASI BERDASARKAN RATING Vigo Angkur Vigo; Try Ana Setyarini Rini; Hasibun Asikin Hasibun
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5749

Abstract

Manggarai Regency possesses significant tourism potential, yet remains constrained by the lack of integrated digital information, which hinders tourists from accessing accurate data and determining visit priorities. This research aims to design and develop a tourism information website integrated with an Item-Based Collaborative Filtering recommendation system to facilitate data-driven decision-making. The development employs the Research and Development (R&D) method using the Waterfall model, encompassing requirements analysis, system design, Implementation using PHP and MySQL, and rigorous functional testing. The final system was evaluated using Black Box testing, which confirms that all features, including the automated rating-based recommendation engine, function correctly according to specifications. The results demonstrate that the website effectively presents comprehensive information and generates accurate top-destination recommendations based on real-time visitor ratings. This platform provides a transparent and interactive tool that successfully assists tourists in selecting destinations while enhancing the digital promotion of Manggarai Regency’s tourism sector.
KLASIFIKASI PENYAKIT JAMUR PADA TANAMAN BAWANG MERAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS CITRA DIGITAL Alfani Septiani Selan; Franky Franky Y. Basilin; Tika Skolastika S. Igon
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5761

Abstract

Shallots are a type of bulb plant widely consumed by Indonesians, both as a cooking spice and herbal medicine. Shallot production in Kupang City has experienced a significant decline, with production dropping from 292.15 quantiles to 255.01 quantiles in 2024. This is a serious concern due to disease attacks on shallot plants that cause economic losses due to crop failure for farmers. Lack of understanding and knowledge about shallot diseases is a major obstacle in overcoming this problem. Therefore, an automated system based on digital image technology is needed that is capable of classifying diseases quickly and accurately. This study aims to implement a Convolutional Neural Network (CNN) in the process of classifying fungal diseases in shallot plants based on digital images. CNN is a deep learning method that has the ability to extract visual features through convolutional, pooling, and fully connected layers. The use of CNN in this study is expected to provide accurate results in classifying fungal diseases in shallot plants based on digital images, thereby reducing the potential for crop failure and increasing production yields. The test results using K-Fold Cross Validation showed that the best model was obtained in fold 5 with an accuracy of 90.00%, a sensitivity of 90.02%, and a specificity of 96.75%. In addition, the system obtained an average accuracy of 86.91%, a sensitivity of 87.61%, and a specificity of 95.61%. Based on these results, the system is able to classify fungal diseases in shallot plants with good performance.
RANCANG BANGUN WEBSITE SISTEM INFORMASI INVENTARIS BARANG MENGGUNAKAN METODE WATERFALL DI DESA PADA - LEMBATA Kornelis Andrian Kabo; Yohanis Malelak; Petrus Katemba
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5765

Abstract

Inventory management at the Pada Village Office in Lembata is currently still conducted manually using physical ledgers, which triggers data inaccuracies and the risk of document loss. This study aims to analyze, design, and develop an integrated web-based inventory information system using the Waterfall method. The system implements a real-time public complaint feature to strengthen asset management transparency and support the implementation of the Electronic-Based Government System (SPBE), enriched with a public infrastructure loan/rental module and an automatic fine control mechanism. The system was built using Native PHP and a MySQL database. Functional evaluation using Black Box Testing on 8 core test scenarios showed a 100% success rate (Pass) without any technical errors. Performance efficiency evaluation through Google Chrome Network Tools demonstrated optimal server response speed, with an overall average Page Load Time of 751 ms (under 1 second). Compatibility testing verified that the public interface is fully responsive and adaptive when accessed through both desktop and mobile browsers. The integration of these various features establishes the proposed system as a practical solution for realizing accountable village-level administrative digitalization.
PENERAPAN METODE HYBRID RANDOM FOREST DAN GENETIC ALGORITHM UNTUK OPTIMASI PENJADWALAN PRODUKSI Widya Monika Sari; Nurmalitasari; Bangun Prajadi Cipto Utomo
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5789

Abstract

The garment industry faces complex production scheduling challenges due to high product variability and inaccurate process time estimation. Inefficient production scheduling often leads to production delays and reduced operational performance. Conventional scheduling methods such as First Come First Serve (FCFS) and Earliest Due Date (EDD) have been shown to be less effective in dynamic production environments. This study aims to optimize flow shop production scheduling in a children's garment manufacturing environment using a hybrid Random Forest–Genetic Algorithm approach. Random Forest is employed to predict the processing time of each job based on a simulated dataset regenerated from the company's historical production data collected in 2025 while preserving the statistical characteristics and relationships among variables. Subsequently, the Genetic Algorithm is used to optimize job sequencing by simultaneously minimizing makespan and weighted tardiness. The study follows the CRISP-DM methodology up to the model evaluation stage. The results show that the Random Forest model achieved satisfactory prediction performance for the cutting, sewing, and finishing stages, with R² values of 0.817, 0.981, and 0.867, respectively. Using a population size of 30, 100 generations, and 10 independent runs, the Genetic Algorithm achieved an improvement of 79.94% compared to FCFS and 39.71% compared to EDD. These findings demonstrate that the proposed hybrid Random Forest–Genetic Algorithm approach can generate a more adaptive, efficient, and data-driven production schedule than conventional scheduling methods.
ANALISIS PERBANDINGAN SISTEM NOTIFIKASI TELEGRAM DAN BERBASIS WEB UNTUK PEMANTAUAN TERNAK IOT Rizki Fikriansyah; Fera Febrianti
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5830

Abstract

Although livestock monitoring is essential for improving farm productivity and supporting food security, conventional monitoring methods remain inefficient and may increase the risk of livestock loss. This study aims to compare the performance of Telegram-based and web-based notification systems for Internet of Things (IoT)-based livestock monitoring and to evaluate the responsiveness of the proposed web-based system. The developed system uses an ESP32 and Neo-6M GPS module to transmit livestock coordinates to a PHP-based web server, where data are stored in a MySQL database and displayed in real time using AJAX and a geofencing mechanism. System performance was evaluated through 12 field experiments conducted during morning, afternoon, and evening conditions. The results show that the proposed system achieved a notification delay of 0.06–3.4 s under normal network conditions, with a maximum delay of 25 s when the device temporarily moved outside the WiFi coverage area. Compared with the previous Telegram-based system, which relied on periodic notifications and external messaging services, the proposed web-based system provides faster and more responsive real-time monitoring. These findings demonstrate that the proposed approach can improve livestock supervision efficiency and has practical potential for supporting smart farming applications and food security.
ANALISIS ROBUSTNESS CONVOLUTIONAL NEURAL NETWORK TERHADAP VARIASI PENCAHAYAAN PADA SISTEM PENGENALAN WAJAH Ezra Ananta Pandie; Franki Yusuf Bisilisin; Dewi Anggraini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5833

Abstract

This study aims to evaluate the robustness of Convolutional Neural Networks (CNN) in face recognition systems under varying illumination conditions. The evaluation was conducted using a dataset comprising 36 subjects, with facial images captured under three distinct lighting scenarios: dim, normal, and bright. The research methodology involved training the CNN model using K-Fold Cross-Validation and assessing its stability against visual disturbances using artificial adversarial attacks based on the Fast Gradient Sign Method (FGSM). The novelty and main contribution of this study lie in the dual-evaluation approach, which simultaneously tests the model's resilience against natural illumination variations and artificial adversarial perturbations. Experimental results demonstrated that the CNN model achieved optimal face recognition performance at 50 epochs, maintaining an average accuracy rate of 81.48%. In conclusion, the evaluated CNN architecture is reliable and stable for face recognition in uncontrolled lighting environments, providing a solid foundation for developing more secure biometric systems against visual disturbances.
SISTEM INFORMASI PENYEWAAN PERALATAN OUTDOOR BERBASIS WEB DI SELEKTA ADVENTURE Fajar Saputra; Wijiyanto; Hanifah Permatasari
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5855

Abstract

The operational management of outdoor equipment rentals at Selekta Adventure still relies on manual processes, potentially leading to errors in transaction recording, delays in reporting, and difficulties in monitoring stock availability. This situation indicates the need for a system capable of effectively integrating all rental activities. This study aims to develop a web-based outdoor equipment rental information system by applying the Waterfall method as a software development approach. The system implementation was carried out using the Laravel framework and MySQL database to support structured data management. The resulting system provides various key features, including real-time inventory management, automatic late payment penalty calculations, and digital payment integration through a payment gateway, making the transaction process more practical and transparent. Functional testing was conducted using the Black Box Testing method, and all test scenarios showed that each feature ran according to user requirements without any functional errors. In addition, the application quality was evaluated using Google Lighthouse with a Performance score of 92, Accessibility 95, Best Practices 96, and SEO 100. The evaluation results indicate that the system not only meets functional requirements but also has a good web interface quality in terms of performance, accessibility, implementation of best practices, and search engine optimization. Thus, the developed system can support increased operational efficiency, reduce potential errors in data management, and improve the quality of rental services at Selekta Adventure.

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