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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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PEMODELAN BANJIR DI PASTEUR JAWA BARAT MENGGUNAKAN SAINT VENANT EQUATION DENGAN METODE BEDA HINGGA Febriana Eka Adkhaniyah; Nimas Nabila Anggraeni; Dian Candra Rini Novitasari
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.v12i2.5908

Abstract

Floods are natural disasters that often occur in Indonesia. Floods occur due to excessive water flow that inundates an area for a certain period of time. Floods can occur suddenly or gradually at any time, resulting in significant losses. According to the National Disaster Management Agency, from 2022 to 2024, Indonesia experienced 4,026 flood disasters, 699of which occurred in West Java Province. On January 25, 2025, one of the areas in West Java, specifically Pasteur, Bandung, experienced a flood disaster triggered by the suboptimal performance of its drainage system and high rainfall intensity. Therefore, this research conducted flood modeling using the shallow water equation with the finite difference method. The purpose of this research is to minimize losses caused by flood disasters. Based on the research results, the Pasteur area has the potential to experience flooding if it experiences high rainfall intensity, which is also exacerbated by the inadequate drainage system. The modeling results indicate that the depth of the floodwater increased significantly, reaching a maximum depth of approximately 1.3 meters, after which it gradually decreased until it receded in about six hours.
IMPLEMENTASI TRANSFER LEARNING DENGAN FINE-TUNING PADA DETEKSI OBJEK MULTI-KELAS MENGGUNAKAN YOLO (STUDI KASUS CAR FREE DAY JALAN EL TARI KOTA KUPANG) Hendrikus Samuel Ola Sogen; Erna Rosani Nubatonis; Hasibun Asikin
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.v12i2.5923

Abstract

Object detection is a computer vision technology used to recognize and determine the location of objects in images or videos. This study aims to implement the transfer learning method with fine-tuning on the YOLOv8m model to detect multi-class objects consisting of persons, vehicles, and umbrellas in the Car Free Day environment on El Tari Street, Kupang City, as well as to develop a web-based object detection system capable of automatically detecting objects in images and videos. The research dataset consisted of 315 images obtained through field documentation and annotated using Roboflow, which was then increased to 757 images through the augmentation process before being divided into training, validation, and testing datasets. The YOLOv8m pretrained model based on the COCO dataset was trained using Google Colab for 100 epochs with the transfer learning and fine-tuning approach. The results showed that the model achieved a precision of 0.903, a recall of 0.801, an mAP50 of 0.868, and an mAP50-95 of 0.625. In addition, the developed web-based system was able to automatically detect objects in images and videos and display bounding boxes, confidence scores, object counts, detection result graphs, and model evaluation metrics. The results of the study indicate that the application of fine-tuning to YOLOv8m is capable of improving the model's adaptability to the characteristics of the local Car Free Day environment, thereby potentially supporting more effective public activity monitoring.
KLASIFIKASI SURAT MASUK DI KANTOR PENGADILAN MILITER III-15 KUPANG MENGGUNAKAN (LSTM) LONG SHORT TERM-MEMORY Asmawati Tuto; Sumarlin; Yohanis Malelak
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.v12i2.5924

Abstract

The increasing volume of incoming correspondence at the Kupang Military Court III-15 Office has made the conventional letter classification process complex and time-consuming. Purpose: this study aims to develop an incoming letter classification system that categorizes letters into four classes (regular letters, circulars, decrees, and orders) using the Long Short-Term Memory (LSTM) method; the main contribution of this study is the application of LSTM to a local military-court correspondence dataset that has not been widely studied, together with a replicable preprocessing pipeline and K-Fold evaluation protocol, providing practical implications for accelerating correspondence administration in military judicial offices. Methods: the dataset consists of 500 incoming letter records in Excel format that underwent a preprocessing stage including cleaning, case folding, normalization, stopword removal, stemming, tokenizing, encoding, and padding, and was then divided into 80% training data and 20% testing data, evaluated using 5-Fold Cross Validation with accuracy, precision, recall, and F1-score as performance metrics. Results: the average model performance results were Accuracy 42.04%, Precision 42.39%, Recall 42.04%, and F1-Score 39.93%, with the highest accuracy obtained in Fold 2 (56.44%) and the lowest in Fold 5 (32.00%), while the model's main difficulty lay in distinguishing between the Circular and Decree categories, which share similar text patterns. Conclusion: the LSTM method is capable of recognizing textual patterns in letters and performing classification; however, its performance remains variable and relatively low on this dataset, so the developed system has the potential to improve the efficiency of incoming letter management at the Kupang Military Court III-15 Office, although further optimization is still needed before independent deployment.
SISTEM PREDIKSI RISIKO KETERLAMBATAN DISTRIBUSI PANGAN PROGRAM MAKANAN BERGIZI GRATIS BERBASIS MACHINE LEARNING Ismail; Nur Fadillah Amiruddin; Hasna; Zinta; Kamis Tati
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.v12i2.5930

Abstract

Food distribution in the Free Lunch Program (MBG) requires timeliness to maintain food quality and service effectiveness to beneficiaries. Delays can be influenced by distribution distance, number of recipients, weather, road conditions, delivery time, and vehicle type. This study aims to develop a machine learning-based prediction model for the risk of delays in MBG food distribution. The research method uses a quantitative approach with a dataset of 100 data samples from operational distribution scenarios in Soppeng Regency. Data were processed through cleaning, categorical variable coding, numeric variable normalization, 5-fold cross-validation splitting, model training, and performance evaluation. Four algorithms were compared: Random Forest, Decision Tree, K-Nearest Neighbor, and Logistic Regression. The test results showed that Random Forest achieved 92.00% accuracy, 92.00% precision, 92.00% recall, and 92.00% F1-score. Feature importance analysis showed that the number of recipients, distribution distance, and distribution time were the most dominant factors in determining the risk of delays. The proposed prediction system can be a tool for MBG distribution managers in identifying potential delays early and formulating more appropriate operational mitigation recommendations.
PENERAPAN WEBGIS UNTUK VISUALISASI DAN ANALISIS LOKASI SEKOLAH DI KECAMATAN NUBATUKAN KABUPATEN LEMBATA Djunus Bruno Djunior; Remerta N. Na’atonis; Skolastika Siba 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.v12i2.5931

Abstract

Nubatukan District, Lembata Regency, has 42 schools ranging from elementary to senior high school level spread across its villages, yet information on their locations and distribution has not been presented spatially in an accessible way. This study aims to design and build a WebGIS application as a medium for visualizing and managing school location data in the district. The system was developed using PHP, HTML, CSS, JavaScript, and a MySQL database, supported by ArcGIS and Google Earth Pro for spatial data processing, and was tested through blackbox testing and a Likert-scale User Acceptance Test (UAT) involving 44 respondents. Blackbox testing results show that all application functions operate according to the designed requirements, while the UAT obtained a user acceptance rate of 89.13%, categorized as very good. The developed WebGIS application therefore effectively displays the distribution of schools interactively and benefits the community and local government of Nubatukan District in accessing educational information.
ANALISIS PEMANTAUAN KUALITAS BBM ECERAN BERBASIS IOT DENGAN FUEL QUALITY SENSOR DAN SVM UNTUK MENENTUKAN KELAYAKAN BERDASARKAN SIFAT FISIK BBM Gabrieno Bunyu; Menhya Snae; Heni
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.v12i2.5947

Abstract

This research aims to design and develop an Internet of Things (IoT)-based monitoring system for the quality of retail petroleum products capable of operating in real time, in order to address the public’s limitations in independently verifying the suitability of retail petroleum products. The system was developed using a proximity sensor as an initial trigger to detect the presence of objects or liquids, and a TDS sensor as a fuel quality sensor to measure changes in conductivity values indicating the water content in fuel samples; each reading is locked for 5 seconds to ensure the stability of the proximity and TDS sensor values before the data is sent to a Flask-based server and analysed using a Support Vector Machine (SVM) algorithm to classify the fuel condition into the categories ‘Suitable’ and ‘Unsuitable’. Tests were carried out on 100 retail fuel samples, comprising 50 samples of pure fuel and 50 samples of fuel mixed with water, with classification rules based on TDS values: a value close to or equal to zero indicates that the fuel shows no signs of water contamination (Acceptable), whilst a value above zero which in the tests varied from 1 to over 700 depending on the level of contamination indicates the presence of water admixture (Unfit). The research results show that the system is capable of performing sensor readings, data transmission and the classification process effectively in real time; furthermore, based on an evaluation using a confusion matrix, the SVM model achieved an accuracy of 0.94, a precision of 0.946, a recall of 0.94 and an F1-score of 0.94. With this performance, the developed system has the potential to be utilised by the public and retail fuel businesses to verify fuel suitability quickly, automatically and objectively without the need for laboratory testing.
ANALISIS PENJUALAN PADA HAPPYMART MENGGUNAKAN ALGORITMA FP-GROWTH Hendrikus Lambertho Laba Kumanireng; 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.v12i2.5953

Abstract

Sales analysis is a crucial process for evaluating transaction data to understand consumption patterns and maximize business performance through a data-driven approach. HappyMart faces the challenge of significant transaction data growth, collecting a total of 1,500 transaction records during the period of August to October 2025. Inefficient manual analysis potentially triggers overstocking due to a lack of understanding of consumer purchasing patterns. This research aims to analyze purchasing patterns using the FP-Growth algorithm to formulate operational recommendations. The analysis stages include data collection, preprocessing, data transformation, and the extraction of association rules. System evaluation was conducted by comparing manual calculations in Excel, Python output, and RapidMiner. This experiment utilized a minimum support parameter of 0.2% and a minimum confidence of 60%. The research results identified product association patterns, where one of the strongest rules indicates: if consumers buy Terigu Kompas 1Kg and Aqua 1500ml, they will also buy Terigu Kompas 500G (support 0.2%, confidence 60%, and lift ratio 21.95). Practically, this highly correlated figure provides a direct contribution to the store in the form of recommendations for placing these products adjacent to each other in the same aisle, as well as implementing bundling promotion strategies to minimize stock accumulation.
KLASIFIKASI SPASIAL TINGKAT KEMISKINAN RUMAH TANGGA MENGGUNAKAN NAIVE BAYES DI KECAMATAN WOTAN ULUMADO Cyrilius Budi De Fe Rento Lewo Manuk; Meliana O.Meo; Tri Ana Setyarini
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.v12i2.5955

Abstract

Household poverty is a social issue that requires accurate identification to ensure that poverty alleviation programs are implemented effectively and targeted appropriately. Wotan Ulumado Subdistrict, East Flores Regency, has diverse socioeconomic characteristics; therefore, a data-driven method is needed to classify household poverty levels. This study aims to analyze the socioeconomic factors associated with household poverty and apply the Naive Bayes method to classify households according to their poverty status. The data were collected through field observations, interviews, and a literature review. The variables examined included household income, number of dependents, the educational level of the household head, housing conditions, and asset ownership. The dataset consisted of 360 training records and 28 testing records. The classification process was conducted by calculating the prior, likelihood, and posterior probabilities for each poverty category. The classification categories comprised poor, moderately poor, and non-poor households. Evaluation using a confusion matrix showed that 26 out of 28 testing records were correctly classified, resulting in an accuracy rate of 92.86%. These findings indicate that the Naive Bayes method performs well in identifying household poverty levels based on socioeconomic indicators. The classification results can further be presented in the form of tables, graphs, and maps to illustrate the distribution of poverty levels and support local government decision-making in determining priority households and areas for poverty alleviation programs in Wotan Ulumado Subdistrict.
PENERAPAN METODE K-MEANS CLUSTERING DAN SUPPORT VECTOR MACHINE (SVM) BERBASIS MODEL RFM UNTUK KLASIFIKASI TIER PELANGGAN Tariq; Nurmalitasari; Faulinda Ely Nastiti
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.v12i2.5958

Abstract

Suboptimal management of large-scale transaction data can lead to marketing inefficiencies, particularly in determining promotional strategies that do not align with customer characteristics. This study aims to map the customer loyalty of CV Ekasa's client partners, by segmenting its customers using an integrated Recency, Frequency, Monetary (RFM) model, K-Means Clustering, and Support Vector Machine (SVM) classification. The dataset comprises 287,512 raw point-of-sale transaction records collected between October 2022 and September 2025, which after preprocessing yielded 341 valid customers for RFM modeling. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, RFM features were log-transformed and standardized before clustering. Silhouette Score evaluation across k = 1–10 identified two customer segments (k = 2, Silhouette Score = 0.482) as optimal, labeled Passive Tier and Active Tier. These cluster labels were then used as classification targets for a linear-kernel SVM, evaluated under two data-splitting scenarios (80:20 and 70:30). The model achieved 97.10% accuracy with the 80:20 split and 98.06% with the 70:30 split, with precision, recall, and F1-scores above 0.97 for both tiers in both scenarios. These findings indicate that the integrated RFM–K-Means–SVM pipeline classifies customer loyalty tiers reliably and stably. The resulting model was deployed as an interactive Streamlit dashboard, giving CV Ekasa's client partner a practical, data-driven basis for designing more targeted and efficient marketing and retention strategies.
ANALISIS AKTOR PENENTU DAN PREDIKSI JENIS KONTRASEPSI PADA AKSEPTOR KB MENGGUNAKAN ALGORITMA RANDOM FOREST Mario Edmon Gasa; Tri Ana Setyarini; 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.v12i2.5960

Abstract

The Family Planning (KB) program aims to control population growth, yet the high discontinuation rate due to mismatched contraceptive choices remains a major challenge in the field. Therefore, this study aims to develop an objective contraceptive prediction model using the Random Forest algorithm to minimize the risk of program failure. The methodology involved processing 4,500 acceptor records balanced into 9 contraceptive classes with 12 demographic variables, optimized via GridSearchCV, and evaluated using 5-Fold Cross Validation. The results indicate that the model operates stably with an average accuracy of 78.87%, achieving the best performance in the Fold-1 test at 81.67%. The model also demonstrated optimal recognition for the MOP and MAL classes (F1-Score 0.98), proving the algorithm's reliability in identifying classes with highly distinctive characteristics despite data overlap challenges within the Injectable and Pill classes. Feature Importance analysis reveals that Age (22.60%), Gender (14.39%), and Age at Marriage (12.44%) are the most dominant determining factors. This prediction model is implemented in a Flask application, serving as a practical decision-support tool for healthcare workers to provide instant, transparent, and targeted contraceptive recommendations.

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