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Tomy Satria Alasi
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Jurnal Armada Informatika
Published by STMIK Methodist Binjai
ISSN : 25980416     EISSN : 2615689X     DOI : https://doi.org/10.36520/jai.v8i2.130
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Jurnal Armada Informatika, an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 188 Documents
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Broiler Chicken Sales Forecasting Using Holt’s Double Exponential Smoothing at UD Annisa Sidabutar Alwam Sidabutar; Ali Hadomuan Hasibuan; Hery Sunandar
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.299

Abstract

Month-to-month fluctuations in broiler chicken sales make it difficult for UD Annisa Sidabutar to determine proportional inventory levels. Decisions based solely on experience may cause excess stock during low-demand periods or shortages when demand increases. This study applies Holt’s Double Exponential Smoothing method to forecast broiler chicken sales using 12 monthly observations from July 2025 to June 2026. The method was selected because the series fluctuates and exhibits a changing tendency, while the available observations are insufficient to estimate a robust annual seasonal pattern. Smoothing parameters were calibrated by testing 81 combinations of alpha and beta ranging from 0.1 to 0.9 at 0.1 intervals. The best combination was alpha 0.9 and beta 0.1, producing a Mean Absolute Percentage Error of 28.60%, a Mean Absolute Error of 197.70 units, and a Root Mean Squared Error of 287.19 units. Using the selected model, sales are projected at 732 units in July 2026 and increase gradually to 1,066 units in June 2027. The forecasts can serve as an initial reference for inventory planning; however, they should be interpreted cautiously because the historical series covers only one year and contains several sharp fluctuations.
Optimization of Tomato Production Prediction Using XGBoost and CatBoost Based on Lag Features in Aceh Province Eva Mufida Padilla; Utari; Rahma Yuni Simanullang
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.305

Abstract

Tomato production in each regency/municipality changes from year to year, necessitating a predictive method that can support production planning, distribution, and agricultural policy decision-making. This study aims to develop and compare machine learning models for predicting tomato production based on historical data from regencies/municipalities in Aceh Province. The dataset consists of 230 observations from 23 regencies/municipalities covering the period from 2015 to 2024, with attributes including region, year, and tomato production measured in quintals. The research stages include data cleaning, handling missing values, data transformation, and the construction of historical features, including production in the previous year, the three-year average production, and the production growth rate. The algorithms evaluated include Linear Regression, Random Forest Regressor, XGBoost Regressor, and CatBoost Regressor. The data are divided chronologically to prevent data leakage, while model performance is evaluated using Mean Absolute Error, Root Mean Squared Error, and the coefficient of determination. Preliminary analysis indicates that the production data have a very wide range and an uneven distribution pattern, requiring logarithmic transformation. This study is expected to identify a predictive model with the lowest error rate and provide supporting information for local governments and stakeholders in planning tomato commodity development in a more measurable and systematic manner.
Optimizing the Number of Trees in the Random Forest Algorithm to Improve Data Classification Accuracy Iqbal Giffari Ritonga; Pius Deski Manalu; Dedi Irawan; Syawaluddin Kadafi Parinduri
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.306

Abstract

Classification is a technique in data mining used to build predictive models based on patterns or characteristics of data. One widely used classification algorithm is Random Forest, an ensemble learning method that combines several decision trees to produce more accurate and stable predictions. However, the performance of the Random Forest algorithm can be affected by the parameters used, one of which is the number of decision trees (n_estimators). This study aims to analyze the effect of variations in the number of trees on the performance of the Random Forest algorithm in classifying and determine the configuration of the number of trees that produces the best accuracy. The research method was carried out by applying the Random Forest algorithm using variations in the number of trees of 50, 100, 150, and 200. Model performance evaluation was carried out using accuracy, confusion matrix, and feature importance analysis. The test results showed that variations in the number of trees had an impact on model performance, although the increase in accuracy obtained was relatively small. The accuracy values ​​for n_estimators 50, 100, 150, and 200 were 89.44%, 89.44%, 89.53%, and 89.42%, respectively, with the best performance obtained when using n_estimators = 150. The confusion matrix results showed that this configuration provided a better classification balance between the Cancel and Not_Cancelled classes. In addition, feature importance analysis showed that lead time, average price, and special requests were the most influential features on the classification results across all variations in the number of trees. Based on the results of the study, the use of the optimal number of trees can improve the stability of the Random Forest model, but adding a larger number of trees does not always result in a significant increase in accuracy. The Random Forest source code and dataset are available on GitHub at https://github.com
Design and Development of a Car Rental Reservation Information System at CV. Alvin Jaya Mandiri Ika Yusnita Sari; Imam Adlin Sinaga; Elvika Rahmi; Khairunnisa Khairunnisa
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.308

Abstract

This study aims to develop a web-based car rental information system at CV. Alvin Jaya Mandiri to replace inefficient manual processes. The software development method used is the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and maintenance. Data were collected through observation, interviews, and documentation studies. The system enables online reservations, real-time vehicle availability monitoring, transaction management, and automated financial and inventory reports. It also provides a user-friendly interface for both administrators and customers. The results show that the system improves operational efficiency, data accuracy, and service quality, supporting digital transformation in the increasingly competitive car rental industry
Design and Development of a Web-Based New Student Admission Information System at SMP Negeri 1 Raya Fathiya Hasyifah Sibarani; Triase Triase; Rahmad Syuhada; Vicky Setia Gunawan
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.309

Abstract

SMP Negeri 1 Raya is one of the schools that needs an information system to help its academic process. One of the academic processes carried out every year is the admission of new students. Currently, the registration system for new student candidates is carried out by visiting the school and bringing the files that have been previously informed. The problems that occur in the admission process are the accumulation of files so that it takes a long time when looking for certain files, losing files, and it takes a long time to manage new student admission data. This makes the new student admission process inefficient. Based on the problems, this study aims to build a student registration information system that help schools and facilitate parents in the registration process of prospective students. This research uses Waterfall as a system development method with sequential stages. The results of the study show that the information system built is in accordance with the needs and facilitates the registration process of prospective students for schools and parents.
Clustering of Stunting, Wasting, and Underweight Case Volumes Using K-Means: An Exploratory Study of Monthly Data in Southeast Aceh Regency Ahmad Taufik Al Afkari; Tomy Satria Alasi; Yuvi Darmayunata
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.310

Abstract

The availability of open data enables computational-based monitoring of toddler nutritional problems, but local administrative datasets are often small, aggregated, and lack target labels for supervised modeling. This study aims to (1) determine the most appropriate number of case volume clusters, (2) characterize the clusters of stunting, wasting, and underweight, and (3) build a simple and reproducible analysis baseline using Google Colab. Secondary data was obtained from the Aceh Government's open sources and consists of 15 aggregate observations from Southeast Aceh Regency between November 2022 and March 2023. The modeling variable is the number of cases in individuals. The research stages include data understanding, quality checking, descriptive analysis, K-Means modeling for k=2 to k=5, and internal evaluation using the Silhouette Coefficient, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The results show no missing values or duplications. The highest silhouette value was obtained at k=2 with 0.8189, alongside a DBI of 0.2286 and a CHI of 150.6841. The high-volume cluster is centered at 1,182.6 individuals and contains five stunting observations, while the lower-volume cluster is centered at 545.7 individuals and contains five wasting observations and five underweight observations. The total of the three indicators changed from 2,469 individuals in November 2022 to 2,122 individuals in March 2023 (-14.05%), but the change was not monotonic as an increase occurred in February 2023. The novelty of this research lies in a minimal baseline that empirically selects k, uses three evaluation indices, incorporates temporal examination, and limits cluster interpretation to relative volumes. The results can serve as an analytic audit prototype for regional nutrition data, but cannot yet be used to infer prevalence, causality, or individual risk. The research testing can be downloaded from github.com. Keywords: Data Mining; K-Means; Clustering; Stunting; Wasting; Underweight; Google Colab
Comparative Analysis of E-Commerce Platforms in Indonesia Using the SMART and TOPSIS Methods Fakhri Fitra Al Taufiq
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.311

Abstract

The rapid growth of e-commerce in Indonesia has been driven by increasing internet penetration and widespread smartphone usage. Tokopedia, Shopee, Lazada, Bukalapak, and Blibli remain the five most widely used platforms, each offering distinct advantages and limitations. However, the abundance of available platforms creates challenges for consumers in selecting the most suitable one, especially since multiple criteria—such as product price, transaction security, application usability, delivery speed, customer service, and product availability—must be considered simultaneously. To address this issue, this study employs a Decision Support System approach using Multi-Criteria Decision Making (MCDM) through the integration of the SMART and TOPSIS methods. Primary data were collected from 20 active e-commerce users who provided both importance weights for the evaluation criteria and performance ratings for each platform.The SMART method was used to normalize the criteria weights based on respondent prioritization. The results show that Transaction Security is the most influential criterion with a weight of 19.8%, followed by Product Availability at 19.4%. These weights were subsequently used in the TOPSIS calculation to determine each platform’s relative closeness to the ideal solution. The findings indicate that Shopee is the best-performing platform with a preference score of 0.911, followed by Tokopedia (0.825), Blibli (0.613), Lazada (0.188), and Bukalapak (0.000). This ranking demonstrates that Shopee consistently excels in high-weighted criteria, particularly product price, application ease of use, and product availability.Overall, the integration of SMART and TOPSIS proves to be effective in generating objective, measurable, and transparent recommendations for selecting the most suitable e-commerce platform based on user perceptions.
Design of a Three-Layer Blockchain Integrity Validation Model for Fraud Prevention in Business Licensing Platforms Edrian Hadinata; Tasya Febriyanti
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.312

Abstract

Data integrity in multi-stage government licensing services is a critical challenge, particularly when each stage involves different actors with distinct authority. Without a permanent recording mechanism for every action in the workflow, licensing data remains vulnerable to manipulation. This study develops a web-based SIUP business licensing information system integrating blockchain technology as an automatic integrity validation mechanism. Every action taken by six actors is permanently recorded as a new block using SHA-256 hashing and Proof-of-Work requiring hash values prefixed with "00". The recording mechanism is implemented exclusively in BlockchainController.php through the validate() function, while block hash and prevHash values are stored in independent log files located at storage/app/private/blockchain/ on the server. A three-layer integrity validation runs automatically: the first layer compares block count in the database against the log file; the second layer verifies prevHash chain continuity; the third layer confirms that every block hash begins with "00" as proof of valid Proof-of-Work mining. When any violation is detected, the system disables all action buttons for every actor except the Head of Agency, who issues an official Blockchain Integrity Violation Certificate. Testing confirms that the three-layer mechanism successfully detects all violation scenarios automatically.
Design and Implementation of a LAN-Based Personal Home Media Streaming Server Mega Christin Morys Lase; Finis Hermanto Laia; Progresif Buulolo; Karuniaman Buulolo; Kristine Wau
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.313

Abstract

Advancements in computer network technology have created a need for efficient multimedia content-sharing systems that do not rely on an internet connection, and one viable solution is to build a personal home streaming media server based on a Local Area Network (LAN), prompting this research to design and implement a media streaming system utilizing Digital Living Network Alliance (DLNA) features within the Windows operating system to enable direct access to multimedia files from other devices on the local network; the study employs a design and implementation methodology encompassing requirements analysis, system design, network configuration, media streaming service configuration, and system testing, which involved IP address configuration, connectivity verification via the ping command, and streaming service validation using Windows Media Player on client computers, with the results demonstrating successful connectivity between the server and client computers within the LAN, proper activation of the DLNA service, and successful access and streaming playback of multimedia files without the need for downloading or experiencing buffering, thus proving that the developed system provides a simple, efficient, and easily implementable multimedia sharing solution suitable for home environments or small-scale local networks.
Application of the K-Means Clustering Algorithm for Sales Data Clustering to Identify Best-Selling Products at PT Cahaya Surga Teknik Indonesia Amran Sitohang; R. Mahdalena Simanjorang; Fittra Ferdiansyah; Angel Nurfadilah
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.315

Abstract

Business competition demands that companies utilize sales data as a basis for strategic decision-making. PT Cahaya Surga Teknik Indonesia has sales data that continues to grow each period, but has not been optimally utilized to identify best-selling products. This study aims to apply the K-Means Clustering algorithm to group sales data so that it can identify the categories of best-selling products, medium-selling products, and less-selling products. The research method uses data mining with KDD (Knowledge Discovery in Database) stages including data selection, preprocessing, transformation, clustering process using the K-Means algorithm, and evaluation of cluster results. The research dataset consists of annual sales data (content year) with attributes of number of transactions, number of sales, and total revenue. The results show that the K-Means algorithm is able to divide the data into 3 main clusters: the very best-selling product cluster, the moderately best-selling product cluster, and the less-selling product cluster. The clustering results can help companies in determining stock strategies, promotions, and sales planning.

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